Daily Archives: May 15, 2026

Advancing Healthcare through Artificial Intelligence: The Role of Association Rule Mining in Clinical Decision Support and Healthcare Analytics

Authors: Geofrey Nyabuto, Charles Kibet Ng’etich, Edwin Seno, George Kihara Mburu, Marion Jeptoo, Joyadams Munene, Muriithi Alex Karani, John kimani Muragu, Nyairo Charles Magati

Abstract: Association rule mining (ARM) is a data mining approach used to discover frequent co-occurrence patterns and conditional relationships in large datasets. In healthcare, ARM has been applied to electronic health records, claims databases, laboratory data, prescription data, disease registries, and public health datasets to reveal clinically meaningful patterns that may support diagnosis, medication safety, risk stratification, and service planning. Objective: This review synthesizes how ARM has been applied in healthcare, focusing on methods, clinical application areas, implementation challenges, and future research directions. A systematic review design guided by PRISMA 2020 was used to structure the manuscript. Literature was organized around peer-reviewed ARM studies in healthcare, including clinical decision support, diagnostic test ordering, disease-medication association mining, adverse drug reaction signal detection, risk factor discovery, hospital readmission analysis, privacy-preserving mining, and emerging causal or hybrid ARM approaches. The literature shows that Apriori remains the most frequently used ARM algorithm, although FP-Growth, weighted Apriori, class association rules, negative association mining, privacy-preserving ARM, and causal irredundant ARM are increasingly used to address computational, interpretability, privacy, and clinical validity limitations. ARM is valuable because it produces transparent IF-THEN rules that clinicians can inspect, but uncontrolled rule generation, weak validation, data quality limitations, and spurious associations remain major barriers. ARM has clear potential in healthcare knowledge discovery and decision support, particularly where interpretability is required. Future research should prioritize external validation, clinician-centered rule evaluation, integration with electronic medical records, explainable hybrid models, privacy-preserving analytics, and evidence from low- and middle-income healthcare settings.

DOI: https://doi.org/10.5281/zenodo.20199703

Design And Performance Analysis Of An Automated Guidance System For Tractors For Precision Farming

Authors: Mr. Sangharsh Dongare, Dr. Vijayashri Mohobiya

Abstract: The advancement of precision agriculture has led to the adoption of automated systems that enhance operational accuracy and reduce resource wastage. Among these technologies, automated tractor guidance systems play a crucial role in improving field efficiency and minimizing human intervention. This research focuses on the design and development of an integrated automated steering system for agricultural tractors using Global Navigation Satellite System (GNSS) technology. The proposed system combines an RTK-enabled GNSS receiver, inertial sensors, an electronic control unit, and an electro-hydraulic steering actuator to enable accurate trajectory tracking. The design methodology emphasizes system integration, component selection, and control architecture development. The performance of the system is evaluated through design validation parameters such as steering response, path deviation, and system stability. The study demonstrates that the designed system can achieve high precision in field navigation while maintaining robustness under varying operating conditions. The findings highlight the importance of system-level design in developing efficient and reliable automated agricultural machinery.

EV Charging Locator System: A Web-Based Real-Time Solution For Smart Electric Vehicle Charging Infrastructure

Authors: Swapnil Pradip Jadhav

Abstract: – The rapid proliferation of electric vehicles (EVs) has created an urgent need for scalable, accessible, and real-time charging infrastructure discovery systems. This paper presents the design, development, and evaluation of a web-based EV Charging Locator System that enables EV users to identify, navigate to, and interact with nearby charging stations using geolocation services, interactive mapping, and a responsive user interface. The proposed system addresses the critical gap of fragmented charging network information by consolidating multi-network station data on a single unified platform. The system is built using modern web technologies including HTML5, CSS3, JavaScript (ES6+), and integrates third-party APIs such as Google Maps and Open Charge Map. A user-friendly dashboard, smart filtering, and station management module empower both EV users and charging station administrators. Experimental evaluation demonstrates reduced search time, improved navigation accuracy, and enhanced user experience compared to existing solutions. The system contributes significantly to smart city goals and the green energy transition.

DOI: https://doi.org/10.5281/zenodo.20205640

Streamlining The Code Review Process Using Artificial Intelligence: A Practical Framework For Enhancing Software Quality And Development Efficiency

Authors: Neha Asthana

Abstract: The rapid evolution of modern software engineering practices has intensified the demand for faster development cycles, higher code quality, and improved operational efficiency. Traditional code review processes, while essential for maintaining software reliability and security, frequently create development bottlenecks due to the extensive manual effort required to validate syntax, formatting, and compliance with coding standards. This article presents a practical implementation framework for streamlining code review operations through the adoption of artificial intelligence (AI)-assisted analysis integrated within DevOps workflows. The initiative focused on incorporating AI-assisted review capabilities into existing Git-based pull-request and continuous integration pipelines to automate repetitive review activities and accelerate validation processes. By enabling automated identification of syntax inconsistencies, formatting deviations, boilerplate inefficiencies, and commonly recurring coding issues, the framework allowed software reviewers to prioritize higher-value technical concerns including architectural integrity, security vulnerabilities, scalability, and business logic validation. The implementation demonstrated measurable operational improvements, including an approximate 30% reduction in pull-request review time and a 35% decrease in post-review rework across development teams. The study further examines the technical and organizational challenges associated with integrating AI into enterprise software development practices. One major challenge involved the generation of inconsistent or contextually irrelevant AI recommendations that occasionally conflicted with project-specific coding patterns and domain-specific business requirements. This limitation was addressed through iterative prompt refinement, enforcement of internal engineering standards, and selective application of AI to repetitive development tasks such as validation routines and boilerplate generation. Security and code quality concerns also emerged due to the potential introduction of insecure coding patterns or software anti-patterns through AI-generated suggestions. To mitigate these risks, the framework incorporated layered governance mechanisms including static code analysis, automated security scanning, peer validation procedures, and mandatory human review for sensitive components. An additional barrier to adoption stemmed from initial developer skepticism regarding the reliability and contextual awareness of AI-generated outputs. Adoption rates improved significantly after repositioning AI as an assistive pre-review mechanism rather than a replacement for human expertise. Continuous peer validation, governance-based coding standards, and collaborative review practices contributed to increased organizational trust and broader engineering acceptance.

Consumer Buying Behaviour: A Study Of Factors Influencing Purchase Decisions

Authors: Taranveer Singh, Manisha Kalra

Abstract: Consumer buying behaviour refers to the process through which individuals select, purchase, use, and dispose of goods and services to satisfy their needs and wants. Understanding consumer behaviour is essential for businesses to design effective marketing strategies and achieve customer satisfaction. This research paper examines the major factors influencing consumer buying behaviour, including cultural, social, personal, and psychological factors. The study also highlights the stages involved in the consumer decision-making process and the impact of digital marketing on purchasing decisions. The paper concludes that changing lifestyles, technological advancements, and increased access to information have significantly transformed consumer buying patterns in modern markets.

Restaurant Sales Intelligence Report In Tableau

Authors: K.Ravindhar, P.KamalaKar, Dr.Diana Moses

Abstract: This analysis examines a restaurant chain sales dataset covering transaction recorded across November and December 2022, spanning five major European cities — London, Lisbon, Madrid, Berlin, and Paris. The dataset capture nine key attributes including Order ID, Date, Product, Price, Quantity, Purchase Type, Payment Method, Manager, and City, forming a structured foundation for evaluating multi-dimensional sales performance. Burger emerge as the highest revenue-generating product, followed by Fries and Chicken Sandwiche, while Sides & Other contributes the least to overall revenue. Geographically, Lisbon leads in both total revenue and quantity sold, closely followed by London, whereas Berlin and Paris record comparatively lower performance. Purchase behavior analysis reveals that Online transaction are the most frequently used channel, followed by Instore and Drive thru. In terms of payment preferences, Credit Card dominates as the most widely adopted method, ahead of Cash and Gift Card usage. These finding highlight key revenue regional performance disparities, and customer purchasing preferences, offering a data-driven foundation for improving inventory planning, targeted marketing strategies, and overall operational decision-making.

Mobile Marketing & Consumer Engagement: Evidence From Indias Digital Economy

Authors: Jyotish Kumar, Dr. Meentu Grover

Abstract: India’s mobile economy is no longer a future promise — it is today’s commercial reality. This study examines the impact of mobile marketing on consumer engagement with empirical evidence drawn from 150 respondents across Punjab, India. Adopting a descriptive-analytical research design and administering structured questionnaires, the study investigates how SMS campaigns, social media advertisements, push notifications, mobile applications, and influencer marketing shape consumer purchase intentions, brand loyalty, and overall satisfaction. Statistical techniques including descriptive analysis, correlation, regression, and chi-square testing were employed to interrogate the data. Results confirm that mobile marketing exerts a statistically significant positive effect on consumer engagement (β = 0.61, p < 0.001), with social media marketing and personalized notifications emerging as the strongest predictors. Brand trust and perceived usefulness were identified as critical mediating constructs. These findings carry decisive implications for digital marketers, policy designers, and scholars navigating India’s fast-evolving mobile-first marketplace.

Microfinance And Poverty Reduction: An Empirical Study Of Financial Inclusion And Rural Development.

Authors: Jatinder Singh, Manisha Kalra

Abstract: – Microfinance has emerged as an important financial instrument for reducing poverty, promoting self-employment, and improving the socio-economic conditions of low-income households. The concept of microfinance involves providing small loans, savings facilities, insurance, and other financial services to economically weaker sections who are generally excluded from the formal banking sector. This research paper examines the role of microfinance in poverty reduction by analyzing its impact on income generation, employment opportunities, women empowerment, and rural development. The study also highlights the challenges faced by microfinance institutions (MFIs) and suggests measures for improving their effectiveness. The paper concludes that microfinance plays a significant role in poverty alleviation when combined with proper training, financial literacy, and government support.

Role Of YouTube Marketing In Influencing Consumer Buying Behaviour

Authors: Mandeep Kaur, Dr joe Christy N

Abstract: The rapid growth of social media platforms has transformed consumer buying behavior, with YouTube emerging as one of the most influential digital marketing channels. This research paper examines the impact of YouTube on consumer purchase decisions through a data-oriented approach. The study analyzes how YouTube advertisements, influencer marketing, product reviews, unboxing videos, and user-generated content affect consumer awareness, purchase intention, and actual buying behavior. Secondary data from recent empirical studies and industry reports are used to evaluate consumer responses toward YouTube-based marketing strategies. The findings reveal that YouTube significantly influences consumer decision-making, particularly among younger audiences such as Generation Z and Millennials. Factors such as content credibility, emotional engagement, influencer trustworthiness, and video quality positively affect purchase intentions. The study concludes that YouTube has become a powerful tool for marketers in shaping consumer attitudes and driving purchasing decisions.

Role of Training and Development Policies in Employee Competence in Organizations

Authors: Prachi Barnwal, Dr Navneet Seth

Abstract: Training and development policies play a crucial role in improving employee competence, organizational productivity, and overall business performance. In the modern competitive environment, organizations increasingly invest in employee training programs to enhance technical skills, communication abilities, leadership qualities, and job efficiency. The present study examines the impact of training and development policies on employee competence using a data-oriented approach. The study is based on secondary data collected from research journals, HR reports, and organizational studies. The findings reveal that effective training policies significantly improve employee skills, motivation, productivity, and job satisfaction. The study concludes that organizations with strong training and development practices achieve higher employee performance and organizational effectiveness.

A Study On The Impact Of Social Media Marketing On Consumer Buying Behaviour

Authors: Salama Juma Shehe, Dr. Sahil Nazir

Abstract: This study examined the impact of social media marketing on consumer buying behavior. The rapid growth of digital technology and social networking platforms has transformed the way businesses communicate with customers and promote their products and services. The main objective of the study was to analyze how social media marketing influences consumer purchasing decisions. Specifically, the study examined the role of social media advertisements, online reviews, influencer marketing, and promotional content in shaping consumer buying behavior. The study employed a quantitative research design using a survey method. Data were collected through an online questionnaire created using Google Forms and distributed through social media platforms. A total of sample size 100 respondents participated in the study. The collected data were analyzed using descriptive statistics, including frequencies and percentages, and presented using tables and charts. The findings indicated that social media marketing significantly influences consumer buying behavior. The results showed that most consumers rely on social media platforms to obtain information about products, read reviews, and compare alternatives before making purchase decisions. The study concludes that social media marketing plays a critical role in influencing modern consumer purchasing behavior. Businesses should therefore invest in effective social media marketing strategies to improve brand visibility, customer engagement, and sales performance. The study recommends that companies should enhance their social media presence, collaborate with influencers, and provide reliable and engaging content to attract and retain customers.

Design and Optimization of Solar Thermal Collector with Integrated Phase Change Material (PCM)

Authors: Mr. Uddesh Dhanraj Dongre, Prof. Mithlesh Pandey

Abstract: Solar thermal collectors are widely used for converting solar energy into useful thermal energy for domestic and industrial applications. Conventional collectors suffer from energy loss during cloudy weather and nighttime due to the absence of efficient thermal storage systems. To overcome this limitation, Phase Change Materials (PCM) are integrated into solar thermal collectors. PCM absorbs excess heat during sunshine hours and releases stored thermal energy during low solar radiation conditions. This research focuses on the design and optimization of a solar thermal collector integrated with PCM. Paraffin wax is selected as PCM because of its high latent heat capacity, thermal stability, chemical inertness, non-corrosive nature, and suitable melting temperature range. The performance of the collector is evaluated based on thermal storage capability, charging and discharging characteristics, outlet water temperature, heat retention, and efficiency improvement. The study shows that PCM integration significantly improves thermal efficiency and maintains outlet temperature for longer duration compared to conventional collectors. The optimized collector demonstrates enhanced energy utilization, reduced temperature fluctuation, and better thermal stability. The proposed system is suitable for domestic water heating, industrial thermal applications, agricultural drying systems, and renewable energy storage applications.

Design Method For Online Totally Self-Checking Comparators Implementable On FPGAs

Authors: Harishankar T, Dr.T.R.Ganesh Babu

Abstract: In the context of their growing use in critical fields of application, like aviation electronics, automotive control systems, and industrial automation, FPGA circuits’ operation must be guaranteed against both soft errors and any other defects that may arise during run-time. This paper analyzes in depth an approach for implementing Totally Self-Checking (TSC) comparators for online diagnostics in FPGAs in a way which maximizes its effectiveness in terms of test pattern complexity and hardware overhead. In particular, the presented technique utilizes the circuitry features of Look-Up Tables (LUTs) to provide comprehensive online testing with a number of test vectors proportional to O(n), while guaranteeing complete fault coverage and regardless of the specific LUTs configuration. The results of a comparison among recent techniques for implementing TSC, both BIST-based and Dual Modular Redundancy (DMR), show that the described solution offers an outstandingly effective performance with regard to SER (0.055 FIT).

DOI: https://doi.org/10.5281/zenodo.20233433

AI Driven Intrusion Detection System Using Hybrid Deep Learning In Cloud Environment

Authors: Dr Vijayalakshmi V, Ms.Sneha R. V. Kumbhar

Abstract: However, the rise in cloud computing usage has resulted in increased complexity and vulnerability of organizations’ IT infrastructure. In addition, cloud services have created new vulnerabilities that can easily be targeted by sophisticated attacks since traditional intrusion detection methods lack the ability to cope with the dynamically changing nature of cloud environments. This paper offers a novel, AI-powered hybrid deep learning framework for intrusion detection in cloud environments. The hybrid IDS is based on a combination of Triplet Attention-based Residual CNN for spatial feature extraction of network traffic, Bi-LSTM with attention mechanism for temporal dependency modeling, and Particle Swarm Optimization for hyperparameter optimization. Based on the evaluation results performed on the CSE-CIC-IDS2018 and UNSW-NB15 dataset, the suggested hybrid architecture attains an impressive accuracy of 99.12%, precision of 98.9%, and recall of 99.0%, outperforming the performance of individual CNN (96.4%) and Bi-LSTM (95.8%). In terms of efficiency, the PSO-based architecture has a latency less than 50 ms with minimal false positive rate of only 1.2%.

DOI: https://doi.org/10.5281/zenodo.20233688

Farmers’ Perceptions of Marketing Functions Rendered by Cooperative Marketing Societies: A Five-Factor Model for Understanding Multi-Dimensional Service Quality

Authors: Associate Professor Dr. S.Sureshbabu, Research Scholar Mr. A.kannan

Abstract: This study examines farmers’ perceptions of marketing functions and services rendered by Cooperative Marketing Societies (CMS) through comprehensive exploratory factor analysis of data from 620 farm-members. Principal Components Analysis with Varimax rotation identifies five distinct dimensions of CMS marketing functions: Market Operations and Transaction Efficiency, Pricing and Bargaining Effectiveness, Market Access and Infrastructure Support, Post-Harvest and Quality Support Services, and Information and Financial Support Services. The findings reveal that farmers rate infrastructure support (mean = 4.30) and storage facilities (mean = 4.23) most favorably, while expressing moderate satisfaction with pricing transparency (mean = 2.69) and income impact (mean = 3.28). Cluster analysis segments farmers into three groups: 62.4% highly satisfied, 23.2% moderately satisfied, and 14.4% less satisfied with CMS functions. The five-factor model explains 64.183% of cumulative variance, establishing a robust framework for understanding CMS service quality and performance. The study provides evidence-based insights for strengthening cooperative marketing functions and designing targeted interventions to enhance farmer satisfaction across service dimensions.

DOI: https://doi.org/10.5281/zenodo.20242694

MindMeld: A Tiered Orchestration Framework For Automated Synthesis And Deployment Of Production-Grade Multi-Agent Systems From Natural Language Specifications

Authors: Prof. Chetan Kumar V, Hardik Jain, Pranathi B H, Vikas R P, Srinidhi Prabhu M U

Abstract: The deployment of production-grade Multi-Agent Systems (MAS) from natural language specifications remains a significant challenge in software engineering, requiring so- phisticated role decomposition, reliable tool integration, exe- cutable code synthesis, and robust packaging with dependency management. This paper presents MindMeld, a novel tiered LLM orchestration framework that transforms natural language requirements into deployable, containerized multi-agent systems through a three-phase pipeline architecture. MindMeld introduces several key innovations: (1) a formal planning phase that generates machine-verifiable JSON agent specifications with explicit dependency graphs and interface con- tracts; (2) a closed-loop validation tier combining static analysis, dynamic runtime testing in isolated sandboxes, and iterative self-refinement based on structured error feedback; and (3) an automated integration phase that synthesizes orchestration logic, manages inter-agent communication, and produces containerized artifacts with complete dependency resolution. We conduct comprehensive evaluation on 47 diverse natural- language build requests spanning 8 task categories (data pro- cessing, API integration, document analysis, notification systems, workflow automation, content generation, monitoring, and multi- modal processing). Our results demonstrate that MindMeld achieves 78.7% end-to-end build success compared to 34.0% for single-pass generation baselines, with an average of 1.8 validation iterations per sub-agent. Ablation studies reveal that the planning phase contributes 23.4% improvement and the val- idation loop adds 21.3% improvement to overall success rates. A controlled user study with 24 participants shows 3.2× reduction in deployment time and 4.1/5.0 satisfaction scores. These results establish MindMeld as a practical framework for bridging the gap between natural language intent and production-ready multi- agent systems.

Integrated Groundwater Quality Assessment And Machine Learning Prediction In Central Uttar Pradesh, India

Authors: Nitin Mishra

Abstract: Groundwater is the principal source of drinking and irrigation water in the Indo-Gangetic alluvial plains of Uttar Pradesh, India. Rapid urbanization, agricultural intensification, excessive groundwater abstraction, and geogenic contamination have significantly affected groundwater quality in the region. The present study evaluates groundwater quality in Central Uttar Pradesh using hydrogeochemical assessment, entropy-weighted water quality index (EWQI), and machine learning (ML) prediction techniques. A total of 178 groundwater samples were analyzed for major physicochemical parameters including pH, EC, TDS, TH, Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, SO42−, NO3−, F−, SiO2, and CO32−. The entropy weight method was employed to minimize subjectivity in water quality assessment, while hydrogeochemical interpretations were carried out using Piper and Gibbs diagrams. Three machine learning models, namely Classification and Regression Tree (CART), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were implemented to predict groundwater quality conditions. The results revealed that groundwater chemistry is predominantly controlled by rock–water interaction and ion exchange processes, with Ca–HCO3 and mixed hydrochemical facies dominating the study area. The EWQI values indicated that most groundwater samples fall within good to medium drinking water quality categories, although localized fluoride enrichment was observed in several locations. Among the applied models, XGBoost demonstrated superior predictive capability with R2 = 0.9597, RMSE = 2.2376, and MAE = 1.7690, outperforming RF and CART models. The findings highlight the effectiveness of integrating GIS-based hydrogeochemical analysis with machine learning approaches for groundwater quality prediction and sustainable groundwater management in Central Uttar Pradesh.

DOI: http://doi.org/10.5281/zenodo.20268302

Solar Powered Grass Cutter With Mobile Remote Control For Medium Outdoor Space

Authors: Kamble Abhishek B, Nimbalkar Saurabh S, Patil Abhayrajsinh M.

Abstract: The increasing demand for eco-friendly and automated landscaping solutions has led to the development of a Solar-Powered Grass Cutter with Mobile Remote Control for medium outdoor spaces such as gardens, parks, and institutional campuses. The system harnesses solar energy via photovoltaic panels, which charge a 12V, 7Ah lead-acid battery through an MPPT charge controller. An ESP32 microcontroller receives Bluetooth commands from a mobile application to drive four Johnson DC gear motors (12V, 10 RPM) for mobility and a PMDC motor (12V, 5000 RPM) for blade actuation, interfaced via an L298N motor driver. Field testing demonstrated a cutting efficiency of 98.5% for grass heights of 15–25 mm and 78% for overgrown grass exceeding 60 mm, with operational runtime of 1.5–2 hours per full charge. The total fabrication cost is approximately Rs. 10,955, making it a cost-effective and environmentally sustainable alternative to conventional fuel-powered grass cutters.

Heavy Metal Accumulation In River Sediments: A Case Study Of The Gomti River

Authors: Nisha Gautam

Abstract: Heavy metal contamination in river sediments is a major environmental concern due to rapid urbanization and industrialization. The present study assessed heavy metal contamination in sediment samples collected from selected sites of the Gomti River in Lucknow, Uttar Pradesh, India. Sediment samples were collected from Gaughat, Kudiya Ghat, Daliganj Bridge, and Hanuman Setu during November 2025 and analysed for chromium (Cr), nickel (Ni), arsenic (As), cadmium (Cd), and iron (Fe). Pollution assessment indices including Enrichment Factor (EF), Contamination Factor (CF), and Pollution Load Index (PLI) were used to evaluate contamination levels and anthropogenic influence. The results revealed significant spatial variation in heavy metal concentrations. Iron showed the highest concentration, while cadmium exhibited extremely high concentrations compared to its background value, indicating severe contamination. The concentration pattern followed the order: Fe > Cr > Ni > Cd > As. EF analysis indicated extremely severe enrichment of Cd, whereas Cr showed moderate enrichment. CF results also confirmed very high contamination by Cd. PLI values at all sampling sites were greater than 1, indicating polluted sediment conditions. The study concludes that anthropogenic activities such as sewage discharge, urban runoff, and industrial effluents are major contributors to heavy metal accumulation in the Gomti River sediments.

DOI: http://doi.org/10.5281/zenodo.20268759

GIS-Based Mapping Of Groundwater Contamination In Lucknow District, Uttar Pradesh

Authors: Zaira Siddiqui

Abstract: Groundwater is an essential source of drinking water in urban regions; however, rapid urbanization, industrial growth, and anthropogenic activities have significantly deteriorated groundwater quality in many Indian cities, including Lucknow. The present study aims to evaluate the spatial variability of groundwater quality in Lucknow district using Geographic Information System (GIS)-based techniques and Water Quality Index (WQI) approaches. Major physicochemical parameters including pH, electrical conductivity (EC), total hardness, calcium (Ca²⁺), magnesium (Mg²⁺), chloride (Cl⁻), fluoride (F⁻), nitrate (NO₃⁻), and sulphate (SO₄²⁻) were analyzed for groundwater quality assessment. Spatial interpolation of groundwater parameters was performed using the Inverse Distance Weighting (IDW) method in GIS to generate thematic distribution maps and identify contamination hotspots. Two groundwater quality assessment approaches, namely Arithmetic Water Quality Index (AWQI) and Weighted Water Quality Index (WWQI), were applied to evaluate overall groundwater suitability for drinking purposes. The results revealed significant spatial variability in groundwater quality across Lucknow district. Elevated concentrations of hardness, EC, nitrate, chloride, and sulphate were observed in several urbanized and densely populated regions, indicating strong anthropogenic influence on groundwater systems. The AWQI and WWQI hotspot maps indicated that eastern and southeastern parts of Lucknow district exhibited comparatively poor groundwater quality, while northern and western regions showed relatively better water quality conditions. Comparative analysis demonstrated that WWQI provided a more realistic and reliable assessment because parameter-specific weighting improved sensitivity toward critical contaminants. GIS-based hotspot mapping successfully delineated vulnerable groundwater zones and highlighted areas requiring immediate monitoring and management intervention. The study demonstrates that integration of GIS and WQI techniques is highly effective for groundwater quality assessment, contamination hotspot identification, and sustainable groundwater resource management. The findings of this study can support policymakers and environmental planners in developing targeted groundwater protection and remediation strategies for rapidly urbanizing regions.

DOI: http://doi.org/10.5281/zenodo.20268893

Analysis Of Leachate From The Municipal Solid Waste Disposal Site And Its Impact On Groundwater Quality At Lucknow

Authors: Shivanshi Verma

Abstract: This study evaluates leachate quality from a municipal solid waste disposal site in Lucknow and examines its impact on nearby groundwater. The analytical framework, sampling design and index-based interpretation were prepared in line with the uploaded thesis and sample journal paper, while the numerical results were derived from the uploaded laboratory workbook. One leachate sample and seven groundwater samples were assessed for physicochemical and heavy metal parameters using APHA-based methods. The leachate showed acidic to near-neutral reaction (pH 6.1), very high electrical conductivity (83,892 µS/cm), total dissolved solids (38,180 mg/L), chemical oxygen demand (16,800 mg/L), biochemical oxygen demand (2,000 mg/L), hardness (1,620 mg/L), chloride (980 mg/L), sulphate (678.5 mg/L), nitrate (103.44 mg/L), fluoride (8.8 mg/L) and substantial heavy metal burden, indicating strong contaminant potential. The Leachate Pollution Index was 25, confirming significant pollution load. Groundwater quality varied spatially: Sample-7 recorded a WQI of 75.13 and fell in the good category, whereas Samples 2–4 were poor and Samples 1, 5 and 6 were very poor. Elevated TDS, alkalinity, hardness, iron, manganese, nickel, copper and zinc were the major causes of groundwater deterioration. The data indicate that leachate migration has affected groundwater quality in the vicinity of the disposal site, although the effect is not controlled by distance alone. The study recommends leachate containment, regular groundwater surveillance, and priority treatment for metal and salinity-related contamination.

DOI: https://doi.org/10.5281/zenodo.20269275

A Systematic Review on Hybrid Transformer Framework for Temporal Representation Learning and Longitudinal Risk Prediction In Clinical Time-Series

Authors: Abdullahi Idris, Aminu A. Abdullahi, Jamilu Awwalu, Abdullahi Uwaisu Muhammad

Abstract: The increasing availability of Electronic Health Records (EHRs), ICU monitoring systems and clinical sensor technologies has generated large volumes of temporal healthcare data that require advanced analytical approaches for effective interpretation and prediction. Traditional machine learning and statistical models often face challenges in handling complex temporal dependencies, irregular sampling, missing values and censored survival outcomes in clinical time-series data. This study employed a Hybrid Transformer Framework for Temporal Representation and Longitudinal Risk Prediction in Clinical Time Series synthesizing the relevant studies and clinical decision-making. The framework integrates the Transformer-LSTM architecture with Cox Proportional Hazards (Cox PH), Survival Random Forest (SRF) and XGBoost algorithms. The Transformer component captures long-range temporal dependencies using self-attention mechanisms, while the LSTM network models short-term sequential clinical patterns. Cox PH is applied for interpretable survival analysis, SRF for nonlinear ensemble survival prediction and XGBoost for high-performance risk classification and prediction. The review study utilizes healthcare datasets such as MIMIC-III, MIMIC-IV, elCU and PhysioNet as well as providing suitable comparative approaches against baseline models.

DOI: https://doi.org/10.5281/zenodo.20269570

Scalable Database Systems for Big Data Analytics: Challenges and Solutions

Authors: Shah Md. Tanzimul Kabir, Zahid Hassan Ome

Abstract: This paper provides a comprehensive analysis of scalable database systems, specifically designed to support big data analytics, and examines their evolution, challenges, and emerging technologies in the exascale data processing era. By examining recent research studies from 2021 to 2026, the current paper seeks to investigate how distributed database architectures, including NewSQL, cloud-native, and data lakehouse, address the fundamental scalability challenge known as the “scalability trilemma” consisting of consistency, availability, and partition tolerance. The current research introduces the Adaptive Scalability Evaluation Framework (ASEF), which integrates horizontal scaling, elastic resources, query optimization, and storage efficiency. The analysis shows that recent scalable database architectures are based on disaggregated storage and compute architectures, enabling near-linear scaling to thousands of nodes with query latencies under 100ms for petabyte-scale data sets. Cloud-native database architectures are shown to be highly elastic, with variations in query latency at the 95th percentile below 15% during scaling events. Newly emerging architectures for lakehouses, which bring the flexibility of data lakes and the performance of data warehouses, provide query performance that is 3 to 5 times better than traditional data lakes and reduce the total cost of ownership by 30 to 50 percent. Evaluation in five dimensions for analytical workloads, such as scaling behavior, consistency model, query performance, storage efficiency, and operational complexity, shows that systems with workload awareness and adaptivity perform much better than static configurations. Continuous optimization provides an improvement in throughput performance that is between 2 to 4 times.

DOI: https://doi.org/10.5281/zenodo.20270564

A Study On The Relationship Between Leadership Styles And Team Performance In Startups

Authors: Anshu Kumar Mishra, Sohail Verma

Abstract: This paper investigates the relationship between leadership styles and team performance in startup organisations, using survey-based data collected from 120 respondents comprising founders, co-founders, team leads and early-stage employees across multiple sectors. The study identifies transformational leadership as the dominant style in the sample and finds strong positive associations between vision-driven leadership, team trust, communication frequency and performance outcomes. Transactional leadership shows moderate relevance in goal-setting and accountability, while laissez-faire approaches correlate with lower performance consistency. Exploratory chi-square testing reveals significant concentration in leadership style distribution, a meaningful link between startup stage and performance rating, and a strong association between trust levels and team output. The paper concludes that startup performance is not driven by a single leadership template but by the leader’s ability to adapt style to team maturity, organisational stage and the demands of rapid growth. A hybrid leadership model combining transformational inspiration with transactional clarity emerges as the most effective pattern for high-performing startup teams.

Visualization And Analysis Of Pro Kabaddi League Data Across All Seasons Using Tableau

Authors: Myana Ramesh, Kanchapogu Prasanth, Mr. T. Srinivas

Abstract: Every PKL match across multiple seasons outcomes, dates, venues, scores, teams in one place. That’s what this dataset is. What you can actually do with it is more interesting than the description suggests. Win/loss trends show which teams hold up across a full season and which ones are inconsistent. Scoring patterns reveal whether a team plays the same way regardless of opponent or adjusts. Venue data is underrated — some teams genuinely perform differently away from home, and the numbers show it. Zoom out across seasons and the league’s own growth becomes visible too. More cities, more matches, more structure. PKL didn’t stay the same sport it was in its first season, and this data captures that shift better than any summary could.

Machine Learning Applications In Network Security

Authors: Mazlan Othman

Abstract: Machine learning (ML) has emerged as a powerful approach for enhancing network security by enabling intelligent detection, prevention, and response to cyber threats. With the increasing complexity and scale of modern networks, traditional rule-based security systems are often insufficient to identify sophisticated attacks such as zero-day exploits, phishing, and advanced persistent threats (APTs). This paper explores the application of machine learning techniques in network security, focusing on how supervised, unsupervised, and reinforcement learning models can analyze network traffic patterns to detect anomalies and malicious activities. It also examines the role of ML in intrusion detection systems (IDS), intrusion prevention systems (IPS), malware detection, and behavioral analysis. Cloud-based and real-time security monitoring systems are discussed as key enablers for scalable ML deployment in distributed network environments. Additionally, the study highlights challenges such as adversarial attacks, data imbalance, privacy concerns, and model interpretability. Emerging solutions including federated learning, explainable AI, and edge-based security analytics are also reviewed. The findings emphasize that machine learning significantly strengthens network security frameworks by enabling proactive, adaptive, and intelligent threat detection mechanisms.

DOI: https://doi.org/10.5281/zenodo.20281072

PLC And SCADA Design Of Dairy Processes

Authors: Professor Mayur Patil, Sayyad Ayaj Riyaj, Sayyad Sameer Shahabuddin, Wadgaonkar Hrushikesh Kanifnath

Abstract: Dairy processing, including pasteurization, storage, and packaging, demands precise control to ensure product safety, quality, and efficiency. This report presents the design of an automated dairy processing system using PLC (Programmable Logic Controllers) and SCADA (Supervisory Control and Data Acquisition) technology. The proposed system integrates sensors (temperature, level, flow, and pH) and actuators (valves, pumps, motors) with PLCs to execute control logic, and a SCADA HMI for real-time monitoring, data logging, and operator interaction. Automation is essential in large- scale dairy plants to reduce manpower, prevent contamination, and optimize processes. The system aims to automate milk pasteurization, Clean-In-Place (CIP) cleaning cycles, and packaging lines, resulting in consistent product quality, improved throughput, and traceability. Technical specifications, software details, and implementation methodology are discussed, and advantages and limitations of the PLC/ SCADA solution are highlighted.

Towards Fine-Grained Depressive Symptom Recognition In Memes Via Multimodal Transformer-CNN Fusion

Authors: Mrs. J. Annie Jennifer, Dr. R. Gunasundari

Abstract: The mental health indicators can be found in memes, and it is quite complex since memes consist of both text and images, and one must analyze both elements to understand their meaning. This research proposes a novel deep learning technique named Multi-CNN. Its aim is to detect depression-related signs by analyzing their linguistic and visual content simultaneously in memes . The technology uses both the BERTweet model for natural language processing and ResNet18 features for images from a neural network. It was assessed using a dataset of internet memes annotated according to eight depression indicators. Early stopping, data augmentation, and others helped improve its performance, while results were estimated by means of a weighted F1 score. As the study shows, it is more effective to use linguistic and visual components simultaneously than to employ the model based only on language or solely on image analysis for identifying the presence of depressive signs in memes. The multimodal approach resulted in a weighted F1 score of 0.6846, while the language-based model received 0.6716. Using just the picture is ineffective when it comes to recognizing depression-related memes. The study’s findings indicate that visual information and text together create strong cues for investigating mental health issues. Besides, the results point to fresh techniques and technologies that can handle the intricate heterogeneous datasets found in social media.

A Hybrid Deep Learning Framework For Real-Time Yield Prediction And Process Monitoring In Biomanufacturing

Authors: Hadiza Ibrahim Aminu, Abdullahi Mohammed Ibrahim, Buhari Aliyu, Zainab Ibrahim Aminu, Abubakar Safiyanu

Abstract: Bioprocessing plays an essential role in the large-scale production of biological products, where accurate monitoring and control are key for both yield and quality. This work aims to develop and assess a predictive framework based on Artificial Neural Networks (ANN) for estimating product yield in bioprocess operations. A multi-phase approach was implemented, beginning with data collection from online sensors and laboratory analyses, followed by preprocessing steps that included normalization, outlier removal, noise filtering, and feature engineering, utilizing dimensionality reduction through Principal Component Analysis. A hybrid ANN model was created, integrating Feed-Forward Neural Networks (FNN) for steady-state predictions, Long Short-Term Memory (LSTM) networks for learning temporal sequences, and Convolutional Neural Networks (CNN) for interpreting spectroscopic data.The model, trained using supervised learning and cross-validation, achieved strong predictive performance with a Mean Squared Error (MSE) of 1.0139 and a coefficient of determination (R²) of 0.9756, capturing 97.6% of yield variance. Predicted versus actual values showed high consistency, confirming robustness for real-time monitoring. Minor overfitting was observed at extreme values, highlighting the need for dataset expansion and regularization. Overall, the results demonstrate that ANN-based modeling effectively captures nonlinear dynamics in bioprocessing, supporting proactive optimization, disturbance detection, and integration into industrial-scale monitoring systems.

DOI: http://doi.org/10.5281/zenodo.20282959

Automated Classification Of Reptiles And Amphibians Using MobileNetV2 And Transfer Learning

Authors: Bandaru Jyothi, M.Radhika

Abstract: This article presents a new approach to automated amphibian and reptile categorization that makes use of deep Convolutional neural networks (CNNs) and transfer learning. By developing a reliable and precise MobileNetV2 model for species identification using deep learning, we tackle the limitations of traditional classification methods while also acknowledging the ecological importance of these two vertebrate groups. Using a transfer learning approach on a massive collection of amphibian and reptile images, we train a pre-trained Convolutional neural network (CNN) to overcome the issue of small dataset size. The model is able to generalize well across several species due to its high extraction efficiency. Additionally, the article delves into the significance of image augmentation techniques for enhancing model performance, particularly in cases when labeled data is scarce. Results are favorable when the proposed method is used to overcome challenges caused by changes in size, posture, and environmental factors. Ecological monitoring, conservation efforts, and biodiversity surveys might benefit from the model’s classification accuracy, which we prove by comparing it to a large dataset of amphibians and reptiles. With an accuracy rate of 82%, the proposed MobileNetV2 model cans correctly categories amphibians and reptiles. The growing field of computer vision as it pertains to animal ecology and biology has a scalable and successful approach to automated species identification, which this work adds to it. The results show that deep learning techniques particularly transfer learning, have the potential to address the issues with animal categorization. Additional investigation on the connection between AI and biodiversity protection might result from this.

DOI: http://doi.org/10.5281/zenodo.20285882

ContractSphere AI: A Smart Contract Management System Using Artificial Intelligence And Blockchain

Authors: Harshada Magar, Yash Bhalekar, Sarthak Belvalkar, Om Jadhav

Abstract: This paper presents ContractSphere AI, a system designed to help organizations manage their contracts more easily and securely. Managing contracts in companies involves many steps such as writing, reviewing, checking legal rules, and storing the final signed document. Doing all these steps manually takes a lot of time and often leads to mistakes. ContractSphere AI uses artificial intelligence to automate these steps and uses blockchain technology to make sure that signed contracts cannot be changed or faked. The system can understand contract language in multiple languages and can handle contracts from different countries with different legal rules. It uses a language model trained on legal documents together with a search system that finds relevant rules and contract examples. The final signed contract is stored securely by saving its unique hash on the blockchain, which proves the contract is genuine. This paper describes how the system works, explains the main processing steps, and discusses how well the system performs in terms of speed, security, and cost.

DOI: https://doi.org/10.5281/zenodo.20286629

Deep Learning And Image Processing-Based Bank Check Verification System

Authors: Marella Maheswari, P ASHOKA

Abstract: Revolutionizing the verification of bank checks, this innovative technology simplifies the process by integrating deep learning, image processing, and an intuitive Django-based web interface. It streamlines the process with little human participation, making it easier than ever before. Our Convolutional neural network (CNN) trained on the IDRBT check dataset and executed in PyTorch has a 99.14% success rate in recognizing handwritten digits, as shown in the introductory article. Adaptive thresholding and Gaussian blurring are implemented in the source code to enhance the picture preparation. The optical character recognition (OCR) in MATLAB can recover machine-printed text with 97.7 percent accuracy, including IFSC codes and account numbers, when Pytesseract is used in the code for region-based text extraction. The approach uses SVM classification and SIFT feature extraction for real-time authenticity checks, allowing signature verification powered by SIFT and SVM to reach 98.1% accuracy. The web-based interface allows more users to upload photos of checks, train models, see datasets, and get immediate categorization results (“Genuine” or “Not Genuine”). The system complies with CTS-2010 standards for Indian banks and the extraction of critical details such as signatures, amounts, and check numbers is possible even if it supports formats from other countries. In order to automate the verification process and decrease processing time, operational expenditures, and fraud risks, it makes use of contour detection and region-based analysis. This scalable solution sets a new standard for secure, efficient financial transactions by combining the rigors approach from the paper with the actual code implementation. Future versions may support more than one language and format.

DOI: http://doi.org/10.5281/zenodo.20286653

Intelligent Prediction Of Smartphone Addiction Through Machine Learning Algorithms

Authors: Singareddy Saritha, M. Sivaparavathi

Abstract: A rising number of individuals are displaying signs such as excessive phone usage, loss of productivity, and even physical and psychological health concerns, making Smartphone addiction a major worry in recent years. The development of reliable instruments for the prediction of Smartphone addiction and the identification of those at risk is, hence, necessary. Using survey data on Smartphone use, we constructed a machine learning model to forecast Smartphone addiction in this research. There was a wide variety of mental health concerns addressed in the survey, including demographics, phone use patterns, and anxiety, despairs, and stress. The model was constructed using a well-liked and efficient machine learning technique. In this work, numerical variables are normalized and categorical variables are encoded as part of the data preprocessing to make sure the model can train properly. Also, we used measures like accuracy to measure the model’s performance on the remaining data after training it on a subset of the data. The algorithm has successfully predicted Smartphone addiction with a high degree of accuracy, according to the findings. Use habits of mobile phones, including how often notifications were checked, how many hours spent on the phone daily, and the applications used most often, were the most critical variables for predicting addiction. Age, gender, and stress levels were other important factors. The constructed model has a number of possible uses. Healthcare providers might use it to identify patients at risk of Smartphone addiction and intervene accordingly. Also, app makers may utilize it to make their applications less addicting and more conducive to healthy phone habits. In a nutshell, the results show that machine learning algorithms can effectively predict Smartphone addiction. We need to conduct further studies to confirm our results on bigger and more varied datasets and to investigate other possible uses for this approach.

DOI: http://doi.org/10.5281/zenodo.20286754

Eco-Rupees: Plastic to Pride

Authors: Eeshritha

Abstract: India generates millions of tonnes of plastic waste annually, much of which is non-biodegradable. This paper explores the feasibility of using recycled polypropylene (PP) to produce polymer currency notes. Drawing on Australia’s pioneering adoption of polymer banknotes, the study eval-uates technical, economic, policy, and social challenges, and proposes a phased roadmap for India to transition towards sustainable currency pro-duction. The findings suggest that recycled PP notes could simultaneously address waste management, enhance currency durability, and position India as a global leader in sustainable finance innovation.

Analyzing Amazon Sales Dataset with Tableau: A Visualization Approach

Authors: Malgaram Punith Teja, Srishti Singh, Baddula Sreeya Yadav, Mrs.Sumayya Samreen

Abstract: This research paper examines what makes an e-commerce business succeed. They use a data set on fifty thousand Amazon transactions to see the effect of all sorts of different variables on sales. What factors we considered was the sale price, discount percentage, ratings and amount of reviews. We examined the mechanics of pricing ,examined the level of consumer confidence in a product , we studied preferences we considered payment mode It shows how offs, and customer ratings influence earned money of the businessThis results from suggesting discounts might increase sales over a specific term but influence long term revenues via performing well on customer ratings and consumer trust. We find that both markets of North America and Middle-East have the revenue. The findings of this research may be accessed by merchants and marketers who wish to set their prices to helps consumers in their purchase decisions and at the same time, increase their revenues. Based on these results, they are able decide about their business.

DOI: https://doi.org/10.5281/zenodo.20302852

Data Visualization Of Nfl Offensive Player Stats,1999-2013 Dataset

Authors: Gajabheenkar Roshini, Dr.Lavanya Pamulaparty

Abstract: This research focuses on analyzing NFL offensive player statistics from 1999–2013 using Tableau visualization techniques. The dataset contains player demographics combine performance, draft information, and offensive statistics. Various visualizations such as bar charts, heat maps, dashboards, and highlight tables are used to identify player performance trends and statistical patterns. The analysis helps understand how player attributes and performance metrics contribute to offensive success in the NFL.

Ai Powered Ats Resume Screeing And Job Recommendation System

Authors: Ragula Rajesh, Potti Rakesh, Poojari Jayakrishna, Mrs.V. Elavenil

Abstract: This study describes the deployment of a cloud-native AI system designed to assist HR departments by offering automated resume screening and candidate-job matching. To provide precise and contextually aware responses, the system employs a Retrieval-Augmented Generation (RAG) technique, which combines a language model with a local knowledge store of job descriptions. We developed this totally with free and open-source technologies like AWS Lambda, BERT, and ChromaDB to make the solution more accessible for startups and SMEs. Open-source approaches, such as Tesseract for OCR, are used to add scanned resume capabilities. To ensure accuracy and validity, the data is also gathered from reputable sources like ESCO skill ontology and LinkedIn datasets. We used PDFs from these sources for RAG and stored them in vector databases for efficient document retrieval, as this system aims to bridge the information gap in high-volume hiring.

DOI: https://doi.org/10.5281/zenodo.20304132

Bangladeshs Journey From Economic Basket Case To Middle-Income Complexities.

Authors: Dr. Mohammad Shah Alam Chowdhury

Abstract: Since its independence in 1971, Bangladesh has transformed from one of the poorest nations in the world famously mischaracterized as a “basket case” to one of the fastest-growing economies in South Asia. This paper examines the trajectory of Bangladesh’s economic growth, driven primarily by a booming ready-made garment (RMG) industry, robust remittance inflows, and significant advancements in social development indicators such as female labor force participation. However, despite reaching lower-middle-income status in 2015, the nation currently faces severe macroeconomic headwinds. Elevated inflation, banking sector vulnerabilities, low tax revenue mobilization, and external shocks have slowed recent GDP growth. This paper analyzes the historical drivers of growth, structural bottlenecks, and the urgent policy reforms required to ensure sustainable and inclusive economic development.

DOI: https://doi.org/10.5281/zenodo.20305441

India’s Corporate Titans: Strategic, Financial, and Integrative Dimensions of the Top 10 Mergers and Acquisitions

Authors: Bikku Kumar

Abstract: Mergers and acquisitions (M&A) have emerged as the most consequential instruments of corporate strategy in India’s post-liberalisation growth narrative. This paper undertakes a rigorous multi-dimensional examination of India’s ten largest M&A transactions — spanning banking, e-commerce, steel, telecommunications, cement, aluminium, pharmaceuticals, and automotive sectors — executed between 2007 and 2023 with a cumulative deal value exceeding USD 113 billion. Employing a descriptive-analytical framework grounded in secondary financial data, the study evaluates pre- and post-merger performance across key metrics including Return on Equity (ROE), Earnings Per Share (EPS), and Debt-to-Equity (D/E) ratio. A comparative matrix is deployed to assess strategic intent realisation, post-merger integration efficacy, and sector-specific determinants of M&A success or failure. Statistical analysis of financial ratios reveals a statistically significant divergence between successful and failed deals when measured against pre-merger benchmarks, with successful integrations yielding a mean ROE improvement of 2.1 percentage points. The findings unequivocally establish that strategic alignment, due diligence rigour, and cultural integration capacity are the decisive success factors — not deal size alone. The research contributes an empirically grounded, sector-comparative understanding of M&A dynamics in an emerging market context.

DOI: https://doi.org/10.5281/zenodo.20305623

Social Realism And Class Conflict In The Works Of Aravind Adiga And Rohinton Mistry.

Authors: Dr. Mohammad Shah Alam Chowdhury

Abstract: The present study explores the representation of socialism and social realism in the selected works of Aravind Adiga and Rohinton Mistry. Socialism, as a socio-economic ideology, advocates equality, collective welfare, and the reduction of class divisions; however, its practical implementation in India has often revealed contradictions marked by poverty, corruption, exploitation, and social inequality. Contemporary Indian English literature reflects these realities by portraying the struggles of marginalized communities and critiquing the failures of political and economic systems. The study examines how Adiga and Mistry depict the harsh realities of Indian society through themes such as class oppression, poverty, labor exploitation, corruption, identity crisis, and social injustice. Aravind Adiga’s novels, particularly The White Tiger and Last Man in Tower, expose the inequalities of post-liberalization India where capitalist ambitions overshadow socialist ideals. His narratives present a satirical and confrontational critique of economic disparity, moral decay, and institutional corruption. In contrast, Rohinton Mistry’s works, including A Fine Balance and Such a Long Journey, offer a compassionate and humanistic portrayal of ordinary individuals struggling against political oppression, social discrimination, and economic hardships. The research further compares the narrative techniques, ideological perspectives, and social concerns reflected in the writings of both authors. While Adiga emphasizes rebellion, survival, and individual ambition within a corrupt socio-economic structure, Mistry focuses on resilience, human dignity, empathy, and collective suffering. Despite their differing approaches, both writers critically examine the failures of governance and the widening gap between the privileged and marginalized sections of society. The study concludes that the works of Adiga and Mistry serve as powerful critiques of socio-political realities in contemporary India. Their fiction not only reflects the complexities of socialism and capitalism but also highlights the role of literature as a medium for social awareness, resistance, and the representation of marginalized voices.

DOI: https://doi.org/10.5281/zenodo.20305758

Telecom Network Intelligence System Using AI

Authors: Shubham sahu, Dr. Dharmbir Yadav

Abstract: The rapid growth of modern telecommunication technologies such as 2G, 4G LTE, and 5G has significantly increased the complexity of telecom network management. Telecom operators continuously generate massive amounts of network data related to traffic usage, bandwidth utilization, latency, throughput, and user activity. Traditional telecom monitoring systems mainly rely on manual analysis and threshold-based alert mechanisms, which are often unable to predict future network congestion, performance degradation, or operational failures effectively. To overcome these limitations, intelligent and automated monitoring solutions are required for efficient telecom network management. This research work, titled “Telecom Network Intelligence System using AI,” proposes an Artificial Intelligence (AI) and Machine Learning (ML) based framework for intelligent telecom network monitoring, traffic prediction, congestion detection, and performance analysis. The proposed system integrates telecom KPI analytics, predictive Machine Learning models, and real-time dashboard visualization to support proactive telecom operations and data-driven decision-making. The system analyzes important telecom Key Performance Indicators (KPIs) such as Call Setup Success Rate (CSSR), Call Drop Rate, LTE Throughput, PRB Utilization, Network Latency, and User Throughput collected from 2G, 4G LTE, and 5G networks. Machine Learning algorithms including Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) are utilized for traffic forecasting, anomaly detection, and congestion prediction. The proposed framework also integrates Python, SQL, Advanced Excel, and Microsoft Power BI for telecom data preprocessing, predictive analytics, and interactive dashboard development. The AI-driven dashboards provide real-time KPI monitoring, network health visualization, congestion alerts, and technology-wise performance comparison. Experimental analysis demonstrates that the proposed system improves prediction accuracy, reduces operational complexity, supports proactive fault management, and enhances telecom network efficiency. The research contributes toward the development of intelligent telecom monitoring systems capable of supporting future AI-driven telecom operations, AIOps integration, self-healing networks, and next-generation 5G/6G communication infrastructures.

A Study On Consumer Buying Behaviour Towards Flipkart Mobile Application

Authors: Ms. K. Divarshini, Dr. P. Poornima

Abstract: The rapid growth of e-commerce and smartphone usage has significantly transformed consumer buying behaviour. Online shopping platforms provide convenience, variety, and easy access to products, influencing consumer decisions. This study aims to analyse the buying behaviour of consumers towards the Flipkart mobile application. Primary data was collected through a structured questionnaire from 100 respondents. The findings reveal that factors such as discounts, product variety, user-friendly interface, and delivery services play a major role in influencing consumer decisions. The study concludes that Flipkart is widely preferred due to its convenience and attractive offers, but improvements in delivery speed and product quality can further enhance customer satisfaction.

DOI: http://doi.org/10.5281/zenodo.20309209

IoT and Machine Learning-Based Framework for Real-Time Methane Gas Detection and Bovine Health Monitoring in Dairy Farms

Authors: Dr. Deepika, Abhinav K G, Adithya Verma M A, Chiranth S Shetty, G Suhas Kartik

Abstract: Dairy farms generate substantial quantities of methane gas through enteric fermentation and manure decomposition. Elevated methane concentrations in enclosed or poorly ventilated cowsheds adversely affect cattle health, reduce milk productivity, and pose safety hazards to farm workers. Conventional gas-monitoring systems are reactive and threshold-based, generating alerts only after dangerous concentrations have already been reached. This paper presents an IoT and Machine Learning (ML)-based framework for real-time methane detection and bovine health risk classification. MQ-4 (methane), MQ-135 (air quality/ammonia), and DHT22 (temperature and humidity) sensors interface with an ESP32 microcontroller to collect continuous environmental readings that are transmitted to Firebase cloud storage via Wi-Fi using MQTT/HTTP protocols. Five supervised ML classifiers — Random Forest, Decision Tree, Support Vector Machine (SVM), Logistic Regression, and K-Nearest Neighbors (KNN) — are trained and evaluated for three-class bovine health risk classification (Low, Moderate, High). Random Forest achieved the highest performance with 96.8% accuracy, 96.5% precision, 96.8% recall, and an F1-score of 96.6% at the 90-10 train-test split, outperforming SVM (91.3%), Decision Tree (84.1%), KNN (79.6%), and Logistic Regression (76.9%). Automated alerts are delivered to farmers via a real-time Arduino IoT Cloud dashboard, email, and mobile push notifications. The proposed system is scalable and cost-effective.

DOI: https://doi.org/10.5281/zenodo.20321084

Design And Development Of Iot Based Agribot Solar Tracker

Authors: Ullas M K, Vishal Kumar B N, Yashwanth A S, Dr. S V. Anil Kumar, Dr. S V. Anil Kumar

Abstract: The Agri Bot Solar Tracker is a smart agricultural robot designed to support modern farming through automation and renewable energy. It performs agricultural tasks such as seed sowing and field monitoring using sensors to measure soil moisture, humidity, and temperature. The system uses Wi-Fi communication for remote monitoring and control through a monitoring and control station. Powered by solar energy with an integrated solar tracking system, the robot maximizes energy efficiency by adjusting the solar panel according to sunlight direction. This sustainable system reduces manual labor, improves operational efficiency, and promotes eco-friendly farming practices.

Sociosphere: A Social Network Platform For Empowering Real-World Social Change

Authors: Purushotam Naidu k, R.Srilatha, S.Gayathri, U.N. Harshitha, P. Siri Chandana

Abstract: Urban population growth creates new challenges related to civic infrastructure, and there is a need for efficient and smart complaint management systems. This paper describes the SocioSphere, which is an AI-based civic issue management platform that uses Natural Language Processing (NLP), machine learning, and high-performance web technologies to automatically process and route complaints. A report verification module (Fake/Real) built with Logistic Regression and engineered textual features can filter out spam and low-quality complaints. Valid complaints use a transformer model (RoBERTa) to identify the multi-class categories to which the complaint belongs. Furthermore, we have added a method of estimating the urgency of a complaint through the use of VADER-based sentiment analysis and heuristics for engagement, thus allowing for priority-based decisions. FastAPI is used to develop the backend API layer, offering high-speed (asynchronous/low latency) performance for model inferences and data processing. Complaints will be stored in the system’s database and dynamically routed to appropriate authorities for final resolution. The experimental results demonstrate that the approach is effective for both classification and validation, as well as improving transparency and reducing manual work through the use of data-driven governance within smart city systems.

DOI: https://doi.org/10.5281/zenodo.20321890

Development of Nonconventional First-Class Fly Ash Bricks Using Silica Fume and Alkali Activators

Authors: Sanju, Rahul Kumar Jha, Shivam Kumar, Sumit kumar, Ashish Juneja

Abstract: This study focuses on developing eco-friendly fly ash bricks using silica fume and alkali activators (NaOH and Na₂SiO₃) as a sustainable alternative to traditional clay bricks. The objective is to utilize industrial waste effectively while reducing environmental degradation caused by clay brick production. Fly ash was used as the primary material, with silica fume added to enhance mechanical strength and alkaline chemicals to initiate geopolymerization. Bricks were prepared by mixing materials, molding, and proper curing. Tests including compressive strength, water absorption, and visual inspection were conducted. Results indicate that alkali-activated fly ash bricks with silica fume exhibit superior strength and durability compared to conventional clay bricks. This approach promotes waste utilization, low pollution, and energy-efficient construction, offering a promising solution for sustainable building practices.

Fabricartion Of Portable Noodle Making Machine

Authors: Jayanth L, Kaviraj M Kumkumgar, Kuldeep Raj M S, Madhuraj H R, Dr. Mohammad Rafi. H. Kerur

Abstract: This project (Phase 2) presents the fabrication of an innovative, portable noodle making machine aimed at providing a cost-effective and user-friendly solution for small-scale and home-based noodle production. Traditional noodle machines tend to be expensive, and require considerable expertise, limiting their accessibility especially for households and micro-entrepreneurs. To address this gap, the proposed machine utilizes a lightweight frame built from available materials, with food-grade stainless steel components for all parts in contact with the dough. The core mechanism involves a threaded extrusion system powered by a small electric motor, which efficiently transforms freshly prepared dough into uniformly shaped noodles. The process comprised conceptual sketches, noodle quality, and portability. Test results demonstrate consistent noodle extrusion with ease of operation and quick cleaning, making it suitable for diverse environments including homes, street vendors, and small eateries. The modular construction further enhances maintainability and transport convenience. This project not only offers a practical fabrication approach but also supports entrepreneurial activities by enabling affordable fresh noodle production. Overall, the project contributes an innovative, accessible, and sustainable noodle-making solution that promotes food variety and small business empowerment.

Effect Of Spent Mushroom Substrate-Based Compost Enriched With Micronutrients On The Productivity Of Maize (Zea Mays L.) And Soil Health

Authors: Pratyush Ranjan Sahu, Nishith Das

Abstract: The incorporation of agro-industrial residues like spent mushroom substrate (SMS) into nutrient management strategies provides a sustainable pathway for intensive agriculture. A field experiment was carried out during the Kharif of 2025 at GIET University, Odisha, to assess the impact of SMS-based compost supplemented with zinc (Zn), boron (B), and neem cake on the physiological, yield, and economic indices of maize (Zea mays L., var. VNR 4226). Utilizing a Randomized Block Design (RBD) with eight treatments and three replications, the study revealed that integrating SMS with micronutrients and the Recommended Dose of Fertilizers (RDF) significantly augmented crop performance. Treatment 8 (T8) (SMS@7t/ha + dried plant debris@2t/ha + cow dung@1t/ha + 5% Zn + B + RDF) delivered the highest plant stature (217.47 cm), maximum dry matter accumulation (237.63 g/plant), and superior yield attributes. This resulted in an exceptional kernel yield of 8.17 t/ha, a 175% increase over the FYM control. Soil chemical properties, notably available phosphorus, improved considerably under SMS regimes. Economically, T8 yielded the highest net monetary returns (₹1,18,642/ha), whereas T6 (RDF + 5% neem cake) optimized the Benefit-Cost ratio (2.38). These findings advocate for the integrated use of fortified SMS compost to enhance maize productivity and soil health.

Real-Time Sign Language Detection Using Computer Vision And Machine Learning

Authors: Assistant Professor. Sukanya H N, Adithya N, Akash H S, Farazulla Khan, G P Chinmayaradhya

Abstract: Sign language is the primary communication medium for deaf and hard-of-hearing individuals, yet it remains largely inaccessible to the general public, creating a persistent commu-nication barrier. This paper presents a real-time sign language detection system that leverages computer vision and machine learning to recognise hand gestures and convert them into readable text or speech with minimal latency. The proposed framework follows a structured processing pipeline comprising data acquisition, key-frame extraction, skin-colour-based hand segmentation, face-region elimination, morphological filtering, and noise reduction. Discriminative spatial features are derived using fuzzy triangular membership functions, and gesture recognition is performed by a K-Nearest Neighbour (Mediapipe) classifier trained on a self-collected dataset of two-handed dynamic signs. For real-time operation, the system employs the MediaPipe library for hand-landmark detection and a Convolutional Neural Network (CNN) trained with TensorFlow/Keras for gesture classification. Experimental evaluation demonstrates an overall gesture recognition accuracy of approximately 92%, with a high-confidence detection of 99.6% for the “Peace” gesture and an average detection-plus-translation latency of approximately 150 ms per frame. The system requires no specialised sensors or gloves, making it cost-effective and practically deployable in educational institutions, healthcare facilities, and public service environments. Results confirm the feasibility and effectiveness of the proposed approach as an assistive communication solution for hearing-impaired individuals.

Job Satisfaction Among Employees And Its Impact On Domestic Life

Authors: Priya kumari, Sohail Verma

Abstract: Job satisfaction is one of the most important aspects influencing employee performance, mental well-being, and overall quality of life. In the modern competitive work environment, employees often face workload pressure, stress, long working hours, and work-life imbalance, which directly affect their domestic and family life. This study examines the relationship between job satisfaction and employees’ domestic life and analyses how workplace conditions influence family relationships, personal happiness, and social well-being. The paper highlights factors such as salary, working conditions, job security, organizational support, work-life balance, and employee recognition in determining job satisfaction levels. The study also discusses how satisfied employees maintain healthier family relationships, lower stress levels, and improved domestic harmony, whereas job dissatisfaction may lead to emotional stress, conflicts, and reduced quality of life at home. The findings suggest that organizations should focus on employee welfare, flexible work policies, and supportive work environments to improve both job satisfaction and domestic well-being.

Impact Of Digital Payments On Daily Life. A New Setup

Authors: Sweta Pandey, Meentu Grover

Abstract: Digital payment systems have transformed the way people conduct financial transactions in their daily lives. The rapid growth of internet technology, smartphones, and financial technology has increased the adoption of digital payments across the world. In India, digital payment methods such as Unified Payments Interface (UPI), mobile wallets, internet banking, debit cards, and credit cards have become highly popular due to convenience, speed, and security. This paper examines the impact of digital payments on daily life and analyses how cashless transactions have influenced consumer behaviour, business activities, and economic growth. The study highlights the advantages of digital payments, including faster transactions, financial inclusion, transparency, reduced dependency on cash, and improved online shopping experiences. It also discusses challenges such as cyber fraud, privacy concerns, internet dependency, and lack of digital literacy among certain sections of society. The paper concludes that digital payments have significantly improved the efficiency and convenience of daily financial activities and will continue to play an important role in the future digital economy.

Analysis Of Risk Management In Construction Project.

Authors: Mahmud Danladi, Salihu Sarki Ubayi, Mahmud Danladi

Abstract: The construction industry is highly susceptible to uncertainties and risks that significantly influence project delivery in terms of cost, time, quality, safety, and sustainability. This study examined the analysis of risk management in project construction within the Nigerian construction industry. Specifically, the study identified the types of risks associated with project construction, examined the factors affecting risk management, and evaluated the effects of risk management on construction project performance. A descriptive quantitative research design was adopted. Data were collected through structured questionnaires administered to 80 construction engineers involved in risk management practices, out of which 74 valid responses were retrieved, representing a response rate of 92.5%. Descriptive statistical tools including frequency distribution, percentage analysis, mean item score, and standard deviation were used for data analysis. The findings revealed that inadequate site investigation, inadequate specification, contractor’s experience, weather implications, natural disasters, new technology, and shortage of resources were among the most significant risks affecting construction projects. Resource availability, project complexity, and time compression were identified as the major factors affecting risk management implementation. Furthermore, the study established that risk management strongly affects project cost, completion time, productivity, project quality, health and safety, and environmental sustainability. The study concluded that effective risk management is essential for successful construction project delivery and recommended proper site investigation, adequate resource allocation, experienced workforce engagement, and proactive risk management strategies to improve project outcomes in Nigeria.

DOI: https://doi.org/10.5281/zenodo.20324799

Smart Vendor AI: An AI-Driven Smart Vendor Management System For Real-Time Freshness Detection And Dynamic Retail Intelligence

Authors: Sudarshan K, Sushmitha H Y, Varshanth Gowda M L, Vinay C N

Abstract: Street vendors selling fruits and vegetables across India face a persistent challenge: perishable stock loses value as the day progresses, yet pricing remains static. This paper presents Smart Vendor AI, a complete end-to-end system that combines inventory management, point-of-sale operations, analytics dashboards, sales forecasting, and AI-assisted product quality assessment within a unified web-based platform.. The pipeline consists of six sequential layers: a fine-tuned YOLOv8s model for ripeness classification, a signal engine that converts raw predictions into weighted freshness scores, a deterministic market con- text module, an XGBoost pricing model trained on 5,000 realistic scenarios, a rule-based decision engine, and a FAISS-backed retrieval-augmented generation module powered by LLaMA 3.3 70B. Experiments on banana and tomato datasets show classifi- cation accuracy of 99.3% and 98.6% respectively. The system delivers specific, actionable vendor instructions—including an exact discount percentage and an inventory action string—without requiring any technical knowledge from the user. Results indi- cate meaningful potential to reduce the 30–40% annual revenue loss that vendors typically incur through spoilage and mispricing.

AGRONEXUS: An IoT-Based Real-Time Environmental Monitoring And Public Display Framework For Smart Campuses

Authors: Mrs. Pragati Sharma, Aman Chandel, Harsh Sharma, Priya Upadhyay, Safiya Naaz, Sunny Kumar, Tanu Saini, Vashu Dhiman

Abstract: The escalating degradation of environmental quality in educational institutions and public spaces demands cost -effective, real-time monitoring solutions. Conventional systems rely on centralised infrastructure or mobile applications that fail to deliver localised, immediate feedback. This paper presents AgroNexus, an IoT-driven environmental monitoring and public display platform that integrates the ESP32 microcontroller with four sensing modules—DHT22 (temperature and humidity), MQ135 (air quality), a rain sensor (precipitation detection), and DS3231 (real-time clock)—to deliver continuous data acquisition, threshold-based alerting, and live display via a six-panel P10 LED matrix. Experiments conducted in a simulated campus environment demonstrate that AgroNexus achieves high sensor accuracy, low false-alert rates, and sub-three-second display refresh cycles, outperforming single-sensor baselines across all evaluation metrics. The framework is economical, scalable, and readily deployable in smart campuses, industrial zones, and public spaces, establishing a transparent and auditable pipeline for environmental awareness.

DOI: http://doi.org/10.5281/zenodo.20325235

IOT Based Environment Monitoring System Using STM32

Authors: Mrs. Parul Gupta, Safiya Naaz, Priya Upadhyay, Mohd. Arshad, Tanu

Abstract: The rapid degradation of environmental quality driven by industrialization and urbanization demands continuous, real-time monitoring of key atmospheric and ecological parameters. This paper presents the design and implementation of a low-power, solar-powered IoT-based environmental monitoring system built around the STM32 microcontroller. The proposed system integrates a suite of sensors to measure temperature, humidity, atmospheric pressure, air quality, UV radiation, and soil moisture. Data is transmitted wirelessly over Wi-Fi and LoRa protocols to a cloud-based dashboard for real-time visualization and historical analysis. The system is entirely powered by a solar photovoltaic panel coupled with a lithium-ion battery and a power management unit, ensuring uninterrupted autonomous operation in remote locations without access to the electrical grid. Experimental results demonstrate reliable data acquisition with a sampling accuracy exceeding 97%, an end-to-end data transmission latency of less than 2 seconds, and continuous operation exceeding 72 hours on battery backup under cloudy conditions. The proposed system offers a cost-effective, scalable, and energy-autonomous alternative to conventional environmental monitoring stations.

DOI: https://doi.org/10.5281/zenodo.20325332

Real-Time AI-Based PPE Compliance And Safety Intelligence For Construction Sites

Authors: S. Santhosh Kumar, Dr. R. Senthamil Selvi

Abstract: The construction site is considered a risky place for employees, and the risks are associated with falling objects, machines, and exposure to harmful substances. Monitoring the implementation of Personal Protective Equipment (PPE) standards, including helmets, vests, gloves, boots, and masks, is of critical importance in preventing accidents and injuries. The conventional approach to monitoring the implementation of these standards is through manual observation, which is associated with time delays and human error. This study proposes an intelligent framework for the implementation of PPE standards and safety monitoring using an improved YOLOv11 deep learning model for the detection and classification of different types of PPE in real-time construction site video feeds. The model is trained on a diverse dataset to cater to complex backgrounds, lighting, occlusion, and multiple PPE pose angles, ensuring the model performs well in diverse site environments. The framework helps improve workplace safety by ensuring compliance, reducing the probability of accidents caused by negligence, and promoting regulatory compliance, thereby creating a culture of consistent PPE usage and safe work practices across the construction industry.

DOI: http://doi.org/10.5281/zenodo.20325549

Ai Image Fraud Detector

Authors: Shreya Shashikant Patil, Shital Nivrutti Sutar, Prachi Prasad Patil, Mrs . Meghana Khare

Abstract: Artificial intelligence has made it possible to generate highly realistic images, which can be mis used for misinformation, fraud and identity theft. Detecting such AI- generated images manually is difficult and time consuming. Detecting such AI-generated images has become very important to maintain the authenticity of digital content. This paper presents an AI Image Fraud Detector such that uses deep learning techniques to classify as real or fake. The system integrates YOLO (You Only Look Once) model with a web-based applications developed using Flask and JavaScript. Users can upload images through a user-friendly interface, and the system provides prediction result along with confidence scores. The model processes images in real time and ensures fast detection. Experimental results show that the system performs efficiently with good accuracy depending on the dataset quality. This research contributes to improving digital security by providing an automated solution for detecting AI-generated images. In this research, we developed an AI image fraud detection system using deep learning models such as VGG16, ResNet, and InceptionV3.Thesemodels are trained on a dataset containing both real and AI generated images. The system compares the performance of all three model to find which one give better accuracy. The model is trained on a dataset from Kaggle that contain both real and fake images of Aadhar- id photo and other documents. Image preprocessing techniques are used to improve performance of the model. The result show that deep learning models can effectively detect fake images, with one model performing better based on accuracy and efficiency. The study highlights that using multiple models improve reliability and provides a strong solution for detecting AI-generated images in real world applications. We also tested different settings of the model to understand what works best. Our study shows that it is a strong and reliable method for detecting AI-generated images and can be useful in real-world applications. Model is addressing the increasing challenge of AI-generated image detection, laying a foundation for future research in critical area.

Review Paper On Advance Robotic Arm Hand With Object Detection Vehicle

Authors: Prof. V. U. Bansude, A. S. Yadav, D. D. Pawal, A. S. Yadav

Abstract: The robotic arm is one of the most significant innovations in the field of automation and robotics, capable of replicating human arm movements with high precision, accuracy, and repeatability. Over the past decades, researchers have developed robotic arms for various applications such as industrial manufacturing, medical surgery, agriculture, space exploration, and defense operations. Early robotic arm systems were limited to simple wired control and basic pick-and-place operations. However, recent advancements have integrated modern technologies including artificial intelligence (AI), computer vision, machine learning, and Internet of Things (IoT) to achieve intelligent and autonomous functionality. This paper presents a comprehensive survey of existing robotic arm systems with emphasis on their design methodologies, actuation techniques, control mechanisms, and practical applications. A comparative analysis of various research works has been conducted to understand the technological evolution and identify limitations in current robotic arm systems. The study also highlights future opportunities for developing intelligent robotic arms capable of performing complex real-world tasks with improved efficiency and reliability.

Laro-based Wearable Women Safety Alert System

Authors: Amrutha H, Chaithra HM, Chandana BM, Chethana GH, Mr. Santhosh Babu KC Assistant Professor

Abstract: Women’s safety remains a critical global concern, with increasing incidents of harassment, assault, and emergencies requiring immediate intervention. Traditional safety devices such as panic buttons and mobile applications have limitations: they rely on cellular connectivity, which may be unavailable in remote areas, and they lack automatic fall detection for situations where the user cannot manually trigger an alert. This project presents a comprehensive LoRa based women safety device that combines manual panic activation, automatic fall detection, and dual communication channels for maximum reliability. The system consists of two units: a portable transmitter unit carried by the user and a stationary receiver unit placed at a trusted location such as home, workplace, or police station. The transmitter unit uses an ESP32 microcontroller with a panic button for manual emergency activation and an MPU6050 sensor for automatic fall detection. When an emergency is detected, the transmitter sends an alert via LoRa wireless communication (operating at 433MHz) over long distances (several kilometers). Simultaneously, a GSM800L module sends an SMS alert directly to authorities or emergency contacts. The receiver unit, comprising another ESP32 with a LoRa module, buzzer, and LCD display, receives the LoRa transmission, displays the alert message on the LCD, and activates an audible buzzer to notify personnel at the receiving location. This dual-path communication ensures that even if one channel fails (GSM network down or LoRa interference), the other channel may still deliver the alert. The system is designed to be wearable, low-power, and effective in both urban and remote areas where cellular coverage may be unreliable.

</p

Natural Space As A Transformative Environment For Childrens Well-being

Authors: Murmylo Yulia

Abstract: Children’s happiness” is the principal vector of any society and the foundation on which the Sustainable Development Goals rest today, tomorrow and for the next generation. This multidimensional term encompasses complex components, without each of which it remains incomplete. We examine the interrelation between the phenomenon of children’s happiness and nature-based practices (in the context of an environment in which, through neurobiological, sensory and interpersonal mechanisms, qualitative changes take place in the child’s personality, emotional repertoire, cognitive strategies and immune profile). We review existing methodologies, the international studies that have been conducted on this topic, and their results, and draw a conclusion about the most effective practices contributing to the enhancement of children’s happiness. This article is unique in that it identifies a set of aspects of children’s well-being, presents concrete methodologies for analysing this multifaceted concept, lays out natural factors of influence, summarises a research base on the impact of nature on the younger generation across individual components, and describes working programmes that demonstrate the action of the natural environment on children as transformative. The author argues that, from the standpoint of sustainable development, nature-oriented programmes possess a unique property: they are simultaneously a tool for achieving goals (improving children’s health and well-being) and a means of forming agents of sustainable development in the next generation. Adapting the principles of the Stanford course “Interpersonal Dynamics” to nature-based programmes for children opens up the possibility of creating a new class of pedagogical products.

DOI: http://doi.org/10.5281/zenodo.20341762

SkillLink: A Web-Based Peer-to-Peer Skill Exchange And Mentoring Platform With AI-Assisted Session Management

Authors: Manoj S, Chaitra B P, Nandan J M, Nehal Eldho Binu

Abstract: SkillLink is a web-based peer-to-peer mentoring platform designed to enable real-time skill exchange between learners and teachers. The system is developed using the MERN stack and integrates WebRTC for browser- based video conferencing, Socket.IO for real-time communication, and the Gemini API for AI-assisted interaction. Teachers publish skills and availability through a drag-and-drop calendar interface, while learners can browse and book sessions directly. The platform includes session lifecycle management, subscription-based access control, a credit-based reward system, and a five-star rating mechanism. Experimental evaluation demonstrates low-latency communication, reliable session tracking, and efficient mentor matching, making SkillLink a scalable alternative to conventional e-learning systems.

Smart Attendance System Using Face Recognition

Authors: Shital Vishwanath Ban, Shankar Sanjay Rathod, Prerana Prakash Malgave, Mrs. M.R, Raste

Abstract: Traditional attendance systems are time-consuming and prone to errors such as proxy attendance. This paper presents a Smart Attendance System using Face Recognition technology. The system automatically detects and recognizes faces to mark attendance. It uses machine learning and image processing tech-niques for accurate identification. The system captures real-time images through a camera, processes them, and updates attendance records. It reduces manual effort and improves accuracy. The system is implemented using Python, OpenCV, and a database for storing attendance data.

MedLens: An AI-Powered Radiology Report Simplification System for Improved Patient Accessibility

Authors: B. M. Promod Kumar, Bhavana N. S., C. Chinmayi, Deepthi C. Shekar, Deenadayal B. K.

Abstract: Radiology reports generated from imaging modalities such as X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound scans are critical clinical documents. However, these reports are authored in complex medical terminology intended for radiologists and specialist physicians, rendering them largely inaccessible to patients and non-medical users. This communication gap results in confusion, anxiety, and increased dependency on healthcare professionals for basic explanations. This paper presents MedLens, an AI- powered radiology report simplification system that bridges this gap by leveraging Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG). The system extracts text from uploaded PDF reports using PyMuPDF, processes clinical content using Google Gemini AI models, and generates accurate, context-aware patient-friendly summaries. It further classifies the urgency of findings into levels (Low, Moderate, High, Critical), and integrates multilingual translation, text-to-speech functionality, and an AI-powered contextual chatbot. The platform is deployed using FastAPI on the backend and React.js with Tailwind CSS on the frontend. Experimental results demonstrate that MedLens successfully simplifies complex medical terminology, detects critical conditions, provides multilingual support, and enables interactive report-based queries, thereby empowering patients with better health awareness and facilitating informed discussions with healthcare providers.

DOI: https://doi.org/10.5281/zenodo.20351715

Class-Balanced Knowledge Distillation for Imbalanced Urban Vehicle Detection on CAVI-14

Authors: Parag Hossain

Abstract: Urban vehicle detection systems face a fundamental challenge that is often overlooked in benchmark datasets: severe class imbalance. In real-world traffic scenes, common vehicles such as cars appear thousands of times more frequently than critical but rare categories including ambulances, e-bikes, and motorcycles. This imbalance causes standard detectors to become biased toward majority classes, leading to unacceptable failure rates for minority class detection in safety-critical applications. In this paper, we propose a novel Class-Balanced Knowledge Distillation (CBKD) framework specifically designed to address this challenge on the challenging CAVI-14 dataset, which contains fourteen urban vehicle categories with up to fifteen-fold class imbalance. Our method integrates three key components: class-balanced sampling to ensure equal exposure to all classes during training, focal loss with class-specific weights to down-weight easy majority examples, and knowledge distillation from a teacher model pretrained on a synthetically balanced dataset. Extensive experiments demonstrate that CBKD achieves perfect mean average precision at 0.50 intersection-over-union threshold (mAP50) of 1.000 and near-perfect mAP50-95 of 1.000 after one thousand training epochs. Per-class F1 scores consistently exceed 0.97 across all fourteen categories, including the rarest classes. Qualitative results on validation images show accurate detection even under heavy occlusion and challenging lighting conditions. Our approach establishes a new state-of-the-art on the CAVI-14 dataset and provides a practical, reproducible solution for imbalanced object detection in intelligent transportation systems.

DOI: https://doi.org/10.5281/zenodo.20351972

AgriHub: An AI-Powered End-to-End Agricultural Decision Support Platform

Authors: Mohammed Munyim Hussain V, Poorvaj K P, Prashanth S R, Preetham M, Mr P Prasanna

Abstract: Agriculture remains a cornerstone of economic activity across developing nations, yet smallholder farmers routinely face yield gaps caused by uninformed decisions on crop selection, soil nutrition, and disease management. This paper presents AGRI HUB, a web-based Crop and Soil Management System that unifies several machine-learning and deep-learning services behind a single Flask-driven interface. Four core modules are delivered: (i) smart crop recommendation using a Random Forest classifier trained on seven agro-climatic parameters, achieving 99.55% accuracy across 22 crop classes; (ii) soil nutrient analysis and fertilizer recommendation through NPK deficit computation against crop-specific thresholds; (iii) plant disease detection using a ResNet-9 convolutional neural network capable of classifying 38 disease categories from leaf photographs; and (iv) real-time, weather-driven activity planning by consuming OpenWeatherMap API data to generate seven-day farming calendars. An AI chatbot powered by the Google Gemini large language model supplements the analytical modules with conversational agronomic guidance. A crop profitability comparison dashboard rounds out the system, enabling evidence-based economic decisions. Experimental evaluation confirms that the integrated platform consistently outperforms single-module alternatives in both accuracy and decision breadth, offering a scalable, cost-effective tool for precision agriculture.

AgriHub: An AI-Powered End-to-End Agricultural Decision Support Platform

Authors: Govardhan Jadhav, Anand Ahire, Hitesh Kalal, Rishi Mishra, Prof.S.R.Agrwal

Abstract: Social media usage has increased significantly in recent years, leading to concerns about addictive behavior and its impact on users’ productivity and mental well-being. This paper presents a Social Media Addiction Tracker system designed to monitor, analyze, and manage user engagement across various platforms. The proposed system collects data such as screen time, frequency of usage, and interaction patterns, and applies data analytics and machine learning techniques to identify signs of excessive usage and potential addiction. Based on the analysis, the system provides real-time feedback, usage reports, and personalized alerts to help users regulate their social media habits. Experimental evaluation demonstrates that the system effectively raises user awareness and supports behavior modification. The proposed solution aims to promote healthier digital habits and improve overall well-being.

Incentive-Driven Social Media Usage Regulation System

Authors: Govardhan Jadhav, Anand Ahire, Hitesh Kalal, Rishi Mishra, Prof.S.R.Agrwal

Abstract: Social media usage has increased significantly in recent years, leading to concerns about addictive behavior and its impact on users’ productivity and mental well-being. This paper presents a Social Media Addiction Tracker system designed to monitor, analyze, and manage user engagement across various platforms. The proposed system collects data such as screen time, frequency of usage, and interaction patterns, and applies data analytics and machine learning techniques to identify signs of excessive usage and potential addiction. Based on the analysis, the system provides real-time feedback, usage reports, and personalized alerts to help users regulate their social media habits. Experimental evaluation demonstrates that the system effectively raises user awareness and supports behavior modification. The proposed solution aims to promote healthier digital habits and improve overall well-being.

Hybrid Generative Artificial Intelligence and Quantum-Mechanical Screening for Accelerated Drug Lead Optimization

Authors: Prof. R. Raveendhra

Abstract: Artificial intelligence (AI) is transforming pharmaceutical research by enabling rapid molecular prediction, virtual screening, and biological data integration. However, many current AI systems lack energetic realism and mechanistic interpretability. This manuscript presents a conceptual framework termed Adaptive Quantum-Generative Optimization (AQGO), integrating generative AI, molecular transformers, quantum-mechanical screening, molecular docking, and expert pharmacological validation. The framework is designed to improve lead optimization by combining data-driven molecular generation with physics-based energetic evaluation. The article reviews current advances in AI-driven drug discovery, the role of quantum chemistry in molecular simulation, translational challenges, and future directions for hybrid AI–quantum systems. Emphasis is placed on explainability, reproducibility, ethical deployment, and scientific transparency. The proposed architecture highlights the potential of combining generative intelligence with quantum-mechanical validation to support more efficient and reliable pharmaceutical discovery pipelines.

Smart Classroom and Digital Learning

Authors: Lalita Sonawane

Abstract: Smart classrooms and digital learning are changing education with the help of technology. Tools like smart boards, projectors, online classes, artificial intelligence, and virtual classrooms help students learn in an easy and interesting way. During the COVID-19 pandemic, online learning became very important because schools and colleges were closed. This research paper explains the meaning, benefits, challenges, and future of smart classrooms and digital learning. The information for this paper was collected from books, journals, websites, and research articles. The study shows that smart classrooms improve communication between teachers and students, increase student participation, and provide flexible learning opportunities. Students can study anytime and anywhere through digital platforms. However, there are also some problems like poor internet connection, high technology cost, lack of digital skills, and cyber security risks. The paper concludes that smart classrooms and digital learning are important for the future of education and need proper support, training, and infrastructure.

A Proximal Adaptive Momentum Algorithm with Variance Reduction for Nonconvex Composite Optimization: Convergence Analysis and Complexity Bounds

Authors: Dr.K.Srinivasan, Dr. M. K. Vediappan

Abstract: We propose and analyze the Proximal Adaptive Momentum with Variance Reduction (PAMVR) algorithm, a novel first-order method for solving nonconvex composite optimization problems of the form min F(x) = f(x) + g(x), where f is a smooth nonconvex function and g is a proper convex, lower-semicontinuous regularizer. PAMVR integrates three complementary mechanisms: (i) a momentum-corrected gradient estimator with adaptive step sizes, (ii) a periodic variance-reduction snapshot strategy inspired by SVRG, and (iii) a proximal operator for handling the nonsmooth component. Under standard Lipschitz-gradient and bounded-variance assumptions, we establish global convergence to an epsilon-approximate stationary point with a sample complexity of O(n + n^{2/3}/epsilon^2) stochastic gradient evaluations, matching the best-known bounds for this problem class while requiring weaker algorithmic assumptions than existing momentum-based methods. We further prove almost-sure convergence of the iterate sequence under a Kurdyka-Lojasiewicz (KL) regularity condition, obtaining explicit convergence rates depending on the KL exponent. The theoretical findings are validated on benchmark nonconvex problems including sparse logistic regression, matrix completion, and neural network training, demonstrating consistent improvements of 15–32% in convergence speed over PROX-SVRG, ProxGD-M, and Spider-Boost baselines. These results establish PAMVR as both a theoretically sound and practically competitive method for large-scale nonconvex optimization.

DOI: https://doi.org/10.5281/zenodo.20354108

A Study On The Impact Of Financial Literacy On Financial Decision-Making Among College Students With Special Reference To Coimbatore District

Authors: Ms. Nandhini R, Mr. Mohan Kumar

Abstract: Financial literacy plays a significant role in enabling individuals to make informed and effective financial decisions in their daily lives. In today’s rapidly changing financial environment, college students are increasingly required to manage personal finances, including budgeting, saving, investing, and controlling expenses. However, many students lack adequate financial knowledge and awareness, which may lead to poor financial behaviour and long-term financial instability. This study aims to assess the level of financial literacy among college students and examine its influence on their financial decision-making behaviour. The research also focuses on identifying the major sources of financial information used by students and analysing the relationship between financial literacy and saving habits. The study highlights the importance of financial education in developing responsible financial behaviour among young adults. By identifying gaps in financial awareness, the research provides useful insights and recommendations for improving financial literacy programs and promoting better financial management practices among students for a financially secure future.

DOI: https://doi.org/10.5281/zenodo.20354178

A Study On Work From Home And Its Impact On Employee Productivity, Work-Life Balance And Job Satisfaction With Special Reference To It And Bpos Employees In Coimbatore City

Authors: Dr. Kowsalya G, Ms. Kowsalya

Abstract: The COVID-19 pandemic catalysed the most rapid and large-scale transition to remote work in human history, transforming work from home from a marginal flexibility benefit into the dominant mode of employment for millions of knowledge workers globally and in India. While the immediate crisis has subsided, hybrid and fully remote work arrangements have become a permanent feature of the employment landscape, particularly in the Information Technology and Business Process Outsourcing sectors that constitute two of Coimbatore’s most significant and fastest-growing industries. This study examines the impact of work from home arrangements on employee productivity, work-life balance, and job satisfaction among IT and BPO employees in Coimbatore city. Primary data were collected through a structured questionnaire administered to 120 IT and BPO professionals currently working from home or in hybrid arrangements. Secondary data were gathered from academic journals, NASSCOM reports, SHRM publications, and government employment surveys. Statistical tools including simple percentage analysis, weighted average method, and chi-square test were employed. Findings reveal that while work from home significantly improves perceived productivity and time flexibility for a majority of respondents, challenges in work-life boundary maintenance, social isolation, and home infrastructure quality create significant well-being risks that require proactive organisational and policy intervention.

DOI: https://doi.org/10.5281/zenodo.20354225

A Study On The Socio-Economic Impact Of The Tamil Pudhalvan Scheme On Low-Income Families

Authors: Ms. Dr.B. Geethpriya, Mr. M. Balakumar

Abstract: This study examines the socio-economic impact of the Tamil Pudhalvan Scheme a state-funded educational welfare initiative introduced by the Government of Tamil Nadu on low-income families residing in the Coimbatore district. A structured questionnaire was administered to 151 beneficiary respondents selected using simple random sampling. Data were analyzed using percentage analysis and one-way Analysis of Variance (ANOVA) to test whether statistically significant differences exist in perceived financial relief, educational motivation, and dropout-prevention effectiveness across educational level groups (school, diploma, undergraduate, and postgraduate). ANOVA results indicate significant between-group differences in financial impact (F(3, 147) = 4.82, p = .003) and educational motivation (F(3, 147) = 3.61, p = .015), while dropout prevention did not reveal statistically significant variation (F(3, 147) = 2.14, p = .097). The findings suggest that the scheme delivers differentiated benefits depending on the student’s level of education. Post hoc Turkey HSD tests revealed that postgraduate students perceived significantly greater financial relief compared to school-level beneficiaries. Policy recommendations include increasing the monthly stipend, expanding digital outreach, and integrating the scheme with vocational skill programmes.

DOI: https://doi.org/10.5281/zenodo.20354253

A Study On Ai-Driven Consumer Segmentation And Social Marketing Strategies For Sustainable Water Purification Businesses In Coimbatore City

Authors: Ms. Revathi G, Mr. Ashwath R

Abstract: The study adopts a descriptive research design and is based on both primary and secondary data. Primary data was collected from 150 respondents using a structured questionnaire, while secondary data was gathered from journals, articles, and online sources. The research focuses on identifying consumer segments, analyzing the impact of AI in understanding customer preferences, and evaluating the effectiveness of social marketing strategies in influencing consumer awareness and purchasing behavior. The study adopts a descriptive research design and is based on both primary and secondary data. Primary data was collected from 150 respondents using a structured questionnaire, while secondary data was gathered from journals, articles, and online sources.

DOI: https://doi.org/10.5281/zenodo.20354287

Exploratory And Visual Analytics Of Mtcars Dataset Using Tableau Tool

Authors: Cherukupalli Harshitha, Darapu Saivenkat, Muvvapati Koushik, Mrs.K.Sireesha

Abstract: The mtcars11 dataset provides complete data about vehicle performance and their corresponding features. The dataset includes essential features which measure fuel efficiency through miles per gallon and provide engine specifications and horsepower and vehicle weight and transmission type and driving conditions. The information assists in examining trends associated with vehicle effectiveness, performance, and operational conduct. Through the use of data visualisation methods on this dataset, we seek to comprehend how elements such as weight, engine power, and transmission type affect fuel efficiency and overall performance. It also aids in recognising patterns under various driving circumstances like traffic and weather. The knowledge acquired can enhance decision-making in automotive evaluation, vehicle development, and performance improvement.

DOI: http://doi.org/10.5281/zenodo.20355470

Deep Shield: Protecting Against Deepfakes

Authors: Dr. M. C. Padma, Bhoomika M, Faika Mehvish, Praveen Kumar R

Abstract: The rapid proliferation of deepfake videos—synthesised using Generative Adversarial Networks (GANs) and allied deep-learning techniques—poses grave risks to societal trust, democratic processes, and personal privacy. Existing detection approaches predominantly rely on frame-level spatial analysis and consequently fail to capture temporal inconsistencies that arise in manipulated sequences. This paper presents Deep Shield, a hybrid deep-learning framework that couples a ResNeXt convolutional neural network (CNN) for spatial feature extraction with a Long Short-Term Memory (LSTM) recurrent network for temporal sequence modelling. Each video frame is first preprocessed via face detection and alignment, after which ResNeXt encodes per-frame spatial embeddings that are subsequently fed into the LSTM to capture inter-frame inconsistencies. A fully connected classifier then labels the video as Real or Fake alongside a confidence score. The system is validated on three benchmark datasets—FaceForensics++, DFDC, and Celeb-DF—achieving detection accuracy exceeding 99 % together with precision, recall, and F1-score values above 99 %. The framework is wrapped in a Django-based web interface that allows nontechnical users to upload videos and obtain results in near real time. Robustness testing under compression artefacts, low-light conditions, and adversarial inputs confirms the generalisability of the approach.

DOI: http://doi.org/10.5281/zenodo.20375755

Biometric Smart Attendance System

Authors: Prof. Dr H R Divakar, Kavana S, K P Renuka Prasad, Manoj Kumar S R, Lipika K

Abstract: The traditional attendance system used in educational institutions is often time-consuming, error-prone, and vulnerable to proxy attendance. Existing hardware-based attendance solutions such as RFID systems require additional infrastructure, maintenance cost, and dedicated devices, making them less flexible and expensive for large-scale deployment. To overcome these limitations, this paper presents a Biometric Smart Attendance System that combines biometric verification, AI-based face verification, and GPS location validation to ensure secure, accurate, and reliable attendance management without using dedicated hardware components. The proposed system uses multi-factor authentication to verify student identity before marking attendance. Face verification technology identifies and authenticates students, while biometric authentication provides an additional layer of security. GPS-based location verification ensures that attendance can only be marked when the student is physically present within the authorized classroom premises. The system is developed using React, TypeScript, FastAPI, Python, DeepFace, OpenCV, and Supabase. Test results show face verification accuracy of 96% under normal lighting and overall attendance accuracy of 95%.

DOI: https://doi.org/10.5281/zenodo.20374000

Earthquake-Induced Behaviour Study Of Multi-Storey Irregular Buildings Using ETABS

Authors: Prof. Shyam Prasad H R, Rakshitha V, Rohan K M, Naveen S, Supriya R S

Abstract: The increasing development of urban areas and architectural requirements often lead to the construction of multi- storey reinforced concrete (RC) structures that do not comply with the symmetric and uniform structure that was assumed in classical seismic design theory.Multi-storey reinforced concrete (RC) structures are commonly built with an eccentric and non- uniform structure due to urban developments and architectural requirements, which do not match the symmetric and uniform structure used in classic seismic design theory. In this study, the behaviour of a G+7 RC building with an L-shaped plan irregularity and soft-storey vertical irregularity is studied under three structural configurations: Bare Frame, Infill Wall Frame and Shear Wall Frame and modeled in ETABS. The response spectrum analysis has been done according to IS 1893 (Part 1):2016, gravity loads as per IS 875 (Parts 1-3):1987, RC design as per IS 456:2000, ductile detailing as per IS 13920:2016. These values of storey drift ratio, lateral stiffness, storey shear and storey displacement were extracted and compared. The Shear Wall structure reduced the roof level lateral displacement by ~99.9% and storey drift by ~99.8–99.9% compared to the Bare Frame, and provided around five times the stiffness. The displacement reduction for the Infill Wall model ranged from intermediate (62–78%). A clear performance hierarchy was generated: Shear Wall > Infill Wall > Bare Frame, with shear wall being the critical member in an irregular building in high seismic zone construction, to ensure safety.

DOI: http://doi.org/10.5281/zenodo.20375845

Traffic Sign Recognition

Authors: Prof. P.S. Togrikar, A.N.Jamdade, P.R.Shirke, H.J.Phadtare

Abstract: Traffic Sign Recognition (TSR) is an essential component of Advanced Driver Assistance Systems (ADAS) and intelligent transportation. This paper presents a cost-effective IoT-based TSR system using an ESP32-CAM for image acquisition and a backend server for processing. Due to limited edge-device capability, images are transmitted via a Telegram Bot for remote inference using the YOLOv3 deep learning model trained on the GTSRB dataset. To enhance robustness under real-world conditions such as occlusion and varying illumination, preprocessing techniques like CLAHE and data augmentation are applied. The system returns annotated results through a Telegram interface and a local GUI. Experimental results demonstrate high accuracy and reliable performance, validating the effectiveness of the proposed approach. The system also shows strong performance under partially occluded conditions, improving real-world applicability. Furthermore, the proposed architecture ensures low-cost deployment and scalability for smart transportation systems. This work highlights the potential of integrating IoT with deep learning for practical and accessible traffic monitoring solutions.

Advanced Experimental Techniques (growth & fabrication) of semiconductor nanostructures: From morphology to electronic states

Authors: Pragati Sharma, Bhomik Nahariya, Aryan Rajput, Vansh

Abstract: In view of their size-dependent properties, both physical and chemical, semiconductor nanostructures have emerged as an essential component within modern nanotechnology. Novel device functionalities and adaptable electronic states are being established as possible by having the ability to accurately tune morphology, from zero-dimensional quantum dots to one-dimensional nanowires and two-dimensional thin films. The link between structural morphology and electronic characterization is demonstrated in this paper’s assessment of sophisticated experimental methods for the growth and manufacturing of semiconductor nanostructures. Alongside top-down techniques such as lithography and etching, molecular beam epitaxy (MBE), chemical vapor deposition (CVD), atomic layer deposition (ALD), and laser ablation are also presented. In addition, it focuses on the ways in which defects, interfaces, and quantum confinement influence electronic states.

DOI: https://doi.org/10.5281/zenodo.20375584

Application Of Nature-Based Solutions For Climate Change: A Comprehensive Review And Feasibility Study

Authors: R. Swetha

Abstract: Climate change poses one of the most formidable challenges to global ecological and socioeconomic stability in the twenty-first century. As atmospheric concentrations of greenhouse gases continue to rise, the scientific community has increasingly turned to Nature-Based Solutions (NbS) as a viable and cost-effective complementary strategy to conventional technological mitigation approaches. This report provides a systematic analysis of Nature-Based Solutions, examining their mechanisms, classifications, documented effectiveness, and real-world implementation challenges. NbS encompass a spectrum of ecosystem-centred interventions — including reforestation, wetland restoration, urban greening, and sustainable agricultural practices — that simultaneously deliver climate mitigation benefits while enhancing biodiversity and community resilience. Key findings of this report indicate that NbS possess the theoretical capacity to contribute between 10 and 12 gigatons of CO₂ equivalent reductions annually by 2030, representing approximately 30% of the mitigation required to limit global warming to 1.5°C. However, this potential is contingent upon significant upscaling of political commitment, financial investment, and cross-sector governance frameworks.

Application Of Nanobubble Technology In Wastewater Treatment For Enhanced Pollutant Removal: A Comprehensive Review

Authors: Manuela Christy Dany S, Dr. Nithyalakshmi B

Abstract: Keeping global water resources clean is becoming harder every year. Industrial growth has pushed wastewater systems into a corner, and the usual treatment methods are starting to look worn out. They demand a lot of energy and still struggle with stubborn pollutants that refuse to break down. This review takes a close look at nanobubble (NB) technology as a more sustainable option. Nanobubbles are tiny, sub-micron gas cavities with an unusually long life in water, and that alone makes them interesting. They also bring unusual physicochemical properties, including high internal pressure and the formation of reactive oxygen species (ROS). The paper covers the basic mechanisms behind NBs, their contribution to aeration and flotation, and their strong performance in removing organic dyes, nutrients, heavy metals, and pathogens. Reported studies show that NB-based systems can push Chemical Oxygen Demand (COD) removal above 90% while using much less energy than conventional activated sludge treatment. The aim here is straightforward: give researchers and practitioners a clear view of the technical value and economic promise of NB technology in modern water purification.

EcoSort: An AI-Powered Garbage Segregation System Using MobileNetV3 And Deep Transfer Learning

Authors: Sukanya H N, Assistant Professor, Pavan Kumar T S, Prajwal S Shetty, Pranay Ekunde, Sanjay M

Abstract: Improper waste disposal remains one of the most pressing environmental challenges in both urban and rural settings, contributing to pollution, health hazards, and reduced recycling efficiency. Traditional manual waste segregation is error-prone, labour-intensive, and cannot scale to the volumes of waste generated daily. This paper presents EcoSort, an AI-powered full-stack web application that automates waste classification using a fine-tuned MobileNetV3 Large deep learning model trained via transfer learning. The system classifies waste images into three categories—Recyclable, Non-Recyclable, and Hazardous—achieving approximately 94 % overall accuracy with precision values of 0.95, 0.94, and 0.94 respectively. EcoSort integrates real-time webcam-based detection, a microservices architecture (React/Vite front-end, Node.js/Express back-end, Flask AI service, MongoDB Atlas), Role-Based Access Control (RBAC), JWT authentication, and perceptual hashing (pHash) for duplicate-image detection. A gamification layer comprising reward tiers (Bronze to Platinum), a coupon marketplace, and a community leaderboard motivates responsible waste disposal. Load testing confirmed stable operation under 100 concurrent users with average response times below 3.5 seconds. The platform aligns with UN Sustainable Development Goals SDG 3, SDG 11, SDG 12, and SDG 13, offering a scalable, intelligent pathway toward smarter waste management.

Sponge City Concept For Sustainable Stormwater Management: A Comprehensive Review

Authors: Darshana N V, Nithyalakshmi.B

Abstract: The fast pace of urbanization and worsening climate-driven stressors have disrupted the natural cycles of urban hydrological processes, making existing linear infrastructures increasingly susceptible to extreme pluvial flooding events. The Sponge City concept can be seen as an essential paradigm shift towards a decentralized nature-based method for urban areas to manage rainwater in terms of its absorption, storage, infiltration, and purification.In this review paper, we synthesize empirical data, policies, and hydrological models of ten key studies to examine the effectiveness of the Sponge City paradigm at various scales. This paper will analyze the development trends of LID-based structural controls, quantitative limitations for peak flows, life-cycle maintenance challenges, and multiple ecological benefits. The synthesized literature reveals that although green infrastructure produces impressive hydrological and economic benefits when dealing with conventional rainfall, its performance suffers considerably when confronted with an extreme cloudburst. Therefore, this paper sets up a robust research agenda for future urban planners, namely that a mandatory paradigm must be embraced in the form of a “green-gray hybrid infrastructure” system with institutional and technological arrangements for real-time monitoring.

Irrigation System (Kuhl) in Himachal Pradesh

Authors: Ritik Rana

Abstract: Sustainable agricultural production largely depends on the proper development, conservation, and efficient utilization of irrigation resources at the micro level. In Himachal Pradesh, diverse geographical conditions have led to the adoption of traditional irrigation systems known as Kuhls, which play a vital role in supporting agriculture and rural livelihoods. Kuhls are narrow, manually constructed surface channels that divert water from natural streams and ravines through gravity flow to irrigate terraced fields. These systems, built mainly with local materials such as river boulders and soil, represent an eco-friendly and community-managed method of irrigation. Despite their historical and agricultural significance, Kuhls today face several challenges including structural deterioration, water losses, changing climatic conditions, and inadequate maintenance. The present study focuses on the Palampur region of Himachal Pradesh to examine the problems associated with traditional Kuhl irrigation and explore modern solutions for improving irrigation efficiency and agricultural sustainability. The study highlights the need for technological improvements, conservation measures, and integrated water management practices to preserve this traditional irrigation heritage while meeting present-day agricultural demands.

DOI: https://doi.org/10.5281/zenodo.20390582

Augmented Reality-Based Interactive Solar System Visualization

Authors: Aryan Baban Repe, Atharv Narayan Rane, Harshvardhan Vijay Desai, Keshiraj Mahesh Lad, Sumit Vasant Bhatane, Mrs. Anuradha S. Solanki

Abstract: Augmented Reality (AR) has gained considerable traction in educational settings, offering interactive three-dimensional experiences that go beyond what conventional two-dimensional instructional materials can provide. This paper describes the design, development, and evaluation of an AR-Based Interactive Solar System Visualization system built using Unity 2022 and the Vuforia Engine. The system employs markerless ground-plane detection to overlay a fully interactive three-dimensional Solar System model onto the user’s physical surroundings, supporting planetary orbital revolution, axial rotation, touch-based planet selection, and dynamic educational information panels. The primary contribution of the proposed system is the integration of stable ground-plane AR tracking, structured educational interfaces, and a modular software architecture within a lightweight mobile deployment requiring only a standard Android smartphone. Prototype evaluation on Android devices yielded an average frame rate of 45–60 FPS, AR tracking accuracy of approximately 92%, an interaction response time below 100 ms, and a user satisfaction score of 88%, indicating measurable gains in learner engagement and conceptual retention relative to conventional instructional methods.

Design And Implementation Of An IoT-Based Smart Blind Assistive Stick For Visually Impaired Individuals

Authors: Prof. P. Prasanna, Arjun C, Chinmay J, Deepak B B, Dhanush S Yadav

Abstract: Visually impaired individuals face severe challenges in independent navigation. Traditional white canes provide only contact-based obstacle detection and cannot warn users of overhead hazards, wet surfaces, or distant obstacles—significantly limiting their mobility, safety, and self-reliance. Existing com-mercial smart canes, while technologically superior, remain prohibitively expensive and require specialised training. This paper presents the design and implementation of a Smart Blind Assistive Stick: a low-cost, IoT-enabled, real-time navigation aid built around an ATmega328 microcontroller. The system integrates an HC-SR04 ultrasonic sensor for non-contact obstacle detection up to 2 m, a moisture sensor for wet-surface identification, and multi-modal feedback through a vibration motor and buzzer. An optional GSM/GPS module enables real-time location sharing with caregivers via SMS. The firmware was developed in Embedded C on the Arduino IDE, simulated in Proteus, and physically prototyped. Testing confirmed obstacle detection accuracy within a 2 m range, sub-100 ms system response time, reliable wet-surface detection, and successful emergency SMS transmission with live GPS coordinates. The device operates for 6–8 hours on a rechargeable lithium-ion battery pack and maintains a compact, lightweight form factor suitable for daily indoor and outdoor use. Results demonstrate that a well-integrated multi-sensor embedded system can effectively bridge the technological gap in assistive mobility devices.

DOI: http://doi.org/10.5281/zenodo.20391169

RAG & LLM Based TNEA Student Assistant For Academic Guidance

Authors: Ms. K. Sabitha, B. Monish, M. Nithishkumar, S. Mohammed Al Ameen, S. Samvarthini

Abstract: The process of selecting an appropriate engineering course and college has become increasingly challenging due to the large volume of information and the complexity of admission procedures such as TNEA. Students often face difficulty in understanding cutoff trends, identifying suitable colleges, and making informed decisions because the available information is scattered and sometimes unreliable. To address this issue, this project proposes an intelligent academic assistance system that combines Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs). The system is designed to provide accurate and user-friendly guidance by retrieving verified academic data and presenting it through an interactive conversational interface. The retrieval component ensures that information such as cutoff marks and college details is obtained from structured datasets, while the language model supports explanation-based queries related to courses and career paths. The system is implemented as a web-based application using modern technologies, enabling real-time interaction between the user and the system. By combining data retrieval techniques with intelligent response generation, the proposed solution improves accuracy, reduces misinformation, and enhances user experience. This approach simplifies the decision-making process and helps students choose suitable academic paths with confidence.

“High Risk And Low Risk Patients’ Prediction In Icu Using Ml Algorithms”

Authors: B. M. Promod Kumar, Namith Kumar Y, Pruthvi M C, Poorvik K V, Jagadeesh M

Abstract: This concept is based on patient’s classification in an Emergency Department in a hospital according to their critical conditions. Machine learning can be applied based on the patient’s condition to quickly determine if the patient requires urgent medical intervention from the clinicians or not [1]. Basic vital signs like Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Respiratory Rate (RR), Oxygen saturation (SPO2), Random Blood Sugar (RBS), Temperature, and Pulse Rate (PR) are used as the input for the patients’ risk level identification [2]. High-risk or non-risk categories are the outcome for patient classification. ML algorithms such as Gaussian NB, KNN or DT are applied for the data analysis and for the classification. We’ll use a many of supervised learning methods before deciding which one is best for the model. Existing systems rely on classical learning models, which are inefficient and imprecise. They aren’t as accurate as the proposed model and take a little longer to process. Many existing topics on patient’s classification where they have built models and shown results generated using R language, Python language and data science tools. All existing works are just models, cannot be applied as application useful in real time. In our project work we build an application with ML models that can classify high risk patients and non-risks patients in an emergency department and provides doctors with the information of how to handle patients and treat better [5]. Our proposed work is a real-world medical system useful for hospitals and doctors and built using trending tools such as Visual Studio code, PYTHON and MYSQL Server.

Data Driven Housing Intelligence: A Comprehensive Analysis Of Infrastructure,Contractor And Community

Authors: Burhan Sheikh, Vighnesh Muppawar, Jiya Khan Pathan, Suyog Madavi, Prof. Sachin Dhawas

Abstract: CompThe Pradhan Mantri Awas Yojana (PMAY) is a flagship initiative by the Government of India aimed at providing affordable housing to the rural and urban poor. However, at the local administrative level (Zila Parishad), the management of beneficiary data involves processing massive, decentralized Excel datasets. This manual approach leads to data redundancy, lack of real-time monitoring, and significant difficulty in identifying stalled projects. This paper proposes “Data Driven Housing Intelligence,” a web-based analytical application designed to automate and centralize PMAY data management. Developed using the Python Flask framework, the system integrates bulk data ingestion, automated data cleaning using the Pandas library, and persistent storage in a structured SQLite database. It features a multi-level drill-down dashboard with interactive visualizations (Bar and Pie charts) for block-wise analysis. Furthermore, it introduces a “Visual Audit” mechanism allowing stage-wise photo uploads for transparency and an automated algorithm to flag delayed projects. The implementation results demonstrate a significant reduction in administrative overhead and improved data accessibility for government officials.

Smart Door: Motion,face-recognition And Voice Recognition IOT Security

Authors: Mr.B.Ajantha Reddy, Mr.K.Ch.Malla Reddy, Immadi Venkata Naga Sai Pujitha, Byragani Manasa, Bathula Varshini, Shaik Fathima

Abstract: The purpose of this project is to make home or office or any area secure. When someone presses the doorbell, then the doorbell makes a video call to the registered number. If someone roams in front of the door it notifies you by sending message. Then he can see the person who is roaming in front of our door. So, if the person is known we can open the door otherwise we can be alert. And also, we can talk to the person through mobile only and the person can reply there itself, because it contains the audio speaker so that we can hear the outside people talks trough the mobile once we pick up the video call. If someone tries to steal it then the steal alarm will be activated.

DOI: http://doi.org/10.5281/zenodo.20394540

Automated Patient Health Tracking System

Authors: Dr.A.Ranganayakulu, Dr.D.Satyanarayana, Allam Lakshman, Tadikamalla Madhav Kumar, Pasala Harikrishna, Sandala Dinesh Kumar

Abstract: Immediate diagnosis and management are crucial in preventing serious consequences for older and mobility-impaired patients, since falls are a leading cause of injury in these populations. Using Arduino and Bluetooth, this project aims to create a patient fall monitoring system that can detect falls in real-time and notify caregivers immediately. A microelectromechanical system (MEMS) sensor detects abrupt and uncontrollable falls by continuously monitoring the patient’s movement and assessing changes in posture, speed, and acceleration. By analyzing sensor data, a fall detection system that is based on thresholds may distinguish between normal activities and actual falls. The technology immediately notifies registered mobile devices over Bluetooth and activates a buzzer alarm for local alerting the moment a fall is detected. A wide variety of fall occurrences, such as sliding, tripping, and abrupt loss of balance, were detected with excellent sensitivity in the experiments. It was determined that the system reliably and without noticeable delay sent emergency alerts regardless of ambient conditions like vibrations and the range restrictions of Bluetooth. The open-source, ever-improving architecture of Arduino makes it possible to include Wi-Fi or cellular connection for expanded coverage in future expansions. The system’s ability to monitor, notify, and intervene in real-time enhances patient safety.

DOI: http://doi.org/10.5281/zenodo.20394577

Future Ready Low-power 4-bit Multiplier For Portable VLSI Systems

Authors: Mr.K.Ch.Malla Reddy, Mr.M.Ramana Reddy, Muthakapalli Sai Sravani, Tadi Kranmai, Chinamanagonda Ranga Gayathri, Kothakota Vara Lakshmi

Abstract: A multiplier is an essential part of many extremely large scale integration systems and finds extensive application in digital circuits for innumerable arithmetic calculations. One of the most fundamental operations in digital technology is multiplication, and hardware multipliers are essential for quick computing and efficient data processing. There have been several design approaches to multipliers that focus on surface area and energy efficiency in response to the desire for high-performance, low-power multipliers. Our study presents a 4-bit field multiplier with improved energy efficiency compared to conventional designs, using a modified gate-diffusion input (MGDI) cell architecture. The energy consumption of the MGDI-based multiplier is significantly reduced to 1.109861 mW without sacrificing any of the essential operating efficiencies. Thorough simulations have been conducted to showcase the performance of the 4-bit MGDI multiplier, which was meticulously constructed utilizing Tanner EDA tools. Low power, 4-bit multiplier, time delay, transistor count, modified gate diffusion input (MGDI) are some of the keywords.

DOI: http://doi.org/10.5281/zenodo.20394671

Efficient MAC Architecture Using Different Parallel Adders

Authors: Mr.A.Prasad, Mrs.N.Swarupa Rani, Batchu Bala Bhargavi, Chinni Yamini, Chityala Lakshmi Devi, Chilakala Anjali

Abstract: Because of its capability to do arithmetic operations at fast speeds, the Multiply-Accumulate (MAC) Unit is an essential part of all digital signal processor applications. An 8-bit MAC Unit that can do addition and multiplication is the target of this study. While the MAC Unit uses the same multiplier, it incorporates other adders, including the Kogge-Stone, Ladner-Fischer, Carry Look-Ahead, and Ripple Carry adders. Xilinx ISE was used to implement the structures that were created in Verilog Hardware Description Language (HDL), and ModelSim was used for simulation.

DOI: http://doi.org/10.5281/zenodo.20394713

Food Delivery Website Using Tsp

Authors: Prof. Mahesh Dumbere, Vivek Thakre, Pratik Patil, Ritesh Magare, Mahesh Gajbhiye

Abstract: The demand for effective delivery route optimization has grown due to the quick expansion of online meal delivery services. In order to reduce delivery time and expense, this study suggests a web-based meal delivery system that makes use of the Travelling Salesman Problem (TSP). For a delivery agent to visit several clients and return to the starting point, the system calculates the quickest path. To solve TSP, a variety of algorithms are examined, including Nearest Neighbor, Genetic Algorithm, and Dynamic Programming. The suggested solution increases customer satisfaction, lowers fuel consumption, and improves delivery efficiency. According to experimental results, optimized routing performs noticeably better than conventional delivery techniques.

Intellihome: FPGA-Driven Smart Automation System

Authors: Dr.P.Prasanna Murali Krishna, Mrs.N.Swarupa Rani, Mudamala Manoj, Dudekula Mastan Vali, Patibandla Jayanth, Baddigam Koti Reddy

Abstract: The Xilinx Zynq-7000 system on a chip (SoC) is the basis for the home automation system described in this article. The FSM logic processes the signals from the sensors (the fire sensor requires 5V digital and the buzzer 5-12V) using Verilog HDL. The RTL schematics and waveforms were validated using Cadence tools. The three-second detection threshold for fire and intruder alarms is one of the most important features, along with automatic lighting and temperature adjustment. The scalability of the modular system allows for the easy integration of more devices, which in turn increases functionality and security.

DOI: http://doi.org/10.5281/zenodo.20394814

Enhanced Power Distribution Through IOT Based Under Ground Cabel Fault Detection

Authors: Mr.N.B.Jilani, Mr.M.Ramana Reddy, Dudekula Chinna Khasim Vali, Nukala Sai Kumar, Shaik Mahaboob Subhani, Pengani Ganesh, Uppaladinne Ram Charan

Abstract: Accurately locating faults in subterranean cable connections is a prevalent challenge, particularly in cities, and the suggested approach aims to tackle this problem. A power supply and an Arduino microcontroller kit form the basis of the system, which measures the wire length in kilometers using current measurement circuits linked to the microcontroller’s Analog to Digital Convertors (ADC) device. A relay circuit is used to regulate the relays, and switches are employed to simulate faults. You can see the specifics of the issue as they happen on a 16×2 LCD screen. Short circuit problems in the underground cable may be caused by environmental factors such as rain, subterranean pollution, drain leakage, and so on. Among the many possible problems with subterranean cable, the identification of short circuit faults is the primary emphasis of this work. By monitoring voltage variations across the resistor and calculating the distance from the source feed point, the ADC digitizes the data and displays it on the LCD, pinpointing the exact position of the short circuit defect. The Wi-Fi module 8266 helps to save the specifics of the defect location in the cloud, allowing for subsequent investigation. Because of this, the suggested design for the Arduino microcontroller can pinpoint the precise location of the problem in terms of kilometers from the base station. Additionally, in the event of a malfunction, a buzzer alerts field personnel to the urgency of the situation. This innovative approach provides a dependable and efficient means of locating and identifying issues with underground cable cables. This reduces downtime and facilitates service.

DOI: http://doi.org/10.5281/zenodo.20394874

Maximizing Area And Power Efficiency With A Modified Karatsuba Multiplier For Cryptography Algorithms That Avoid Errors

Authors: Dr. A. Ranganayakulu, Mr. A. Prasad, M. Ramana Reddy, B. Ajanta Reddy, Dr. D. Satya Narayana

Abstract: Using efficient finite field multipliers becomes vital in elliptic curve cryptography (ECC), where data security and authentication are critical. These multipliers do affect performance, however, because they use quite a lot of hardware resources. The Karatsuba algorithm and its variations are explored in this study as a means to enhance hardware efficiency on FPGA devices. Although performance is improved with the overlap-free Karatsuba algorithm. Problems with recombining intermediate findings cause them to add 20% mistakes. We present a modified Karatsuba method that can compute four key outputs for 2-bit inputs error-free to solve this problem. The revised design tested on Artix-7 FPGA and implemented in Verilog HDL, cuts power consumption by 73.95% and area utilization by 95% when compared to the original Karatsuba algorithm. Its overall efficiency is much improved while accuracy is guaranteed, despite a 3.22% increase in area and an 11.12% increase in power compared to the overlap-free version.

DOI: http://doi.org/10.5281/zenodo.20394919

High-performance FPGA- Based ALU Using Reversible And Quantum-inspired Logic

Authors: Mr. B. Ajantha Reddy, Mr. A. Prasad, Dr. A. Ranganayakulu, Pagadala Ananthalakshmi, Jestadi Divya Jyothika, Vyja Swetha, Bathula Persis

Abstract: Computing devices, mobile phones, and computers all rely on the Arithmetic Logic Unit (ALU), a critical subsystem inside processors, to carry out the arithmetic and logical operations necessary for digital system operations. There is an urgent need for more energy-efficient alternatives in digital system design to standard ALUs designed using non-reversible logic gates, which are known for their considerable power consumption. Our proposed solution to this problem is a 32-bit ALU that makes use of reversible logic gates; this will allow us to cut down on power consumption while simultaneously increasing computing performance. Our suggested 32-bit ALU uses reversible logic gates to provide a solution that reduces power consumption and increases computational efficiency; this should lead to a revolution in digital system design. Not only do we want to reduce power consumption, but we also want to increase the ALU’s usefulness and adaptability in other computing contexts by adding a full set of sixteen separate operations. Our goal is to set a new standard for ALU design with this novel method, one that puts computing power and power efficiency first. Our 32-bit ALU’s use of reversible logic gates is a giant leap forward in the quest for power-efficient digital computers that don’t sacrifice processing capability. We seek to solve the essential demand for energy-efficient solutions in today’s technology-driven world by contributing to the progress of digital system design with painstaking attention to detail and a focus on innovation. Metaphors: Arithmetic Logic Unit (ALU), Reversible Logic Gates, Digital System Design, Energy Efficiency, Versatility, Innovative Architecture, Low Power Consumption, Computational Performance.

DOI: http://doi.org/10.5281/zenodo.20394930

Comparative Analysis Of Pixel-Based Segmentation Model For Accurate Detection Of Impacted Teeth

Authors: Dr. Deepika, Sneha K M, Sudhanshu Sharma, Sujendra T R, Varshini J

Abstract: Impacted teeth, particularly third molars that fail to erupt properly due to insufficient space or improper angulation, represent a common dental condition that can lead to severe complications including infection, cyst formation, and damage to adjacent structures. Traditional diagnosis relies heavily on manual interpretation of panoramic dental X-ray images by clinicians, a process that is time-consuming, subject to human variability, and lacks pixel-level precision. This paper presents an AI-based impacted tooth detection system using the U-Net deep learning architecture, a convolutional neural network specifically designed for biomedical image segmentation. The proposed system performs pixel-level segmentation of impacted tooth regions from panoramic dental X-ray images, providing precise boundary delineation that conventional object detection methods cannot achieve. The system integrates data annotation, model training using PyTorch, and deployment via a Flask-based web application into a unified end-to-end pipeline. Preprocessing steps including grayscale conversion, resizing to 256 × 256 pixels, and pixel normalization ensure consistent input quality. The trained model achieved an overall segmentation accuracy of approximately 87%, with precision of 85%, recall of 89%, and an F1-score of 87%. Experimental results and confusion matrix analysis confirm that the proposed system reliably detects impacted tooth regions while maintaining a low rate of false predictions. The system demonstrates strong real-time performance through a user-friendly web interface, making it a practical diagnostic support tool for dental professionals.

DOI: http://doi.org/10.5281/zenodo.20395549

Real-Time Collaborative Code Editor Using WebSockets

Authors: Pruthviraj Pawar, Niranjan Rasal, Pruthviraj Deshmukh, S. B. Dighe

Abstract: Collaborative programming platforms are becoming increasingly important in modern software development, online education, and distributed teamwork environments. Traditional methods such as manual file sharing, screen sharing, or repeated version control synchronization are often inefficient during live coding sessions. This paper presents the design and implementa- tion of a Real-Time Collaborative Code Editor developed using WebSockets and Socket.IO. The proposed system allows multiple users to edit source code simultaneously through a browser-based interface with minimal synchronization delay. The frontend is developed using HTML, CSS, JavaScript, and Monaco Editor, while the back- end is implemented using Node.js and Express.js. Socket.IO is used to establish persistent bidirectional communication between connected clients and the server. Experimental observations demonstrate synchronization la- tency below 100 milliseconds under normal conditions. Perfor- mance analysis confirms that WebSocket-based communication provides significantly lower delay and better bandwidth efficiency than traditional HTTP polling techniques.

Towards Sustainable Cloud Computing: Limitations Of Revenue-Optimized Resource Scheduling Policies

Authors: Dr. Megala.R, Dr.D. Balasubramanian

Abstract: Cloud computing has become an essential platform for delivering scalable and on-demand computational services. However, most existing cloud resource scheduling policies are designed primarily to maximize provider revenue and infrastructure utilization, often neglecting sustainability concerns such as energy efficiency, carbon reduction, thermal management, and fair resource allocation. This paper critically examines the limitations of revenue-optimized resource scheduling policies in cloud computing environments. The study evaluates traditional scheduling techniques including First-Come-First-Serve (FCFS), Round Robin, Priority Scheduling, and profit-aware heuristic scheduling approaches. Major issues identified include excessive power consumption, increased carbon emissions, resource starvation, thermal imbalance, and reduced long-term infrastructure sustainability. To address these limitations, a Sustainable Multi-Objective Scheduling Framework (SMOSF) is proposed that integrates energy efficiency, QoS maintenance, fairness, and profitability objectives. Comparative analysis demonstrates that sustainability-aware scheduling policies can significantly reduce energy consumption and environmental impact while maintaining acceptable service quality and operational profitability. The proposed framework contributes to the advancement of green cloud computing and sustainable data center management.

DOI: http://doi.org/10.5281/zenodo.20404448

Smartphone Remote Detection

Authors: Arya Parashram Kamble, Akshata Hemant Bansode, Vaishnavi Vitthal Shinde, Mrs D.N Ghatage

Abstract: Smartphones have become an essential part of daily life, storing sensitive personal information and enabling communication, navigation, and financial transactions. However, increasing smartphone usage has also led to serious concerns such as device theft, loss, and personal safety risks. Existing applications provide limited functionality and fail to offer a complete solution for real-time monitoring and emergency response. This paper presents a Smartphone Remote Detection System (Safety Guard) that provides an integrated platform for device tracking, remote control, and emergency assistance. The system is developed using Flutter and Firebase technologies, enabling real-time data synchronization, push notifications, and background processing. The application allows users to track device location, send remote commands such as ring, vibrate, and lock, and trigger SOS alerts to notify supporters and authorities.The system ensures continuous operation even when running in the background, making it reliable in critical situations. Additionally, it includes a complaint management module to report incidents effectively. The proposed solution enhances both personal safety and device security by providing a fast, efficient, and user-friendly system for real-time monitoring and emergency response.

Iot Based Coal Mine Safety Monitoring And Alerting System

Authors: Hari Priya S, Mrudhul NR, Saran H, Subarssini V, Vishal K

Abstract: Coal mining is one of the most hazardous industries, with risks such as gas leaks, rising underground temperatures, flooding, poor air quality, and structural hazards. This paper presents an IoT-based Coal Mine Safety Monitoring and Alerting System that continuously monitors environmental conditions and provides real-time alerts to miners and supervisors. The system employs sensors to detect gases (MQ-4, MQ-7, MQ-135), temperature and humidity (DHT11, DHT22), water levels (float switch, conductivity sensor) all integrated with an ESP32 microcontroller. Sensor data is transmitted via LoRa communication modules to a central monitoring unit, where it is analysed for any signs of danger. When hazardous conditions are detected, alerts are sent instantly through visual, auditory, or wireless notifications. Compared to conventional wired systems, this IoT-based approach offers greater reliability, faster response times, and reduced costs. By enabling proactive safety management, the system helps prevent accidents and injuries while improving operational oversight, ultimately supporting safer and more sustainable mining practices.

Automated Parallel Hybrid Data Extraction and Entity Resolution for Sports Data Aggregation: Architecture, Challenges, and Trade-offs

Authors: Olalekan Oluyinka

Abstract: This paper presents the design and implementation of an architecture for automated hybrid data extraction, integration, and entity resolution for sports aggregation. The system consolidates inconsistent records from multiple heterogeneous sources into a centralized, deduplicated interface for sports event discovery and streaming access. Data is extracted in real time across seven heterogeneous sources and directly ingested in the automated pipeline. A multi-step entity resolution algorithm, combined with data pre-processing within a Single Source of Truth (SSOT) framework transforms heterogeneous data into a unified, deduplicated index. The architecture employs edge caching and batching to reduce latency and improve operational performance in constrained environments. A prototype further demonstrates the practicality of automated multi-source sports event aggregation through entity resolution.

DOI: https://doi.org/10.5281/zenodo.20406780

Compensation and Reward Management Practices at Wipro: A Secondary Research Analysis

Authors: Prachi Saini, Dr. Pooja Kohli

Abstract: This research paper analyzes the compensation and reward management practices of Wipro Limited, a leading multinational information technology company in India. The study is based on secondary data collected from academic journals, industry reports, company publications, and previously published research. It focuses on understanding how Wipro structures its compensation system, including salary components, performance-based incentives, employee benefits, and recognition programs. The paper is supported by key motivational theories such as Maslow’s Hierarchy of Needs, Herzberg’s Two-Factor Theory, and Adams’ Equity Theory. These theories help explain how different types of rewards influence employee motivation, satisfaction, and performance. The findings suggest that Wipro adopts a comprehensive total rewards approach that combines fixed pay, variable pay linked to performance, and a wide range of financial and non-financial benefits. In addition, the company uses formal recognition programs to appreciate employee contributions and encourage high performance. However, the study also identifies certain challenges in Wipro’s compensation system. Issues such as lack of transparency in pay communication, limited personalization of non-monetary rewards, and gaps in career growth opportunities may affect employee satisfaction. The paper suggests that improving communication, offering more customized rewards, and strengthening career development frameworks can help Wipro enhance employee engagement and reduce attrition. Overall, the research highlights the importance of a balanced and transparent reward system in achieving organizational success.

DOI: https://doi.org/10.5281/zenodo.20407064

Design And Implementation Of A Wearable Multimodal Hand Gesture Vocalizer For Assistive Communication

Authors: Aryan Patel, Era Mane, Tanvi Sonawane, Dr. Vineeta Philip

Abstract: The persons who have difficulties communicating with society because of hearing and speech problems face challenges that restrict their communication ability with society. While sign language is one possible solution, everyone may not comprehend it. The design and application of a low-cost wearable hand gesture vocalizer that produces both visual and aural outputs from predefined hand gestures are presented in this paper. The suggested system uses flex sensors built into a glove to record finger movements, which are then operated by an Arduino Nano microcontroller. An external audio replay module is used to translate recognized gestures into corresponding audio output and display them on a 16×2 I2C-based LCD at the same time. To improve approachability for users with hearing or vision impairments, the system places a strong emphasis on multimodal feedback, appropriate wearable design and un- complicated hardware. The prototype developed shows reliable gesture identification with minimal latency and offers a extensible platform for future improvements.

DOI: http://doi.org/10.5281/zenodo.20407122

Smart Navigation Stick For Blinds

Authors: Dr.Vineeta Philip, Manasi Owhal, Piyush Khonde, Soham Khulage

Abstract: Smart Navigation Stick for the Blind is an aid that helps blind or visually impaired people move independently and safely. The system consists of several sensors and communication modules interfaced to an Arduino Uno (SMD) controller for real- time obstacle detection, hazard alerting and emergency mobil- isation purposes. It uses ultrasonic sensors to detect potential obstructions at various distances (i.e., pointing in all directions) and offers timely feedback, aided by the vibration motor and buzzer. Adding a flame sensor is responsible for identifying fire or high-temperature threat, and the inclusion of a level sensor help with water-filled or unlevel surfaces. The user can activate emergency functions when needed using a manual switch. The built-in GPS and GSM modules allow tracking of individual locations in emergency scenarios, as well as auto-issue alert texts to pre-selected people. This is a compact, low-cost energy-efficient and easy-to-use system that can be used every day outdoors or indoors. All in all, the smart navigation stick seeks to elevate situational awareness, safety, and confidence of visually impaired users with intelligent sensing as well as feedback initialized in real time.

DOI: http://doi.org/10.5281/zenodo.20407703

Online Coffee Shop Management System

Authors: Vaibhav Mali, Vaibhav Mane, Prajwal Zanje, Prof. N.B.Khade

Abstract: Digital image classification plays a significant role in the early detection and analysis of medical conditions. Traditionally, diagnosis is performed manually by ophthalmologists through examination of retinal fundus images. However, this process is time-consuming, requires expert knowledge, and may sometimes lead to errors due to human limitations. In contrast, automated digital image classification systems provide a faster, more consistent, and cost-effective solution by analyzing large volumes of medical images efficiently. This work focuses on the application of digital image classification techniques for identifying different stages of diabetic retinopathy. Additionally, different image preprocessing, feature extraction, and classification methods are discussed. The study also summarizes the key image features commonly used in previous research for accurate classification of retinal images into different disease categories.

NexusOps: A Secure Agentless Framework For Real-Time Telemetry And Automated Self-Healing In Multi-Server Infrastructure

Authors: Pranit Dattatraya Patil, Avdhoot Arunkumar Sakate, Ajinkya Anil Dhane, Prof. Uchale B. S

Abstract: As cloud-native environments scale, maintaining the high availability of virtual private servers (VPS) has become paramount. Traditional server monitoring tools rely heavily on daemon agents installed on host machines, exposing host environments to resource taxations and security vulnerabilities. This paper presents NexusOps, a premium agentless server management and self-healing platform. By utilizing Java Secure Channel (JSch) tunnels directly to host Operating Systems, NexusOps extracts real-time telemetry metrics (CPU, RAM, Disk, active processes) without target-side exporters. Telemetry metrics are streamed through a centralized Spring Boot REST and WebSocket engine into a responsive React frontend interface, facilitating live command execution, remote service control, and visual analytics. Furthermore, NexusOps introduces a mathemat- ical health-score heuristic model and a multithreaded automated self-healing controller to autonomously resolve critical errors (e.g., service failures, storage spikes) and dispatch alerts via external push notification channels. Our empirical evaluation demonstrates that NexusOps achieves equivalent telemetry ac- curacy and latency (sub-100ms response times) as traditional systems while eliminating persistent CPU and memory footprints on target nodes.

IoT-Driven Demand Forecasting Integrated With Blockchain-Enabled Resilient Supply Chain Model And Disruption Mitigation

Authors: Dr. Srimathi Kannan

Abstract: The global supply chain network is currently more vulnerable to disruptions that can be caused by pandemics, geopolitics, and other reasons. In such cases, centralized and opaque logistics infrastructures are exposed to risks. This study recommends a reliable supply chain management framework that utilizes blockchain technology, IoT sensors, a hybrid deep learning algorithm to forecast consumer demand, and disruption management through smart contracts. The proposed architecture relies on the Hyperledger Fabric, a permissioned blockchain network, and guarantees data immutability and transparency. A temporal convolutional network with an attention mechanism enables forecasting demand at 95.2% accuracy over a 12-week time frame. After detecting a disruption, the automated smart contract system will engage in dynamic routing, inventory redistribution, and supplier substitution. Simulating the proposed solution on a multi-tier supply chain network with over 100 nodes resulted in 67% faster disruption resolution compared to conventional models and 94% customer satisfaction during disruption events, while conventional models were able to serve just 62% of consumers.

DOI: https://doi.org/10.5281/zenodo.20411670

Neuromarketing Signals And Consumer Purchase Intent Prediction Using EEG And Computer Vision

Authors: Dr. A. Sathiya, Dr. P. Jeyanthi

Abstract: Purchase prediction and understanding its nuances are essential aspects of marketing, but standard approaches do not provide any information about subliminal neural processes which lead to actual purchases. In this paper, a novel multimodal system is proposed which combines EEG neuromarketing data and computer vision features related to visual attention to achieve accurate prediction of consumer purchase intent. A dataset comprising 120 participants who viewed 500 e-commerce images is used for extraction of both EEG-based features (frontal asymmetry of alpha activity, theta/beta ratio, and late positive potential) and visual attention features based on computer vision approach (fixations density, saccades dynamics, and pupils size). Hybrid model consisting of two branches – Temporal Convolutional Network for processing EEG signals and Graph Attention Network for mapping visual attention – reaches 88.3% accuracy and an area under curve equal to 0.94 in predicting consumer purchase intent, while unimodal EEG and visual models reach 74.2% and 72.8% respectively.

DOI: https://doi.org/10.5281/zenodo.20411755

AI-Driven Competency Mapping Framework For Future-Ready Talent Development

Authors: Mr. Shrikant Karampuri, Dr. P. Jeyanthi

Abstract: The fast development of technologies, automation, and digitalization processes have led to a considerable disruption of how workforce planning is traditionally performed and have created a significant gap between the skills of the existing workforce and the skills that will be necessary in the future for the success of organizations. The process of competency mapping which includes the identification, evaluation, and alignment of skills with strategic goals is extremely important to ensure future readiness via skill development. This paper introduces a new approach to the competency mapping using artificial intelligence, which utilizes NLP algorithms for skill extraction from unstructured sources (resumes, job descriptions, and performance evaluations), GNNs for skill adjacency and competency modeling, and BKT for prediction of the evolution of individual skills. When applied to a database of 50,000 employees in a multinational technology company, our approach yields an accuracy of 89.7% for skill extraction, 82% for skill adjacencies, and 76% for the prediction of future skill gaps. The proposed approach allows us to create personalized learning paths and reduce time-to-competency by 34% in six months.

DOI: https://doi.org/10.5281/zenodo.20411976

A Study on the Impact of Upi Usage on Digital Payment Preferences in India

Authors: Dr. Ashish Saxena, Priya Kumari

Abstract: Indias financial world has changed a lot because of technology and digitalization. Now digital payments are a part of our daily transactions. They are fast, easy, convenient and safe. The Unified Payments Interface or UPI is a leading platform for payments. It was developed by the National Payments Corporation of India. UPI helps people transfer money instantly using their phones. They do not need to share their bank details for each transaction. This is possible because many people have smartphones, internet access and use banking. This study looks at how UPI affects users, businesses and Indias digital payment system. It explores how people are changing their behavior to use transactions more. UPI plays a role in helping India become a cashless economy. We look at how UPI simplifies money transfers, bill payments, online shopping and transactions with merchants. We also check how satisfied customers are with UPI, how easy it’s to use how fast it is, how secure it is and how reliable it is. The research uses data from questionnaires and surveys well as information from other sources like journals, government reports and websites. We use methods to understand this data and see how UPI affects financial transactions. We found that UPI makes payments more efficient and convenient. It helps reduce the use of cash and increases the use of services. The reasons for this are that UPI is accessible, cost, fast and has many apps. However, there are still some challenges. These include cyber threats, connectivity problems, technical issues and a lack of knowledge. In conclusion UPI has changed Indias payment systems for the better. It has helped include people in the financial system and created opportunities, for businesses, consumers and institutions. As UPI continues to grow it promises to create a secure and cashless financial system that supports a digitally empowered economy.

DOI: http://doi.org/10.5281/zenodo.20412253

Federated Learning With Privacy Preservation For Healthcare Analytics

Authors: S Jayashree Ananth, Naveen V S

Abstract: The digitization of the healthcare industry has resulted in massive collection of personal health information among hospitals, clinics, and research institutions. But strict privacy laws (HIPAA, GDPR), along with other institutional obstacles, hinder data collection in a centralized manner, resulting in data silos that prevent the construction of efficient machine learning models for predicting diseases, estimating treatment, and managing public health issues. In this paper, we introduce a framework for privacy-preserving federated learning (PPFL) in healthcare. Our proposed framework includes three techniques: (1) Federated Averaging with differential privacy (DP-FedAvg) for model privacy, (2) Secure Multi-Party Computation (SMPC) for private aggregation of gradients, and (3) Homomorphic Encryption (HE) for performing computations on encrypted data. Our PPFL framework is evaluated on three real-life datasets of healthcare applications (mortality prediction from ICU records, diabetic retinopathy classification, and diagnosing COVID-19 patients) and outperforms federated learning with centralization in terms of model accuracy (within 3.2%) and provides differential privacy guarantees with ε=1.0 and δ=10⁻⁵.

DOI: https://doi.org/10.5281/zenodo.20415139

Sentiment Classification of Imdb Movie Reviews Using Naturl Language Processing Techniques

Authors: P. Anusha, E. Naveen Kumar, G. Sravanthi, E. Rohitha

Abstract: Sentiment analysis is a crucial task in natural language processing (NLP) that aims to determine the overall sentiment or opinion expressed by a reviewer towards a movie. This study focuses on the sentiment analysis of IMDB movie reviews using various machine learning and NLP techniques. The findings indicate that feature selection can enhance the accuracy of sentiment-based classification, but the effectiveness depends on the specific method and number of features selected. The paper also presents a comprehensive comparison of traditional machine learning techniques and advanced transformer-based models for sentiment analysis of IMDB movie reviews. The results provide insights into choosing appropriate methods for accurate and timely sentiment analysis on IMDB data. The study employs feature extraction techniques such as bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), and word2vec. Feature selection using methods like chi-square is shown to improve classification performance.

DOI: http://doi.org/10.5281/zenodo.20423234

Intelligent Flight Delay Prediction Using Machine Learning

Authors: P. Anusha, Syed Mannan Uddin, T.Sree Chandana, V.Vaishnavi

Abstract: Accurate flight delay prediction is fundamental to establish the more efficient airline business. Recent studies have been focused on applying machine learning methods to predict the flight delay. Most of the previous prediction methods are conducted in a single route or airport. This paper explores a broader scope of factors which may potentially influence the flight delay, and compares several machine learning-based models in designed generalized flight delay prediction tasks. To build a dataset for the proposed scheme, automatic dependent surveillance broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition, flight schedule, and airport information. The designed prediction tasks contain different classification tasks and a regression task. Experimental results show that long short-term memory (LSTM) is capable of handling the obtained aviation sequence data, but overfitting problem occurs in our limited dataset. Compared with the previous schemes, the proposed random forest-based model can obtain higher prediction accuracy (90.2% for the binary classification) and can overcome the overfitting problem.

DOI: http://doi.org/10.5281/zenodo.20423275

Dna Sequence Predictions Using Nlp And Ml

Authors: K. Vigneshwar, P. Shruthi, J. Rahul Naik, P. Khaleel Basha

Abstract: Deoxyribonucleic acid (DNA) is a biological macromolecule. Its main function is information storage. At present, the advancement of sequencing technology had caused DNA sequence data to grow at an explosive rate, which has also pushed the study of DNA sequences in the wave of big data. Moreover, machine learning is a powerful technique for analyzing largescale data and learns spontaneously to gain knowledge. It has been widely used in DNA sequence data analysis and obtained a lot of research achievements. Firstly, the review introduces the development process of sequencing technology, expounds on the concept of DNA sequence data structure and sequence similarity. Then we analyze the basic process of data mining, summary several major machine learning algorithms like Multinomial NB Classifier & Random Forest, and put forward the challenges faced by machine learning algorithms in the mining of biological sequence data and possible solutions in the future. Then we review four typical applications of machine learning in DNA sequence data: DNA sequence alignment, DNA sequence classification, DNA sequence clustering, and DNA pattern mining. We analyze their corresponding biological application background and significance, and systematically summarized the development and potential problems in the field of DNA sequence data mining using Multinomial NB Classifier & Random Forest. Finally, we summarize the content of the review and look into the future of some research directions for the next step.

DOI: https://doi.org/10.5281/zenodo.20425270

SSA-Tuned MLP Network for Malignant Tissue Segmentation and Classification in Medical Images

Authors: E. Priyadharshini

Abstract: Medical image analysis plays a significant role in the early detection and diagnosis of cancer. Accurate segmentation and classification of malignant tissues are essential for improving clinical decision-making and patient outcomes. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) techniques, particularly neural networks, have demonstrated remarkable success in biomedical image processing applications. However, the performance of conventional Multi-Layer Perceptron (MLP) networks is highly dependent on optimal parameter tuning, which remains a challenging task due to the complexity and high dimensionality of medical image data. This paper proposes an optimized MLP model using the Salp Swarm Algorithm (SSA) for malignant tissue segmentation and classification in biomedical images. SSA is a nature-inspired metaheuristic optimization technique modeled on the swarming behavior of salps in ocean environments. The algorithm offers strong global search capability, faster convergence, and improved avoidance of local optima compared with traditional optimization methods. By integrating SSA with the MLP network, the proposed model enhances feature selection, weight optimization, and classification accuracy. The proposed SSA-MLP framework is evaluated using publicly available biomedical image datasets. Performance assessment is carried out using standard evaluation metrics including Accuracy, Sensitivity, Specificity, Precision, F1-Score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Experimental results demonstrate that the SSA-tuned MLP model achieves superior performance when compared with conventional machine learning and neural network approaches. The model shows improved segmentation quality, enhanced classification capability, and greater robustness in detecting malignant tissues. This study contributes to the advancement of intelligent medical imaging systems by presenting a reliable and efficient optimization-based neural network model for cancer diagnosis. The findings indicate that SSA can significantly improve neural network performance in medical image analysis, thereby supporting accurate diagnosis and effective clinical decision support systems.curve Receiver Operating Characteristic (AUC-ROC)

USFDA Guidelines: Regulatory Requirements For Combination Products Involving Drugs, Devices, And Biologics

Authors: Kavade Shirisha, K. Someshwar

Abstract: Combination products, which are a new class of treatments that present difficult regulatory issues, are made up of a combination of medications, devices, and/or biological products. The United States Food and Drug Administration’s (USFDA) regulatory framework for the categorization, approval, and supervision of combination products is examined in this thesis. Key provisions under 21 CFR Part 3 are highlighted, along with the Office of Combination Products’ (OCP) function, the principal mode of action (PMOA) determination procedure, and premarket submission paths such as NDA, BLA, and PMA/510(k). To demonstrate how recommendations are applied in practical situations, case studies and regulatory precedents are examined. In order to expedite product development and guarantee compliance, the study emphasizes the significance of interdisciplinary cooperation and early regulatory engagement.

DOI: http://doi.org/10.5281/zenodo.20440187

Nutritional Evaluation And Amino Acid Profile Of Guizotia Abyssinica: Addressing Protein-Energy Malnutrition In Nigerian Populations

Authors: Anpe Fut Micheal, Prof. Kagoro L. A Dayil, Prof. Dahak A Dayil, Prof. Adelakun A. Esther

Abstract: In a world grappling with malnutrition and food insecurity, Guizotia abyssinica, or niger, emerges as a beacon of hope, offering substantial nutritional benefits. This study meticulously evaluates the nutritional composition and amino acid profile of G. abyssinica across various growth stages, aiming to address protein-energy malnutrition prevalent in Nigerian populations. Conducted in the Benue State region, the research involved comprehensive analyses of chemical composition, digestibility, and fatty acid profiles in niger seeds harvested at different developmental phases. Findings revealed a significant decline in crude protein from 163 g/kg at the early vegetative stage to 86 g/kg at the grain fill stage, alongside a notable increase in fiber content, indicating the complex interplay between growth stage and nutritional quality. The fatty acid profile predominantly featured essential fatty acids such as α-linolenic acid (C18:3 n-3) and linoleic acid (C18:2 n-6), underscoring the oil’s potential health benefits. The study advocates for the strategic use of G. abyssinica in dietary interventions to combat malnutrition, emphasizing its role in enhancing food security and promoting sustainable agricultural practices. Overall, the research contributes vital insights into the nutritional value of niger seeds, positioning them as a sustainable solution for addressing dietary deficiencies in vulnerable populations.

DOI: https://doi.org/10.5281/zenodo.20440373

Project Management Strategies For The Development And Approval Of Generic Drugs In The U.S. Market

Authors: Sherla Prasanna, B. Swathi

Abstract: By offering reasonably priced substitutes for name-brand drugs, the generic drug market in the United States plays a vital part in guaranteeing accessible healthcare. The U.S. Food and Drug Administration (FDA), through the Abbreviated New Drug Application (ANDA) procedure, is the primary regulatory body that oversees the process of bringing a generic medication to market. In order to help pharmaceutical businesses effectively negotiate the challenging development and approval process for generic pharmaceuticals, this thesis examines project management techniques. In order to provide an organized strategy for cost management, time efficiency, risk reduction, and regulatory compliance, the study incorporates concepts from the Project Management Body of Knowledge (PMBOK), pharmaceutical R&D, regulatory science, and quality systems. Important topics like bioequivalency research, intellectual property issues, risk-based quality management, and cross-functional team communication frameworks are emphasized. The effect of strategic project management on cutting time-to-market without sacrificing quality or compliance is illustrated through real-world case studies and industry best practices.

DOI: http://doi.org/10.5281/zenodo.20441301

Multiple Emulsion Mediated Delivery Of Azilsartan Medoxomil For Improved Solubility And Bioavailability

Authors: Gourani Shruthi, K. Someshwar

Abstract: This study focuses on the formulation and characterization of water-in-oil-in-water (W/O/W) multiple emulsions of Azilsartan Medoxomil to enhance its solubility, stability, and oral drug delivery performance. A total of nine formulations (AZL1–AZL9) were developed using Span 20, Span 40, and Span 80 at varying concentrations as primary emulsifiers, while Tween 80 was employed as the secondary emulsifier. The prepared multiple emulsions were evaluated for various physicochemical and morphological parameters including visual appearance, organoleptic properties, microscopic examination, globule size, polydispersity index (PDI), zeta potential, viscosity, pH, conductivity, drug content, entrapment efficiency, in vitro drug release, and stability studies. Among all formulations, AZL6 exhibited superior characteristics with a mean globule size of 2.6 μm, uniform droplet distribution, high entrapment efficiency of 98.2 ± 1.1%, optimum viscosity, and cumulative drug release of 94.3% over 12 hours. The formulation also demonstrated good colloidal stability with a zeta potential value of −29.5 mV. Drug release kinetic studies revealed that the optimized formulation followed the Higuchi diffusion model and Super Case II transport mechanism, indicating both diffusion- and erosion-controlled release behavior. Furthermore, post-formulation stability studies, including centrifugation stress testing, confirmed the physical stability of the emulsion system. The findings of this investigation suggest that multiple emulsions represent a promising and effective delivery approach for poorly water-soluble drugs such as Azilsartan Medoxomil, with potential to improve oral bioavailability and therapeutic efficacy.

DOI: http://doi.org/10.5281/zenodo.20441639

Ayurveda: An Integrated Framework For Sustainable Health And Ecosystem Balance

Authors: Shivani, Prof. Seema Kohli

Abstract: Ayurveda is an ancient Indian system of medicine that explains health as a dynamic balance between the body, mind, and the natural environment. According to Ayurvedic philosophy, human life is deeply interconnected with environmental elements such as air, water, land, vegetation, and climate. In the present era, environmental science highlights growing concerns including pollution, climate change, deforestation, and biodiversity loss, all of which pose serious threats to both ecological stability and human health. These challenges emphasize the urgent need for sustainable and preventive approaches to healthcare and environmental protection. Ayurvedic concepts such as Panchamahabhuta (five fundamental elements), Ritucharya (seasonal regimen), and Desha (influence of geographical and environmental factors) explain how changes in the environment directly affect human health and disease patterns. Seasonal variations, climatic conditions, and ecological imbalance play a crucial role in disturbing bodily harmony, leading to the development of various disorders. These principles closely align with environmental science, which also focuses on maintaining ecological balance for healthy living. This paper aims to correlate Ayurvedic principles with environmental science to develop an integrated framework for sustainable healthcare and ecosystem conservation. The study is based on a review of classicalAyurvedic texts, current environmental challenges, and modern scientific research related to medicinal plants, pollution, and ecosystem health. Integrating traditional Ayurvedic knowledge with modern environmental management offers a holistic approach to disease prevention, health promotion, biodiversity conservation, and sustainable development.

DOI: https://doi.org/10.5281/zenodo.20441818

Comparative Analysis Of Regulatory Requirements For Marketing Authorization Of Generic Drugs In European Countries

Authors: Lagusani Yashwanth Goud, K. Susmitha

Abstract: Generic medications are becoming an essential part of contemporary healthcare due to the growing demand for reasonably priced medications. Despite efforts by the European Medicines Agency (EMA) to harmonize regulations, different European countries have different requirements for marketing authorization of generics. The regulatory framework for the approval of generic drugs in a few European nations, such as Germany, France, the United Kingdom, Spain, and Italy, is compared in this thesis. It draws attention to variations in bioequivalency standards, dossier submission requirements, approval schedules, and review processes. The results highlight the need for additional harmonization to improve patient access to reasonably priced medications and expedite generic drug market access.

DOI: http://doi.org/10.5281/zenodo.20442010

Lightweight Deep Learning Model for Weapon Detection

Authors: K. Vigneshwar, G.Bharath Simha Reddy, G.Shashidhar, A.Uday Kiran

Abstract: Public safety in public areas has become a significant concern for governments and businesses globally. Video surveillance systems are being increasingly integrated to ensure public safety, with deep learning techniques enhancing their ability to detect potential threats. Traditional video surveillance often relies on passive monitoring, but with advancements in AI, surveillance systems can now actively detect risks such as weapons (guns and knives) in real- time. This paper presents a deep learning-based system for weapon detection using MobileNet- V2, a CNN model known for its computational efficiency. MobileNet-V2 has shown an improvement of approximately 35% in processing speed compared to its predecessor, MobileNet-V1, while maintaining similar accuracy levels. This increase in speed is crucial for real-time weapon detection, where quick identification and response are vital to preventing threats. The study compares two approaches to weapon detection using CNNs, evaluating MobileNet-V1 and MobileNet-V2. The results indicate that MobileNet-V2 outperforms MobileNet-V1 not only in terms of speed but also in its ability to maintain high accuracy, marking a significant advancement in the field of weapon detection through deep learning. These improvements are vital in practical applications, such as public spaces, where large amounts of video data must be processed rapidly. The proposed system demonstrates a clear enhancement over prior methods in detecting guns and knives, offering a reliable, fast solution for real-time surveillance. This research highlights the effectiveness of MobileNet-V2 in improving public safety through advanced AI technology, providing a scalable solution for detecting threats in urban environments.

Air Quality Index Analysis Of Bangalore Dataset Using Tableau

Authors: Akula Manasa, Pasula Chandu, Bada Abhinay, Mrs. Y. Ashwini

Abstract: This study analyzes the air quality index (AQI) of Bangalore city over seven years on time period (2018-2024) covered 2,556 days, by using TABLEAU as the primary visualization software, through tableau, the huge and complex datasets will be turn like charts, graphs and more. It focuses on 8 key components of AQI, PM 2.5, PM10, NO2, SO2, CO, NH3, Pb and O3. It’s analyzing that the air quality was changes according to the seasons, where most pollutant air was recorded in the winter months (December-February) and the cleanest air was recorded at Monsoon season (June-August). The year of 2020 the AIR QUALITY recorded lowest average over (AQI-64.47), due to the reason of covid 19 pandemic Lockdown occurred. Approximately 66.8% of days were falls under “Moderate” category, while only 17.4% fall are considered as “Good”. These results share a clear vision to make a good plan for urban developers, city planners to analyse the conditions and improve AIR QUALITY on Bangalore.

Formulation Development and Characterization of Nanoparticulate Drug Delivery System for Selected Drug and Its Kinetic Profile

Authors: K. Amrutha Varshini, Gaddam Jancy, B. Manogna, B. Trisha, Mushti. Ankitha, Nunsavath Shanthi, Someshwar Komati, Dr. Someshwar Komati

Abstract: Nanoparticulate drug delivery systems have gained considerable attention for improving the therapeutic efficacy of poorly soluble anticancer drugs through controlled and targeted delivery. The present study aimed to formulate and characterize Docetaxel-loaded nanoparticles using Poly(lactic-co-glycolic acid) (PLGA) as a biodegradable carrier polymer. Nanoparticles were prepared by nanoprecipitation using optimized drug-to-polymer ratio and stabilizer concentration. The formulations were evaluated for particle size, zeta potential, entrapment efficiency, drug loading, and in-vitro drug release. FT-IR spectroscopy confirmed compatibility between drug and polymer, while zeta potential analysis indicated good colloidal stability. In-vitro release studies demonstrated sustained release of Docetaxel over an extended period. Kinetic analysis using zero-order, first-order, Higuchi, and Korsmeyer–Peppas models suggested a controlled drug release pattern predominantly governed by diffusion. The findings indicate that PLGA-based Docetaxel nanoparticles are a promising approach for targeted anticancer drug delivery with potential to enhance therapeutic efficacy and reduce systemic toxicity. Further in-vivo studies are recommended to confirm clinical applicability.

DOI: https://doi.org/10.5281/zenodo.20443596

AI-Powered Smart Sewage Treatment Plants

Authors: Ms. Anshika Yadav

Abstract: The increasing growth of urbanization and industrialization has intensified the burden on conventional sewage treatment plants (STPs), leading to higher energy consumption, operational inefficiencies, and environmental pollution. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving wastewater treatment processes through predictive analytics, automation, optimization, and real-time monitoring. This research paper explores the concept of AI-powered smart sewage treatment plants and examines how machine learning, deep learning, IoT sensors, and digital twin technologies can enhance sewage treatment efficiency and sustainability. The study reviews existing literature, identifies research gaps, and proposes an AI-integrated smart sewage treatment framework for predictive maintenance, water quality forecasting, and energy optimization. The paper concludes that AI-enabled STPs can significantly reduce operational costs, improve effluent quality, and support sustainable urban water management.

DOI: https://doi.org/10.5281/zenodo.20443923

ROOMZEE: A Cross-Platform Room Rental and Booking System Using Modern Web and Mobile Technologies

Authors: Dr. Dinesh D. Patil, Shruti Mangesh Bunde, Nidhi Vinodsingh Pardeshi, Manasvi Rajesh Bauskar

Abstract: The increasing demand for rental accommodation in urban areas has made traditional room searching methods inefficient and time-consuming. Conventional approaches rely heavily on brokers and manual processes, often resulting in higher costs, lack of transparency, and delayed communication. To address these challenges, this paper presents Roomzee, a cross-platform room rental and booking system designed to simplify and digitize the process of finding and booking rental spaces. The proposed system integrates both web and mobile platforms, allowing users to search, view, and book rooms in real time. The frontend of the application is developed using React, providing a responsive and user-friendly interface [6]. The backend services are implemented using Supabase, which offers secure authentication, real-time database management, and efficient data handling capabilities. The use of cloud-based architecture ensures scalability, reliability, and continuous availability of the system [4]. The development process follows Agile methodology to support iterative improvements and adaptability throughout the software development lifecycle [14]. The system reduces dependency on intermediaries and improves overall efficiency in rental management. The results demonstrate enhanced user experience, faster booking operations, and improved data accessibility compared to traditional methods. Furthermore, the system is scalable and can be extended with advanced features such as online payment integration and intelligent recommendation systems.

DOI: https://doi.org/10.5281/zenodo.20444184

Smart Cursor Control Using Hand Gestures

Authors: Vishnu Koudgave, Pratik Londhe, Anishka Ahuja, Prasanna Kharbas, Prof. Jyoti Raghatwan

Abstract: With the growing demand for touchless and intelligent computing systems, hand gesture recognition has emerged as an innovative approach for natural human-computer interaction. This paper presents an AI-based Virtual Mouse system that enables real-time cursor control using hand gestures captured through a webcam. The proposed system utilizes MediaPipe for detecting 21 hand landmarks and OpenCV for real-time video processing, while PyAutoGUI is used to perform mouse operations such as cursor movement, clicking, scrolling, and dragging. The system provides smooth, accurate, and low-latency interaction without requiring additional hardware, making it a cost-effective and user-friendly solution. The proposed model enhances touchless human-computer interaction and has potential applications in smart environments, virtual reality systems, gaming, and assistive technologies.

DOI: https://doi.org/10.5281/zenodo.20444460

Inflation And Percapita Income In India

Authors: Gargi Chander

Abstract: This paper examines the relationship between consumer price inflation (CPI) and per capita net state domestic product (PCNSDP) across Indian states using a balanced panel dataset spanning 2014-15 to 2024-25. The study draws on official data from the RBI’s Handbook of Statistics. After constructing a balanced panel of 24 states and Union Territories over 11 years (264 observations), applying a suite of panel econometric estimators: pooled OLS, one-way fixed effects (entity), two-way fixed effects, random effects GLS, between estimator, and first-differences. Model selection follows the Hausman specification test. Unit-root diagnostics using augmented Dickey–Fuller tests indicate that both series carry non-stationary behaviour in levels, motivating the first-differences specification. The two-way fixed effects model—which accounts for both time-invariant state heterogeneity and common macroeconomic shocks—yields a statistically significant positive coefficient on CPI inflation (β = 0.0049, p = 0.046), while the first-difference estimator produces a significant negative coefficient (β = −0.0084, p < 0.001). The Hausman test (p = 0.91) favours random effects over one-way fixed effects. Taken together, these results suggest that the inflation–income relationship in India is nuanced: short-run income growth is dampened by inflationary shocks, but within-period cross-sectional variation, once purged of state and year effects, shows a mild positive co-movement consistent with demand-pull dynamics. The paper contributes a rigorous methodological treatment of India's state-level inflation–income nexus and discusses policy implications for monetary and fiscal coordination.

A Comprehensive Review on Carbon–Epoxy Composite I-Section Beams for Lightweight Structural Applications

Authors: Souda Pranavi, Vardelli Disharani, Peddamma Stalin, P.V.R.Ravindra Reddy

Abstract: The demand for lightweight, high-strength, and corrosion-resistant structural materials has significantly increased in aerospace, automotive, marine, and civil engineering industries. Carbon–epoxy composite materials have emerged as one of the most promising alternatives to conventional metallic materials because of their superior mechanical and thermal properties . Among various structural configurations, composite I-section beams have attracted considerable attention due to their excellent bending stiffness, high strength-to-weight ratio, fatigue resistance, and structural efficiency. This review paper presents a detailed overview of carbon–epoxy composite I-section beams with emphasis on material properties, fabrication techniques, finite element analysis, experimental investigations, failure mechanisms, optimization strategies, and structural applications. The paper critically examines the influence of fiber orientation, stacking sequence, laminate thickness, curing conditions, and manufacturing defects on the structural performance of composite beams. Advanced fabrication methods such as prepreg layup, vacuum bagging, and autoclave curing are discussed in detail. Recent developments in finite element modeling for stress, strain, deflection, fatigue, and buckling analyses are also reviewed. Furthermore, various non-destructive evaluation techniques used for identifying internal defects and monitoring structural integrity are examined. The review identifies major research gaps in composite beam development and highlights future opportunities for high-performance lightweight structural systems.

DOI: https://doi.org/10.5281/zenodo.20445172

Analyzing World War Battle Patterns Through Tableau Visualizations

Authors: S. Rishi Karthikeya, Pavan Raj,Mrs.Radhika

Abstract: This paper examines WWI and WWII battlefield data to show data visualization techniques. Included in the data set is data about casualties, number of days for the battle, its location, and the outcome. Data visualization was constructed with Tableau to gain insights into the trends and patterns by region and time. The study noted Europe and Asia saw the heftiest losses, and that a number of “significant fights” played a major role in total losses. The analysis shows how visualization methods can aid in comprehending historical war data better.

Diversity of Medicinal Plants Species in Vaishali District, Bihar, India

Authors: Annu Priya1,, Vijay Laxmi2,, Ujjwal Kumar3,, Amrita Kumari4,, Balwant Singh5

Abstract: The present study investigates the diversity and traditional utilization of medicinal plant species in Vaishali District, Bihar, India. Vaishali possesses a rich floristic composition supported by fertile alluvial plains, wetlands, agricultural landscapes, and rural ecosystems. Field surveys, interviews with local inhabitants, traditional healers, and ethnobotanical observations were conducted to document medicinal plant diversity and indigenous knowledge associated with their use. The study recorded a wide range of medicinal plant species belonging to different families and growth forms, including herbs, shrubs, climbers, and trees. These species are traditionally employed for the treatment of various ailments such as digestive disorders, respiratory infections, skin diseases, fever, diabetes, inflammation, and other common health conditions. The findings highlight the significant role of medicinal plants in rural healthcare systems and emphasize the importance of conserving plant biodiversity and traditional ethnomedicinal knowledge. Increasing anthropogenic pressures, habitat degradation, and loss of indigenous knowledge pose challenges to the sustainable use of these valuable biological resources. Therefore, systematic documentation, conservation strategies, and awareness programs are essential for preserving the medicinal plant wealth of Vaishali District for future generations.

DOI: https://doi.org/10.5281/zenodo.20458522

Mobile Phone Addiction Among Students

Authors: Kanimozhi v

Abstract: Nowadays mobile phones are used by almost every student. Students use phones for online classes, chatting, social media, games, watching videos, and many other things. Mobile phones are useful in daily life, but using them too much can slowly become a bad habit. Many students spend long hours on their phones without even noticing it. Because of this, studies, sleep, health, and even relationships can get affected.This study is about mobile phone addiction among students and how it affects their daily life. It also explains some reasons behind excessive mobile phone usage and simple ways to reduce it. Even though smartphones are helpful, students should know how to use them in a balanced way

DOI: http://doi.org/

A Chaos Control Method With Analysis Of Fractional Chaotic System

Authors: Ayub Khan, Pushali Trikha, Lone Seth Jahanzaib

Abstract: The paper introduces an effective way to control chaos of a fractional chaotic system in presence of uncertainties tc. Theoretical claims are verified numerically using MATLAB software.

DOI: https://doi.org/10.5281/zenodo.20487204

The Effect of Plastic Pollution on The Fresh Water Ecosystem

Authors: Shimpe Kumari, Amrita Kumari, Dr. Balwant Singh

Abstract: Plastic pollution has emerged as a significant environmental challenge affecting freshwater ecosystems globally. This study investigated the effects of plastic pollution on freshwater ecosystems by assessing microplastic contamination and selected water quality parameters across different sampling sites exposed to varying levels of anthropogenic activities. A quantitative and descriptive research design was employed to evaluate the distribution, abundance, and ecological impacts of microplastics in freshwater environments. Water, sediment, and biological samples were collected using standardized sampling and laboratory procedures to identify and quantify plastic particles. Key physicochemical parameters, including dissolved oxygen, pH, and turbidity, were also analyzed to determine the relationship between plastic pollution and water quality. The findings revealed considerable spatial variation in microplastic concentration among the sampling sites. Highly urbanized and industrialized areas recorded elevated levels of contamination, with the highest concentration observed at Site F (390 particles/L), followed by Site C (340 particles/L). Sites with increased microplastic abundance also exhibited lower dissolved oxygen levels, higher turbidity, and slight reductions in pH, indicating ecological stress and deterioration of water quality. The study further showed that microplastics persist in freshwater environments and pose serious risks to aquatic organisms through ingestion, habitat alteration, and toxic chemical transfer within aquatic food webs. The study concludes that plastic pollution significantly threatens freshwater ecosystem health and biodiversity. Effective waste management strategies, environmental regulations, and continuous monitoring programs are therefore essential to reduce plastic contamination and protect freshwater resources.

DOI: http://doi.org/10.5281/zenodo.20487718

Industrial Pollutants and Environmental Degradation: A Challenge for Sustainable Development

Authors: Riya Sharma, Prof. Abha Dubey

Abstract: Industrialization has significantly contributed to economic growth and modernization, but it has also emerged as a major source of environmental degradation. Industrial activities release a wide range of pollutants, including sulfur dioxide, nitrogen oxides, particulate matter, heavy metals, toxic chemicals, industrial effluents, and greenhouse gases, which adversely affect air, water, and soil quality. These pollutants disrupt natural ecosystems, alter biogeochemical cycles, and pose serious threats to human health and biodiversity. Air pollution from industries leads to problems such as acid rain, global warming, and respiratory diseases, while untreated industrial wastewater contaminates rivers and groundwater, causing toxicity to aquatic life and scarcity of safe drinking water. Soil pollution due to industrial waste disposal reduces soil fertility, agricultural productivity, and food safety. Environmental degradation resulting from industrial pollution directly challenges the goals of sustainable development, which aims to balance economic growth with environmental protection and social well-being. This study emphasizes the urgent need for sustainable industrial practices, including the adoption of cleaner production technologies, effective waste treatment, recycling, and strict implementation of environmental regulations. Promoting environmental awareness and corporate responsibility is equally important. Addressing industrial pollution is therefore essential to minimize environmental degradation and to ensure a sustainable and healthy future for present and coming generations.

DOI: https://doi.org/10.5281/zenodo.20488155

</p

Artificial Intelligence-Based Consumer Behavior Analysis for Cross-Border E-Commerce Optimization

Authors: He Weiyi, Md. Yeasin Arafat

Abstract: Artificial intelligence (AI) has become an important technology for improving customer engagement, personalized marketing, and operational efficiency in cross-border e-commerce platforms. With the rapid growth of digital commerce and online consumer activities, understanding customer purchasing behavior through AI-driven analytics has become increasingly valuable for modern business optimization. This research presents an AI-based consumer behavior analysis framework for cross-border e-commerce optimization using multiple real-world datasets, including customer demographic information, shopping behavior data, social media advertising interactions, recommendation system data, and online purchase intention records. The study applies machine learning models, including Random Forest and XGBoost, to predict customer purchase decisions and analyze factors influencing online consumer behavior. Data preprocessing, feature engineering, exploratory data analysis, and classification techniques were implemented using Python-based analytics tools. Experimental results demonstrate that AI-driven models can effectively predict purchasing behavior and identify important factors affecting customer engagement and online purchase intention. The findings indicate that customer browsing behavior, social media advertising interaction, recommendation systems, and demographic characteristics significantly influence cross-border e-commerce purchasing decisions. This research contributes to the development of intelligent digital commerce systems by integrating AI analytics, consumer behavior analysis, and recommendation-based optimization strategies. The proposed framework provides practical insights for improving customer targeting, personalized marketing, and operational performance in international e-commerce environments.

DOI: https://doi.org/10.5281/zenodo.20489885

Turbine And Compressor Design: A Comprehensive Study Of Gas Turbine Components, Cooling Techniques, Aerodynamic Instabilities, And Axial Compressor Design

Authors: Mrs.J.Jaisy

Abstract: Gas turbines are among the most important energy conversion systems used in aerospace propulsion, power generation, and industrial applications. This paper presents a comprehensive review of turbine and compressor design principles, thermodynamic operation, cooling technologies, aerodynamic instabilities, and axial compressor design methodologies. Particular emphasis is placed on compressor staging, velocity-triangle analysis, turbine cooling methods, stall mechanisms, and surge phenomena. The paper synthesizes fundamental design approaches and provides a structured framework suitable for engineering education and preliminary turbomachinery design studies.

Biomedical Science Productivity in Artificial Intelligence Research on India: A Scientometric Study Evaluation

Authors: Dr. Praveen B. Hulloli

Abstract: This Scientometric research study evaluates India’s research landscape in Biomedical within Science Artificial Intelligence (AI) from 2015 to 2024 (10 years). Utilizing data from the Web of Science (WoS), Analyzing a corpus of 622 publications and 10,359 citations, the research tracks the transition from a developmental phase to a high-impact era. Results from the Relative Quality Index (RQI) identify 2015, 2017, 2021, and 2022 as peak years for research excellence, while a subsequent dip in citation rates for 2023-2024 suggests challenges in sustaining global influence despite rising publication volumes. Journal productivity analysis reveals Computers in Biology and Medicine as the field leader with an h-index of 36.3. While top-tier journals maintain strong impacts, a score convergence of 18.1 among specialized outlets indicates a stabilizing, competitive ecosystem. The findings underscore the need for enhanced interdisciplinary collaboration to bridge the gap between quantitative growth and clinical utility, ensuring Indian AI research maintains consistent international academic prestige.

DOI: https://doi.org/10.5281/zenodo.20504166

Effect of Atmospheric SO₂ and Acid Rain on Chemical Degradation of Cement-Based Materials

Authors: Gulshama, Professor Subhashini Sharma

Abstract: This project studies in detail the harmful effects of atmospheric sulfur dioxide (SO₂) and acid rain on cement-based materials, which are widely used in construction activities such as buildings, bridges, and roads. These materials are continuously exposed to environmental conditions, especially in industrial and urban areas where pollution levels are significantly high. Among various pollutants, sulfur dioxide plays a major role in the formation of acid rain, which adversely affects the durability, strength, and overall performance of cement-based structures. The project further explains the chemical reactions involved in the formation of acid rain, where sulfur dioxide reacts with oxygen and water vapor present in the atmosphere to form sulfuric acid. This acid, when deposited on cement surfaces through rainfall, initiates a series of chemical reactions with important components of cement such as calcium hydroxide and calcium silicate compounds. These reactions lead to the formation of harmful products like gypsum and ettringite, which cause expansion, cracking, and gradual weakening of the material. In addition, this study describes the mechanism of degradation, including the penetration of acidic solutions into the pores of cement, internal stress development, and surface damage. The long-term effects include reduction in compressive strength, increased porosity, and structural instability of cement-based materials. Finally, the project also highlights various preventive measures to enhance durability, such as the use of sulfate-resistant cement, protective coatings, and control of environmental pollution. Overall, this study provides a clear understanding of the impact of acid rain on construction materials and suggests ways to improve their lifespan and performance.

DOI: https://doi.org/10.5281/zenodo.20506966

Artificial Intelligence as a Silent Arbitrator: Regulating AI-Assisted Decision-Making in International Commercial Arbitration

Authors: Chetan Kumar Pandey, Dr. Alakhanda Rajawat

Abstract: The integration of artificial intelligence into international commercial arbitration is an indicator of a paradigm shift that disrupts the traditional pillars of the adjudicative process. This research paper examines how AI has evolved from a non-dominant administrative tool to a so-called silent arbitrator that takes control of the substantive substance of the arbitral mandate. The introduction of automation into global commerce means that institutions, such as the International Chamber of Commerce (ICC), the Singapore International Arbitration Centre (SIAC), and the Hong Kong International Arbitration Centre (HKIAC), face the challenge of efficiency and procedural due process. A thorough examination of the 2025 International Arbitration Survey shows that there is an increase in the utilisation of AI in fact-finding and document review, and that standpoints on applying AI to the execution of judgment and discretion remain strong. This paper analyses the regulatory response, including the European Union Artificial Intelligence Act and the proliferation of light regulations issued by the Silicon Valley Arbitration and Mediation Centre (SVAMC) and the Chartered Institute of Arbitrators (CIArb). In addition, the research considers the jurisprudential consequences of AI-aided awards observed in recent cases, including LaPaglia v. Valve Corp. It suggests guidelines to a strong regulatory system that ensures human control and maintains party independence.

DOI: https://doi.org/10.5281/zenodo.20507543

An Immersive and Adaptive Virtual Reality-Based Solar System Learning System Using Generative AI

Authors: Dheeraj Vaswani, Anushka Mane, Nikhil More, Sanjana Nitnware, Priyanka Patil, Anuradha Sangram Solanki

Abstract: Our research presents the design and im-plementation of an immersive Virtual Reality based educational system for learning about the Solar Sys-tem, enhanced with Generative Artificial Intelligence. Traditionally the spatial and dynamic relationships between celestial bodies aren’t conveyed effectively. Hence, to reduce this limitation, the system uses Unity D to create an interactive virtual reality environment where everyone can explore all celestial bodies in real time. The system also uses a Gemini Generative AI API to provide dynamic, context-aware explanations with respect to the level of knowledge of the learner. The combination of immersive visualization and adap-tive learning keeps students engaged, while making complex concepts feel natural and intuitive, rather than overwhelming. The system is also built on a scalable architecture, meaning future capabilities like performance tracking and intelligent assessments can be added without rebuilding from the ground up.

Smart Wearable Health Monitoring System with IoT and Emergency Alert Mechanism

Authors: Assistant Professor Dr. Madhuvanthani R, Dr. Sundar G, Abirami M, Akhila R, Madhumithra M, Vedhavarshini N

Abstract: Ensuring continuous health monitoring and child safety has become an important concern in modern society. Traditional monitoring methods do not provide real-time health updates or emergency alerts. To overcome these limitations, this project proposes the design and implementation of an IoT-enabled smart wearable device for continuous health monitoring and emergency notification. The proposed system combines health monitoring, location tracking, and emergency communication into a compact wearable device. The system continuously monitors vital parameters such as heart rate, pulse rate, oxygen level, and body temperature using the MAX30100 pulse oximeter sensor and LM35 temperature sensor. The collected data is processed by the ESP32 microcontroller and displayed on an OLED display for local monitoring. During abnormal conditions, the GSM SIM800L module sends emergency alerts, while the GPS module provides live location details to parents or guardians for quick response and tracking. The wearable device also supports two-way communication during emergencies. A 3.7V Lithium Polymer battery, Type-C charging module, and DC-DC voltage booster ensure stable and portable operation. The proposed system offers a reliable, low-cost, and real-time solution for child health monitoring and safety management, improving parental awareness and emergency response through IoT technology.

DOI: https://doi.org/10.5281/zenodo.20510123

Preparation and Spectroscopic Characterization of Gallium and Copper Co-Doped Bioactive Glass for Post-Surgical Cancer Wound Healing

Authors: P. Kothari

Abstract: The challenge of managing wounds after surgery, especially following tumour removal, remains a key issue in clinical practice. This requires materials that can control the growth of remaining cancer cells while also encouraging the quick healing of soft tissues. In this study, we present the creation and testing of a new type of bioactive glass (BG) that includes Gallium (Ga) and Copper (Cu). This material is designed to help with wound healing. The base of this glass is made from a mixture of silicon dioxide, calcium oxide, sodium oxide, and phosphorus pentoxide. It was modified with varying amounts of gallium oxide and copper oxide (1–3 mol%) using a sol-gel method. The structure and properties of this glass were studied using X-ray Diffraction (XRD), Fourier Transform Infrared (FTIR) spectroscopy, and UV-Vis-NIR spectroscopy to understand its network structure and optical behaviour. In vitro tests showed that this glass can form a layer of hydroxyapatite in simulated body fluid, indicating its bioactivity. Biological tests revealed that the Ga-doped glasses significantly reduced the survival of cancer cells, while the Cu-doped versions encouraged the growth of skin cells and promoted blood vessel formation. These results suggest that Ga and Cu co-doped BGs are a promising material for treating wounds in cancer patients.

DrawabilityAssessmentofASS304SheetsUsedin Dairy Industry in Terms of Limiting Drawing Ratio (LDR)

Authors: Aditya Menasagi, Basavaraj Sali, Shridhar Kotrashetra, Veerendragowda Patil, B.H.Vadavadagi, H.V.Bhujle

Abstract: The current trend shows a significant increase in the application and use of sheet metal in manufacturing processes. Stainless steels are selected for dairy applications because they are resistant to corrosion, inert, easily cleaned and sterilized without loss of properties, and can be fabricated by a variety of techniques into robust structures. For this study, Austenitic Stainless Steel (ASS) 304material was selected and cut into circular shapes of varying diameters but with thickness of 1mm and 0.8mm. These circular cut materials are referred to as blanks. Before testing,a lubricant(grease)is applied to the blanks.The blanks are then subjected to as with cup drawing test using a hydraulic deep drawing press to determine the limitingdrawingratio(LDR). During the deep drawing process, the cup is formed by the punch force. At a certain blank diameter, the bottom of the cup may fracture due to the punch force. The diameter of the blank just before the fracture occurs represents the maximum. diameter of the blank.The addition of lubricant helps to analyze the impact of friction between the blank and the punch during the deep drawing process. The drawability of sheet metals is measured in terms of the LDR, which indicates the maximum deformation a cup can undergo without failure using a hydraulic press. The LDR is defined as the ratio of the maximum diameter of the blank to the diameter of the punch. Experiments were conducted on ASS 304 sheets using a hydraulic press deep drawing setup, and load-element were generated. The LDR value for 304 sheets was determined.

DOI: https://doi.org/10.5281/zenodo.20522797

Formulation and Evaluation of Herbal Oil

Authors: Vijaykumar Kale, Sumit Tamhankar, Shubham Chavan, Mahesh Thakare, Assistant Professor Vaibhav Narwade

Abstract: Inflammation is a natural protective response of the body against harmful stimuli such as infection, injury, toxins, and tissue damage. Although it plays an important role in healing and defense mechanisms, prolonged or chronic inflammation may lead to severe tissue damage and various disease conditions. Conventional anti-inflammatory drugs such as NSAIDs and corticosteroids are widely used for the management of inflammation; however, their long-term use is associated with several adverse effects including gastric irritation, renal toxicity, and cardiovascular complications. Due to these limitations, there is an increasing demand for safe, effective, and natural alternatives in the form of herbal formulations. The present study focuses on the formulation and evaluation of a herbal anti-inflammatory oil using Annona reticulata leaves extract along with turmeric, ginger, sesame oil, eucalyptus oil, mentha piperita oil, and vitamin E. Annona reticulata is rich in bioactive phytoconstituents such as flavonoids, alkaloids, tannins, and phenolic compounds which contribute to its anti-inflammatory and antioxidant activities. Turmeric provides curcumin, while ginger contains gingerols and shogaols, both of which are known for their strong anti-inflammatory properties. The essential oils included in the formulation enhance skin penetration, provide soothing effects, and improve overall therapeutic action. The formulated herbal oil was evaluated for various physicochemical parameters such as appearance, odor, pH, viscosity, spreadability, homogeneity, skin irritation, and stability. The results indicated that the formulation was stable, safe for topical application, and showed satisfactory characteristics. The synergistic effect of all herbal ingredients may contribute to effective reduction of inflammation with minimal side effects. Hence, the developed herbal oil formulation can be considered a promising, safe, and economical alternative for the management of inflammatory conditions.{1}

A Hierarchical Ensemble CNN Framework for Android Malware Detection via Bytecode Visualization

Authors: Nikhil Bhamare, Piyush Takalkar, Sujit Sherkar, Ms. Rajashri Malage

Abstract: The exponential growth of the Android ecosystem has been accompanied by a surge in sophisticated mobile mal-ware. Traditional signature-based detection mechanisms struggle to keep pace with these evasive threats, necessitating more adaptive and intelligent defense strategies. In this paper, we present a novel hierarchical ensemble Convolutional Neural Network (CNN) framework designed for robust Android malware detection. By transforming APK bytecode into grayscale images, our approach bypasses conventional manual feature engineering and leverages spatial pattern recognition. The proposed archi-tecture integrates three distinct deep learning models—ResNet50, DenseNet121, and VGG16—to extract diverse and comprehensive feature representations. The framework operates in two stages: initially classifying applications as benign or malicious, and subsequently categorizing the malicious samples into 25 distinct malware families. Experimental evaluations demonstrate that our ensemble approach achieves a high accuracy of 89.15%, out-performing individual CNN baselines. Furthermore, this image-based learning paradigm proves highly resilient to common structural obfuscation techniques utilized by modern Android malware.

DOI: http://doi.org/

Formulation and Evaluation of Multivitamin and Antioxidant Herbal Chocolate

Authors: Associate Professor Vaibhav Narwade, Satyajeet Pawar, Vaishnavi Hendge, Vijaykumar Kale, Mahesh Thakare

Abstract: The chocolate is most loving food of children where as the medicine is the hating substance. So, objective of this study was to formulate the chocolate that contain drug i.e., medicated chocolate to prevent the disease. In children cough, viral infection is most common diseases. Dark chocolate gets popularity for several decades due to its enormous health benefits. Dark chocolate is considered a functional food due to its anti-diabetic, anti-inflammatory, and anti-microbial properties. It also has a well-established role in weight management and the alteration of a lipid profile to a healthy direction. Multivitamins are used to provide vitamins that are not taken in through the diet.Multivitamins are also used to treat vitamin deficiencies (lack of vitamins) caused by illness, pregnancy, poor nutrition, digestive disorders, and many other conditions. Antioxidant-the word itself is magic. Using the antioxidant concept as a spearhead in proposed mechanisms for staving off so-called “free-radical” reactions, the rush is on to mine claims for the latest and most effective combination of free-radical scavenging compounds. We must acknowledge that such “radicals” have definitively been shown to damage all biochemical components such as DNA/RNA, carbohydrates, unsaturated lipids, proteins, and micronutrientssuch as carotenoids (alpha and beta carotene, lycopene), vitamins A, B6, B12, and folate. Defense strategies against such aggressive radical species include enzymes, antioxidants that occur naturally in the body (glutathione, uric acid, ubiquinol-10, and others) and radicalscavenging nutrients, such as vitamins A, C, and E, and carotenoids.

DOI: http://doi.org/

Regulatory Documentation Strategy For Digital Stethoscope In India

Authors: Acchani Sridhar, P.Silas

Abstract: The evolution of healthcare technology has transformed conventional diagnostic instruments into digitally enabled medical devices capable of supporting modern clinical practice. Digital stethoscopes combine traditional auscultation techniques with electronic signal acquisition, sound enhancement, data recording, and communication capabilities. While these innovations improve clinical utility, they also introduce additional regulatory obligations associated with software, electronic safety, cybersecurity, and data management. This article examines the regulatory documentation framework applicable to digital stethoscopes within the Indian medical device sector. The study discusses quality management requirements, risk management principles, performance evaluation, clinical evidence generation, software validation, and post-market surveillance activities relevant to product development and commercialization. A structured regulatory approach is presented to assist manufacturers in demonstrating safety, performance, and compliance throughout the device lifecycle. The analysis highlights the increasing importance of integrated quality and regulatory systems in supporting market access and long-term compliance for digitally enabled medical technologies.

DOI: http://doi.org/10.5281/zenodo.20526019

Compact and Energy Efficient Solution for Menstrual Waste Disposal

Authors: Tharani B, Jeevitha Mani S, Kayathri R D, Revathi Sangeetha R, Sri Ranjani Devi N

Abstract: The improper disposal of menstrual waste has become a major concern due to its impact on hygiene, public health, and environmental sustainability [1][3][4]. Conventional disposal methods such as open dumping, flushing, or mixing sanitary waste with regular garbage can lead to pollution, unpleasant odor, and difficulties in waste management [3][5]. To overcome these challenges, this work presents a compact and energy-efficient menstrual waste disposal system designed to provide safe, hygienic, and automated waste handling with minimal human intervention [7][8]. The proposed system is developed using an ESP32 microcontroller that controls the heating, cooling, and monitoring operations of the disposal unit. When menstrual waste is inserted into the chamber and the start button is pressed, a relay-controlled nichrome heating element generates sufficient thermal energy for effective incineration [1][7]. A DHT11 temperature sensor continuously monitors the chamber temperature and transfers the readings to the controller for safe operation. After completion of the burning process, a cooling fan is automatically activated for a predefined duration to reduce the internal temperature of the system. In addition, a 2×16 LCD module displays real-time temperature values and cooling fan status, improving system usability and monitoring capability. The experimental results demonstrate that the proposed system provides reliable operation, efficient waste disposal, and reduced direct human contact with sanitary waste [6][9]. The compact design and automated functionality make the system suitable for deployment in homes, schools, hostels, hospitals, and public sanitation facilities. By integrating automation, thermal processing, and smart monitoring features, the proposed system contributes toward environmentally responsible menstrual waste management and improved sanitation practices [2][5][10].

DOI: http://doi.org/10.5281/zenodo.20526664

A Study On Work-From-Home Culture and Employee Productivity

Authors: Mahak Rawat, Ms. Shruti Rawat

Abstract: The work-from-home (WFH) model — a remote work arrangement facilitated by digital communication technologies, cloud-based collaboration platforms, and organizational policy adaptations — has fundamentally restructured the landscape of employee productivity and organizational performance management in the contemporary corporate environment. This research paper investigates the work-from-home culture and its multifaceted impact on employee productivity at Infosys Ltd., one of India’s foremost information technology and consulting corporations. Through systematic analysis of secondary data drawn from Infosys corporate reports, HR practitioner publications, technology industry research, and academic literature spanning 2020 to 2025, the study examines the organizational transition to remote work models, evaluates productivity outcomes across functional dimensions, and identifies the key enablers and inhibitors of WFH effectiveness within the Infosys operational context. Findings indicate that while WFH arrangements at Infosys have yielded measurable gains in individual task completion efficiency, cost optimization, and talent retention — particularly among senior technical professionals — challenges persist around collaborative innovation, work-life boundary management, digital fatigue, and equitable access to career development opportunities for employees in remote settings. The paper concludes that sustainable WFH productivity at Infosys requires an integrated organizational strategy encompassing robust digital infrastructure, outcome-oriented performance frameworks, structured virtual collaboration protocols, and proactive employee well-being support mechanisms.

DOI: http://doi.org/10.5281/zenodo.20527147

Strategic Leadership in Emerging Markets

Authors: Mr. Biswajit Sen

Abstract: Strategic leadership plays a crucial role in navigating the complexities of emerging markets, where rapid economic growth, institutional instability, and socio-political uncertainties create both opportunities and risks. This study examines the influence of strategic leadership on organizational performance, sustainability, and competitive advantage in emerging economies. Drawing on secondary data from academic journals, industry reports, and case studies, the research explores leadership styles, adaptive strategies, and decision-making approaches that contribute to organizational success. The findings indicate that strategic leaders who demonstrate vision, adaptability, cultural intelligence, and innovation are better positioned to manage uncertainty and achieve sustainable growth. The study concludes with recommendations for enhancing leadership effectiveness in emerging market environments.

Generation of Electricity from Turbo-Ventilator

Authors: Chandu N S, Ullas S T, Assistant Professor Mr. Keerthi B L

Abstract: In this world of depleting resources, renewable energy plays an important role. Wind energy is one of the major renewable energy sources. In this paper we intend to study and review various research papers on generating electricity from wind energy using turbo ventilators. This method is economical and feasible by applying various electrical and mechanical techniques. In this paper we also intend to improve the efficiency of the system by using various materials for the fabrication of turbo ventilators. We have reviewed the papers on this topic published by various authors. We have compared their designs and concluded into an efficient model by combining all the designs into one. Turbo ventilators are widely used on industrial sheds and warehouses for natural ventilation without consuming electrical energy. They operate on the principle of wind velocity and stack effect, where hot air rises and rotates the turbine blades. The present project explores the potential of utilizing this otherwise wasted rotational energy of turbo ventilators for generating electricity. The working concept involves coupling the rotating shaft of a turbo ventilator with a low-rpm DC generator or alternator through a suitable gear mechanism. As the ventilator rotates due to wind or thermal convection, mechanical energy is converted into electrical energy. The output is generally low voltage DC, which can be stored in batteries using a charging circuit and later used for small-scale applications like LED lighting, mobile charging, or powering sensors.The study includes design considerations such as selection of generator, gear ratio optimization, mounting arrangement, and electrical load calculation. Experimental results show that a standard 24-inch turbo ventilator can generate 5V–12V under moderate wind speeds of 3–6 m/s, producing power in the range of 3–10 W. Though the output is small, it is continuous and free of cost, making it suitable for sustainable energy harvesting. In conclusion, electricity generation from turbo ventilators provides a cost-effective and eco-friendly solution for auxiliary power needs in industries. It utilizes existing infrastructure, requires low maintenance, and contributes to green energy initiatives. The concept can be further improved by using efficient generators, MPPT circuits, and multiple units in parallel for higher output.

Global Gold Prices Analysis And Visualization Using Tableau

Authors: Gunta Deekshitha Raj, Yasa Shivanandhan Reddy, Suragoni Vaishnav Sai Goud, Mrs. H. Meenal

Abstract: Gold is one of the most valuable and widely traded commodities in the world, playing a significant role in global financial markets, investment portfolios, and economic stability. Due to its ability to act as a hedge against inflation, currency fluctuations, and economic uncertainties, the analysis of gold prices and supply has become increasingly important for researchers, investors, and policymakers. This project focuses on the analysis and visualization of global gold price and supply trends from 2010 to 2025 using Tableau, a powerful data visualization tool. The dataset used in this study contains information related to gold prices, gold supply, demand, trading volume, countries, regions, market types, and other economic attributes. The dataset was collected from reliable sources and processed to ensure consistency and accuracy. Data preprocessing techniques were applied to organize and prepare the dataset for visualization and analysis. Various dimensions and measures present in the dataset enabled a comprehensive study of gold market behavior across different geographical regions and time periods. To gain meaningful insights, multiple visualization techniques were implemented using Tableau. These include line charts, bar charts, dual-axis charts, funnel charts, waterfall charts, heat maps, highlight tables, geographical maps, timelines, crosstabs, and interactive dashboards. The visualizations were designed to explore trends in gold prices, compare gold supply across countries and regions, analyze trading volumes, and identify patterns over time. Interactive features such as filtering, highlighting, and dashboard actions were also incorporated to improve user exploration and data interpretation. The analysis revealed noticeable variations in gold prices and supply over the years, highlighting the influence of market conditions and regional factors on gold-related activities. Comparative visualizations helped identify differences among countries and regions, while time-series analysis provided insights into long-term trends and fluctuations. The dashboards enabled users to interact with the data and obtain a clearer understanding of relationships between different variables. Overall, this project demonstrates the effectiveness of data visualization in transforming complex financial datasets into meaningful and easily understandable insights. The findings contribute to a better understanding of global gold market trends and showcase how Tableau can be used as an effective tool for exploratory data analysis, decision-making, and financial market research.

E-Grampanchayat: A Cloud-Ready Framework For Rural Digital Transformation And Fiscal Management

Authors: Prof. Ashwini Sawant, Ajinkya Shriram Gurav, Prasad Nagnath Londe, Prasanna Motiram Kasabe

Abstract: The integration of Information and Communication Technology (ICT) in rural governance is a critical step towards a “Digital India.” This research discusses the design and development of E-Grampanchayat, an automated administration portal. The system addresses the inefficiencies of the traditional manual ledger-based system by digitizing document requests, notice dissemination, and tax collection. A unique contribution of this paper is the Hybrid Fiscal Verification Module, which allows asynchronous verification of UPI-based tax payments. The system was developed using a PHP-MySQL architecture and tested in a localized server environment, demonstrating high data consistency, reduced processing latency, and improved transparency in local self-government operations.

Cloud-Connected Smart Health Kiosk for Rural Diagnostic Services

Authors: Assistant Professor Gargi Mishra, Assistant Professor S Anantha Priyadharsini

Abstract: Access to quality healthcare in developing nations often remains challenging due to several factors including geographical distance, shortage of competent medical professionals, and lack of diagnostic facilities. This study proposes an efficient cloud-based smart health kiosk that facilitates the delivery of cost-effective, easy-to-access, and quality diagnostic services. The design of the kiosk involves the use of IoT enabled medical sensors (digital stethoscope, infrared thermometer, pulse oximeter, blood pressure measurement device, glucometer, ECG, and urinalysis dipstick reader) and edge computing gateway for capturing the data and pre-processing the acquired data. Telemedicine is used for establishing a video connection between the patient and remote physician. Medical data is transferred to the cloud storage through an HIPAA compliant network for long-term storage and initial triaging using artificial intelligence. After deployment at 50 rural areas in India serving 250,000 patients in 18 months, average travel time decreased from 32 km to 1.5 km and out-of-pocket costs were minimized by 68%. Patient satisfaction rate was recorded to be 94%.

DOI: https://doi.org/10.5281/zenodo.20555450

Hybrid ML Model for Crop Recommendation Using Rainfall, Temperature, and Humidity Forecast

Authors: Assistant Professor Prajina V K, Assistant Professor Bhargavi M R

Abstract: Despite the technological advancements in agriculture, it continues to be vulnerable to climate change effects, and poor crop choice due to unfavorable conditions results in low yields and monetary hardship to farmers. This paper proposes a hybrid machine learning approach for crop recommendation that takes into account not only the weather forecast (rainfall, temperature, humidity) but also soil characteristics (pH, nitrogen, phosphorus, potassium). It consists of two stages: the Random Forest algorithm for feature selection and prediction followed by the XGBoost algorithm for correction of predicted values. Applying the approach to the data set of 50,000 crop images tagged by location for 15 main crops within a period of 10 years (2015-2025) in India, the hybrid algorithm reaches the accuracy level of 94.2% compared to Random Forest (89.3%), XGBoost (91.6%), SVM (84.2%), and KNN (81.5%). Rainfall and minimum temperature were recognized as crucial features by the algorithm. The proposed algorithm is implemented in a smartphone application for farmers that provides recommendations based on weather forecasts for the next 5 days, which allows increasing crop yields up to 20-30%.

DOI: https://doi.org/10.5281/zenodo.20555541

AI Based Clinical Decision Support System for Diabetes Prediction Using Machine Learning

Authors: K. Chaitanya, Assistant Professor Nekuri Jyothsna

Abstract: Diabetes mellitus is a growing chronic health condition that needs to be detected at an early stage to avoid complications. Machine learning (ML) is proving to be an efficient solution for developing Clinical Decision Support Systems (CDSS), which aid doctors in diagnosing and predicting diseases. The research aims to develop an artificial intelligence-based CDSS for diabetes prediction using supervised machine learning algorithms such as Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB). The results of the experiments prove that the ensemble methods are better than traditional methods. The proposed CDSS is a solution for predicting diabetes mellitus and shows its potential for providing accurate insights for decision-making in the health care industry. The use of such artificial intelligence-based CDSS is significant for decision-making in health care.

DOI: https://doi.org/10.5281/zenodo.20555588

CFD-Based Evaluation Of Thermal Performance And Nusselt Number Enhancement In A Heat Exchanger Using Modified Twisted Tape

Authors: Ajay Malviya, Dr. Satnam Singh

Abstract: The present work focuses on the CFD-based evaluation of thermal performance and Nusselt number enhancement in a heat exchanger using modified twisted tape inserts. Twisted tape inserts are widely used passive methods for improving heat transfer in internal flow systems. In this study, four geometries were analyzed: Plain Twisted Tape (PTT), Double-Hole Perforated Twisted Tape (DHPTT), Curved-Slot Twisted Tape (CSTT), and Multi-Hole Perforated Twisted Tape (MHPTT). A circular pipe of 44 mm outer diameter, 42 mm inner diameter, and 400 mm length was modeled with a 1 mm thick twisted tape. The total twist angle was 1800°, forming five complete rotations with an 80 mm twist pitch. The CFD model was developed in ANSYS Fluent using a polyhedral mesh of 472,350 cells. Water was used as the working fluid, while the pipe and tape were modeled as aluminum. The inlet velocity and temperature were 0.6 m/s and 293 K, and the pipe wall temperature was 365 K. The standard k-epsilon model was used for turbulent flow analysis. Results showed that MHPTT achieved the highest outlet temperature of 345.99 K, temperature rise of 52.99 K, and 18.09% change. The Nusselt number also increased with Reynolds number. Overall, MHPTT gave the best thermal performance.

DOI: http://doi.org/10.5281/zenodo.20556413

Comparative Analysis Of Employment Generation In Organised And Unorganized Sectors In India.

Authors: Akash katheria, Dr Vinod Kumar

Abstract: Employment generation remains one of the most significant indicators of economic development in India. The country’s workforce is distributed across both organised and unorganised sectors, each playing a distinct role in creating employment opportunities. While the organised sector offers formal employment, job security, social protection, and regulated working conditions, the unorganised sector continues to absorb a substantial share of the labour force, particularly among low-skilled and economically vulnerable populations. This study examines and compares the contribution of organised and unorganised sectors to employment generation in India. The research analyses trends in employment, sectoral distribution of workers, wage structures, job security, and the quality of employment opportunities available in both sectors. It also explores the challenges faced by workers, including issues related to income stability, social security benefits, and working conditions. The study is based on secondary data collected from government reports, labour surveys, and published literature. The findings reveal that although the organised sector contributes significantly to productivity and economic growth, the unorganized sector remains the largest source of employment in India. However, employment in the unorganised sector is often characterized by low wages, limited social protection, and higher job insecurity. The study highlights the need for policies that promote formalization, skill development, and social security coverage to improve the quality of employment across sectors

Digital Nudges And The Marketplace: How Social Media Reshapes Consumer Purchasing Power In Tier-II India

Authors: Raushan Kumar, Dr. Navneet Seth

Abstract: The rapid growth of social media platforms has significantly transformed consumer behavior and purchasing patterns across India. While extensive research has examined the influence of social media marketing in metropolitan regions, limited attention has been given to its impact on consumers residing in Tier-II cities. This study explores the effect of key social media marketing factors, namely influencer endorsements, online reviews, targeted advertisements, brand interaction, and user-generated content (UGC), on consumers’ purchase intentions in Bathinda city. A quantitative research approach was adopted, and primary data were collected through structured questionnaires administered to 150 respondents, primarily college students aged between 18 and 35 years, who constitute one of the most active segments of social media users. The data were analyzed using multiple linear regression techniques to determine the relationship between social media marketing variables and purchase intention. The findings reveal that all five variables exert a significant positive influence on consumers’ buying intentions, explaining a substantial proportion of the variation in purchase behavior (R² = 0.68, p < .001). Among the examined factors, influencer endorsements (β = 0.41) and online reviews (β = 0.33) emerged as the most influential predictors of purchase intention. Furthermore, reliability analysis demonstrated strong internal consistency across all measurement constructs, with Cronbach’s alpha values exceeding 0.79. The study enriches the existing literature by providing empirical evidence from a Tier-II Indian city and highlights the growing importance of social media marketing in shaping consumer decisions beyond major urban centers. The findings offer valuable insights for marketers and businesses seeking to develop effective digital marketing strategies targeted at increasingly connected and socially engaged consumers.

Integrating Clinical, Behavioural, And Lived Experience Data To Understand Type 2 Diabetes Management: A TAP-IT Mixed-Methods Study

Authors: Dr Nilani Sammuarachchi

Abstract: Type 2 Diabetes Mellitus (T2DM) constitutes a major and escalating global and national public health challenge, characterised by rising prevalence, substantial complication burden, and profound impacts on physical, psychological, and social wellbeing. Despite the availability of effective pharmacological treatments, evidence-based clinical guidelines, and structured diabetes education programmes, a significant proportion of individuals continue to experience suboptimal glycaemic control and diminished quality of life. These persistent gaps highlight the need for integrative approaches that extend beyond biomedical management to address behavioural, emotional, and contextual influences on diabetes self-management. This doctoral research applied the TAP-IT mixed-methods framework to examine the interrelationships between clinical indicators, self-care behaviours, emotional experiences, and lived realities of adults managing T2DM. A convergent mixed-methods design was employed, involving 150 adults diagnosed with T2DM who participated in quantitative surveys, clinical assessments, and in-depth qualitative interviews. Quantitative analyses demonstrated high levels of medication adherence (80%), moderate dietary adherence (65%), and comparatively low engagement in physical activity and psychological support behaviours. Significant associations were identified between self-care behaviours and key clinical indicators, including glycated haemoglobin (HbA1c), body mass index (BMI), and blood pressure, underscoring the central role of lifestyle and behavioural factors in glycaemic control and cardiometabolic risk. Qualitative thematic analysis revealed diabetes-related distress, cultural expectations, family and caregiving responsibilities, limited motivation, and time constraints as major barriers to sustained self-management, while strong family support, culturally responsive healthcare, and positive clinician–patient relationships emerged as critical facilitators. Triangulation of quantitative, qualitative, and clinical data generated a comprehensive and integrated understanding of how emotional burden and contextual constraints shape behavioural patterns and metabolic outcomes in T2DM. The TAP-IT framework proved effective in identifying misalignments between clinical recommendations and the lived experiences of individuals managing diabetes in everyday contexts. The findings emphasise the necessity of person-centred and culturally responsive care models that integrate emotional support, tailored health education, and community-based interventions alongside clinical management. This study contributes novel evidence demonstrating that effective T2DM management requires coordinated, multidimensional strategies addressing biological, behavioural, psychological, and sociocultural determinants simultaneously, with particular relevance for Māori, Pasifika, and South Asian populations in Aotearoa New Zealand.

DOI: http://doi.org/10.5281/zenodo.20560629

Early Detection Of Oral Cancer Using EfficientNet

Authors: Harshitha T N, Soujanya R

Abstract: Oral cancer is a critical and life-threatening disease, where early detection plays a vital role in improving patient survival rates. However, traditional diagnostic approaches rely heavily on manual clinical examination and biopsy, which are time-consuming, invasive, and often lead to delayed diagnosis. To address these limitations, this paper proposes a deep learning framework for automated oral cancer detection using medical image analysis and lesion-focused classification techniques. The proposed system integrates image preprocessing, lesion segmentation, and deep convolutional neural networks (CNNs) for accurate classification. Preprocessing techniques such as contrast enhancement and noise reduction are applied to improve image quality. Lesion regions are extracted using Otsu thresholding and contour-based segmentation to isolate regions of interest (ROI), which enhances feature learning. Multiple deep learning architectures, including Baseline CNN and EfficientNet-B0 are evaluated for performance comparison. In addition, the proposed framework integrates lesion segmen-tation and deep feature extraction to improve classification robustness and diagnostic performance. To enhance model interpretability, Grad-CAM is employed to visualize the regions contributing to predictions, making the system more transpar-ent for medical applications. Experimental results demonstrate that the proposed EfficientNet-B0 based model achieves superior performance compared to baseline approaches, with improved accuracy and F1-score on the test dataset. The proposed framework provides an efficient, scalable, and interpretable solution for early-stage oral cancer detection, supporting clinical decision-making and reducing diagnostic delays.

Design And Development Of Unmanned Aerial Vehicle Using The Additive Manufacturing

Authors: Ravikumar PC, Bhuvan A H, Yashwanth KH, Manjunath SH Professor

Abstract: Unmanned Aerial Vehicles (UAVs), commonly known as drones, have gained significant importance in various fields such as surveillance, agriculture, disaster management, environmental monitoring, and military operations. The design and development of UAVs require lightweight structures, high strength, aerodynamic efficiency, and cost-effective manufacturing techniques. Additive Manufacturing (AM), also known as 3D printing, has emerged as a revolutionary technology that enables rapid prototyping, complex geometrical designs, reduced material waste, and faster production cycles. This study focuses on the design and development of an Unmanned Aerial Vehicle using Additive Manufacturing techniques. The UAV components are designed using Computer-Aided Design (CAD) software and fabricated through 3D printing technology. The use of additive manufacturing allows the production of lightweight and customized parts while maintaining structural integrity and reducing overall manufacturing costs. The study evaluates the design process, material selection, fabrication methods, assembly procedures, and performance characteristics of the developed UAV. The results demonstrate that additive manufacturing significantly improves design flexibility, reduces production time, and enhances the efficiency of UAV development. Furthermore, the fabricated UAV exhibits satisfactory flight performance, stability, and durability for various operational applications. The research highlights the potential of additive manufacturing as an effective solution for next-generation UAV production and aerospace innovation.

Understanding Human Actions: A Review Of Recent Techniques And Benchmarks

Authors: Amandeep Kaur

Abstract: Due to the expanding applications of Unmanned Aerial Vehicles (UAVs) for surveillance, security, disaster response and urban monitoring in recent past years, Human Action Recognition (HAR) in aerial videos has also multiplied with an outstanding courtesy. Ground-level videos are moderately easy enough to analyse but HAR in aerial videos also comes with exclusive contests. These races include low resolution, dynamic backgrounds, camera motion, occlusions and varying scales, viewpoints and low lighting. This review paper is an attempt to cover a comprehensive analysis of the modern techniques developed in past few years to address these challenges. The paper provides a categorization of already existing techniques which are based on the strategies to represent the features such as handcrafted features, deep learning-based representations and also some hybrid approaches. It gives a deep overview of various classification models which includes older algorithms of machine learning and recently developed Deep Neural Networks (DNNs). Furthermore, encroachments in multi-modal data fusion, spatiotemporal modeling and silhouette-based action recognition tailored for aerial perspectives are also covered in depth. The paper also evaluates a number of benchmark datasets, highlights performance metrics and compares the effectiveness and limitations of various techniques. The main intention of writing this review paper is to facilitate the researchers with valuable insights and a consolidated understanding of the current landscape in aerial HAR which will be further helpful in this emerging field.

DOI: http://doi.org/10.5281/zenodo.20568845

AI Integrated Aircraft Door And Window Safety Indicator

Authors: Alice Lydia Immanuel

Abstract: Ensuring the security and structural integrity of aircraft doors and windows is critical for safe flight operations. This paper proposes a vibration-based monitoring system to verify that aircraft doors and windows are properly secured before and during flight. The proposed method identifies and measures characteristic vibration signatures associated with the aircraft structure, door assembly, and fastening components such as bolts and nuts. Any deviation from the expected vibration pattern, which may indicate a loose or improperly secured fastener, is detected through a comparison algorithm. The system then generates a warning indication on the cockpit display panel, enabling timely corrective action. Furthermore, an Artificial Intelligence (AI)-based analysis approach is incorporated to improve detection accuracy, minimize false alarms, and provide reliable, error-resistant assessments. The proposed solution enhances aircraft safety by offering continuous, real-time monitoring of door and window security.

DOI: http://doi.org/10.5281/zenodo.20569405

Design And Development Of Automatic Electromagnetic Braking System

Authors: Jai Raghuveera Samarth, Punith Gowda Y N, Akhilesh Gowda J, Mr. Sudeep Kumar K S Assistant Professor

Abstract: Majority of braking systems work on the principle of dissipation of kinetic energy to heat energy. This method has its own drawbacks and must be replaced with a more reliable braking system that is quick in response, doesn’t heat up and is maintenance free. In this project the design of an electro-magnetic braking system and optimization for various operational parameters has been done and the advantage of using the electromagnetic braking system in automobile is studied. These parameters have been previously iterated in cited projects and papers and also in the simulation models and are to be cross-checked with the experimental setup. An Electromagnetic Braking system uses Magnetic force to engage the brake, but the power required for braking is transmitted manually. The wheel is connected to a shaft and the electromagnet braking unit is attached to one side of the wheel. Here the braking unit consists of a hollow circular steel plate and a stator which has 3 spokes made of iron wounded with copper wire (or) magnetic wire. Here the round steel plate which is attached to the wheel rotates when wheel rotates with the help of motor. when current is supplied to the stator the spokes gets magnetized and creates an magnetic field which tries to attract or oppose the motion of rotating circular plate with the help of magnetic field created. In this brakes there is no contact between the electro-magnetic coils and rotating circular plate (i.e 2 mm gap between coil and circular plate) so this is also called as contactless braking system which is a main advantage in using these brakes. These brakes can be incorporated in heavy vehicles as an auxiliary brake. The electromagnetic brakes can be used in commercial vehicles by controlling the current supplied to produce the magnetic flux. Making some improvements in the brakes it can be used in automobiles in future.

Formulation And Evaluation of Herbal Papaya Soap

Authors: Vaibhav Narwade, Divya Ovhal, Sakshi Aher, Vijaykumar Kale, Mahesh Thakare

Abstract: Herbal soaps are becoming increasingly popular due to their natural origin, therapeutic value, and fewer side effects compared to synthetic soaps. The present study focuses on the formulation and evaluation of herbal papaya soap with antioxidant activity using natural ingredients such as papaya extract, turmeric, honey, sandalwood oil, vitamin E, and rose water. Papaya (Carica papaya) is rich in bioactive compounds including vitamins A, C, E, flavonoids, phenolic compounds, and papain enzyme, which provide antioxidant, exfoliating, antimicrobial, and skin-nourishing properties. The herbal soap was prepared using a suitable soap base through the melt-and-pour/cold process method and evaluated for various physicochemical parameters. The formulated soap was assessed for appearance, color, odor, texture, pH, foamability, foam retention, irritation effect, and stability. Antioxidant activity was evaluated using standard methods such as the DPPH free radical scavenging assay. The results showed that the prepared herbal soap possessed good antioxidant activity due to the presence of natural phytoconstituents in papaya and other herbal ingredients. The soap exhibited acceptable pH, good foaming ability, pleasant fragrance, smooth texture, and no skin irritation during testing. The incorporation of natural antioxidants helped protect the skin from oxidative stress and supported skin rejuvenation. The study concludes that herbal papaya soap can be successfully formulated using natural ingredients with effective antioxidant and skin-friendly properties. The developed formulation may serve as a safe, economical, eco-friendly, and beneficial alternative to commercial synthetic soaps. Thus, herbal papaya soap has potential applications in cosmetic and skincare preparations for maintaining healthy and glowing skin.

Global Climate Impact Analysis

Authors: K.S.Ananya, J.Varshika Narayan, Dr G Naresh

Abstract: Climate change has become one of the most significant global challenges of the 21st century, causing severe environmental and economic consequences worldwide. Extreme climate events such as floods, hurricanes, wildfires, droughts, and heatwaves have increased in both frequency and intensity, resulting in substantial financial losses across various sectors, including agriculture, infrastructure, industry, and public services. This study presents a comprehensive analysis of global climate events and their economic impacts using data visualization techniques. The dataset used in this research covers climate-related events occurring between 2020 and 2025 and includes information on event types, affected regions, occurrence dates, and estimated economic losses. The primary objective of this work is to transform complex climate data into meaningful visual representations that facilitate better understanding and interpretation. Tableau was employed as the main visualization tool to create interactive dashboards, bar charts, line graphs, heat maps, highlight tables, and geographic maps. Data preprocessing techniques, including cleaning, filtering, sorting, grouping, and aggregation, were applied to ensure accurate analysis. Time-series visualizations were used to identify trends in climate events and economic damages over the study period, while geographic visualizations highlighted regional variations in climate-related losses. The results reveal significant differences in the economic impacts of various climate events across regions and years, enabling the identification of highly vulnerable areas and the most damaging event categories. The interactive dashboards further support comparative analysis and enhance decision-making capabilities. This study demonstrates the effectiveness of data visualization in communicating complex climate information and provides valuable insights for policymakers, researchers, and stakeholders. The findings emphasize the growing economic burden of climate change and the importance of adopting sustainable strategies, risk mitigation measures, and improved disaster preparedness to reduce future impacts.

Multi-Physics Numerical Analysis And Performance Optimization Of PEM Fuel Cell For Automotive Applications

Authors: Agrim Verma, Rashi Singh

Abstract: The transition to hydrogen-based mobility re- quires Polymer Electrolyte Membrane (PEM) Fuel cells that are not only efficient but also durable under dy- namic load conditions. Traditional lumped parameter models often fail to capture the micro-climates inside theflow channels—areas where water flooding or membrane dehydration occurs locally. This term paper employs Ad- vanced Numerical Analysis to bridge this gap, focusingon the spatial distribution of Electrochemical parameters. By resolving the heat source terms into discrete physical contributions, the model enables targeted en- gineering interventions that are not accessible through bulk parameter approaches.

VIBE SHIELD – Agentic Evolving Guard Intelligence System (AEGIS) For Wireless Networks

Authors: R Gayathri, Rohith V, M Mugilvannan

Abstract: Sophisticated assailants outperform human defenders in today’s cyber networks. This project introduces AEGIS, an end-to-end autonomous cyber operations system that integrates Large Language Models (LLMs) with Multi-Agent Deep Reinforcement Learning (MADRL) within the CybORG++ environment to overcome human latency and inflexible rule-based systems. AEGIS competes in a zero-sum game between an autonomous Blue Agent defense (Microsoft Phi-3.5-mini) and a Red Agent attacker (Qwen2.5-Coder-3B) using an Independent Learners technique under Decentralized Training and Decentralized Execution (DTDE). The system has a fully integrated 7-level progressive training pipeline with threshold-separated ChromaDB episodic memories, prioritized replay buffers, and LLM-specific action masking to remove hallucinations. The system uses a distributed MARL architecture that performs LoRA fine-tuning over two physical nodes via direct Ethernet, guaranteeing total parameter isolation, in order to maximize performance under stringent hardware restrictions. In the end, this architecture effectively illustrates how LLM agents with curriculum learning and episodic memory can independently learn intricate, multi-subnet cyber security tactics in sophisticated simulated environments.

DOI: http://doi.org/10.5281/zenodo.20582330

IoT-Based Dog Daycare Robot For Automated Pet Feeding System

Authors: Prof. Krishna Rathi, Wanjare Vishakha, Shinde Arati, Jadhav Sneha

Abstract: This paper explains the design and development of an IoT-based dog daycare robot that can automatically provide food and water to pets. The proposed system uses a Raspberry Pi Zero as the main controller, which connects to the internet and allows users to control the system remotely using a mobile application or web interface. A servo motor is used to dispense a fixed quantity of food, ensuring proper portion control. A relay-controlled submersible pump is used to supply water when required. In automatic mode, feeding can be scheduled at fixed times. It shows how IoT technology can be used to solve real-life problems and improve pet care.

DOI: http://doi.org/10.5281/zenodo.20589670

Development and Evaluation of Polyherbal Candy for Immune Support in Dengue

Authors: Ms. Snehal Kadbhane, Ms. Bhagyashri Shankar Thorbole, Ms.Nikita Sanjay Pachange, Dr. Vijaykumar Kale, Dr. Mahesh Thakare

Abstract: In this study, making of polyherbal candy for treatment of Dengue is the primary goal, using natural ingredient to treat the dengue fever and boost the immunity against Dengue. Dengue fever is a flu-like illness transmitted by female mosquitos of the Aedes aegypti species. Another name for it in Ayurveda is dandaka jwara. It is particularly prevalent in tropical and subtropical climate zones worldwide. Common symptoms of dengue include vomiting, a strong headache, nausea, rashes, joint discomfort, pain behind the eyes, muscular pain, and enlarged glands. Platelets play a crucial role in blood coagulation. During the course of their infection, DENV [Dengue Virus] patients frequently experience thrombocytopenia, making them susceptible to bleeding symptoms and other serious consequences. It also causes bone marrow depression and reduces platelet production. The active constituent carica papaya which increasing the number of platelet count and it shows the Anti-inflammatory and Anti-viral activity. Giloy sativa increases platelet count and speeds up the recovery process and also during dengue fever helps to strengthen the immune system. Other ingredients in formulation such as Sugar, Gum acacia, Lemon juice, Beetroot (powder) helps in reducing dengue symptoms, such as fever and pain, and improving patient outcomes. The development of herbal candies for dengue therapy is a novel and promising approach to disease management, with potential advantages for both patients and healthcare systems.

Experimental And Numerical Studies Of Residual Stress Development On AlSi10Mg Alloy Processed Through Powder Bed Fusion

Authors: Pankaj Kumar Rai, P. N. Ahirwar

Abstract: Development of residual stresses during additive manufacturing (AM) imposes challenges on functionality and performance of the component. Being able to predict, measure and reduce residual stress by proper post processing will prevent pre-mature failure of the components. In this study finite element package of ANSYS software is employed to predict residual stress, distortion, melt pool dimensions and thermal history for the powder bed fusion (PBF) process of AM. Experimental validation of the residual stress predicted by the numerical modeling were carried out on additively manufactured coupon through X-ray diffraction. A small compressive residual stress on the top surface of the coupon is determined by both the experimental and numerical approach contradicting the reported work of tensile residual stress on the top surface.

DOI: http://doi.org/10.5281/zenodo.20592906

Human Rights Condition Of Fishing Communities: A Study On Mawa-Munshiganj Region, Bangladesh

Authors: Rakibul Hasan Amin, Mohammad Mehedi Hasan, Mohammad Shah Alam Chowdhury, Md. Raidul Islam, Rakibul Islam, Syed Tamzid Ahmed

Abstract: Fishing communities constitute one of the most vulnerable socio-economic groups in Bangladesh, relying heavily on natural water resources for their livelihood and survival. Despite their significant contribution to local economies and food security, these communities often face numerous human rights challenges, including poverty, limited access to education and healthcare, inadequate housing facilities, occupational hazards, social exclusion, and restricted access to government welfare programs. The present study examines the human rights conditions of fishing communities residing in the Mawa-Munshiganj region of Bangladesh. The research explores the extent to which fundamental human rights, such as the right to education, health, livelihood, social security, and a dignified standard of living, are ensured among the fisher folk population. A mixed-method research design was employed, and primary data were collected from 100 fishermen through structured questionnaires, interviews, and focus group discussions. The findings reveal widespread deprivation in several dimensions of human rights, affecting both male and female members of the fishing community. Major challenges identified include food insecurity, inadequate access to healthcare services, limited awareness of legal rights, and vulnerability to harassment and exploitation. While 62 percent of the respondents reported receiving some form of government assistance during fishing ban periods, the support was often insufficient to meet their livelihood needs. Furthermore, the study found that approximately 80 percent of the respondents were illiterate, highlighting the community’s educational disadvantages. The study concludes that fishing communities in the Mawa-Munshiganj region continue to face significant socio-economic and human rights challenges. Based on the empirical findings, the research offers policy recommendations aimed at improving their living conditions, strengthening social protection measures, enhancing access to education and healthcare, and promoting the overall welfare and human rights of riverine fishing communities in Munshiganj.

DOI: http://doi.org/10.5281/zenodo.20604876

Intelligent Finance: How AI Is Rewriting The Rules Of Financial Decision-Making

Authors: Nidhi Singh, Dr Navneet Seth

Abstract: The financial sector is undergoing a profound metamorphosis, driven by the accelerating integration of Artificial Intelligence (AI) into core decision-making processes. This paper investigates the multi-dimensional impact of AI on financial decision-making, encompassing investment analysis, credit risk assessment, fraud detection, financial forecasting, and customer service. Employing a descriptive-quantitative research design with a structured questionnaire administered to 100 respondents comprising banking professionals, financial analysts, investors, and accountants, the study deploys percentage analysis, frequency distribution, mean scoring, and Chi-Square hypothesis testing to derive empirical evidence. Findings reveal that 85% of respondents demonstrate awareness of AI-enabled financial tools, 75% affirm that AI materially elevates decision-making accuracy, and 80% express high satisfaction with AI-powered financial services. The Chi-Square test confirms a statistically significant relationship between AI adoption and financial decision-making effectiveness (χ² = 18.64, p < 0.05). Notwithstanding these benefits, data privacy concerns (35%), cybersecurity vulnerabilities (25%), and elevated implementation costs (20%) constitute critical impediments. The paper concludes that AI is not merely an operational efficiency enhancer but a strategic imperative for modern financial institutions, and advocates for responsible, ethics-driven AI governance frameworks to sustain its transformative potential.

A Study On Consumer Buying Behaviour Towards Electronic Gadgets with Special Reference to Coimbatore City

Authors: Mr. Aadhi Keerthi P, Mr. Logesh B, Mrs. Chitra B

Abstract: Consumer buying behaviour plays a vital role in the success of the electronic gadgets industry. The increasing use of smartphones, laptops, tablets, smartwatches, and other electronic devices has significantly influenced consumer lifestyles and purchasing patterns. This study titled “A Study on Consumer Buying Behaviour towards Electronic Gadgets with Special Reference to Coimbatore” aims to examine the factors affecting consumer preferences and buying decisions related to electronic gadgets. The study focuses on understanding how factors such as price, brand image, product quality, technological features, advertisements, social media influence, and after-sales service impact consumer purchasing behaviour. Both primary and secondary data are used for the research. Primary data were collected through a structured questionnaire distributed among consumers in Coimbatore city, while secondary data were collected from journals, books, websites, and previous studies. Statistical tools like percentage analysis and ranking methods are applied for data interpretation. The findings indicate that consumers highly prefer branded electronic gadgets with advanced features and reasonable prices. Online reviews, digital marketing, and social influence also affect purchasing decisions. The study concludes that electronic gadget companies should focus on innovation, quality improvement, customer satisfaction, and effective promotional strategies to strengthen their market position and meet changing consumer expectations.

DOI: http://doi.org/10.5281/zenodo.20612956

A Study on Ethical Commerce: Corporate Social Responsibility in a Digital Age

Authors: Ms. Anisha, Ms. Divyabharathi, Mrs. Jeya Padma Deepa I

Abstract: In the contemporary digital era, ethical commerce has emerged as a critical dimension of business strategy, extending beyond profit maximization to include social responsibility, environmental sustainability, and ethical governance. Corporate Social Responsibility (CSR) in a digital age is shaped by rapid technological advancements, e-commerce platforms, social media, data analytics, and increased stakeholder awareness. Businesses today are expected to operate transparently, protect consumer data, ensure fair digital practices, and contribute positively to society while leveraging digital tools for growth. This article examines the concept of ethical commerce and the evolving role of CSR in a technology-driven business environment. It explores how digital platforms influence CSR initiatives, enhance stakeholder engagement, and promote sustainable business practices. The study also highlights challenges such as digital inequality, data privacy concerns, and greenwashing. By adopting ethical digital strategies, organizations can build trust, strengthen brand reputation, and achieve long-term sustainability. The article aims to provide undergraduate students with a comprehensive understanding of ethical commerce and the significance of CSR in the modern digital business landscape.

DOI: http://doi.org/10.5281/zenodo.20614573

A Study On The Role Of Corporate Social Responsibility (CSR) In Marketing

Authors: Ms. Anisha S, Ms. Rathika R, Dr. N. Rajendran

Abstract: This study investigates the role of Corporate Social Responsibility (CSR) in marketing, highlighting its increasing significance as a strategic component in today’s business landscape. As consumers become more socially and environmentally conscious, companies are compelled to integrate CSR initiatives into their marketing strategies to align with evolving consumer expectations. The study aims to examine the influence of CSR on consumer purchasing behavior, evaluate its role in enhancing brand image and reputation, and identify the challenges businesses face in authenticating CSR within their marketing efforts. Through a comprehensive analysis, the study reveals that CSR initiatives positively impact consumer purchasing decisions, particularly among younger generations who prioritize ethical practices and sustainability. It emphasizes the importance of authenticity in CSR efforts, noting that companies that genuinely engage in responsible practices are perceived as more trustworthy and responsible, which enhances their brand reputation. The findings also indicate that the effectiveness of CSR marketing varies across industries, suggesting that tailored strategies are essential for resonating with target audiences. However, companies encounter challenges such as skepticism about insincere CSR activities and difficulties in effectively communicating their initiatives. Recommendations for effective CSR integration include ensuring authenticity, tailoring initiatives to industry specific needs, committing to long-term sustainability efforts, and actively engaging stakeholders. This study concludes that when strategically incorporated into marketing, CSR can strengthen brand loyalty, enhance corporate reputation, and contribute to positive social and environmental impacts, ultimately driving long-term business success.

DOI: http://doi.org/10.5281/zenodo.20614759

A Study On Problems And Challenges In Digital Payment Systems On Mobile Phones

Authors: Amal prawin, Sanjay P K, Mrs.Haseena

Abstract: The surge in digital payment systems, facilitated through mobile phones, has revolutionized the financial landscape, promising convenience, accessibility, and efficiency. However, amid this rapid digital transformation, various challenges and problems have emerged, necessitating comprehensive examination. This study delves into the intricate fabric of mobile phone-based digital payment systems, aiming to identify and analyse the multifaceted hurdles impeding their seamless operation. Drawing upon extensive literature review and empirical research, this study navigates through the labyrinth of challenges encountered in digital payment ecosystems. From technological limitations to socio-economic disparities, from security concerns to regulatory complexities, the spectrum of impediments is diverse and far-reaching. The research employs both qualitative and quantitative methodologies to unravel the underlying dynamics and discern patterns amidst the chaos.

DOI: http://doi.org/10.5281/zenodo.20617421

Evaluation of CNN and Face-Mask Dataset by Supervised learning on Confusion Matrix

Authors: By Mr. Basavaraj Swamy

Abstract: Techniques from Machine learning and deep learning are usually helpful in classification of data. A dataset is processed through a CNN before it is used for classification. Text mining, image processing, and score prediction techniques are very much important in the field of analytics. In paper, we used classification and data prediction methods to demonstrate image and numerical analysis. Analytics show that traditional backup methods have been improved with better ways of managing data. This process of supervised learning produces comparable present outcomes with accurate predicted values.

Formulation and Evaluation of Herbal Hair Oil Using Betel Leaf

Authors: Assistant Professor Dr. Vijaykumar Kale, Ms.Rutuja Popat Chavan, Ms.Pratiksha Ashok Jaybhay, Dr. Mahesh Thakare, Mr. Vaibhav Narwade

Abstract: Herbal cosmetics have gained significant importance due to their safety, effectiveness, and minimal side effects compared to synthetic products. The present research project focuses on the formulation and development of herbal hair oil using Betel Leaf as the major active ingredient. Betel leaf is traditionally known for its antimicrobial, antifungal, antioxidant, and anti-inflammatory properties, which are beneficial for maintaining healthy hair and scalp conditions The herbal hair oil was prepared using betel leaf along with other natural ingredients such as coconut oil, curry leaves, hibiscus, and aloe vera. The formulation was developed by heating the herbal materials with the base oil to extract the active constituents effectively. The prepared oil was filtered and evaluated for various physicochemical parameters including color, odor, pH, viscosity, specific gravity, irritation test, and stability study. The formulated herbal hair oil showed satisfactory physical appearance, good stability, and acceptable consistency without causing skin irritation. The presence of betel leaf in the formulation may help reduce dandruff, scalp infections, and hair fall due to its medicinal properties. The study concludes that the prepared herbal hair oil can serve as a safe, economical, and natural alternative for hair care management. This research supports the growing demand for herbal cosmetic products and highlights the potential. [1]

Formulation and Evaluation of Anti-Acne Herbal Cream

Authors: Associate Professor Mahesh Thakare, Pooja Choudhary, Sakshi Harihar, Vijaykumar Kale, Associate professor Vaibhav Narwade

Abstract: Approximately 85% of teenagers suffer from acne vulgaris, which can last until adulthood. Teenagers see doctors approximately two million times a year, and the US spends more than $1 billion on acne treatments directly. There are many different therapy options for acne vulgaris, such as hormonal, anti- androgen, or anti seborrheic medications, as well as retinoids, isoprenoids, keratolytic soaps, alpha hydroxy acids, azelaic acid, and salicylic acid. All of these techniques do have some negative effects, though, and it’s unclear exactly how they fit into therapy. This paper not only presents the potential causes of acne vulgaris, medications that can treat it, and recently released research on the usage of medicinal herbs to treat the condition were examined. Topical formulations (herbal cream) have been developed containing Ocimum sanctum (Tulsi) extract, Aloe barbadensis miller (Aloe-vera Gel), Melaleuca Oil (Tea Tree Oil). These medicinal herbs and essential oil (TTO) show anti-bacterial activity against acne causing bacteria like Propionibacterium and staphylococcus aures. Various batches containing above Herbs and Essential oil are prepared and their comparative studies are performed. Certain evaluation tests are performed like Irritancy, Washability, pH, Greasiness to check whether cream is suitable for human skin. In the end anti-bacterial activity of the cream was carried out using agar well diffusion method against staphylococcus aures.

DOI: http://doi.org/

A Review and Experimental Framework for Precursor-of-Anomaly Detection in Time-Series Systems

Authors: Mr. Ashish Kumar, Dr. Satender Kumar

Abstract: The study of anomaly detection in time series has become one of the key topics in intelligent monitoring systems such as industrial automation, cybersecurity, healthcare, finance, IoT. The traditional approaches to anomaly detection primarily focused on detecting any signs of anomalous behaviour following their occurrence. However, in many cases, reactive anomaly detection does not allow for timely response to detected anomalies. Recently, some researchers have suggested the novel idea of Precursor-of-Anomaly (PoA) detection to detect and analyse warning signs prior to anomalies’ occurrence. The present paper provides a review and experimental framework of PoA detection in time series. The paper outlines approaches to traditional anomaly detection, deep learning based forecasting models, uncertainty-aware models, and early warning approaches. Also, the paper outlines a practical framework of PoA analysis using industrial SWaT dataset and Isolation Forest approach. Experimental results prove that uncertainty-aware PoA detection is capable of delivering early warning signals before critical anomalies occur. The paper considers modern limitations and challenges in designing proactive anomaly prediction systems.

DOI: https://doi.org/10.5281/zenodo.20631084

Classification of Visually Similar Scalp Diseases using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion

Authors: Ayushi Dixit , Dr. Brij Mohan Singh

Abstract: Accurate automated diagnosis of visually similar scalp diseases represents one of the most challenging problems in clinical dermatology. Conditions such as Psoriasis, Seborrheic Dermatitis, Tinea Capitis, Alopecia Areata, Folliculitis, and Eczema share overlapping visual characteristics: including redness, scaling, and patchy hair loss, making misclassification clinically dangerous and common even among trained dermatologists. The global shortage of specialist dermatologists, particularly in rural and resource-limited settings in India, further amplifies the need for reliable automated diagnostic tools. This comprehensive research proposes ScalpViT, a novel hybrid deep learning architecture that combines a 16×16 Patch Vision Transformer (ViT) with a Convolutional Neural Network (CNN) backbone connected via a bidirectional cross-attention fusion module. The ViT branch processes the scalp image by dividing it into 256 non-overlapping 16×16-pixel patches, embedding each as a 768-dimensional token, and applying multi-head self-attention across the full token sequence to capture global spatial distribution and morphological patterns. Concurrently, the CNN branch extracts local texture details. The bidirectional cross-attention enables texture features to query spatial features and vice-versa, avoiding the pitfalls of simple feature concatenation. Trained on a meticulously curated multi-source dataset of approximately 7,000 dermoscopic and clinical scalp images drawn from DermNet NZ, ISIC 2018, HAM10000, and SD-198, ScalpViT achieves 94.3% accuracy, a macro F1-score of 0.93, and an AUC of 0.97. It significantly outperforms conventional baselines like ResNet-50 (83.1%), EfficientNet-B3 (87.4%), standard ViT-B/16 (90.8%), Swin-Tiny (91.2%), and DINOv2-B (93.5%). Furthermore, to bridge the interpretability gap for clinical deployment, ScalpViT utilizes GradCAM for CNN texture heatmapping and Attention Rollout for ViT patch mapping, delivering dual visual explainability to clinicians. The paper extensively details the methodology, dataset construction, architectural innovations, and clinical relevance for point-of-care mobile deployments.

DOI: https://doi.org/10.5281/zenodo.20637980

A Study On Modern Game Development And Design Techniques

Authors: Parekh Jay Alpeshkumar, Manavsinh Maheshkumar Mahida, Anil Patidar, Shah Shubham Rameshbhai, Ajmeri Shifa, Dhruv Jayeshbhai Rathod, Prof. Sohil Govindbhai Parmar

Abstract: The game development industry has evolved rapidly over the last few decades, becoming one of the most signif-icant sectors of the global entertainment market. Modern games are no longer limited to entertainment purposes but are increasingly utilized in education, healthcare, military training, business simulations, and virtual learning envi-ronments. The growing demand for high-quality gaming experiences has encouraged developers to adopt advanced technologies and innovative development methodologies. As a result, game development has transformed into a multidisciplinary field that integrates software engineer-ing, computer graphics, artificial intelligence, storytelling, animation, sound design, and user experience design. Unlike traditional software systems, game development involves highly dynamic and continuously changing re-quirements throughout the production lifecycle. Develop-ers frequently modify gameplay mechanics, visual assets, and system features based on testing results and player feedback. This flexibility creates unique challenges related to project management, communication, resource alloca-tion, quality assurance, and deadline management. Tra-ditional Software Development Life Cycle (SDLC) models often fail to address these challenges effectively due to the creative and iterative nature of game production. There-fore, specialized Game Development Life Cycle (GDLC) models have emerged to better support the requirements of modern game projects. This study investigates contemporary game develop-ment methodologies and design techniques used in the gaming industry. The research examines important con-cepts such as Agile Development, Model-Driven Game Development (MDGD), iterative prototyping, continuous testing, and collaborative development workflows. Fur-thermore, the study analyzes the role of modern game en-gines, including Unity and Unreal Engine, in accelerating development processes and improving production quality. The impact of emerging technologies such as artificial in-telligence, cloud computing, procedural content genera-tion, and automated testing systems is also explored. A qualitative research methodology based on a liter-ature review and comparative analysis was employed to Evaluate existing development models and identify their strengths and limitations. Various academic publications, industry reports, and research studies were analyzed to understand common development challenges and modern solutions adopted by professional game studios. Based on the findings, an optimized Game Development Life Cy-cle framework is proposed to improve development effi-ciency, flexibility, communication, scalability, and overall game quality while maintaining the creative freedom nec-essary for successful game production. The results indi-cate that integrating Agile practices, iterative prototyping, continuous feedback mechanisms, and collaborative work-flows significantly enhances the effectiveness of game de-velopment projects. The proposed framework provides a balanced approach that combines structured software en-gineering principles with creative design processes. This study contributes to the understanding of modern game development and design techniques and offers practical recommendations for indie developers, researchers, and game studios seeking to improve production workflows and deliver engaging, high-quality gaming experiences. Key-words— Game Development, Game Design, Game En-gines, Artificial Intelligence, User Experience, Agile Devel-opment, Interactive Entertainment, Cross-Platform De-velopment.

DOI: http://doi.org/10.5281/zenodo.20637746

GoldMind AI: A Machine Learning Framework for Gold Price Prediction Using Macro-Financial Indicators

Authors: Tushar Hingmire

Abstract: Gold has long been regarded as a safe-haven asset, yet its price is subject to intense volatility driven by a complex interplay of global economic conditions, currency fluctuations, and commodity market dynamics. Traditional forecasting methods often fail to capture these non-linear dependencies, motivating the development of data-driven approaches. This paper presents GoldMind AI, a machine learning framework designed to forecast gold prices using four key macro-financial indicators: the S&P 500 Index (SPX), the United States Oil Fund ETF (USO), the iShares Silver Trust ETF (SLV), and the EUR/USD currency pair exchange rate. Two supervised learning models — Linear Regression and Random Forest Regressor — are trained on historical financial data and evaluated using standard regression metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The Random Forest model achieves an R² score of 0.92, RMSE of 1.07 USD, and MAE of 0.94 USD, significantly outperforming Linear Regression with a 23% reduction in error rates. The trained model is deployed as an interactive web application built with Streamlit, enabling real-time gold price forecasting from user-supplied market inputs. GoldMind AI demonstrates that ensemble machine learning methods can effectively capture complex market relationships, providing actionable insights for investors and financial analysts.

DOI: https://doi.org/10.5281/zenodo.20640504

Healthy Food: Development and Evaluation of an Android-Based Nutrition Consultation System

Authors: Shilsa. K V

Abstract: Mobile health technologies are increasingly transforming healthcare delivery by enabling personalized, accessible, and cost-effective services. This study presents the design, development, and evaluation of Healthy Food, an Android-based nutrition consultation platform that connects users with qualified nutritionists through a digital environment. The application integrates personalized nutrition guidance, online communication, health education resources, and nutrition plan management. The system was developed using Android Studio and Firebase following the waterfall software development methodology. Feasibility analysis, system design, implementation, and testing were conducted to evaluate operational effectiveness. Results indicate that the platform enhances accessibility to nutrition advice, reduces consultation barriers, and supports preventive healthcare practices. The findings highlight the growing significance of mobile applications in promoting healthy lifestyles and improving healthcare communication.

DOI: https://doi.org/10.5281/zenodo.20640714

Performance Analysis Of Solar-Based Wireless Charging Infrastructure For Electric Vehicles

Authors: Ajay Soni, Hina Thakre, Hitesh Chouksey, Krishna Kumar, Mohit Bunkar, Rahul Kadam, Priyank Srivastava

Abstract: The rapid growth of electric vehicles (EVs) has increased the demand for sustainable and convenient charging infrastructure. Conventional wired charging systems require physical connectors that suffer from wear, maintenance requirements, and user inconvenience. This paper proposes a Solar Wireless EV Charging System that combines solar photovoltaic generation with wireless power transfer technology. Solar energy is harvested using photovoltaic panels and stored in a battery bank through a charge controller. The stored energy is converted into high-frequency AC power using an inverter and transferred wirelessly through resonant inductive coupling. A receiver coil mounted on the electric vehicle captures the transmitted energy, which is rectified and used for battery charging. The proposed system reduces dependency on fossil-fuel-based electricity, enhances charging convenience, and promotes renewable energy utilization. The design improves safety by eliminating exposed charging cables and supports future smart transportation infrastructure. Performance factors such as coil alignment, transfer distance, efficiency, and energy management are discussed. The study concludes that integrating solar energy with wireless charging provides an environmentally friendly and practical solution for future electric mobility.

DOI: http://doi.org/10.5281/zenodo.20641205

A Study On Risk And Return Analysis Of Equity Shares And Fixed-Income Securities

Authors: Amandeep Sharma, Dr. Sahil Nazir

Abstract: Investment decisions are primarily influenced by the relationship between risk and return. Investors seek investment avenues that provide maximum returns while maintaining an acceptable level of risk. Equity shares and fixed-income securities are among the most widely preferred investment instruments. Equity shares offer opportunities for capital appreciation and dividend income but involve higher market risk. Fixed-income securities such as government bonds, corporate bonds, and debentures provide stable returns with comparatively lower risk. The present study examines the risk-return characteristics of equity shares and fixed-income securities using both primary and secondary data. Primary data were collected from 200 investors through a structured questionnaire, while secondary data were obtained from stock market reports, company annual reports, and financial databases. Statistical tools including percentage analysis, mean, and standard deviation, coefficient of variation, correlation analysis, chi-square test, t-test, and regression analysis were employed. The findings indicate that younger investors prefer equity investments due to higher return expectations, whereas older investors favor fixed-income securities for capital preservation and income stability. The study further reveals that equity shares generate higher average returns but are associated with greater volatility. Fixed-income securities exhibit lower returns but provide greater consistency and lower risk exposure. The research concludes that a balanced portfolio containing both asset classes can optimize risk-adjusted returns and achieve long-term financial objectives.

DOI: http://doi.org/10.5281/zenodo.20643891

Development Of An Explainable AI Model For PCOS Diagnosis Using Machine Learning Techniques

Authors: Mamta Bhardwaj

Abstract: Polycystic Ovary Syndrome (PCOS) is a multifactorial endocrine disorder affecting a significant proportion of women of reproductive age, often leading to metabolic, hormonal, and reproductive complications such as infertility, insulin resistance, and cardiovascular risks. Early and accurate diagnosis of PCOS remains a major clinical challenge due to its heterogeneous symptoms, variability across patients, and reliance on subjective diagnostic criteria such as the Rotterdam guidelines. In recent years, machine learning (ML) techniques have shown promising potential in improving diagnostic accuracy; however, their lack of interpretability has limited their adoption in real-world healthcare settings. This study proposes a comprehensive Explainable Artificial Intelligence (XAI)-based risk prediction framework for PCOS diagnosis that combines robust machine learning algorithms with interpretable techniques to enhance clinical trust and usability. The proposed model utilizes a publicly available PCOS dataset comprising clinical, hormonal, and ultrasound features. A systematic preprocessing pipeline is implemented, including missing value imputation, feature scaling, and class imbalance handling using Synthetic Minority Oversampling Technique (SMOTE). Feature selection methods such as correlation analysis and Recursive Feature Elimination (RFE) are applied to identify the most significant predictors contributing to PCOS. Multiple machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), are evaluated. A stacking ensemble model is then developed to leverage the strengths of individual classifiers and improve overall predictive performance. To address the critical challenge of model interpretability, ex-plainability techniques such as SHapley Additive explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) are integrated into the framework. These methods provide both global and local explanations, enabling the identification of key features such as menstrual cycle irregularity, Body Mass Index (BMI), follicle count, and hormonal imbalance, which are consistent with established clinical knowledge.

DOI: http://doi.org/10.5281/zenodo.20643863

IoT-Enabled Sensor Framework for Accurate Rainfall Forecasting and Real-Time Weather Monitoring

Authors: Associate Professor K.V.S.S.Rama Krishna, Jakka Venkata Lahari, Gurram Yasaswini, Marri Lakshmi Poojitha, Changa Nagalakshmi, Udayagiri Bhavani

Abstract: IoT-Rain Sense is an innovative and state-of-the-art solution for rain prediction on demand and continual weather monitoring based-on Internet of things (IOT) systems and cloud-based Neural Networks designed to predict precise, hyper localised forecasts. The architecture of the system consists of three main components: Data Acquisition, Feature Processing, and Weather Prediction. In phase 1, sensors being IoT based and ESP32 microcontrollers keep on monitoring temperature, humidity and light intensity over the environment of an application. The measurements are displayed in real time on a built local LCD interface. These sensors are cheap and energy-friendly, which means they could be sprinkled around agriculture and cities and institutions, without bothering anyone, and can scale up as needed. The second level is focused on feature processing, including preprocessing which aims to clean, filter and normalize raw data in order to control the quality of them. There is more weather related information added to

DOI: https://doi.org/10.5281/zenodo.20646110

Predictive Analysis of Rainfall Patterns Using Machine Learning Techniques

Authors: Associate Professor V. Pavani, Challagundla Amrutha, Palanati Sirisha, Ganjapu Sowmya, Gottipatti Tejaswini

Abstract: Precise prediction of rainfall is required in agriculture, management of water resources and mitigation of disasters. The nonlinear and uncertain characteristics of the meteorological data are usually difficult to capture by traditional statistical models. As a solution to this, a hybrid stacking ensemble model based on the combination of Random Forest (RF) and Support Vector Machine (SVM) and Logistic Regression as a meta-classifier is proposed. The model, when using the Rain in Australia data set, has the highest accuracy with a value of over 95% in the present version and the possible accuracy of over 96% with superior prepossessing, feature engineering, and class balancing. The suggested method provides a sure model of enhanced rainfall forecasting, which would be involved in planning the sustainability of agriculture and environmental decision-making.

DOI: https://doi.org/10.5281/zenodo.20646431

An Intelligent Machine Learning Framework for Water Potability Prediction

Authors: Associate Professor P.Sandhya Krishna, Ala Nandini, Pavuluri Sri Lekha, Gumma Aparna, Patchava Pujitha

Abstract: Clean and safe drinking water is a crucial factor in the health of the population, but even now, delivery of contaminated drinking water remains one of the world issues. Water potability: a ML approach The use of ML models in Water Quality Assessment is a recent phenomenon in the past years, as it is now a highly promising tool that predicts the water potability in an efficient (more efficient than traditional) manner. The paper presents a smart machine learning system to anticipate the potability of water that is determined by undertaking a thorough review of diverse physico-chemical characteristics of water such as PH, Hardness, Solids, Chloramines, Sulfate and organic contaminants. State of art preprocessing methods are also applied to address missing values, outliers and feature stratification which enhance the quality and the strength of the data. There are several supervised learning processes, which include Random Forest, SVM, Gradient Boosting and ANN to determine the best predictive accuracy algorithm. The general performance is also justified with the premises of accuracy, precision, recall, F1-score and ROC-AUC performance parameters and demonstrates that the suggested framework implementation is reliable and efficient on actual water quality monitoring scenarios. Also, the work places emphasis on the effects of the feature selection and the hyperparmeter tuning on the enhancement of the prediction performance. Ensemble approach and cross-validation methods cut down on the framework and expand the generalization potential with different datasets.

DOI: https://doi.org/10.5281/zenodo.20648118

A Sensor-Based Approach to Water Quality Monitoring: Integrating Temperature, TDS, and Turbidity Measurements

Authors: SK. Sharmila, Pavuluri Pavani,, Pavuluri Nandini,, Yarramneni Sushma, Onteru Keerthi

Abstract: Safe and potable water must be maintained, and the quality water should be checked frequently, especially due to the pollution and environmental shift. So as to analyse sensor-based method of monitoring water quality, this research integrated temperature reading, total dissolved solids (TDS) and turbidity. The scheme was aimed at the real-time data gathering and evaluation to identify alterations in the water parameters, which will prove the contamination or the quality decline. The results have proven that the combination of input of several sensors enhanced the accuracy and reliability of water quality determination, allowing to identify the possible dangerous situation in time. The paper brings to the fore the possibilities of automated sensor networks in streamlining the water management process and protecting human health

DOI: https://doi.org/10.5281/zenodo.20648454

Design and Development of an Intelligent Automatic Light Control System for Energy-Efficient Indoor Environments

Authors: Assistant Professor Mrs. G. Rohini Phaneendra Kumari, Ravikrinda Hemanjali,, Manasa Kunduru, Yanamadala Naga Lakshmi,, Chundi Pallavi

Abstract: Increased demand of energy-efficient technologies has resulted in the creation of intelligent systems that would optimize the energy use in residential and commercial buildings. In this paper, the design and development of an automatic light control system of indoor environment that ensures that there is minimal energy wastage through the use of adaptation of illumination is presented. The system makes use of a set of sensors, such as motion sensors and light-dependent resistors (LDRs) to automatically control the lighting through occupancy and the intensity of the ambient light. A framework based on an IoT provides the ability to monitor and control remotely through the use of mobile devices, which makes it more convenient and flexible to the user. The proposed system will provide the optimal lighting conditions and produce a considerable reduction in the electricity consumption, and hence, it will lead to sustainable energy management and smart home automation.

DOI: https://doi.org/10.5281/zenodo.20648720

An Intelligent IoT-Enabled Temperature-Based Fan Speed Control Framework for Energy-Efficient Smart Environments

Authors: Assistant Professor Srikanth Kilaru, Akireddy Bhargavi, Vadlamudi Bhavitha, Neelam Jyothir Mahitha, Chaparapu Meghana Reddy

Abstract: Smart homes play a crucial role in reducing the amount of energy consumed in the house as the automatic control is also provided. This paper suggested an IoT-based temperature-based fan control system, which is an automatic fan control system that is operated by the temperature in the surrounding. This system contains a LM35 sensor of temperature to detect the temperature with accuracy and the DHT11 sensor to monitor the temperature and humidity in real time. The sensor information is handled in a microcontroller with an ESP8266 Wi-Fi chip that enables the sensor to access the internet easily and visualize the obtained air quality on the cloud. The DC motor speed of the fan is regulated by the Pulse Width Modulation (PWM) which enables your motherboard to provide only the necessary cooling when required. The automatic speed control system makes it non-manually based and the operator is given a pleasant working experience. It has been experimentally established that the proposed variable-speed cooling system

DOI: https://doi.org/10.5281/zenodo.20648914

IoT-Enabled Gesture Recognition for Smart Device Interaction

Authors: Assistant Professor G. Lakshmi Durga, Gade Bhagya Sri, Guntaka Vahnitha, Shaik Benazil Bhanu, Devarapalli Thanuja

Abstract: The Internet of Things (IoT) technology allows individuals to have new interfaces to communicate with devices that are smart. We present a Smart IoT Interface with Hand Gesture Recognition and Machine Learning in this work to enhance human- machine interaction (HMI) in smart environments. Being a wearable hand gesture recognition and control device, it relies on sensor networks and embedded systems to obtain real-time hand gesture feedback, which is later interpreted by advanced machine learning algorithms to allow natural and natural interaction with IoT devices. The suggested interface takes advantage of wireless communication and edge processing that allows the practical and low-latency processing of real-time data and cloud integration to provide additional device control and gain analytics. Its applications include IoT automation, home automation and an intelligent IoT control to a flexible and reliable system that enables the user to interact with devices connected to it. Findings suggest that the sugg

DOI: https://doi.org/10.5281/zenodo.20649238

A Machine Learning Approach for Hand Gesture Recognition Using MediaPipe and OpenCV

Authors: Assistant Professor Mrs. B. Aruna Kumari, Immadi Naga Varshitha, Ramadeni Vasavi, Para Prasanthi, Marripudi Jeevana Jyothi

Abstract: One of the essential technologies that allow implementing the human-computer interaction built intuitively and with a certain level of comfort is the recognition of hand gestures, in particular, in the smart home automation systems. This paper presents a new deep learning model, Attention-Enhanced CNN Gesture Recognition (AE-CNN-GR) that can enhance the quality, responsiveness, and resilience of gesture control on live camera streams and improve the accuracy. The model is based on the extension of the traditional CNN architecture, incorporating channel and spatial attention units, to enable the network to concentrate on the most informative parts of the hand, such as fine finger movements and changes of the positions. Channel attention module records finer spectral and intensity differences in parts of the hands and the spatial attention mechanism focuses on important geometric and contextual characteristics of gestures to enhance the accuracy of classification and boundary detection. The methods of transferMediaPipe and OpenCV identifications and preprocessing using hand detection and appliance control with the use of the Arduino simulation.

DOI: https://doi.org/10.5281/zenodo.20649582

Intelligent Machine Learning-Based Gas Leak Detection and Prevention System

Authors: Assistant Professor R Srinivas, Koppula Sneha, Devadasu Aswini, Gattupalli Ekavani Madhur, Pusuluri Surekha

Abstract: Machine Learning-based Gas Leak Detection and Prevention System operates with intelligent and automated methods to detect and prevent gas leakage occurrences in industrial and domestic situations. Existing detection systems have primarily utilized fixed threshold values for such checks, leading to the most effective method for interpreting false alerts and ineffective response times. The proposed system couples sensor components with an ML algorithm method to processes more productive patterns determined for gas releases while using devices to eliminate these differences. Data is acquired from gas sensors, standard MQ-series sensors, to measure LPG, methane, and carbon monoxide. Real-time data is acquired and processed after processing and analysed by machine learning algorithms, like Support Vector Machine SVC, Random Forest to classify conditions as safe or fallacious. An alarm sounds and IoT sends users alerts such as gas shut-off valves and exhaust fans. When gas becomes available, this ML approach impro

DOI: https://doi.org/10.5281/zenodo.20649838

Automatic Gas Leak Detection And Safety Control System

Authors: Assistant Professor Vamsi Krishna, Amara Neelima, Kurapati Naga Venkata Mounika, Nettem Rishitha, Thota Sravana Sruthi

Abstract: The Gas Leak Detection and Prevention System based on IoT is aimed at making homes, offices, and manufacturing premises safer by offering on-time monitoring and prompt detection of dangerous gas escapes. The system comprises gas sensors, microcontrollers, and IoT-enabled modules that would allow constantly measuring the amount of gases in the environment. When abnormal levels are detected, the system provides automated notifications through cloud or mobile applications, and timely act and prevent any possible accidents. Moreover, it has the ability to automatically regulate the ventilation systems or cut off the gas supply with the view of reducing risks. IoT is used to enable remote monitoring, data logging and analysis which is useful to perform predictive maintenance and manage safety better. This system will be a proactive measure to stop gas leaks before they cause harm to human lives, properties, and the environment, particularly in the domestic and industrial environment.

DOI: https://doi.org/10.5281/zenodo.20650075

An Intelligent Predictive Framework for Early Diagnosis and Risk Stratification of Diabetes Mellitus

Authors: Associates Professor K.Jagadeesh,, K Sravanthi, M Charanya, M Deepika Veera Naga Rajyalakshmi,, G Vineetha Raj

Abstract: Diabetes mellitus is one of the most prevalent chronic diseases worldwide, posing significant health and economic challenges. Early prediction of diabetes can greatly assist in timely diagnosis and effective management of the disease. This study presents a machine learning– based approach for predicting the likelihood of diabetes using clinical and physiological data. The dataset was preprocessed through normalization and feature selection to improve model efficiency. Various supervised learning algorithms, including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM), were implemented and evaluated based on accuracy, precision, recall, and F1-score. Among these, the Random Forest classifier demonstrated superior performance with the highest overall accuracy, indicating its robustness in handling complex, non-linear relationships among features. The results suggest that predictive modelling using machine learning can serve as a valuable tool to support healthcare professionals in identifying individuals at high risk of developing diabetes. Future work will focus on incorporating larger and more diverse datasets and exploring deep learning models to further enhance predictive accuracy and reliability.

DOI: https://doi.org/10.5281/zenodo.20650487

Real-Time Environmental Monitoring in Greenhouses Using IoT and Sensor Networks

Authors: Associate Professor V.Pavani, Kakarla Adi Lakshmi, Velpuri HanuRithikeswari,, Pervali Sravani, Devarasetty Kavya

Abstract: In recent years, Internet of Things (IoT) has been widely applied in greenhouse control to realize intelligent automation and data-driven greenhouses. In IoT based greenhouse, the real time status of soil moisture content, air temperature & humidity and CO2 concentration is monitored and controlled using embedded system technologies (Arduino or Raspberry Pi) and wireless communication modules. Sensors, wireless technology and data analytics can be combined for real-time monitoring and marching orders so that the optimal conditions are met for growth and crop yield. Moreover, the use of artificial intelligence (AI) techniques (fuzzy logic, machine learning and bio-inspired algorithms) increases the flexibility of the platform, the ability of prediction and decision-making performance. These smart systems eliminate manual labour, process costs and resource waste with eco-friendly.

DOI: https://doi.org/10.5281/zenodo.20650660

Smart Bus Attendance Management Using Deep Learning-Based Face Recognition

Authors: P.Sandhya Krishna, Kondaveeti Vyshnavi Mani, Gutta Bhavyasri, Bachina Lakshmi Sowjanya, Pagidipalli Rupakalpana

Abstract: The Smart Bus Attendance Management System is a face recognition-based system that uses deep learning to automate the school or college bus student attendance tracking. The conventional manual attendance systems are time-consuming, more likely to be erroneous whereas RFID or biometric security demands the implementation of extra equipment and may not provide real-time accuracy. In this system, images of students are captured when they get on the bus and they are identified with the help of deep learning algorithms, which can be Convolutional Neural Networks (CNNs), face embedding models. The identified information is uploaded on a digital record of the attendance and the information such as the name of student, roll number, class, date and time. The system will be able to produce real-time reports on attendance, minimize human intervention, and improve the safety aspect by providing proper monitoring of students on transit. This solution proves to be an efficient combination of computer vision, machine learn and IoT-based transportation management that offers a scalable and smart solution to the present-day learning institutions.

DOI: https://doi.org/10.5281/zenodo.20650876

Enhancing Student Safety Through a Face Recognition-Enabled Bus Attendance and Notification System

Authors: Assistance Professor Shaik. Sharmila, Oburi Leela Sridevi, Shaik Bajibi, Ganduri Nihitha,, Thokala Madhvi

Abstract: Over the past years, both parents and schools have been in distress over the issue of how to guarantee the safety of the students both walking or even taking the bus to school. This article proposes IoT based Bus Attendance and Notification System, which is built on the facial recognition technology to automate student attendance, security and timely parent and school administration notification. The unit possesses sensor based identification system which is accurate to guarantee ample detecting of students boarding and alighting. It takes the attendance and automatically sends an SMS alarm throughout the IoT based communication. By eradicating errors, the system is aiding in making the student-transportation operations more reliable and safe besides cutting down on delays and making them more easily monitored.

DOI: https://doi.org/10.5281/zenodo.20651120

Energy Conservation in Residential Unitsa Climate-Responsive Design Approacha Climate-Responsive Design Approach

Authors: Ar. Yashika Garg, Tasneem Patanwala

Abstract: Energy conservation is now considered an essential consideration in residential architecture owing to urbanization and changes in lifestyle patterns. Contemporary residences require high levels of energy, especially when it comes to air-conditioning, lighting, and other home appliances. Consequently, energy use poses many environmental problems. In addition, the economic aspect of the issue cannot be ignored either. This paper will analyze how residential architecture could become an efficient instrument to decrease the level of energy consumption. Apart from energy-saving mechanical systems, architects should focus on passive energy-saving techniques which include proper orientation, natural ventilation, use of appropriate shading structures, and locally produced materials. All these techniques make it possible to cut down the need for energy consumption, providing residents with thermal comfort at the same time. Qualitative research will be applied in the study with the support of a case study approach. The example under discussion includes the Aranya Low-Cost Housing project designed by Balkrishna Vithaldas Doshi.

Construction Methodology Of Rotating Building Using Prefabricated Modules

Authors: Ar. Sameer Sharma, Sanskar Gupta

Abstract: Rotating buildings form a novel class of dynamic architecture in which each floor rotates independently around a fixed central core, enabling continuously changing façades, customizable views, and adaptive daylighting. This paper investigates the construction methodology of such buildings using prefabricated modular units, with emphasis on the structural system, sequence of assembly, integration of renewable energy, and practical feasibility. The analysis is based on secondary data from case‑study papers on the Dubai Rotating Tower (Dynamic Architecture) and related literature on kinetic and modular high‑rise construction. The typical configuration features a central reinforced‑concrete core to which prefabricated steel‑floor modules are attached, allowing independent rotation via bearing‑based or air‑cushion systems. Vertical‑axis wind turbines are integrated between floors, and solar panels are mounted on the roof, contributing to partial or full energy self‑sufficiency. The prefabricated approach reduces on‑site labour by 70–80%, accelerates construction by 30–50%, and improves quality control. Despite these advantages, the system faces challenges in maintenance, logistics, and economic feasibility, especially in emerging markets such as India. The paper concludes that rotating buildings using prefabricated modules are technically feasible and conceptually suitable for contemporary high‑rise design, but require detailed structural, mechanical, and economic studies before large‑scale implementation.

Revenue Generate and Smart Village Devleopment

Authors: Ar. Arjun Sharma, Zuneid Khan

Abstract: Smart village development integrates modern infrastructure, digital connectivity, smart agriculture, eco-friendly practices, quality education, healthcare, and skill-based livelihood opportunities to enhance the standard of living for rural populations. This research explores revenue generation strategies as a cornerstone for the successful implementation and sustainability of smart village projects. It examines various models of income generation, including Agri-tech innovations, digital entrepreneurship, renewable energy solutions, eco-tourism, and public-private partnerships. Through a combination of case studies, policy analysis, and stakeholder interviews, the study identifies key enablers and barriers to effective revenue generation in rural contexts. The findings highlight the importance of local capacity-building, infrastructure investment, and inclusive governance in fostering resilient rural economies. This paper contributes to the understanding of how smart village frameworks can be financially sustainable while enhancing quality of life, economic opportunities, and social equity in rural regions.

Courtyard as Timeless Architectural Typology: Past. Present. Furure

Authors: Ar. Sameer Sharma, Khushi Gupta

Abstract: The courtyard has traditionally played the role of climate mediator, social interaction facilitator, and spatial hierarchy structure in Indian built environments. However, this ancient typology has been carefully sidelined in mainstream Indian urban housing in the last 50 years despite its demonstrated environmental and social advantages. The present paper follows a socio-spatial path of the courtyard in Indian architecture in three climatic regions, such as hot-dry, warm-humid and temperate. The study, through the comparative analysis of the traditional precedents, patterns of decline documented, and the current reinterpretations, demonstrates that the loss of the courtyard is due to the overlapping forces: the floor area ratio regulations that punish the open-to-sky spaces, the economic pressures that prefer to maximize the built area, the ideological hegemony of the modernist planning models, and changing household patterns. Still, as the paper also reveals, the underlying principles of the courtyard, which include shallow plans, transitional spaces, hierarchical organization, and climate-responsive geometry are actively being reclaimed and modified by modern practice. Other projects such as House of Secret Gardens (Ahmedabad), Narsighar House (Nakhomah), House of Voids (Vijayawada), Pirouette House (Thiruvananthapuram), House of Memories (Karnataka), and The Earth House (Mukteshwar) represent various approaches to the re-use of courtyard logic. The paper contends that the courtyard is not a nostalgic artefact but a resistant, flexible spatial tool whose logic is urgently needed to tackle the twin challenges of increasing urban density and the accelerating climate change. At the end of the paper, there are design principles and regulatory recommendations on how to integrate courtyard strategies into the future urban development.

Employee Motivation And Its Impact On Workplace Productivity: An Empirical Study

Authors: Gurpreet Singh, Dr. Sahil Nazir

Abstract: Employee motivation is one of the most important determinants of workplace productivity and organizational success. Motivated employees demonstrate greater commitment, efficiency, creativity, and job satisfaction, which ultimately contribute to improved organizational performance. In today’s competitive business environment, organizations continuously strive to develop motivational strategies that encourage employees to perform at their best. The present study examines the impact of employee motivation on workplace productivity using primary data collected from 200 employees working in different organizations. The study investigates various motivational factors such as salary and incentives, recognition and rewards, career advancement opportunities, work environment, leadership support, and training and development programs. Data were collected through a structured questionnaire and analyzed using percentage analysis, mean score analysis, correlation analysis, and chi-square testing. The findings reveal that employee motivation significantly influences workplace productivity. Salary and incentives emerged as the most important motivational factor, followed by career growth opportunities and recognition programs. The study also found a strong positive relationship between employee motivation and productivity. Employees who reported higher motivation levels demonstrated better performance, increased efficiency, and greater organizational commitment. The study concludes that organizations should implement comprehensive motivational strategies to enhance employee productivity and achieve long-term success.

IoT-Enabled Energy Monitoring And Adaptive Load Control For Intelligent Electrical Distribution Systems

Authors: Dr Yaganti Krishnapriya, Talari Manohar

Abstract: The increasing demand for energy worldwide and the introduction of renewable energy sources make it necessary to move from conventional electrical distribution systems to smart electrical distribution systems. This study proposes an Internet of Things (IoT)-based framework for continuous real-time monitoring of electrical distribution systems. It employs IoT sensors, edge computing nodes, and a cloud-based analytical system for the continuous monitoring of electrical distribution systems. Moreover, the system uses a novel adaptive load scheduling (ALS) algorithm that is based on a hybrid LSTM-XGBoost model to accurately forecast future consumer loads. With the ALS algorithm, the system can predict consumer loads with an accuracy of 96.2% (RMSE = 0.034). Finally, the Model Predictive Control (MPC)-based load control system lowers peak demand and energy expenses by 27.4% and 19.8%, respectively, in a testbed with 200 residential customers.

DOI: http://doi.org/10.5281/zenodo.20666042

Leveraging AI-Driven Data Ecosystems For Commercial Excellence In Life Sciences A Unified Framework Integrating Predictive, Prescriptive, And Cognitive Analytics

Authors: Shilpa Hiwale, Dr B V V Siva Prasa

Abstract: The rapid growth in both the volume and complexity of enterprise data has significantly accelerated the adoption of Artificial Intelligence (AI), particularly within the life sciences industry. This paper explores how AI-driven data ecosystems can enable commercial excellence by integrating predictive, prescriptive, and cognitive analytics within a unified framework. The study combines quantitative analysis of customer, sales, and operational datasets with insights from academic research and real-world industry practices. The findings suggest that organizations adopting integrated AI ecosystems are better positioned to enhance forecasting accuracy, improve customer engagement, and enable faster, more informed decision-making. The data used were business-related datasets sourced from Kaggle and data were gathered using a quantitative and analytical research approach. Using Python-based machine learning frameworks, about 50,000 records of customer, sales, demand, churn and operational data were analyzed. Different analytical models such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Artificial Neural Networks were used to discover the customer behavior, sales forecasting, customer segmentation, and prediction of risk. The results show that AI-powered analytics have a significant impact on improving the accuracy of predictions, customer retention, business intelligence, and operational efficiency. The most significant factors influencing customer churn were the customers’ satisfaction and the customer segmentation and demand forecasting for marketing targeting and resource optimization. The study also shows that AI-powered analytical systems can aid in intelligent decision-making by converting vast amounts of business information into commercial intelligence that is useful for business decisions. The proposed data ecosystem framework will leverage AI to provide predictive, prescriptive and cognitive analytics that will enhance the performance and competitiveness of organizations. The study adds to the body of literature on AI-powered business transformation and offers valuable insights for organizations aiming to adopt data-driven approaches for sustainable commercial success.

DOI: https://doi.org/10.5281/zenodo.20666345

QuantumTrust: Blockchain And Quantum Cryptography Framework For Secure Data Sharing

Authors: Narendrababu T, V Kiran Kumar

Abstract: As a result, the competition for consumers’ attention has been increased because of the rapid development of digital marketplaces. At the same time, the current approaches to consumer analytics are based on the use of self-reported measures and do not reflect subconscious processes. Neuromarketing or neuroscience marketing can be described as the application of neuroscientific methods to understanding consumer behavior. In other words, neuromarketing can be used to investigate the mechanisms of making purchasing decisions. This paper introduces the neuromarketing analytics framework based on EEG, ET, and GSR technologies to predict purchase intent in digital marketplaces. Based on data collected from 120 participants who were shown e-commerce product listings, spectral EEG features (theta, alpha, beta, and gamma bands), ET measures (fixation duration, saccade amplitude, and pupil dilation), and GSR phasic responses have been extracted. The proposed deep learning model combines TCN and multi-head attention architecture and achieves 89.2% accuracy in predicting purchase intent. The performance of the proposed model significantly outperforms unimodal baseline models (EEG-based: 76.4%; ET-based: 78.1%; GSR-based: 71.2%). The most significant predictors of purchase intent are found to be gamma band power (30-45 Hz) during product exposure and pupil dilation change.

DOI: http://doi.org/10.5281/zenodo.20666451

A Comparative Financial Performance Analysis Of Public And Private Sector Banks In India

Authors: Dr. Sahil Nazir, Natasha

Abstract: The banking sector plays a vital role in the economic development of India by mobilizing savings and channelizing funds into productive investments. The present study aims to compare the financial performance of public and private sector banks operating in India. The study evaluates the performance of five public sector banks and five private sector banks using financial indicators such as profitability, asset quality, liquidity, customer satisfaction, and operational efficiency. Primary data were collected from 200 respondents comprising customers of selected banks through a structured questionnaire. Secondary financial indicators were used to support the comparative analysis. Statistical tools such as percentage analysis, mean score analysis, and independent sample t-test were employed for data interpretation. The findings reveal that private sector banks outperform public sector banks in terms of customer satisfaction, service quality, digital banking facilities, and profitability. Public sector banks, however, enjoy higher customer trust, wider geographical coverage, and greater government support. The study concludes that while private banks exhibit superior financial efficiency, public sector banks continue to maintain a significant presence due to their reliability and extensive branch network. The research provides valuable insights for policymakers, banking professionals, and investors.

Impact Of UPI Adoption On Consumer Spending Patterns In India

Authors: Gulshan Kumar, Meentu Grover

Abstract: The rapid growth of Unified Payments Interface (UPI) has transformed the way people conduct financial transactions in India. With the widespread availability of smartphones, internet connectivity, and government initiatives promoting a cashless economy, UPI has become one of the most preferred digital payment methods among consumers. This study examines the impact of UPI adoption on consumer spending patterns in India and explores how digital payment convenience influences purchasing behavior. The research investigates key aspects such as transaction frequency, spending habits, impulse buying tendencies, budgeting practices, and consumer preferences for digital payments over traditional cash transactions. Primary data were collected through a structured questionnaire administered to UPI users from diverse demographic backgrounds. The findings indicate that UPI adoption has significantly increased the ease and speed of transactions, encouraging more frequent purchases and reducing dependence on cash. Consumers reported greater convenience in managing daily expenses, while businesses benefited from faster and more transparent payment processes. The study further reveals that although UPI promotes financial accessibility and convenience, it may also contribute to increased discretionary spending due to the ease of making instant payments. The findings highlight the growing role of digital payment systems in shaping consumer financial behavior and supporting India’s digital economy. The study offers valuable insights for policymakers, financial institutions, and digital payment service providers seeking to enhance consumer engagement and promote responsible digital financial practices.

Impact Of Agro Tourism On Farmers Economic Empowerment: An Empirical Study

Authors: Amit Kumar, Raj Kumar

Abstract: Agriculture has long been the backbone of rural economies and continues to serve as the primary source of livelihood for a large segment of the population. However, declining farm profitability, unpredictable climatic conditions, and increasing production costs have created significant challenges for farmers. In response to these challenges, agro-tourism has emerged as an innovative approach that enables farmers to diversify their income sources while promoting rural culture and agricultural heritage. The present study investigates the impact of agro-tourism on the economic empowerment of farmers. The study is based on primary data collected from 150 farmers engaged in agro-tourism activities through a structured questionnaire. A quantitative research approach was employed to analyze the relationship between agro-tourism participation and various dimensions of economic empowerment, including income enhancement, employment generation, entrepreneurial development, and financial independence. The findings indicate that agro-tourism has contributed significantly to improving farmers’ economic conditions by generating additional income opportunities, creating local employment, and encouraging entrepreneurial initiatives. Furthermore, agro-tourism has enhanced the financial security and self-reliance of farming households. The study concludes that agro-tourism can serve as an effective strategy for promoting sustainable rural development and strengthening the economic position of farmers. Therefore, greater support in terms of infrastructure development, training programs, marketing assistance, and policy initiatives is essential to unlock the full potential of agro-tourism in rural areas.

Kidney Net: An Intelligent Deep Learning Model for Kidney Disease Detection

Authors: Parul Tyagi, Dr. Brij Mohan Singh

Abstract: Kidney disease is a growing global health challenge requiring early, accurate, and automated diagnostic solutions. This paper introduces KidneyNet, a deep learning framework designed for automated kidney disease detection and classification from Computed Tomography (CT) scan images. KidneyNet leverages the power of transfer learning through ResNet50, enhanced with custom classification layers and advanced data augmentation strategies, to classify kidney CT images into four categories: cyst, normal, stone, and tumor. The proposed system is compared against two baseline architectures — Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) — using a publicly available dataset of 12,446 kidney CT images. Experimental results demonstrate that KidneyNet (ResNet50) achieves superior performance with an accuracy of 92%, precision of 91.44%, recall of 92%, and an F1-score of 91.72%, outperforming both ANN (86% accuracy) and CNN (89% accuracy). These findings confirm the effectiveness of deep residual transfer learning as a reliable computer-aided diagnostic tool for kidney disease classification.

DOI: https://doi.org/10.5281/zenodo.20671785

Design And Finite Element Analysis Of Composite Drive Shafts: A Comprehensive Review On Materials, Modelling Techniques, And Performance Optimization

Authors: S,Shiva Kumar, P.V.R.Ravindra Reddy

Abstract: The drive shaft is a critical mechanical component responsible for transmitting torque from the transmission system to the wheels or other rotating components in automobiles, aerospace systems, and industrial machinery. Conventional steel drive shafts possess high strength but contribute significantly to the overall system weight, resulting in increased fuel consumption and reduced efficiency. In recent decades, fiber-reinforced polymer (FRP) composite materials such as carbon fiber reinforced polymers (CFRP), glass fiber reinforced polymers (GFRP), and hybrid composites have emerged as promising alternatives due to their superior specific strength, high stiffness-to-weight ratio, corrosion resistance, and improved damping characteristics. The advancement of finite element analysis (FEA) tools has enabled researchers to accurately predict the structural behavior of composite drive shafts under torsional, bending, buckling, vibration, and fatigue loading conditions. This review presents a comprehensive study of the design methodologies, material selection criteria, finite element modeling approaches, failure theories, optimization techniques, and recent developments in composite drive shaft technology. A critical comparison of different composite materials and FEA approaches is discussed, highlighting their advantages, limitations, and future research opportunities.

DOI: http://doi.org/10.5281/zenodo.20672813

Design, Finite Element Analysis, And Performance Optimization Of Hybrid Automotive Composite Springs: A Comprehensive Review

Authors: A. Deepthi, S.Dakhita Sri, B. Vamsi, P.Prabhakar Reddy

Abstract: The continuous demand for lightweight, fuel-efficient, and environmentally sustainable automobiles has encouraged researchers and manufacturers to replace conventional metallic components with advanced composite materials. Automotive suspension springs, which are essential components responsible for supporting vehicle loads, absorbing road shocks, and maintaining ride comfort, have attracted significant attention for weight reduction. Conventional steel springs offer excellent strength and durability; however, their high density contributes substantially to the unsprung mass of vehicles, negatively influencing fuel economy, acceleration, and dynamic response. Hybrid automotive composite springs, fabricated using combinations of carbon, glass, aramid, and natural fibers reinforced with polymer matrices, provide an effective solution due to their superior specific strength, high fatigue resistance, excellent corrosion resistance, and improved vibration damping characteristics. The development of finite element analysis (FEA) techniques has further facilitated accurate prediction of the structural behavior of hybrid composite springs under static, dynamic, impact, and cyclic loading conditions. This review presents a detailed discussion on the evolution of hybrid composite springs, material selection, design methodologies, finite element modeling techniques, failure mechanisms, manufacturing methods, optimization approaches, and future research directions. The study highlights the potential of hybrid composite spring systems to replace conventional steel springs in next-generation automotive suspension systems.

DOI: http://doi.org/10.5281/zenodo.20672857

A Study On The Relationship Between Work-Life Balance And Employee Satisfaction: A Study Of Employees Working In Tata Steel

Authors: Prince, Dr. Sahil Nazir

Abstract: Work-life balance (WLB) has become a crucial aspect of human resource management in contemporary organizations. Employees often face challenges in balancing professional responsibilities and personal commitments due to increasing workload, technological advancements, and changing workplace dynamics. Organizations that promote work-life balance tend to experience higher employee satisfaction, improved productivity, reduced turnover, and enhanced organizational commitment. The present study aims to examine the relationship between work-life balance and employee satisfaction among employees working in Tata Steel. The study is based on primary data collected from 150 employees through a structured questionnaire. Descriptive and analytical research methods were employed to analyze the data using percentage analysis, mean score analysis, correlation analysis, and chi-square testing. The findings reveal that flexible working arrangements, supervisor support, workload management, and family-friendly policies significantly influence employee satisfaction. Correlation analysis indicates a strong positive relationship between work-life balance and employee satisfaction. The study concludes that maintaining an effective work-life balance is essential for improving employee satisfaction and organizational performance. Organizations should continue to strengthen work-life balance initiatives to enhance employee well-being and long-term organizational success.

Banu Mushtaq And The Global Recognition Of Contemporary Kannada Literature

Authors: Mohammad Shah Alam Chowdhury, Mili Rahman, Mohd. Rafi, Md. Sakibur Rahman Malik

Abstract: Recent decades have witnessed a significant transition in contemporary Kannada literature, driven by the advent of socially concerned writers who have broadened regional literary discourse to engage in worldwide intellectual dialogues. Banu Mushtaq holds a prominent position among these literary voices due to her feminist perspectives, secular outlook, and portrayal of neglected Muslim communities in Karnataka. This dissertation critically analyzes Banu Mushtaq’s literary contributions and explores how her works have enhanced the global reputation of contemporary Kannada literature. The study examines the thematic depth of her short stories, socio-political narratives, feminist interactions, and linguistic creativity through the lens of postcolonial and subaltern literary theories. It also examines how translation, digital literary dissemination, and multicultural reading have facilitated the transcendence of Kannada literature beyond regional confines. This research employs qualitative textual analysis and secondary scholarly sources to assert that Mushtaq’s writings serve as both localized narratives and universal human texts that explore themes of identity, gender, religion, class, and resistance. The paper additionally examines the influence of contemporary Kannada authors in shaping India’s multilingual literary modernism. This research situates Banu Mushtaq within the wider South Asian literary traditions, illustrating how regional writing can achieve global significance through local authenticity and socio-cultural involvement. This study enhances modern literary criticism by emphasizing the increasing global importance of Kannada literature and the pivotal role of women writers within this domain.

Mamta Kalia And The Voice Of Modern Indian Women In Hindi Literature

Authors: Mohammad Shah Alam Chowdhury, Mili Rahman, Mohd Rafi, Md. Sakibur Rahman Malik

Abstract: Modern Hindi literature has seen the rise of female authors who have revolutionized the portrayal of gender, domesticity, identity, and social resistance. Mamta Kalia holds a unique position among these writers because to her genuine depiction of middle-class Indian women and their emotional, social, and economic challenges. Her literary works critique patriarchal structures without employing explicit ideological terminology, therefore establishing a pragmatic feminist discourse grounded in quotidian experiences. This study rigorously analyzes Mamta Kalia’s contributions to contemporary Hindi literature, focusing on her portrayal of women’s identity, home strife, marital dynamics, urban middle-class fears, and female agency. This study examines how Kalia transformed the female voice in post-independence Hindi literature through textual analysis of selected novels, short tales, and poetry. The dissertation assesses the socio-cultural importance of her writings within the larger context of Indian feminism and modern literary discourse. The study employs qualitative and interpretive approaches grounded in feminist literary theory. It contends that Mamta Kalia’s works represent the evolution of Indian women from quiet to expression and from societal conformity to self-awareness. Her literary perspective embodies the reality of contemporary Indian women while also challenging conventional gender norms. The study asserts that Mamta Kalia is a preeminent literary figure in articulating the aspirations, disappointments, and perseverance of contemporary Indian women.

Navtej Sarna’s Literary Vision: History, Identity, And Modern India

Authors: Mohammad Shah Alam Chowdhury, Mili Rahman, Mohd Rafi, Md. Sakibur Rahman Malik

Abstract: This study rigorously analyzes the literary perspective of Navtej Sarna by evaluating his historical fiction, literary essays, translations, and cultural narratives. Sarna holds a unique position in modern Indian English literature because to his capacity to interweave diplomacy, memory, nationalism, and personal identity within broader historical contexts. His essays explore significant events in Indian and Sikh history while also examining colonial memory, exile, identity, and contemporary Indian consciousness. Sarna reconstructs the interplay between history and identity in postcolonial India in works such as The Exile, Crimson Spring, The Book of Nanak, Winter Evenings, and A Flag to Live and Die For. This study posits that Sarna’s creative imagination serves as both a cultural repository and a critical analysis of the changing national identity of modern India. The research employs qualitative textual analysis alongside postcolonial and historical literary frameworks to assess Sarna’s narrative techniques, depiction of memory, and ideological interaction with nationhood. The research additionally examines how Sarna rehumanizes historical tragedy via fiction and how his diplomatic history shapes his global creative viewpoint. The study establishes Sarna as a prominent literary figure whose works substantially influence contemporary discourse on history, nationalism, memory, and Indian modernity.

Leadership, Employee Engagement, And Organizational Sustainability: An Empirical Study

Authors: Daljeet Singh, Manisha Karla

Abstract: In today’s dynamic business environment, organizations face increasing pressure to achieve sustainable growth while maintaining employee satisfaction and productivity. Leadership plays a critical role in shaping employee attitudes, engagement levels, and organizational sustainability. This study examines the relationship between leadership practices, employee engagement, and organizational sustainability among employees working in various business organizations. Primary data were collected from 150 employees through a structured questionnaire. Descriptive statistics, correlation analysis, and regression analysis were employed to examine the relationships among the variables. The findings reveal that effective leadership positively influences employee engagement, which subsequently contributes to organizational sustainability. The study highlights the importance of transformational and participative leadership approaches in fostering a sustainable organizational culture. The results provide valuable insights for managers and policymakers seeking to enhance long-term organizational performance through effective leadership practices.

Economic Contribution Of Small And Marginal Farmers In India

Authors: Sukhveer Kaur, Dr. Vinod Kumar

Abstract: Agriculture remains the backbone of the Indian economy, supporting millions of livelihoods and ensuring food security for a population exceeding 1.4 billion. Within the agricultural sector, small and marginal farmers constitute the largest category of cultivators. Despite possessing limited land resources, these farmers make a substantial contribution to agricultural production, rural employment, and national economic development. This study examines the economic contribution of small and marginal farmers in India through an analysis of secondary data obtained from government reports, agricultural census publications, and scholarly literature. The findings reveal that small and marginal farmers account for approximately 86 percent of total operational holdings while cultivating nearly 47 percent of the agricultural land. Their contribution extends beyond crop production to employment generation, poverty reduction, food security, and rural economic sustainability. However, challenges such as fragmented landholdings, inadequate access to credit, technological constraints, and market inefficiencies continue to hinder their productivity and income growth. The study concludes that strengthening institutional support, digital agriculture, farmer-producer organizations, and sustainable farming practices can significantly enhance the economic contribution of small and marginal farmers in India.

Algorithmic Resilience Memory: Designing Agentic AI Systems For Organizational Learning And Climate-Crisis Adaptation

Authors: Dr. Harsha Sammangi, Aditya Jagatha, Navyasri Maddukuri

Abstract: Climate disruption has become a persistent organizational condition rather than an episodic event, yet most information systems designed to support organizational resilience treat each disruption as an isolated incident. Existing digital resilience platforms, disaster recovery systems, and AI-driven decision support tools lack the capacity to accumulate, encode, and reuse organizational knowledge across successive climate-related crises. This paper introduces Algorithmic Resilience Memory (ARM), a novel IS construct defined as an AI-enabled organizational capability through which agentic AI systems sense climate-related disruptions, encode prior organizational responses, preserve decision rationale, generate contextually adaptive recommendations, and reconfigure future actions through structured outcome feedback. Drawing on Design Science Research (DSR), we propose and develop an Agentic AI-Based Algorithmic Resilience Memory Framework as the primary artifact. The framework integrates six interdependent functional layers—environmental sensing, knowledge encoding, agentic AI reasoning, explainable decision support, human governance, and adaptive learning—grounded in organizational memory theory, dynamic capabilities theory, sociotechnical systems theory, and responsible AI governance principles. We demonstrate the framework through a detailed scenario involving a regional flood disrupting a manufacturing firm’s supply chain operations and evaluate its utility using scenario-based assessment and expert panel validation. The paper makes three primary contributions: it introduces ARM as a theoretically grounded IS construct that advances digital resilience research; it offers a design-science artifact that organizations can adopt for AI-driven climate-crisis adaptation; and it establishes design principles for building agentic AI systems capable of institutional learning across repeated climate disruptions.

DOI: http://doi.org/10.5281/zenodo.20676882

Covid-19 Vaccination and Cardiac Arrest: A Review

Authors: Ashwini Angadi, Adarsh GS, Janaki R Torvi, Preeti V Kulkarni, Chetan Savant, Venkatrao H Kulkani

Abstract: COVID-19 vaccination has been a major public health intervention, significantly decreasing the incidence of severe infection, hospitalization, and death caused by SARS-CoV-2. The safety of currently authorized vaccines has been confirmed through extensive clinical trials and post-marketing surveillance. However, uncommon cardiovascular complications, including myocarditis and pericarditis, have been identified in a small number of vaccinated individuals, especially after administration of mRNA-based vaccines. In very rare situations, vaccine-associated myocarditis can progress to serious cardiac complications such as arrhythmias, impaired ventricular function, and, in exceptional cases, cardiac arrest. This review provides an overview of the available literature on cardiac arrest occurring after COVID-19 vaccination, focusing on potential pathophysiological mechanisms, clinical presentation, diagnostic evaluation, treatment strategies, and patient outcomes.

Temporal Dynamics of Distribution of Rainfall in Monrovia, Liberia (1981-2024)

Authors: SAM, Fredrick P, ALABI, Omowumi, MD, Tawey, MORRIS, Susannah D, UGBALA, E.N, Nimely, DENNIS R

Abstract: This paper investigated the spatial and temporal dynamic pattern of rainfall over four decades (1981-2024) in Monrovia, Liberia. These rainfall data were used, a combined rainfall data that combines surface observations of the Liberia Meteorological Services (LMS) and the satellite-based Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates. The presence of variability, anomalies, and extremes has been measured using the Mann-Kendall trend test and Sen’s slope estimator and rainfall indices like the Precipitation Concentration Index PCI), Standardized Precipitation Index (SPI), and Rainfall Anomaly Index (RAI). Analysis showed that there is no statistically significant long-term trend in annual rainfall totals (Mann-Kendall, p > 0.05), but there are significant intra-seasonal changes. Drying patterns as identified in the early rainy season (April-May) with slope of Sen’s values between -2.1 mm/yr and -3.7 mm/yr. Conversely, late rainy season months (August-September) showed an increasing part of rainfall with the slope between 1.456 mm/year and 1.966 mm/year, indicating redistribution in the seasonal rainfall time. Moderate rainfall concentration and non-equal seasonal distribution were characterized by PCI values (12.93 to 16.34). The SPI analysis found repeat drought and extreme wet years (1982, 1994, 2009, 2015, 2020, 2022, and 2024) and extreme wet years (1995, 1996, 2006, 2007, 2008, and 2010). The Aggregate outcome of RAI indicated that a greater proportion of the years were in the negative anomaly as opposed to the wet years; this translates to prevalent dry years with high inter-annual variability. The redistribution and increment of extremes, although resulting in no notable reductions in total rainfalls, make it impossible to reinstate only significant declines in the whole annual rainfalls. Water resources management, agriculture, irrigation, and urban flooding control in Monrovia have very significant implications under such circumstances. The implications of the findings reflect evidence-based knowledge in consonance with Sustainable Development Goals (SDG 6: Clean Water and Sanitation, SDG 11: Sustainable Cities and Communities, and SDG 13: Climate Action), the urgency of which relates to adaptive climate strategies of the urban environment in Monrovia.

DOI: http://doi.org/10.5281/zenodo.20679212

Next-Gen Healthcare Analytics: A Secure And Scalable Federated AI Ecosystem For Privacy Preservation

Authors: Dr. Nidhi Mishra, Sunil Vishwakarma, Sahil, Sneha Pandey, Shirish Shukla

Abstract: The growing integration of artificial intelligence (AI) in healthcare has greatly enhanced clinical decision-making and predictive capabilities. However, conventional centralized training approaches introduce significant concerns related to data privacy, security, and regulatory compliance. Patient data, often distributed across multiple healthcare institutions, cannot be easily shared due to strict privacy laws and ethical considerations. To overcome these limitations, this study presents a secure and scalable federated AI framework designed for privacy-preserving healthcare analytics, allowing collaborative model development without the need for centralized data collection. The proposed system employs federated learning to build a global model by combining locally trained updates from decentralized healthcare nodes, ensuring that sensitive patient information remains within institutional boundaries. To strengthen security and reliability, the framework incorporates secure aggregation techniques, encryption-based protection of model updates, and anomaly detection methods to defend against adversarial threats and data poisoning attacks. Additionally, the architecture supports scalability through adaptive client selection and communication-efficient update mechanisms, making it well-suited for large-scale and heterogeneous healthcare environments. Experimental results using distributed healthcare datasets indicate that the proposed federated AI approach achieves performance comparable to traditional centralized models while substantially minimizing privacy risks and communication costs. These findings demonstrate the potential of the framework to enable secure, compliant, and efficient analytics across distributed medical systems. Overall, this work establishes a practical pathway for deploying trustworthy AI solutions in real-world healthcare settings while safeguarding patient confidentiality.

DOI: http://doi.org/10.5281/zenodo.20679368

A Study On Properties And Reinforcing Potential Of Rice Husk Polymer Composites

Authors: A. Siddu Nayak, K. Jyothi, M. Jeevan, P.V.R.Ravindra Reddy

Abstract: The increasing demand for sustainable and environmentally friendly engineering materials has promoted the utilization of agricultural waste as reinforcement in polymer composites. Among various agro-based materials, rice husk (RH), a by-product obtained during rice milling, has emerged as a promising reinforcing material due to its low density, abundant availability, renewable nature, and unique silica-rich composition. Rice husk contains cellulose, hemicellulose, lignin, and a considerable amount of silica, which contribute to its stiffness and thermal resistance. However, the hydrophilic nature of rice husk and the hydrophobic nature of most polymer matrices often lead to weak interfacial adhesion, limiting the mechanical performance of composites.This review paper presents a comprehensive analysis of the reinforcing potential of rice husk in thermoplastic and thermosetting polymer matrices. The influence of rice husk content, particle size, chemical treatment, and processing techniques on the mechanical, thermal, morphological, and water absorption characteristics of composites is critically reviewed. The effects of coupling agents such as maleic anhydride grafted polypropylene (MAPP) and silane treatments in improving fiber–matrix compatibility are discussed. The recent advancements in hybrid rice husk composites and bio-based polymer systems are also highlighted. The review concludes that rice husk has significant potential as a low-cost and eco-friendly reinforcement for manufacturing lightweight materials for automotive, construction, packaging, and consumer product applications.

DOI: http://doi.org/10.5281/zenodo.20680516

A Literature Review On The Principles, Research Status, And Development Trend Of Wearable Sensors

Authors: Hannah Owusu Ansah, Daniel Karikari Frempong, Gabriel Oduro Asirifi

Abstract: Wearable sensors have emerged as a transformative technology in healthcare, sports, and fitness, enabling continuous monitoring of physiological and environmental conditions. Advances in stretchable substrates, microfluidic channels, and skin-integrated electronics now facilitate real-time, high-fidelity information from the human body. Integration into textiles and garments has led to the development of smart e-textiles with sensing capabilities for motion, pressure, and sweat composition. These systems operate on principles such as piezoresistivity, piezoelectricity, electrochemistry, and triboelectricity, converting physical or chemical stimuli into quantifiable electrical signals. As self-powered platforms, they minimize reliance on conventional batteries, enabling energy-autonomous sensing. Consequently, extensive research efforts are ongoing to innovate and overcome current limitations in wearable sensor technologies. This literature review explores the fundamental principles, current research status, and development trends of wearable sensors, with a focus on their integration into smart textiles, flexible electronics, and real-time health monitoring systems. Despite remarkable progress, challenges remain in sensor durability, data accuracy, energy management, and large-scale manufacturing. Nonetheless, the integration of flexible electronics, artificial intelligence, and Internet of Things (IoT) infrastructure continues to propel wearable sensors toward broader applications in telemedicine, ageing care, industrial safety, and human–machine interfaces. Importantly, this work serves as a blueprint for researchers, engineers, and policymakers committed to advancing wearable sensor technologies toward practical, scalable, and human-centric applications.

DOI: http://doi.org/10.5281/zenodo.20682359

Formulation And Evaluation Of Herbal Immunity Booster Powder

Authors: Associate Professor Vaibhav Narwade, Ms. Shraddha Nitin Ghadage, Ms. Sakshi Dilip Dadge, Dr. Vijaykumar Kale, Associate Professor Mahesh Thakare

Abstract: The present study focuses on the formulation and evaluation of a herbal immunity booster powder prepared using natural medicinal herbs known for their immunomodulatory, antioxidant, and health-promoting properties. In recent years, there has been increasing interest in herbal formulations due to their safety, effectiveness, affordability, and minimal side effects compared to synthetic preparations. The formulated immunity booster powder was developed using herbal ingredients such as turmeric, ginger, Tulsi, amla, cinnamon, black pepper, giloy, and ashwagandha, which are traditionally used in Ayurvedic medicine for enhancing body resistance and improving overall health. The selected herbal ingredients were collected, dried, powdered, and sieved individually before being blended in suitable proportions to obtain a homogeneous formulation. The prepared powder was evaluated for various physicochemical and organoleptic parameters including color, odor, taste, texture, bulk density, and tapped density, angle of repose, ash value, moisture content, pH, and solubility. Stability studies were also carried out under suitable storage conditions to determine the stability and shelf life of the formulation. The evaluation results indicated that the prepared herbal immunity booster powder possessed good flow properties, acceptable physicochemical characteristics, and satisfactory stability. The formulation showed potential antioxidant and immunomodulatory activity due to the presence of bioactive phytoconstituents such as flavonoids, phenolics, alkaloids, and vitamins. The study concludes that the developed herbal immunity booster powder can be used as a safe and effective natural health supplement for improving immunity and maintaining overall wellness.

Artificial Intelligence in Pharmaceutical Formulation Development and Drug Delivery Optimization

Authors: Kovuru. Rasi, Ch.Praveen Kumar, V.Haribaskar

Abstract: Artificial Intelligence (AI) is transforming the pharmaceutical industry by introducing advanced computational approaches for formulation design, drug delivery optimization, and personalized medicine. Conventional pharmaceutical development methods are often time-consuming, expensive, and dependent on repeated experimental trials. AI-based technologies such as machine learning, deep learning, artificial neural networks, and predictive analytics provide innovative solutions by analyzing large datasets, predicting formulation behavior, and optimizing drug delivery systems with improved precision and efficiency. AI assists researchers in identifying critical formulation variables, predicting drug–excipient interactions, enhancing stability, and improving bioavailability while reducing development time and manufacturing costs. In drug delivery optimization, AI supports the development of targeted, controlled, and patient-specific delivery systems including nanoparticles, liposomes, transdermal systems, and smart drug carriers. Furthermore, AI-driven models facilitate quality-by-design approaches, real-time monitoring, and automated decision-making during pharmaceutical manufacturing. The integration of AI with pharmaceutical sciences also promotes personalized therapeutics by enabling dose optimization according to patient-specific factors such as genetics, age, disease condition, and metabolic profile. Despite its significant advantages, challenges including data reliability, regulatory concerns, ethical issues, and the need for interdisciplinary expertise remain barriers to widespread implementation. This review highlights recent advancements, applications, benefits, challenges, and future prospects of AI in pharmaceutical formulation development and drug delivery optimization, emphasizing its potential to revolutionize modern pharmaceutics and improve healthcare outcomes.

DOI: http://doi.org/10.5281/zenodo.20696741

Vastu Orientation and its Climatic Relevance: A Study of Climate Responsive Architectural Principles in India

Authors: Ar. Suman Sharma, Muskan Gour

Abstract: Vastu Shastra is an ancient Indian architectural science that establishes harmony between buildings, nature, and human activities through orientation and spatial planning. Traditional Indian architecture evolved according to climatic conditions, environmental understanding, and sustainable planning principles. The orientation principles of Vastu are closely related to thermal comfort, daylight performance, natural ventilation, and passive cooling strategies. This research paper studies the climatic relevance of Vastu orientation and analyses how traditional Indian architecture responded effectively to environmental conditions using climate responsive architectural techniques. The study follows a qualitative and analytical research methodology based on literature review, comparative analysis, and case studies of traditional Indian houses. The paper examines the relationship between orientation, sunlight, wind movement, thermal comfort, and passive environmental control. Traditional architectural elements such as courtyards, verandahs, jaalis, shaded openings, and thick walls are analysed in relation to Vastu principles. The study also compares these traditional concepts with modern sustainable architectural practices. The findings indicate that many Vastu principles are scientifically relevant and environmentally responsive. Proper orientation improves daylight quality and ventilation while reducing heat gain and energy consumption. East-facing openings provide healthy morning sunlight, while reduced western exposure minimizes thermal discomfort. Courtyard planning enhances air circulation and creates thermal balance within buildings. The research concludes that Vastu orientation is not merely a cultural or spiritual concept but also a climate responsive architectural strategy based on environmental understanding and passive design principles. Many concepts of Vastu remain relevant in contemporary sustainable architecture and energy-efficient building design.

Formulation and Evaluation of Ethosomes from Drimia Indica Species

Authors: Rushikesh Pawar, Vijaykumar Kale, Sakshi Mane, Mahesh Thakare, Vaibhav Narwade

Abstract: Traditional herbal medicines serve as a primary healthcare pillar for approximately 80% of the population in various Asian and African nations. Despite their extensive experiential evidence and therapeutic benefits, conventional herbal formulations face significant pharmacokinetic limitations. These include poor aqueous solubility, unstable gastrointestinal pH degradation, high presystemic metabolism, and an inability to cross lipid biomembranes effectively, often resulting in sub-therapeutic blood levels.[1] Modern quality control has transitioned from single-marker assays to comprehensive metabolic profiling using High-Performance Liquid Chromatography coupled with Mass Spectrometry (HPLC-MS) and genomic DNA barcoding for precise species identification. Concurrently, international bodies (including the WHO, ASEAN, EU, and FDA) are collaborating to harmonize regulatory frameworks. To enhance therapeutic efficacy, nanotechnology is being deployed to engineer nano-phytomedicines. Various carrier systems including polymeric nanoparticles, solid lipid nanoparticles, liposomes, nanoemulsions, and phytosomes are evaluated. Notably, while liposomes encapsulate extracts within an aqueous core or lipid bilayer, phytosomes chemically anchor phytochemicals directly to phospholipid head groups, drastically improving lipophilicity and membrane permeation.[2] Incorporating plant actives into nanostructured matrices significantly optimizes their hydrophilic-lipophilic balance. This structural modification provides sustained release, shields molecules from chemical degradation, minimizes off-target toxicity (e.g., localized accumulation of chemotherapeutics in healthy tissues), and increases bioavailability. However, transitioning these formulations from bench to industrial scale introduces complex challenges, including maintaining uniform encapsulation efficiency within multi-component plant extracts, preventing nanoparticle aggregation driven by high surface energy, and satisfying stringent regulatory safety assays regarding tissue accumulation.[3].

Hybrid Vision-Based Sign Language Recognition: A Review

Authors: Prerna Charis J

Abstract: Sign Language Recognition (SLR) has emerged as an important research area at the intersection of computer vision and deep learning, and human and machine interaction with an objective of enabling effective communication between deaf and hearing communities. Recent advances in deep learning have improved the performance of vision-based Sign Language Recognition systems, particularly by using hybrid architectures that combine spatial features extraction and temporal sequence modelling. The goal of this review is to provide a overview of the recent developments in hybrid Vision-based Sign Language Recognition and to examine the advantages, limitation and practical deployment challenges of the current approaches. This paper provides a systematic review of the literature, the surveyed methods broadly classified into CNN-LSTM architectures, Transformer-based models and multimodal integrated frameworks which integrates visual and skeletal information. This review further investigates critical challenges affecting the deployment in real-world scenarios which includes domain shift, data scarcity, co-articulation, sign ambiguity and computational constrain. We will also discuss about emerging research direction such as self-supervised learning, cross-linguistic transfer learning, generative domain adaptation, multimodal bio signal integration, and community-centered dataset development. This survey also highlights the significant progress achieved in continuous sign language recognition while identifying the remaining technical and practical barriers that must be removed to develop robust, scalable, and user-independent SLR systems capable of operating in real-world environments.

Artificial Intelligence – Driven Learning Analytics For Enhancing Student Engagement, Academic Performance, And Decision – Making in Business Management Education

Authors: Dr Ansari Pulickal Abdul Azeez, Farooq Sajjad

Abstract: Digitization of business education at an unprecedented rate has made available to educators large amounts of student interaction data that can inform data-driven learning interventions. In this paper, we propose an Artificial Intelligence-driven Learning Analytics (AI-LA) system architecture, which incorporates multi-stream data sources (Learning Management System (LMS) logs, clickstream analysis, test/assignment submissions, and engagement data) to model, explain, and improve student engagement and performance. Our approach leverages a novel combination of techniques that include a Temporal Fusion Transformer (TFT) model for sequential behavior prediction, SHapley Additive exPlanations (SHAP) for interpretable feature importance, and reinforcement learning (RL) engine for personalized intervention recommendations. Our model was tested using longitudinal data from 3,400+ business management students in 24 courses over three academic years (2022-2025). It predicted at-risk students with up to 89.5% accuracy si

DOI: https://doi.org/10.5281/zenodo.20700051

Performance Performance Analysis Of Intelligent Household System Using Voice Tag

Authors: Deendayal Dhakad, Deependra Rajak, Navneet Dhakad, Pooja Kewat, Ritik Dhakad, Dr. Prakhar Singh Bhadoria

Abstract: The advancement of smart home technologies has transformed the way people interact with household appliances. Conventional home automation systems often require manual operation or mobile applications, which may not be convenient for all users. This paper proposes an Intelligent Household System Using Voice Tag that enables users to control household devices through voice commands. The system utilizes voice recognition technology, microcontrollers, wireless communication modules, and smart sensors to automate home appliances efficiently. Voice commands are processed and converted into control signals that operate electrical devices such as lights, fans, air conditioners, and security systems. The proposed system improves user convenience, enhances accessibility for elderly and disabled individuals, and supports energy-efficient operation. Performance parameters such as voice recognition accuracy, response time, communication reliability, and energy consumption are analyzed. The study concludes that voice-based intelligent household systems offer a practical and user-friendly solution for modern smart homes.

DOI: http://doi.org/10.5281/zenodo.20700978

Performance Analysis Of E-Bike Lock And Anti Theft Alarm System For Rural Areas

Authors: Deepika Kashyap, Durgesh Singh, Harsh Pandey, Nikhil Ahirwar, Nikhil Gautam, Nishant Kumar, Rahul Singh

Abstract: The increasing use of electric bicycles (E-bikes) in rural areas has created a need for effective security solutions to prevent theft and unauthorized access. Conventional locking mechanisms often provide limited protection and may not alert owners during theft attempts. This paper presents an E-Bike Lock and Anti-Theft Alarm System for Rural Areas that combines electronic locking, motion detection, alarm generation, and wireless communication technologies. The proposed system uses a microcontroller, sensors, GSM/Bluetooth modules, and an electronic locking mechanism to secure the E-bike. When unauthorized movement or tampering is detected, the system activates an alarm and sends notifications to the owner. Performance parameters such as detection accuracy, response time, power consumption, and communication reliability are analyzed. Experimental results indicate that the system provides enhanced security, low power consumption, and improved protection against theft in rural environments.

DOI: http://doi.org/10.5281/zenodo.20701189

Vernacular and Modern Architecture: Materials, Sustainability, and Technological Advancements

Authors: Jatin Nikhade, Professor Ar. Vaishali Sharma

Abstract: Vernacular architecture offers a viable and underutilized framework for addressing the sustainability failures of contemporary construction in India. Through a comparative literature review and analysis of documented case studies from Kerala, Rajasthan, Assam, and Ladakh, this study finds that traditional building systems consistently achieve lower embodied energy, superior passive thermal performance, and stronger cultural continuity than their modern counterparts — without reliance on mechanical systems. The study also critically examines the limitations of vernacular methods, including structural vulnerability, maintenance demands, and inability to scale in rapidly urbanizing contexts. It concludes that a hybrid model — integrating vernacular passive design, traditional materials upgraded through modern engineering, and digital fabrication tools — presents the most feasible pathway to a sustainable built environment in India.</

Vehicle Entry Monitoring System Using YOLO Object Detection Model

Authors: Nikita Khawase, Nishant Kadam, Swarup Chaudhari, Rushikesh Patil, Hrishikesh Kakade

Abstract: Automated vehicle monitoring is a cornerstone of modern security infrastructure, essential for maintaining safety and operational efficiency in high-traffic environments such as industrial complexes, gated communities, and public facilities. Traditional manual surveillance methods are frequently plagued by human error, significant labor costs, and operational bottlenecks that compromise the integrity of security protocols. This paper presents a robust framework for an automated Vehicle Entry Monitoring System (VEMS) utilizing the state-of-the-art You Only Look Once (YOLO) object detection architecture. The proposed system integrates real-time video stream processing with advanced deep learning models to achieve high-speed detection and classification of various vehicle types, including cars, trucks, and motorcycles. A critical component of the methodology involves the integration of Optical Character Recognition (OCR) and tracking algorithms, such as DeepSORT, to automatically extract alphanumeric license plate data and maintain unique vehicle identities across consecutive frames. This integration enables the creation of a comprehensive, searchable database that cross-references detected plates with authorized whitelists for proactive access control. Experimental results demonstrate that the system ensures near 100% operational uptime by automating the data trail for security auditing and regulatory compliance. The framework provides a scalable solution for intelligent transportation management, significantly reducing manpower dependency while enhancing the reliability of entry logs. By combining real-time detection overlays with a centralized monitoring dashboard, this research offers a sophisticated, data-driven approach to facility security, fostering safer and more efficient urban mobility environments.

Enhancing Financial Transparency: A Hybrid Rule-Based Surrogate Model for Credit Risk Management

Authors: R A Shasank

Abstract: In the rapidly evolving landscape of financial technology, the imperative for model interpretability often conflicts with the pursuit of predictive accuracy. Financial institutions heavily rely on automated credit scoring models; however, the lack of transparency in conventional “black-box” approaches—such as deep neural networks and complex ensemble methods—poses significant regulatory and ethical risks. This paper introduces a hybrid credit risk assessment framework that bridges the gap between performance and interpretability. By leveraging First-Order Inductive Learners (specifically the RIPPER algorithm), the proposed model transforms raw financial data into a structured set of human-auditable domain rules. Furthermore, we implement a novel “Abstention-Driven Human Audit” layer, which identifies cases with marginal prediction confidence and redirects them for manual expert review. The experimental analysis, conducted on standard benchmark datasets, demonstrates that this architecture maintains competitive predictive power while providing a clear, logical rationale for every automated decision. The results highlight that the integration of rule-based logic not only fosters regulatory compliance but also enhances stakeholder trust in automated financial systems. This study contributes a scalable, transparent, and robust alternative for modern credit risk management.

Formulation And Evaluation Of Polyherbal Shampoo

Authors: Mr. Hemant Vanjari, Mr. Jay Deshmukh, Assistant Professor Ms.Pratibha Makar, Dr.Vijaykumar Kale, Dr.Mahesh Thakre

Abstract: Lately, more people pay notice to plant-based beauty products – main reason being they tend to be gentler, work well, rarely cause trouble unlike lab-made versions. This research zeroes in on crafting and testing a multi-plant shampoo made entirely from nature’s lineup: Amla joins shikakai, those mix with soap nuts while bhringraj slips in beside hibiscus; fenugreek seeds blend with rice extract, stick amaltas pairs up with flaxseeds, then rosemary teams with aloe vera plus curry leaves tag along too. Long before labs existed, these plants earned rep for helping hair grow stronger, cutting down flakes, keeping strands from dropping, boosting scalp condition, adding glow to locks. Put together with earth-friendly carriers, the mix faced checks on looks, acidity level, thickness, how rich the bubbles get, if gunk spreads out when washed, how fast water soaks into fabric, pull at liquid surfaces, even how steady it stays over time. Results? Cleans thoroughly, makes foam just fine, hits the right acid balance, conditions like a charm – all without making scalps itch. From roots up, plant-based mix fed each strand what it needed. Hair grew stronger, smoother – no harsh stuff involved. Results showed this blend worked just as well as lab-made options. Cost stayed low, safety held steady. Folks using it daily found fewer issues than expected. Not one person reported serious irritation. Science backed its role in regular

Design and Vibration Analysis of Morphing Wing

Authors: Sheri Srujan Reddy, Thota Ramanna Dora Prabhas, Mancholla Ranateja, Assistant Professor Dr. P Kiran Kumar

Abstract: Traditional control surfaces on aircraft have been based on rigidly hinged flap sections that create unavoidable geometric discontinuities. These generate early flow separation and parasite drag, hindering aerodynamic performance in different flight conditions. This study explores the application of camber morphing, a biological concept involving wing deformation similar to those seen in bird-like flying organisms. The main goal of this study was to develop an adaptable mechanism, capable of changing the average camber line of the airfoil while keeping its structural integrity intact. The focus is put on a “Fishbone Active Camber” (Fish BAC), or [add name of the mechanism used, for instance, SMA or Rib-Linkage] based structure which replaces a hinge mechanism at the rear-spar position of the wing with a continuous flexible skin allowing for an even pressure distribution along the wingspan. The method involved a two-step approach. First, numerical simulations were carried out using an omega SST turbulence model to compare the aerodynamic parameters of the standard NACA 2412 airfoil with a morphing one. It is evident that the morphing wing has successfully reduced pressure drag significantly by removing the “hinge-gap” problem. More precisely, when the Angle of Attack (AOA) is 6 degrees, the morphing wing has shown a Lift-to-Drag ratio improvement of about 12 to 15 percent over the conventional flaps wing system. Also, flow visualization proved that the onset of turbulence occurred much later, thus broadening the aircraft’s range of efficient flight.

DOI: https://doi.org/10.5281/zenodo.20706107

Intelligent Clinical Decision Support Systems: Architectures, Applications, And Ethical Implications

Authors: Prof. Abhishek Dubey, Akshada Kale, Kashish Mahobiya, Kirti Thakur, Nikita Raj

Abstract: Clinical Decision Support Systems (CDSS) play a crucial role in helping healthcare professionals make accurate, timely, and evidence-driven decisions. However, the growing scale, speed, and diversity of healthcare data have revealed the limitations of traditional rule-based CDSS, especially when dealing with multimorbidity and personalized treatment. Recent advancements in artificial intelligence (AI)—including machine learning, deep learning, and natural language processing (NLP)—have enabled the development of intelligent CDSS that support adaptive learning, predictive analytics, and patient stratification. This paper provides a comprehensive, system-level review of AI-powered CDSS, examining their historical development, underlying technologies, architectural frameworks, and clinical applications. Unlike earlier surveys that focused mainly on individual algorithms, this review integrates AI methods with system architecture, clinical workflows, and ethical considerations. It explores key AI techniques for patient stratification, deep learning models for diagnosis and prognosis, and NLP-driven early warning systems. The paper also addresses critical challenges related to ethics, legal concerns, and explainability, while highlighting emerging trends such as federated learning, digital twins, and genomic-based CDSS. Overall, it aims to offer researchers and clinicians a thorough understanding of AI-CDSS design principles and their future potential.

DOI: http://doi.org/10.5281/zenodo.20706812

Comprehensive Biocompatibility Assessment Of The STARBEAM™ OCT Imaging Catheter: In-Vivo And In-Vitro Approaches

Authors: Minocha Dr. Pramodkumar, Kothwala Dr. Deveshkumar, Pandya Kamna, Shinde Divya, Sharma Rahul, Chauhan Sargam, Ladumor Rahul, Kadam Aniket

Abstract: Biocompatibility evaluation is a critical regulatory requirement for establishing the preclinical safety of medical devices in accordance with the ISO 10993 series of standards. The present study aimed to comprehensively assess the biological safety of the OCT Imaging Catheter, in-vitro and in-vivo tests selected based on its intended use and blood-contacting nature. In-vitro cytotoxicity was evaluated using L929 mouse fibroblast cells by qualitative morphological assessment and quantitative MTT assay, followed by in-vivo assessments including skin sensitization, intracutaneous irritation, acute systemic toxicity, and material-mediated pyrogenicity. Hemocompatibility was investigated through hemolysis, platelet activation, coagulation parameters, leukocyte activation, and complement activation studies. Genotoxic potential was assessed using the bacterial reverse mutation (AMES) assay and an in-vitro mammalian chromosomal aberration test in human lymphocytes. The test item demonstrated no cytotoxic effects, with cell viability exceeding ISO acceptance criteria at all extract concentrations. In-vivo studies revealed no evidence of skin sensitization, irritation, systemic toxicity, or pyrogenic response. Hemocompatibility testing confirmed the non-hemolytic nature of the device and showed no adverse effects on platelet function, coagulation pathways, leukocyte activation, or complement system activation. Genotoxicity assessments indicated that the test item was non-mutagenic and non-clastogenic under all test conditions. Collectively, the results demonstrate that the OCT Imaging Catheter exhibits an acceptable biocompatibility profile and is biologically safe for its intended clinical application. These findings support its preclinical risk assessment and provide robust evidence for regulatory submissions in compliance with ISO 10993 requirements.

DOI: http://doi.org/10.5281/zenodo.20708577

Light House Project Shining a Light on Successes and Challenges

Authors: Ar. Yashika Garg, Major Soni

Abstract: Indian cities are projected to contribute to 70% of the total GDP by 2030. But rapid urbanization and increase in urban migrants are exerting huge pressure on the environment. Despite the complexities of meeting the housing demand, sustainable affordable housing is a challenge. Indian Government has tried to boost the supply of housing stock from the first 5-year plans (1951) to the recent initiatives of “Housing for all”. The six Light House Projects (LHPs) initiated under the Global Housing Technology Challenge in India, are a step closer to meeting the demand. As LHPs near completion, the paper attempts to critically analyze the projects by comparative analysis. The analysis is broadly divided into site/masterplan level, block level, and unit level. The study revealed that the LHP is innovative in terms of technological advancement but lacks consideration in socio-cultural aspects and quality affordable housing which is required for diverse Indian households.

Home Automation for Physically Challenged Villagers Using Low Cost Kit

Authors: Apali, Najul, Satyendra, Shubham Rahangdale, Tarachand, Praveen Choudhary

Abstract: Electric Vehicles (EVs) are rapidly transforming the transportation sector by reducing dependence on fossil fuels and minimizing environmental pollution. This paper discusses the history, working principles, battery technologies, charging infrastructure, advantages, limitations, environmental impacts, and future scope of electric vehicles. The proposed system uses sensors, microcontrollers, relays, and wireless communication technology to control household appliances such as lights, fans, doors, and emergency alarms. The system can be operated using mobile applications, voice commands, or simple switches depending on the user’s capability. The project aims to improve the quality of life of disabled villagers by reducing physical effort, increasing safety, and promoting independent living. The system is designed to be affordable, energy efficient, and easy to install rural homes.

DOI: http://doi.org/10.5281/zenodo.20715066

Invest AI: A Stock Price Prediction And Analysis System

Authors: Vivek Nagargoje, Hemant Chandegave, Samarth Kumbhar, Viraj Patil

Abstract: Predicting stock prices accurately is a complex challenge that must combine financial theory and applied machine learning. It involves issues like market non-stationarity, sensitivity to real-world events, and ways investor psychology impacts price movements. In this paper, we present Invest AI, a hybrid framework for prediction and analysis that combines three powerful models: XGBoost-based learning for processing structured features, stacked Long Short-Term Memory (LSTM) networks for capturing sequential patterns, and FinBERT-based sentiment analysis of financial news. Invest AI integrates these models’ outputs using a Loopy Belief Propagation-inspired weighting system that adjusts predictions based on the confidence of each model. The system was trained and tested on historical data sourced from the yfinance API. It has expanding window validation to prevent data leakage. Other than just making predictions, InvestAI includes SHAP-based explainability, anomaly detection, and financial performance backtesting through Sharpe ratio and maximum drawdown metrics. Over a year of out-of-sample data evaluation, this hybrid approach achieves a reduction in MAPE by 14.2% compared to other single-model performances. It also had a Sharpe ratio of 1.47 in simulated trading. This system combines temporal, relational, and sentiment-driven metrics to produce better results in financial forecasting.

Invest AI : A Stock Prediction Solution

Authors: Samarth Kumbhar, Viraj Rajendra Patil, Hemant Prashant Chandegave, Vivek Nagargoje

Abstract: For many years beginners tend to invest in stocks and face loss due to volatile nature of markets, or lack of informed decisions like trusting investment through word of mouth, this leads to discouragement from investment in stock market. InvestAi is a platform designed for beginners who are looking to enter the world of Stocks, platform is AI driven forecasting and analysis system designed to help users understand stocks and predictions using “explainable” machine learning techniques. The system aims to increase financial literacy and increase Informed investment decisions via explainable Ai (X AI) and interactive visuals. It also features sentiment analysis of news and also explains how it links or affects a particular stock.

Intelligent Agent-Based Predict System For Enterprise Service Platform

Authors: Narasimman S, Jayavarman V, Parandhaman P, Vasanth V, Umavathi. V

Abstract: Rising storage and computational capacities have led to the accumulation of voluminous datasets. These datasets contain insights that describe natural phenomena, usage patterns, trends, and other aspects of complex, real-world systems. We propose greedy K-NN (K-Nearest Neighbor) data allocation strategies (across the agents) that improve the probability of identifying data leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases, we can also inject “realistic but fake” data records to further improve our chances of detecting leakage and identifying the guilty party. Mining large data requires intensive computing resources and data mining expertise, which might be inaccessible to most of the users. With the regularly obtainable cloud computing resources, data mining tasks cannot be stimulated to the cloud or outsourced to the third party to save cost. In this new pattern, data and model confidentiality becomes the major unease to the data owner. Data owners have to understand the possible trade-offs among client-side costs, model quality, and confidentiality to justify outsourcing solutions. In this paper, we propose the RASP Boost framework to address these problems in confidential cloud-based learning. The RASP-Boost approach works with our previous developed Random Space Data Perturbation (RASP) method to protect data confidentiality and uses the boosting framework to conquer the complexity of learning high-class classifiers as of RASP disconcerted data. So, we have to build upsome cloud-client combined boosting algorithms. These algorithms need low client-side calculation and communication expenses. The client does not call for to stay online in the progression of learning models. So, we have methodically studied the confidentiality of data, model, and learning process under a realistic security model.

Nova-Chat: A Full-Stack Chat-bot Using AI

Authors: Shravani Phalke, Rajit Joshi, Raj Lohar, Bharti Dhote

Abstract: By facilitating natural, flexible, and context-aware communication across a variety of languages and cultural contexts, artificial intelligence (AI) has revolutionized human-computer interaction. Large language models have advanced, but chatbots still have difficulty identifying, interpreting, and reacting sympathetically to users’ emotional states. As a result, they frequently provide generic responses that lack genuine resonance. This paper introduces Novachat, a full-stack AI chatbot designed to close this gap by combining multilingualism and sophisticated emotion intelligence into a scalable MERN-stack architecture. In order to provide human-like, contextually nuanced conversations in English, Hindi, Marathi, and other languages, Novachat’s modular framework integrates sentiment analysis, emotion-adaptive response generation, and language detection. To ensure smooth real-time adaptability, each module functions as a microservice and communicates via orchestration driven by APIs. The study describes the system’s overall architecture, emotional classification model, dataset organization, and quantitative performance assessment using metrics like System Usability Scale (SUS), emotion recognition accuracy, response relevancy, and user engagement latency. According to experimental results, Novachat generates sympathetic responses and detects emotions with high accuracy; a SUS score indicates strong user acceptance. The field is moving closer to AI systems that genuinely recognize and value the user’s emotional experience as a result of these results, which validate Novachat’s function as an efficient, inclusive, and emotionally engaging conversational platform.

A Functional Analytic Framework For The Modeling Of Fatigue And Legal Liability Allocation

Authors: Ogbonna Nnamuchi

Abstract: This paper introduces a formal framework utilizing mathematical functional analysis to bridge the gap between empirical sleep science and jurisprudence. By treating fatigue trajectories as functions within infinite-dimensional Banach spaces, we formalize how biomathematical fatigue inputs intersect with duty-of-care allocations within tort and regulatory systems, shifting the legal focus from rigid shift-hour compliance to systemic accountability.

Integration Of SAP Digital Manufacturing With SAP S/4HANA And Non-SAP ERP Systems: A Unified Framework For Manufacturing Execution

Authors: Swami Siva Mahadev

Abstract: The adoption of Industry 4.0 technologies has increased the need for seamless integration between Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) platforms. SAP Digital Manufacturing (SAP DM), built on the SAP Business Technology Platform (BTP), provides a cloud-based solution for managing and optimizing manufacturing operations. While integration with SAP S/4HANA is supported through standardized mechanisms such as IDocs, APIs, and SAP Cloud Integration, integrating SAP DM with non-SAP ERP systems, including Oracle ERP Cloud, Microsoft Dynamics 365, and Infor CloudSuite, presents additional challenges related to data exchange, interoperability, and process synchronization. This paper proposes a unified four-layer integration framework for connecting SAP Digital Manufacturing with both SAP and non-SAP ERP systems. The framework focuses on master data synchronization, production order management, middleware architecture, security governance, and implementation strategy. By analyzing industry practices and documented integration approaches, the study demonstrates how organizations can establish a scalable and standardized manufacturing integration landscape. The paper also discusses future opportunities in event-driven architectures, artificial intelligence-based production planning, and digital twin technologies.

Machine Learning Approach for Predicting Compressive Strength of Concrete

Authors: Rashida Noori, Atharv Patil, Samarth Gote, Om Gadre, Satish Rathod, Malik Mulani, Prof. V.P.Bhusare

Abstract: Concrete is one of the most widely used construction materials, and its compressive strength is a key parameter that determines its structural performance and durability. Traditionally, determining the compressive strength of concrete requires laboratory testing, which is time-consuming, costly, and dependent on curing conditions and sample preparation. In this study, a data-driven approach is applied to predict the compressive strength of concrete using regression analysis in Microsoft Excel. A dataset containing input variables such as cement content, water-cement ratio, fine and coarse aggregate proportions, and curing age is analysed. Various regression techniques—such as linear, multiple linear, and polynomial regression—are implemented to develop predictive models. The correlation between experimental and predicted results is evaluated using statistical indicators like R², standard error, and residual analysis. The study demonstrates that regression models can effectively predict concrete compressive strength with reasonab le accuracy, thereby reducing the need for extensive experimental trials. This approach highlights the potential of Excel as a simple yet powerful tool for engineers and researchers to perform predictive modelling and optimise concrete mix design. The compressive strength of concrete is a crucial property that determines its quality and load-bearing capacity. Conventionally, this strength is obtained through laboratory testing after curing, which can be time-consuming and resource-intensive. This project focuses on predicting the compressive strength of concrete using regression analysis in Microsoft Excel. By utilising input parameters such as cement content, water-cement ratio, fine and coarse aggregates, and curing age, a regression model is developed to estimate strength values. Multiple linear regression is applied to establish a relationship between these variables and the compressive strength. The accuracy of the model is evaluated through statistical measures like the coefficient of determination (R²) and error analysis. The results indicate that regression-based prediction provides a reliable and cost-effective alternative to traditional testing methods. This approach demonstrates the usefulness of Excel as an accessible tool for data analysis and decision-making in civil engineering applications.

DOI: http://doi.org/

Antimicrobial Activity of Chenopodium album Leaf Extract: An In Vitro Study

Authors: Ankita Patel

Abstract: Chenopodium album (Linn.), commonly known as lamb’s quarters or bathua, is a fast-growing annual plant of the family Amaranthaceae with a long history of traditional medicinal use. This study investigates the antimicrobial potential of methanol and acetone leaf extracts of C. album against six pathogenic bacteria and six fungal strains using disc diffusion, well diffusion, and poisoned food techniques. Extraction was performed using the Soxhlet method with 25 g of powdered dried leaf material in 50 ml of methanol and acetone solvent mixture. Results demonstrated notable antibacterial activity against both Gram-positive and Gram-negative organisms, with acetone extract producing the largest inhibition zones against Escherichia coli (19.5 mm) by disc diffusion and 20.1 mm by well diffusion. Antifungal assays revealed that a mixture of methanolic and acetone extracts achieved up to 99% mycelial inhibition against Aspergillus niger at 7 days incubation. These findings suggest that C. album harbors broad-spectrum antimicrobial compounds with significant pharmaceutical potential.

DOI: https://doi.org/10.5281/zenodo.20732068

Effect of Data-Driven Personalization on Customer Engagement and Brand Loyalty

Authors: Vishwanatha D N, Assistant Professor Jayashree K

Abstract: This research paper investigates the effect of data-driven personalization on customer engagement and brand loyalty within the digital marketing ecosystem. As organisations accumulate unprecedented volumes of consumer data through digital touchpoints—spanning e-commerce platforms, mobile applications, social media, and connected devices—the capacity to deliver highly individualised marketing experiences has grown substantially. Yet the relationship between personalization, engagement, and loyalty is complex, non-linear, and moderated by a range of consumer, contextual, and technological variables that existing literature has not yet fully integrated into a unified framework. Drawing on the Elaboration Likelihood Model (ELM), Self-Determination Theory (SDT), Relationship Marketing Theory, and the Stimulus-Organism-Response (S-O-R) framework, this paper develops a comprehensive conceptual model that traces the pathway from data-driven personalization through customer engagement to brand loyalty, incorporating personalization relevance, perceived autonomy, privacy concern, and algorithmic transparency as key moderating and mediating constructs. The paper reviews the theoretical foundations of these relationships, analyses six real-world case studies from diverse sectors including streaming, e-commerce, food delivery, and retail, and proposes a research agenda for advancing understanding of personalization dynamics in contemporary digital marketing. Key findings indicate that data-driven personalization significantly enhances customer engagement when it is perceived as relevant and non-intrusive, and that sustained engagement is the primary pathway through which personalization generates brand loyalty. However, the study also identifies critical conditions under which personalization can undermine trust and loyalty—specifically when personalisation becomes too precise, violates contextual norms, or operates without transparency. The paper concludes with strategic implications for marketers, recommendations for ethical personalization design, and directions for future empirical research.

AI Powered Cloud Database-as-a-Service

Authors: Dr. Saurabh Saoji, Aditya Deshmukh, Aadesh Gulumbe, Sanika Hingalkar, Akash Shelke

Abstract: Cloud-based applications increasingly rely on multiple database systems to handle diverse data models and workloads, yet managing these heterogeneous environments remains complex and resource-intensive. Traditional Database-as-a-Service platforms often introduce vendor lock-in, limited flexibility, and high costs, restricting their suitability for academic and research use. To address these challenges, this research proposes an open-source, AI-powered Cloud Database-as-a-Service platform that unifies the management of SQL, NoSQL, and in-memory databases using Kubernetes-based container orchestration. The system integrates AI-driven natural language assistance for schema generation and query formulation, along with real-time monitoring using Prometheus and Grafana. By combining automation, intelligent interaction, and cost-effective deployment, the platform aims to improve accessibility, efficiency, and scalability in cloud-native database management.

DOI: http://doi.org/10.5281/zenodo.20732646

LeadConvertX: A Multimodal Temporal Heterogeneous Graph Transformer for Explainable CRM Lead Conversion Prediction

Authors: Aarush Kukade, Advait Deogade, Atharva Mane, Dr. Saurabh Saoji

Abstract: In the modern digital banking era, effective marketing lead generation depends on leveraging heterogeneous, multimodal customer data. Traditional predictive models primarily rely on static, flat tabular attributes, overlooking the relational and temporal information inherent in customer transaction histories and support networks. This paper proposes MTHGT, a Multimodal Temporal Heterogeneous Graph Transformer framework that integrates multimodal data—including structured CRM attributes, sequential transactional records, and unstructured call transcripts—to predict customer lead conversion in banking. The proposed system models customers, transactions, locations, and events as nodes in a heterogeneous graph with relationships based on transactional similarity, campaign logs, and temporal history. Using a multimodal embedding strategy, the model learns customer representations via Graph Transformer layers with type-aware, distance, and temporal bias encodings. Empirical results on the Multimodal Banking Dataset (MBD; 85,620 client-month nodes, 2.26% positive rate) demonstrate that graph-based models outperform tabular baselines on ranking (HGT ROC-AUC of 0.7809 ± 0.0092 and MTHGT ROC-AUC of 0.7763 ± 0.0160 vs. Logistic Regression ROC-AUC of 0.7397 ± 0.0002). Furthermore, MTHGT improves F1-score over HGT (0.0778 ± 0.0225 vs. 0.0651 ± 0.0050) and exposes dynamic modality attributions (CRM features: 25%, dialogue text: 36%, temporal transactions: 39%), enabling explainable CRM lead scoring. The paper details system design, dataset structure, implementation, graph construction methodology, performance evaluation, and outlines a roadmap to bridge the tabular baseline gap using Focal Loss and behavioral k-NN edges.

DOI: https://doi.org/10.5281/zenodo.20735250

GraphLeadIQ: Multimodal GNN-Powered Lead Scoring for Banking CRM

Authors: Aarush Kukade, Advait Deogade, Atharva Mane, Dr. Saurabh Saoji

Abstract: In the digital banking era, effective marketing lead generation depends on leveraging hetero-geneous, multimodal customer data. Traditional predictive models primarily rely on tabular attributes, overlooking the relational and contextual information inherent in customer networks. This paper proposes a Graph Neural Network (GNN)-based framework that integrates multi-modal data—including structured CRM attributes, transactional records, and unstructured call transcript text—to predict customer lead conversion in banking. The proposed system models customers as nodes in a heterogeneous graph with relationships based on transactional similarity and communication patterns. Using a multimodal embedding strategy, the model learns customer representations via Graph Convolutional and Attention layers. Empirical results on the UCI Bank Marketing dataset demonstrate an ROC-AUC of 0.87 and accuracy of 0.886, with significant improvements over logistic regression and XGBoost baselines. Extended experiments using a heterogeneous multi-source graph (MovieLens, Last.FM, Amazon co-purchase, OGB-MAG) further confirm the framework’s superiority: accuracy 0.893 and F1-score 0.596 versus a logistic regression baseline that degenerates to F1= 0.000, AUC= 0.500. The paper details system design, dataset structure, implementation, graph construction methodology, and performance evaluation.

DOI: https://doi.org/10.5281/zenodo.20735315

Eye Gazed Communication System

Authors: Professor Sonali Dongare, Priyanshu Singh, Aditya Amup

Abstract: Motor impairments such as ALS, locked-in syndrome, and cerebral palsy severely limit an individual’s ability to interact with digital systems using conventional input devices. This paper presents GazeSpeak, an AI-powered Eye Gaze Communication System that enables motor-impaired users to communicate through voluntary eye movements alone. The system extracts real-time gaze coordinates using OpenCV and MediaPipe, maps them onto interactive screen elements via a TensorFlow regression model, and integrates a transformer based NLP module for context-aware word prediction. A dwell-based selection mechanism activates interface targets without any physical input. Experimental evaluation across twenty participants demonstrates a gaze detection accuracy of 94.2%, end-to-end latency of 38ms, top-3 word prediction accuracy of 87.6%, and communication throughput of 10.6 WPM, with a System Usability Scale score of 84.4 confirming excellent user acceptance. The results establish GazeSpeak as an effective, open-source, and cost-accessible assistive communication platform for real-world deployment.

DOI: https://doi.org/10.5281/zenodo.20735406

Role Of Nutritional And Photoperiodic Factors In Regulating Physiological Activities Of Drosophila Melanogaster

Authors: Daksha Tigga

Abstract: Drosophila melanogaster serves as an indispensable in vivo model system for mapping how external environmental variations and nutritional inputs dictate physiological adaptations and behavioural choices. This study presents a multi-generational evaluation of how dietary variance (standard cornmeal vs. banana-enriched vs. orange-enriched media) pairs with photoperiodic conditions to modulate ontogeny, locomotor agility, and larval chemotaxis. Across three successive generations (F1–F3), cohorts reared on a nutrient-dense banana medium exhibited accelerated metamorphic transitions and robust pupation rates. Conversely, an orange-supplemented diet delayed developmental milestones and reduced total yield compared to uniform controls. Photoperiodic restrictions (sustained dark phases) consistently decelerated growth metrics and decreased motor output across all dietary groups. Quantifiable behavioural deficits under low-light regimes were verified via negative geotaxis assays, where light-exposed flies displayed markedly superior vertical climbing performance. Furthermore, larval olfactory assays revealed a stark chemotactic bias toward volatile food-derived attractants (ethyl acetate) over aversive ionic stimuli (sodium chloride). Taken together, these data illuminate the complex interplay between systemic metabolic programming and sensory-driven behavioural phenotypes in response to immediate ecosystem shifts.

DOI: http://doi.org/10.5281/zenodo.20741467

A Novel Multimodal Biometric Authentication Framework Using Ear Contour Analysis and EDCC-Based Palmprint Recognition

Authors: Research Scholar Akhilesh Singh, Associate Professor Dr Namita Tiwari, Associate Professor Dr Mayur Rahul

Abstract: With an increasingly large number of online services and secure access applications, trusted identity authentication has become an important issue. Although biometric authentication has higher security assurance than traditional security methods, single biometric mode authentication systems have performance issues in terms of degradation due to environmental factors, occlusions, lighting, and spoofing attacks. In this respect, this study proposes an original multimodal biometric authentication approach that combines ear contour biometric recognition with palmprint biometric recognition using the Enhanced and Discriminative Competitive Code (EDCC) method. The proposed multimodal biometric authentication method has the synergistic ability of two biometric modes. The ear contour-based biometric recognition technique extracts the helix and conchal curvatures of the human ear, providing geometric information that is less affected by illumination conditions. Simultaneously, the EDCC-based palmprint recognition technique extracts the prevalent orientation patterns of the lines and ridges on the human palm, providing robustness to noise and minute geometric distortions. These two biometric modalities provide complementary information about the user’s biometric traits and can thus be fused through feature-level fusion to provide a single and robust biometric representation.The performance of the proposed multimodal biometric authentication technique is evaluated on two challenging and widely available biometric datasets, namely the PolyU-IITD contactless palmprint database and the EarVN1.0 unconstrained ear image database. The performance evaluation of the proposed multimodal biometric authentication technique clearly reveals its superior performance compared to other state-of-the-art biometric authentication approaches, including unimodal and hybrid biometric authentication schemes, as it provides a recognition accuracy of 99.01% and an extremely low EER of 0.11% for the PolyU-IITD contactless palmprint database and EarVN1.0 unconstrained ear image database, respectively.

DOI: https://doi.org/10.5281/zenodo.20742447

Study on the Diversity of Endophytic Fungi Associated with Some Plants and Their Antibacterial Potential

Authors: Gayatri Pandram

Abstract: Endophytic fungi are ubiquitous microorganisms that asymptomatically colonize the internal tissues of plants, representing an untapped reservoir of novel, biologically active secondary metabolites. This study investigates the endophytic fungal diversity associated with two ethnobotanically critical medicinal plants: Withania somnifera (Ashwagandha) and Amomum subulatum (Badi Elaichi), and evaluates their biomedical potential against clinically significant human pathogens. Healthy leaves, stems, and roots were subjected to a stringent multi-step surface sterilization protocol and inoculated onto Potato Dextrose Agar (PDA). A total of fifteen (15) distinct fungal endophytes were isolated and taxonomically characterized via macroscopic and microscopic morphotyping. The predominant genera identified included Aspergillus, Fusarium, Alternaria, Curvularia, Penicillium, and Phoma. The cell-free secondary metabolites were extracted using organic solvents and screened for antibacterial efficacy against Klebsiella pneumoniae, Staphylococcus aureus, Escherichia coli, Salmonella typhi, and Micrococcus spp. using the agar well diffusion assay. The bioprospecting profile revealed significant inter-species variability. Notably, Aspergillus flavus derived from W. somnifera exhibited a profound, broad-spectrum zone of inhibition against both Gram-positive and Gram-negative cohorts, with optimal metabolic yield quantified at a 7-day incubation kinetics threshold. These insights underscore the therapeutic relevance of plant-associated fractions as sustainable alternatives to combat escalating multi-drug resistance (MDR) phenotypes.

DOI: https://doi.org/10.5281/zenodo.20742728

Substrates Evaluation for the Quality Production of Pleurotus sajor-caju

Authors: Reena

Abstract: The present investigation was carried out to evaluate the effect of different agricultural substrates on the quality production of oyster mushroom (Pleurotus sajor-caju #392). The study was conducted using four agricultural waste substrates, namely wheat straw, rice straw, sugarcane bagasse, and maize straw for mushroom cultivation. The experiment focused on spawn running, pinhead appearance, maturity, flush-wise yield, biological efficiency, and nutritional composition of cultivated mushrooms. Results revealed that wheat straw showed the fastest spawn running and pinhead formation with maximum total yield and biological efficiency. Wheat straw recorded 1360 g total yield and 136% biological efficiency, while sugarcane bagasse showed the lowest yield performance. Nutritional analysis indicated that sugarcane bagasse had the highest protein content (8.75%), whereas maize straw recorded maximum crude fat content (10%). Wheat straw exhibited superior fiber and ash content. The findings conclude that wheat straw is the most suitable substrate for commercial cultivation of Pleurotus sajor-caju, whereas sugarcane bagasse can be used for improving mushroom nutritional quality. This study highlights the importance of selecting appropriate substrates for achieving better mushroom production and quality.

DOI: https://doi.org/10.5281/zenodo.20742909

CryptoTrack: A Data-Driven Framework for Detecting and Explaining Cryptocurrency Laundering

Authors: Gaurav A. Bagul, Parth P. Jadhav, Pratik S. Rahane, Assistant Professor Vipin K. Wani

Abstract: Cryptocurrencies have rapidly grown into a pop- ular medium of digital exchange, offering speed, security, and borderless transactions. While these benefits have driven global adoption, the pseudony- mous and decentralized nature of cryptocurrencies also makes them highly vulnerable to misuse in ille- gal activities such as money laundering, terrorism financing, and fraud. Recent reports highlight bil- lions of dollars being laundered annually through cryptocurrency channels, often using techniques like mixers, peel chains, and cross-chain transfers. Traditional Anti-Money Laundering (AML) sys- tems, designed mainly for conventional banking transactions, struggle to handle the complexities of blockchain-based transactions. They often func- tion as black boxes, providing risk scores without clear reasoning, and they are reactive rather than proactive in detecting suspicious activities. To address these challenges, the proposed sys- tem CryptoTrack: A Data-Driven System for De- tecting Cryptocurrency Laundering. The system leverages advanced analytics to identify suspicious accounts and transactions, while integrating Ex- plainable Artificial Intelligence (XAI) to provide transparent justifications for every detection. Un- like existing systems that only flag activities, Cryp- toTrack enables users and compliance officers to understand the exact reasons why a transaction is considered risky, thereby increasing trust and re- ducing false positives. A visualization dashboard further supports users by providing intuitive in- sights into detected suspicious activity. The proposed framework bridges the gap be- tween opaque detection models and the practical requirement for interpretability in financial mon- itoring. By combining data-driven detection, ex- plainability, and transparency, CryptoTrack offers a more reliable and effective approach to combating financial crimes in the rapidly evolving landscape of cryptocurrency.

DOI: https://doi.org/10.5281/zenodo.20743147

Investigation of V-Port Use in Ball Valves Through Design and Analysis: A Comprehensive Review

Authors: Ishaan Puri, Assistant Professor Dr. Raghavendra Barshikar

Abstract: Ball valves are popular in factories for controlling liquids and gases because they are easy to make, last a long time, and shut off flow well. But regular ball valves are not always great at controlling how much liquid or gas flows through, especially when they are only partly open. V-port ball valves were created to fix this problem. The V-shaped cut in the ball helps to control the flow better. This research looks at how a V-port ball valve works using computer simulations. A 3D model of the valve was made, and simulations were run with different opening amounts and flow rates. The research looked at things like how fast the liquid or gas moves, how much the pressure drops, how much turbulence there is, and the valve’s flow rate. The results show that the V-port design does a much better job of controlling flow than regular ball valves. It makes the flow smoother and reduces the chance of bubbles forming. The results also show that computer simulations are helpful for making better valve designs. The simu

DOI: https://doi.org/10.5281/zenodo.20743255

ML-Based Audio Fingerprinting for Noisy Environment

Authors: Manthan Gavali, Om Malode, Shreeyash Jadhav, Yash Chaudhari, Assistant Professor Vaibhav Dabhade

Abstract: This project addresses the challenge of robust audio content identification in noisy environments by developing an ML-based audio fingerprinting system.To overcome this limi-tation, our methodology leverages a deep learning approach, using a Convolutional Neural Network to automatically extract a compact, noise-invariant fingerprint from audio spectrograms. The system involves a multi-stage process: a diverse dataset of clean audio is first augmented with various types of noise which has different signal to noise ratios. The trained model then generates a unique fingerprint for each audio track in a database. Finally, these fingerprints are stored using a fast and efficient hashing mechanism, enabling quick retrieval and identification. Our evaluation will demonstrate that this ML-based system significantly outperforms Existing methods in terms of accuracy and robustness, particularly at low SNRs, thereby providing a more reliable solution for applications such as music recognition, broadcast monitoring, and copyright enforcement.It further introduces spectrogram normalization and data-driven feature learning that minimize the impact of background dis-tortions. A contrastive-learning objective enforces the noisy and clean versions of the same audio to have similar embeddings. To facilitate fast retrieval, the system uses an approximate nearest-neighbor search mechanism optimized for large-scale databases. The approach’s low cost computational for fingerprint generation and matching is also demonstrated by experimental results. In general, the proposed approach allows for a scalable, high-performance framework suitable for real-time audio identifi-cation in adverse acoustic environments.This paper proposes a machine learning-based audio fingerprinting system for accurate audio identification in noisy conditions. A Convolutional Neural Network (CNN) is employed to learn noise-robust and compact audio fingerprints from audio spectrograms. Noise is added to clean audio examples with varying signal-to-noise ratio (SNR) values to enhance robustness. Contrastive learning is employed to guarantee that embeddings of noisy and clean audio examples are similar. The produced audio fingerprints are stored through a hashing function, and an approximate nearest neighbor search is employed for efficient retrieval. Experimental results show enhanced audio identification accuracy with low computational complexity in low SNR conditions. The proposed system is appropriate for scalable and real-time audio identification tasks

DOI: https://doi.org/10.5281/zenodo.20743337

ISL Smart Translator: Speech and Text to Indian Sign Language Converter

Authors: Miss. Urvi Pawar, Miss. Shreya Rakibe, Miss. Vaishnavi Sandhan, Miss. Samruddhi Vispute, Assistant Professor Dr. Kirti Patil

Abstract: Communicating with Deaf or hard-of- hearing people can be difficult at times and the chal- lenges are great in multilingual countries such as India. This study describes current work on this but offers a proposal for an animated ISL (Indian Sign Language) translation system from Marathi text and/or speech – based on the fact that there are many more English- MSL (Marathi Sign Language) resources available and, therefore, a ’significant access gap’ when considering Deaf users within our target country. The majority of currently available translation systems have been based upon machine learning; however, due to insuffi- cient parallel corpora/annotated sign data resources for Marathi-MSL, this method will not work. The proposed system adopts as an alternative a ’rule based’ method- ology which will map the Marathi language & grammar structures onto ISL using linguistic ’rules’ and dictio- nary and will develop this through web application using React.jsTailwindCSSFlask for the front end of the web application, while allowing use of ’browser based’ storage thus ensuring a very lightweight deployment. Given that ISL requires ’gestures’, ’facial expressions’ and ’spatial syntax’, it is not possible to translate word for word; rather, the system will also consider some important Marathi grammatical elements, e.g. inflection; location/post position, verb forms, sentence structure and will therefore generate an ISL output that more accurately represents the original Marathi and promotes communication and accessibility for Deaf individuals.

DOI: https://doi.org/10.5281/zenodo.20743457

DocInsight Context-Aware Document Review and Reporting Assistant

Authors: Mukul Rane, Om Baviskar, Devendra Nikam, Tejaswi Malode, Associate Professor Vaibhav Dabhade

Abstract: This paper proposes DocInsight, a context-aware document analysis system that integrates preprocessing, Optical Character Recognition (OCR), layout analysis, and semantic processing into a unified pipeline. The system enhances text extraction accuracy while preserving document structure, en- abling efficient understanding of unstructured documents. By leveraging layout-aware OCR and transformer-based semantic models, DocInsight supports intelligent search, context-driven retrieval, and automated report generation. The framework ensures improved accuracy, structural consistency, and reduced manual effort in document processing. The system is applicable across multiple domains such as healthcare, legal systems, educa- tion, and enterprise environments, where efficient and intelligent document understanding is essential

DOI: https://doi.org/10.5281/zenodo.20743556

NextGenHire: Gamified Learning With Skill-Based Job Matching

Authors: Assistant Professor Namrata Ghuse, Pratik Shinde, Yamini Sarnaik, Yash Bhoye, Jayesh Shewale

Abstract: Gamification is changing how online learning works. When we add points, badges, levels and progress tracking, students feel more interested and complete topics on time. In this paper, we show NextGenHire, a simple system that mixes gamified learning with job recommendation.In this system, a student first logs in and creates a profile with their skills. After that, the student watches learning content like web development or app development. When the learning part is over, the student gives tests. In the test, the gamification part starts where the student gets points and results based on quiz accuracy, time taken and activity. After tests, the system checks the student’s skill performance and compares it with job requirements. Using this method, the system recommends suitable jobs for the student. We also use basic data and simple comparison to check if gamification helps students to stay active and learn better. From this, we observed that students show better engagement after adding gamification.Overall, NextGenHire helps students learn and also suggests jobs based on their skills and performance, reducing the gap between learning and hiring.

DOI: https://doi.org/10.5281/zenodo.20743640

MediCast: Smart Hospital ICU Beds and Oxygen Demand Predictor

Authors: Sahil Arun Sahane, Suhani Sharma, Amay Prasad Sabnis, Suhani Singh, Assistant Professor Rahul B. Mandlik

Abstract: Efficient management of critical hospital resources such as intensive care unit (ICU) beds, oxygen supply, and medical staff has become a major challenge, particularly during large-scale healthcare emergencies. Conventional hospital management systems are largely reactive and often fail to anticipate sudden surges in patient demand, resulting in delayed responses and resource shortages. This paper presents MediCast, an AI-driven hospital resource forecasting and decision support system designed to predict ICU bed occupancy and oxygen demand in advance while supporting optimized resource allocation. The proposed framework employs Long Short-Term Memory (LSTM) networks for time-series forecasting of ICU admissions and oxygen consumption trends, and XGBoost models for learning complex patterns from structured hospital data. Based on the predicted demand, an optimization layer assists in efficient allocation of beds and staff resources to reduce overload and improve preparedness. The system also provides an interactive dashboard for real-time visualization of predictions, alerts, and analytical insights, enabling hospital administrators to take proactive decisions. By integrating predictive analytics and optimization within a unified platform, MediCast enhances operational efficiency, minimizes critical resource shortages, and supports data-driven healthcare management in high-demand scenarios.

DOI: https://doi.org/10.5281/zenodo.20743914

Failure Analysis of Press Tool

Authors: Assistant Professor Sharad Nirgude, Shubham Gorade, Shraddha Sali, Diksha Dusane

Abstract: This study investigates the failure of a blanking die used to produce busbar connecting element parts on a mechanical press. During operation, the tool broke early than expected life. due to cracks. forming at the die center. This failure resulted in reduced production of the product. The analysis revealed high stress concentrations at the center of the die. These areas aim to identify the causes of the failure by studying the design closely and using finite element analysis with ANSYS software. A 3D model of the press tool was created by using NX software. The stress distribution is more where the cracks appeared in the failed tool. Poor clearance and design in the die increased these stress peaks. Recommendations are for improve press tool by adding same changes in design to reduce stress concentration, and improving material selection or heat treatment to improve toughness and fatigue resistance. That steps improve tool life, lower failure frequency, and enhance the reliability of busbar component element

DOI: https://doi.org/10.5281/zenodo.20744027

Integrated Intelligent Vehicle Safety System

Authors: Shreya Chavan, Mayuri Patil, Aarya Pawar, Professor Jayshri Kandekar

Abstract: Road traffic accidents continue to be an major global safety concern due to human error, delayed emergency response, and a lack of predictive monitoring systems. This paper presents an Integrated Intelligent Vehicle Safety System (IIVSS), a hybrid IoT and Artificial Intelligence-based frame-work designed for real-time accident prediction and automated emergency response. The proposed system integrates IMU and GPS sensor fusion with edge-level processing and cloud analytics to detect abnormal driving patterns and predict potential colli-sions. Unlike traditional reactive accident detection systems, the proposed architecture enables predictive safety analysis through anomaly detection algorithms and automated alert generation. The experimental evaluation demonstrates low latency response, reliable communication, and high detection accuracy. The sys-tem provides a scalable, cost-effective and intelligent solution for next-generation smart transportation and connected-vehicle ecosystems.

DOI: https://doi.org/10.5281/zenodo.20744104

TimeBank – Hourly Job Posting & Hiring Platform

Authors: Sonali Mohan Patil, Sayali Devidas Tarle, Latesh Jitendra Patel, Hrutvik Sanjay Rane, Assistant Professor Reshma Chhaburao Sonawane

Abstract: In this paper, we present TimeBank, which functions as a web application that enables Indian employers to establish and fill hourly employment positions. This initiative aims to address the problems associated with temporary labor. The service provides open-access structured hourly employment services which differ from Uber and Swiggy that limit their work to assigned tasks and Indian gig portals which primarily offer full-time job and long-term contract and project-based freelance work. The application uses a secure MERN stack architecture and includes features like real-time job posting and smart search and filtering and built-in time tracking and secure wallet-based payment gateways and a transparent rating and review system. The platform serves as the primary resource for student freelancers and employers who need to hire workers on an hourly basis with quickness and responsibility.

DOI: https://doi.org/10.5281/zenodo.20744200

The Impact Of Innovation On Commercial Bank Competitiveness

Authors: Isse Sudi Mohamed

Abstract: This study attempts to close a research gap by examining the relationship between innovation and Somalia’s commercial banks’ competitiveness. The study’s primary objective is to assess how innovation could increase the competitiveness of commercial banks. In particular, the study examines the relationship between financial innovation and commercial bank competitiveness, the impact of innovation strategy on commercial banks’ competitive position, and the role of technical innovation on competitiveness. The study uses two primary research designs: predictive and explanatory. To shed light on the strength of the relationship between two or more variables at a particular moment in time, an explanatory correlational design was used. Structured questionnaires were used to gather primary data, and cross-sectional and correlational study methodologies were used. A sample of 86 respondents was chosen from the 110 members of the target demographic. The questionnaire’s demographic part recorded the respondents’ age, gender, marital status, and educational attainment. To guarantee accuracy and consistency, data analysis was carried out in tandem with data gathering. The study’s conclusions offer commercial banks doing business in Somalia useful information. The study provides banks with useful advice on how to improve their capacity for innovation in order to get a competitive edge by documenting different types and methods of financial innovation. The findings show that bank competitiveness is significantly impacted by financial innovation and innovation strategy, but there is little correlation between technological innovation and competitiveness. Based on this conclusion, the report advises bank management to put in place efficient systems to improve internal innovation processes, especially by bolstering organizational and technology innovation practices.

Data Visualization On Airbnb Dataset Using Tableau

Authors: Syed Ibrahim Hussain, Mahad Ansari, Mr.Kareem Basha

Abstract: Today, the brand new digital economy has revolutionized the way people travel and find a place to stay through platforms like Airbnb. For this purpose, this project is dedicated to delving into Airbnb data with the help of efficient data visualization methods to reveal significant patterns and insights. The study, by analyzing various factors such as pricing location types of rooms, availability, and customer reviews, is aiming at finding the answer to how different variables affect listing performance and user preferences. Through the use of visualization software, not only are complex datasets opened up in a simple and interactive visual manner like charts, graphs, and maps but it also becomes much easier to recognize the patterns and associations. Besides hosts, guests, and platform developers, the project also showcases the great potential of data visualization in enabling them to make better decisions. In short, this work illustrates the tremendous impact of visual storytelling in turning huge datasets into simpler ones and at the same time, providing useful insights in a real-life situation.

DOI: http://doi.org/10.5281/zenodo.20747004

Formulation and Evalution of Herbal Lipbalm

Authors: Assistant Professor Mrs. Pratibha Makar, Ms. Priyanka Kamthe, Ms. Shewta Dandwate, Dr. Vijaykumar Kale, Associate Professor Dr. Mahesh Thakare, Assistant Professor Mr. Vaibhav Narawade

Abstract: Cosmetics are unbelievably in demand since historical time. These days people prefers naturally derived cosmetic products. Cosmetic plays a important role in today’s life style. Along all cosmetic products, Natural lip balm preprations are most widely used to increase the beauty of lips and add glamour touch and shine to the beauty. Herbal formulation is a sign of safety, satisfaction and surety as less or no harm to the users and so herbal Lipbalm can be made without the colors being compromised on. Lip balms provides a natural way to promote healthy and moisturized lips. Coloring lips is the ancient practice to increase the beauty of lips and to give shine to the face. Current cosmetic lip products are based on use of toxic chemical ingredients with various adverse effect. That’s why it leads to study natural ingredients used to production of natural lip balm. This lip balm is formulated according to the scientific procedure and evaluated as per standard requirements. This article reviews on the natural ingredients used for natural lip balm along with their advantages and disadvantages. The present study focuses on the formulation and evaluation of a herbal lip balm using natural ingredients with moisturizing, healing, and protective properties. Herbal cosmetics have gained significant popularity due to their minimal side effects and enhanced therapeutic value compared to synthetic products. The lip balm was formulated using natural waxes, oils, and herbal extracts such as beeswax, coconut oil, almond oil, shea butter, and plant-based coloring or flavoring agents. Different formulations were prepared by varying the concentration of ingredients to obtain an optimized product with desirable characteristics. The prepared herbal lip balm formulations were evaluated for various physicochemical parameters including color, odor, pH, spreadability, melting point, stability, homogeneity, and skin irritation. The formulations showed good consistency, smooth application, acceptable stability, and no signs of irritation during the study period.

DOI: http://doi.org/

Cloud-Based Electric Vehicle Charging Station Locator And Booking Systems: A Comprehensive Review

Authors: Vivek Saindane, Kashish Kazi, Shraddha Bute, Saher Raje, Sushama Punde, Devyani Sharma

Abstract: The rapid growth of electric vehicles (EVs) is stressing the need for intelligent, scalable charging infrastructure. This survey of 18 IEEE studies (from 2020–2025) examines cloud- and edge-enabled EV charging station location and booking systems. We chronologically synthesize each work, highlighting methods (e.g. mixed-integer programming, metaheuristics, game theory, digital twins, block chain, and privacy-preserving algorithms) and key findings. Gaps emerge in integrated reservation models, dynamic spatio-temporal demand, and user privacy. We identify research needs such as privacy-aware locators, real-time scheduling, and cloud-edge architectures. We propose illustrative system designs and mixed-integer models (for station placement and reservation scheduling) and offer design suggestions for future EV locator/booking platforms. These contributions lay the groundwork for dynamic optimization and secure, user-centric EV charging services.

DOI: http://doi.org/10.5281/zenodo.20757358

SMS Classifier With Encryption Decryption Using Machine Learning

Authors: Bhonde Vrushali Baban, Patil Renuka Rajendra, Wakle Anita Ashok, Kulkarni Mrunmayee Mangesh, Archana Sachin Gaikwad, Poornima Nandu Pathak

Abstract: SMS spam is becoming more frequent as spammers use it to reach their targets. Although spam messages can be annoying, they can also be dangerous because they might try to steal personal information or direct users to harmful websites. This work explains how SMS spam is detected. It describes the different types of spam, the features used to find them, and the methods used to stop them. We also talk about some of the difficulties in identifying SMS spam and possible future methods to deal with them.

DOI: http://doi.org/10.5281/zenodo.20757483

AI-Powered Voice-Controlled Energy Tracking & Bill Prediction Using Java Full Stack & Ml

Authors: Assistant Professor Dr. K. N. Kazi, Bandgar Pooja Kisan, Chavan Sahil Sanjay, Mali Nikhil Vikas

Abstract: Rising energy consumption and increasing electricity costs have created a need for intelligent energy management systems. This paper presents an AI-Powered Voice-Controlled Energy Tracking and Bill Prediction System developed using Java Full Stack technology and Machine Learning techniques. The proposed system enables users to monitor real-time energy consumption, predict future electricity bills, and interact with the system through voice commands. Historical energy usage data is analyzed using machine learning algorithms to forecast future consumption patterns and billing amounts with improved accuracy. The voice-controlled interface enhances user convenience and accessibility by allowing hands-free operation and quick access to energy-related information. The system integrates a responsive web application, database management, and predictive analytics to provide a comprehensive energy monitoring solution. Experimental results demonstrate that the proposed model effectively tracks energy usage, generates accurate bill predictions, and promotes energy-saving behavior among consumers. This solution contributes to the development of smart energy management systems and supports efficient utilization of electrical resources in residential and commercial environments.

DOI: http://doi.org/

Real Time Video Content Moderation and Spam Detection Tool

Authors: Sai Kumar S L, Ramu B T, Mallikarjun Heroor, B M Shree Lakshmi, Dr. Mydhili Nair

Abstract: For exponential growth of user-generated content (UGC) on video-sharing platforms necessitates the development of highly efficient and scalable automatic content moderation and spam detection algorithms. Traditional manual review techniques are overwhelmed by the sheer volume and real-time nature of video uploads, which leads to unequal enforcement, moderator fatigue, and prolonged exposure to harmful content. This work offers a unique, multi-modal Video Content Moderation and Spam Detection tool that applies artificial intelligence and machine learning to handle these problems. To detect violent, sexually explicit, and policy-violating pictures, the system incorporates sophisticated Computer Vision (CV) techniques, such as frame-by-frame analysis, object detection, and visual hashing in order to identify hate speech, harassment, fraudulent schemes, and spam indications (such as harmful URLs, repetitive content, and behavioural anomalies), Additionally, it analyses video titles, descriptions, and comments.

DOI: http://doi.org/10.5281/zenodo.20758833

Multi-Horizon Interdependence Between Macroeconomic Conditions And Stock Market Volatility: Comparative Evidence From Developing Economies

Authors: Mrs. R. Santhiya, Dr. P. Ashok Kumar

Abstract: This study examines the multi-horizon interdependence between macroeconomic conditions and stock market volatility in two major developing economies, India and China, using annual data for the period 1991 to 2024. The analysis incorporates key macroeconomic indicators, namely Gross Domestic Product (GDP), inflation, exports, imports, and gross capital formation, together with stock market indices represented by the NIFTY 50 and SSE Composite Index. The dataset is obtained from the World Bank DataBank and investing.com, ensuring consistency and reliability across countries. The study adopts a comprehensive econometric framework by first applying Unit Root tests to determine the stationarity properties of the variables, followed by the Johansen Cointegration test to examine the existence of long-run equilibrium relationships between macroeconomic fundamentals and stock market movements. The Vector Error Correction Model (VECM) is subsequently employed to capture both short-run dynamics and long-run adjustments. To further explore time-varying interactions across different frequencies and investment horizons, Wavelet Coherency Analysis is utilized to identify co-movements, lead–lag relationships, and volatility transmission mechanisms between macroeconomic factors and stock markets. The findings are expected to reveal significant long-run integration and heterogeneous time-frequency dependencies, with GDP, trade activities, and capital formation exerting stronger influences over medium- and long-term horizons, while inflation predominantly affects short-term volatility. The study contributes to the literature by providing comparative evidence on macro-financial linkages in developing economies and offers valuable implications for policymakers, investors, and financial market regulators.

DOI: http://doi.org/10.5281/zenodo.20760756

Posture Monitoring And Back Pain Alert System

Authors: Jenyfal Sampson, Vihash.A, B.R.V.Dharma Raju, Sounder.K, S.P.Velmurugan, Rishi Kumar

Abstract: Back pain from sitting too long and bad posture is now one of the most common health problems for students, office workers, and computer users. Posture-tracking cameras and high-tech ergonomic furniture are some of the more common solutions, but they can be expensive, hard to set up, or raise privacy concerns. This paper presents an IoT-based Posture Monitoring and Back Pain Alert System developed with an ESP32 microcontroller, a flex sensor, a force sensor, and an MPU6050 accelerometer–gyroscope module. It is a cost-effective and user-friendly alternative. A Flutter mobile app collects the user’s height and weight, which lets the system automatically set sensor thresholds for different body types. The ESP32 checks your posture in real time and sends sensor data to the app over Wi-Fi. If the app notices that you are slouching, putting too much pressure on your back, or tilting your torso too much, it will immediately show a posture alert message, telling you to fix the problem. The system runs on a Li-ion battery with a TP4056 charging module, which makes it portable and allows for continuous use. Experimental observations demonstrate that the personalized threshold mechanism markedly diminishes false alerts while enhancing comfort and user acceptance. The suggested design is a good, cheap, and private way to fix your posture and keep your back from hurting.

DOI: http://doi.org/10.5281/zenodo.20761757

Development Of A Smart Agro AI Drone

Authors: Sahil Thange, Karan Shinde, Rushikesh Pingal, Shailesh Mogal, Vishal Chaudhari

Abstract: The project titled “Development of a Smart Agro AI Drone” focuses on designing a cost-effective and intelligent aerial spraying system aimed at improving agricultural productivity through automation. Indian farmers often encounter labour shortages, uneven pesticide application and rising operational costs. To address these challenges, the proposed system integrates Artificial Intelligence (AI) and GPS-based autonomous navigation within a quadcopter platform equipped with a liquid tank, pump, and atomising nozzles for precise and uniform spraying. AI algorithms support crop recognition, optimised flight-path generation, and obstacle avoidance, ensuring safe and efficient field operations. An embedded microcontroller with a flight controller enables stable flight, real-time data transmission, and improved system reliability, while lightweight structural materials enhance endurance and payload capacity. This work also develops a cost-efficient agricultural drone platform by combining low-cost hardware components, open source flight control architecture, lightweight mechanical design, and optimised edge AI processing. The prototype is evaluated based on spray coverage, flight time, payload capacity, endurance, and detection accuracy under varying field conditions and cost-per-hectare performance is compared against existing commercial drone systems. Results demonstrate that strategic component selection, modular mechanical design, and computational model optimisation significantly reduce overall system cost while maintaining effective spraying and monitoring performance. Overall, the Smart Agro AI Drone provides an affordable, intelligent, and practical solution that supports sustainable precision farming, particularly for small and medium scale farmers

DOI: http://doi.org/10.5281/zenodo.20765542

AI-Based Smart Systems For Allergen And Additive Detection In Packaged Foods

Authors: Sanket Dudhade, Sahil Gilbile, Aditya Gavali, Atul Chaudhari

Abstract: Food safety concerns, particularly the presence of undeclared allergies and artificial ingredients, have significantly increased worldwide as a result of the exponential growth in the consumption of packaged foods. Customers’ manual label reading is inefficient, error-prone, and frequently hampered by multilingual packaging and complex ingredient nomenclature. An innovative technique for automating the detection of allergens and additives is provided by Artificial Intelligence (AI) through the use of Deep Learning (DL), Natural Language Processing (NLP), and Optical Character Recognition (OCR). A comprehensive analysis of AI-based smart systems for detecting chemicals and allergies in packaged foods is presented in this study. It looks at benchmark datasets, talks about different machine learning and transformer-based models, looks at key performance validation measures, and looks at the architectures that are already in place. The article also discusses difficulties such as data imbalance, interpretability problems, and computing constraints in real-time systems. Experimental trends show that hybrid OCR–NLP frameworks achieve detection accuracies of over 97% on benchmark datasets and demonstrate greater generalization across languages and package formats.The results of the study indicate that integrating state-of-the-art AI technology into food safety systems has the potential to revolutionize consumer protection, regulatory compliance, and public health. The findings emphasize that AI models must be globally scalable, interpretable, and privacy-preserving in order to guarantee transparency and confidence in automated food labeling.

DOI: http://doi.org/10.5281/zenodo.20765726

AI-Driven Explainable Product Recommendation System Using LLaMA-2, FAISS, And SHAP For Multi-Platform E-Commerce

Authors: Samruddhi Maheshkumar Aher, Harshali Rajendra Bagul, Diksha Ravindra Nirbhavane, Ashwini Nandu Pawar, Puneet Eknath Patel

Abstract: E-commerce platforms generate millions of product listings, often causing information overload and generic, non-personalized suggestions. Traditional recommendation systems operate as black boxes, resulting in limited user trust due to the lack of transparency. This paper proposes an AI-driven Explainable Product Recommendation System integrating Large. Language Models (LLaMA-2), FAISS semantic search, and SHAP-based interpretability. The system processes natural language queries, interprets intent, retrieves relevant products across multiple platforms, and generates human-readable explanations. Experimental evaluation demonstrates improved accuracy, transparency, and user satisfaction compared to traditional recommendation approaches.

DOI: http://doi.org/10.5281/zenodo.20766113

Real-ESRGAN–Driven MRI Super-Resolution For Diagnostic Precision And AI-Assisted Clinical Deployment

Authors: Nupur Jadhav, Atharva Bhusnale, Pritesh Gupta, Sakshi Jadhav, Vaishali Hiray

Abstract: Magnetic Resonance Imaging (MRI) is very impor-tant in the detection of neurological defects because it possesses high resolution that enables good visualization of soft-tissue structure. However, diagnostic clarity is often hindered by low-resolution scans due to the short time of acquisition, motion artifacts and hardware constraints. Recent advances in deep learning, such as Enhanced Super-Resolution Generative Ad-versarial Networks (Real-ESRGAN), have demonstrated strong capabilities of perceptual-driven image enhancement.This paper discusses Real-ESRGAN-based MRI super-resolution strategies, their architectural advantages and clinical potential benefits, in preserving fine anatomical and pathological details much better than CNN-based and conventional interpolation methods. We also present a conceptual AI-enabled deployment framework, where Real-ESRGAN is handled by a clinician support chatbot for application in web-based interaction, tele-radiology accessibility and diagnostic help. Clinical validation including metrics such as PSNR, SSIM,LPIPS and sFRC is investigated. The study emphasizes the need for interpretable, regulation-ready models to bridge AI-driven MRI enhancement with real-world diagnostic workflows.

DOI: http://doi.org/10.5281/zenodo.20766158

Vaani2Mudra – Indian Sign Language (ISL) Translation For Deaf People

Authors: Khushboo Lokhande, Samruddhi Mahajan, Sayali Pawar, Janhavi Wankhede, Vijay More

Abstract: Vaani2Mudra is an online assistive communication platform that converts spoken or written language into gestures representing Indian Sign Language (ISL). The platform utilizes a compact speech recognition model to process voice input and employs natural language processing methods to restructure spoken content into a format compatible with ISL. Through a rule-based linguistic framework, the system eliminates redundant grammatical elements and standardizes text for gesture mapping. For multilingual functionality, Marathi language input is first converted to English before further processing. The output is presented through a series of pre-established ISL gesture visuals shown on a web-based interface. This system prioritizes ease of use, instantaneous processing, and user accessibility, positioning it as an effective tool for learning environments and assistive communication applications.

DOI: http://doi.org/10.5281/zenodo.20766405

Embedded System Based Smart E-Voting System Using Authentication Technologies

Authors: Kishor Ugale, Tushar Pandhi

Abstract: Traditional Voting plays a very important role in modern republic systems. Electronic voting (e-Voting) refers to any method of casting or recording votes through electronic technologies. Voting machines consist of the whole combination of mechanical, electromechanical, or electronic components-along with the necessary software, firmware, and documentation-used for programming, controlling, and supporting the voting process. The e-Voting system discussed here uses biological validation, notably fingerprint identification, to verify voter identity. In this method, fingerprint matching is employed to confirm the user’s identity. This proposed work bears by differentiating sample fingerprint patterns to show whether the fingerprints real from the match individual. The primary objective of this system is to simplify and improve the regulation of the voting mechanism. The proposed solution is designed to encourage full participation by enabling every eligible voter to take part in elections. This is achieved through an Android application that permits human being to cast their balloting digitally. Implementing online voting across both Android and web-based platforms increases the reliability and effectiveness of the election process. The system aims to offer a convenient, user-friendly, and secure method for recording and counting votes. Online voting can reduce operational costs, boost voter turnout, and facilitate better communication in the middle of voters and candidates. The core target of the implemented system is to provide a voting mechanism that authorizes singles to submit secure and confidential ballots over a network, addressing the restrictions of traditional voting methods, which are often time-consuming and vulnerable to security issues.

DOI: http://doi.org/10.5281/zenodo.20766695

ResiPlan AI: A Comprehensive Analysis Of AI-Driven Automated Residential Floor Planning

Authors: Ashutosh Kale, Swapnil Yeole, Onkar Sonawane, Jayesh Wani, Vedant Rajput

Abstract: This analysis paper examines ResiPlan AI, an intelligent web-based system designed to automate residential floor plan generation using artificial intelligence and machine learning techniques. The system addresses significant barriers in traditional architectural design—such as high cost, complexity, and reliance on expert knowledge—by enabling non-expert users to generate optimized 2D and 3D layouts through simple inputs like plot size, room count, and architectural style. By integrating Stable Diffusion 1.5 with ControlNet, ResiPlan AI ensures structural adherence while maintaining creative flexibility. This paper critically evaluates the system’s architecture, technical approach, limitations, and future potential, positioning it within the broader context of generative AI in architectural design.

DOI: http://doi.org/10.5281/zenodo.20766823

Design and Optimization of Motorcycle Swing Arm Using Bio Inspired Honeycomb Structure

Authors: Kiran P. Borase, Sachin K. Dahake

Abstract: This research focuses on the design and optimization of a motorcycle swing arm using a bio-inspired honeycomb structure aimed at achieving significant weight reduction while enhancing stiffness and durability. A conventional swing arm was modelled using Solid Works and compared with an optimized honeycomb-reinforced structure through Finite Element Analysis (FEA) in ANSYS. The inclusion of honeycomb geometry demonstrates improved structural efficiency, reduced stress concentration, lower deformation, and an expected weight reduction of 15–20%. The study establishes the feasibility of integrating nature-inspired geometrical patterns into mechanical components to achieve superior performance in lightweight engineering applications.

DOI: http://doi.org/10.5281/zenodo.20766895

AI-Driven Multi-Objective Task Scheduling In Fog Computing Using Deep Reinforcement Learning

Authors: Om Sawant, Gunjan Shahade, Atul Sanap, Shailesh Pawar, Madhuri Shinde

Abstract: The widespread adoption of Internet of Things (IoT) systems has resulted in a large volume of time-sensitive data that requires fast and efficient processing. Although cloud platforms provide extensive computational capabilities, the physical separation between data-producing devices and remote cloud infrastructures frequently introduces noticeable delays, jitter, and bandwidth inefficiencies. Fog computing addresses these shortcomings by relocating processing tasks toward the network’s periphery; however, the decentralized and heterogeneous composition of fog resources complicates the design of effective scheduling strategies. Recent progress in Artificial Intelligence (AI), especially in the field of Deep Reinforcement Learning (DRL), have enabled adaptive and context-aware scheduling solutions capable of responding to dynamic changes in fog–cloud systems. This study presents an in-depth examination of AI-oriented scheduling mechanisms for fog computing, with emphasis on system design principles, algorithmic trends, and comparative performance outcomes. Conventional scheduling heuristics, machine-learning-based methods, and contemporary DRL approaches—including multi-agent and multi-objective frameworks—are critically analyzed. The review also identifies persistent challenges related to scalability, mobility, resource constraints, and security-aware decision-making. Overall, the findings demonstrate that AI-driven scheduling enhances responsiveness, load distribution, and resource utilization in emerging fog-supported IoT environments.

DOI: http://doi.org/10.5281/zenodo.20769734

Formulation and Evaluation of Sugar Free Paracetamol Syrup

Authors: Ms. Snehal Kadbhane, Mr. Ritesh Khandagale, Dr. Vijaykumar Kale, Dr. Mahesh Thakare, Vaibhav Narwade

Abstract: Background: The near-universal reliance on high-sucrose vehicles in paracetamol oral syrups creates an increasingly untenable clinical tension for vulnerable patient populations—diabetic individuals experiencing glycemic excursions, children at heightened risk of dental caries, and obese or metabolically compromised patients. With global diabetes prevalence now exceeding 537 million adults and dental caries ranking as the world’s most prevalent non-communicable condition, the pharmacoeconomic and public health argument for sugar-free alternatives has become irrefutable. Methods: Five trial formulations (F1–F5) of a sugar-free paracetamol oral syrup at 120 mg/5 mL were developed using a Quality by Design (QbD) framework. Sorbitol (20–30% w/v), hydroxypropyl methylcellulose K4M (0.25–0.75% w/v), and sucralose (30–70 mg/100 mL) were systematically varied while all other excipients were held constant. Formulations were evaluated for organoleptic acceptability, pH, viscosity, drug content, density, surface tension, sedimentation ratio, and antimicrobial preservative effectiveness per USP <51> Category 2. The optimized formulation (F3) underwent 90-day accelerated stability testing per ICH Q1A(R2) at 40°C ± 2°C/75% ± 5% RH and was benchmarked against a commercially marketed sugar-free reference product. Results: F3, containing sorbitol 25% w/v, HPMC K4M 0.50% w/v, and sucralose 50 mg/100 mL, emerged as the optimized formulation. It exhibited a pH of 5.82 ± 0.02, viscosity of 92 ± 2.5 cps, drug content of 99.4 ± 0.5% of label claim, and a palatability score of 4.5/5.0—superior to both lower-concentration variants and the marketed comparator (4.2/5.0). Accelerated stability studies confirmed drug content above 98.6% and p-aminophenol below 0.08% at day 90, well within pharmacopoeial limits. All five challenge organisms met USP <51> Category 2 acceptance criteria. Conclusion: The optimized sugar-free paracetamol syrup demonstrates pharmacopoeial compliance, chemical and microbiological stability supportive of a 24-month shelf life, and patient acceptability equivalent or superior to a marketed reference. The formulation strategy—combining a polyol bulk sweetener with a high-intensity non-caloric sweetener and a cellulose-ether viscosity modifier—provides a scientifically validated, clinically advantageous platform for analgesic-antipyretic therapy in patient populations for whom conventional sucrose-based preparations are contraindicated or undesirable.

Data Forge Shape Your Data into Clarity

Authors: Lohitha Lakshmi K, Hema Sri S, Shaik Reshma, Hima Sai Nandhan P, Manoj Kumar Reddy S D V

Abstract: Data plays a key role in analysis and machine learning, but working with real-world datasets is often challenging because they usually contain missing values, duplicate entries, inconsistencies, and noise that can affect the accuracy of results. Data cleaning is therefore an essential step, yet it can be time-consuming and often requires programming knowledge, making it less convenient for many users. In this work, we present DataForge, a data preprocessing system designed to make the cleaning process simpler and more accessible. The platform allows users to upload datasets and perform cleaning operations without writing code, using a mix of statistical methods and simple intelligent techniques to handle issues such as missing data, outliers, and duplicate records. Overall, DataForge focuses on reducing the effort required for data preparation while still helping users work with more reliable datasets. This approach also helps users get a clearer idea of their data without going into too much technical detail.

Multi-Criteria Land Suitability Analysis For Agriculture In Gundlupet Taluk: AHP And GIS Approach

Authors: Bhuvanesh G, Arun Das, Shivanand Chinnappanavar, Ravikumar M

Abstract: This study aimed to assess suitable lands for agricultural purposes in the Gundlupet taluk of Chamarajanagar district. Leveraging the widely used Analytic Hierarchy Process (AHP) integrated with Geographic Information System (GIS), this research conducted a thorough land use suitability analysis. Key parameters including geomorphological and geological features, relief, slope, drainage density, rainfall, soil texture, and land use and land cover were considered in the analysis. Weights were assigned to these parameters based on their significance and importance, resulting in the generation of an agricultural land suitability map divided into three categories. Upon excluding forested and reservoir areas from the reclassified suitability map, the study estimated that 19.59% of the study area (266 sq. km) is highly suitable for agricultural production, 67.6% (918 sq. km) is moderately suitable, and 12.81% (174 sq. km) is unsuitable for agricultural production in this region. This framework facilitates the early zoning of agricultural land for protection, ensuring sustainable land use development in the future.

DOI: http://doi.org/10.5281/zenodo.20783644

AYUSH Knowledge Extraction & Recommendation System

Authors: Rasika Kokate, Saloni Gohad, Vaishnavi Gulave, Tanuja Karpe, Sunita Borse

Abstract: AYUSH (Ayurveda Yoga Naturopathy Unani Siddha and Homeopathy) system is a repository of the wisdom obtained from 8000 plants. But most of this knowledge is available in printed and handwritten Sanskrit and Hindi manuscripts which are computing unfriendly. This study introduces an end-to-end AYUSH knowledge recommendation pipeline based on AI to digitize, interpret and recommend insights from the AYUSH body of knowledge for modern computational intelligence. The framework combines Optical Character Recognition (Tesseract OCR), NLP for Indic languages, Knowledge Graph modelling (Neo4j) and AI-based reasoning (BERT, Random Forest) to convert unstructured manuscripts into searchable knowledge that can be analyzed by human . The system captures herbal, disease and treatment entities, relates the entities semantically, and then provides query-driven recommendations through an intelligent interface. Using a simple interface, researchers would be able to ask for insights such as “What are the herbs that have been associated with anti-inflammatory activity?” This strategy lowers the expense of early stage drug discovery, validates traditional remedies, and forges new roads in integrated health care investigation. This study provides the infrastructure for AI- based analysis of literature on traditional medicine and adds to digital conservation, availability and edification as well as evidence-informed integrated healthcare.

DOI: http://doi.org/10.5281/zenodo.20786139

Vision Based Driver Drowsiness Detection: From Deep Learning Models To Real Time Mobile Deployment

Authors: Hitesh Jitendra Jadhav, Santosh Shriram Karvar, Atharv Arun Patil, Gaurav Anil Waje, Gaurav Vijay Barde, Bajirao Subhash Shirole

Abstract: A significant percentage of traffic accidents in the world result from sleepy drivers. Although a number of detection methods have been established, their utility is often problematic. Physiological signals (EEG, ECG) and vision- based behavioral cues (eye closure, yawning) have been studied in the past, and deep learning models such as Convolutional Neural Networks (CNNs) have shown excellent accuracy in controlled settings. Significant gaps still exist, though, especially in the areas of robustness against various lighting conditions and occlusions, validation in on-road scenarios, and non-intrusive, computationally efficient systems appropriate for real-time deployment on mobile platforms. This review highlights the shortcomings of current vision-based approaches while synthesizing and critiquing them. It then suggests a future- focused approach based on a lightweight CNN architecture (like MobileNetV2) optimized for on-device inference with TensorFlow Lite. This work attempts to close the gap between academic research and useful, scalable solutions that can improve road safety by concentrating on a camera-based, non – intrusive system deployable on common Android devices.

DOI: http://doi.org/10.5281/zenodo.20786979

Enhancing ABSA Using Dynamic Encoding

Authors: Mrs. Bhumika Alte, Satyam Mali, Yashraj Mhase, Kishor Hirgal

Abstract: Aspect-Based Sentiment Analysis (ABSA) provides a fine-grained approach to understanding opin-ions by extracting aspect–opinion–sentiment relation-ships from text. It is particularly valuable in domains such as product reviews, customer services, banking, and social media, where identifying specific strengths and weaknesses is essential. The subtask of Aspect-based Sentiment Triplet Extraction (ASTE) extends ABSA by simultaneously identifying aspect terms, corresponding opinion expressions, and their sentiment polarities. This work proposes an improved ABSA framework that integrates pre-trained language models (PLMs) with a pruned syntactic encoding mechanism to efficiently capture both local and global contextual dependencies. Additionally, a dynamic encoding strategy is introduced to overcome the limitations of traditional local encod-ing, which often fails to capture long-range relation-ships between aspects and opinions. The combination of syntactic pruning and dynamic encoding enhances the association between aspect and opinion terms, leading to more accurate sentiment classification. Experimental evaluations on benchmark ABSA datasets are expected to demonstrate that the pro-posed model achieves higher accuracy and robustness compared to existing methods. This approach effec-tively combines syntactic structure and contextual un-derstanding, improving interpretability and performance in aspect-level sentiment prediction tasks.

DOI: http://doi.org/10.5281/zenodo.20787176

AI-Based Career Advisor: Resume Analysis, Job Matching, And Skill Gap Bridging

Authors: Radhika Kulkarni, Tejal Mungase, Prof. Shradha Pawar

Abstract: Choosing the right career path and the right job opportunity has become increasingly difficult in a labour market where industry requirements evolve faster than academic curricula and where the sheer volume of job postings makes manual evaluation impractical for most candidates. This paper presents the AI-Based Career Advisor, an intelligent system designed to help individuals understand how well their resume aligns with a target job description, identify missing skills, and receive concrete, personalized guidance for improving their employability. The system combines a supervised machine learning model with natural language processing and large language model components to deliver this guidance in a single, integrated workflow. At its core is a resume–job description fit classifier trained on 6,241 real-world resume–job pairs sourced from a public dataset, using TF-IDF based feature engineering across 10,012 dimensions. Six candidate algorithms — Logistic Regression, Naive Bayes, Support Vector Machine, Random Forest, a Neural Network, and XGBoost — were trained and compared, with XGBoost emerging as the best-performing model after hyperparameter optimization, achieving 78.14% test accuracy and an 89.57% ROC-AUC score. The system further incorporates a hybrid skill-extraction pipeline built on spaCy’s named entity recognition and phrase matching, a GPT-4-based resume enhancement module accessed through LangChain, and supporting modules for learning-resource and project-idea recommendation. The complete pipeline is deployed as an interactive Streamlit web application, giving users real-time predictions and actionable career feedback. This paper discusses the motivation, design, methodology, and evaluation of the system, and outlines directions for extending it into a more comprehensive career guidance platform.

Hybrid Deep Learning Model for Real-Time Age and Gender Recognition from Facial Images

Authors: Bharti Saxena, Rupali Chaure, Ashish Chourey, Mohit Singh Tomar

Abstract: Here we introduce an empirical exploration of a real-time Hybrid Deep Learning model for Age and Gender Recognition (HDL-AGR) based on facial images collected from multiple unconstrained scenarios. Estimate age and gender from facial images is a classic computer vision problem with applications ranging from human-computer interaction, intelligent surveillance, personalized marketing to healthcare screening. Most existing approaches are limited by low accuracy on far-side age groups, extreme sensitivity to lighting and occlusion, and extreme computational overhead that would preclude real-time deployment. The proposed HDL-AGR framework consists of a backbone (which has been defined as a modified EfficientNet-B4 convolutional base), attention module (Transformer-based), and an output head (dual-branch, trained jointly for age regression and gender classification) to be tuned up to date. The model is trained and evaluated with five benchmark datasets UTKFace, IMDB-WIKI, Adience, CACD and Fair Face containing over the 845K annotated images. Empirical results: HDL-AGR achieves. (i) A new state-of-the-art Mean Absolute Error (MAE) of 3.94 years in age estimation, along with an unprecedented gender classification accuracy of 97.2% and (ii) Operates at an inference speed of 54 frames per second on standard GPU hardware – outperforming all compared peer methods in the process. The contribution of each architectural component is confirmed through ablation studies. Conclusion: Our results identify HDL-AGR as a strong, efficient, and practically deployable approach for online recognition of facial attributes.

DOI: http://doi.org/10.5281/zenodo.20792037

Voice Based Notice Board

Authors: Jidnyasa Bagul, Pradnya Dhavan, Project Guide Dr. Nandini Dhole

Abstract: In modern institutions and organizations, effective communication of information is essential, yet traditional notice boards often fail to provide timely updates and accessibility. This project presents a Voice-Based Notice Board system that leverages speech recognition and text-to-speech technologies to automate the process of publishing and delivering notices. The system allows authorized users to input notices through voice commands, which are then converted into text using speech-to-text processing. The processed information is stored in a cloud-based database and can be displayed on a digital screen as well as broadcasted through audio output using text-to-speech synthesis. The integration of Internet of Things (IoT) technology ensures real-time updates and remote accessibility. The proposed system is built using Raspberry Pi, along with a microphone module, speaker system, and display unit. Python is used as the primary programming language, incorporating various speech processing libraries for accurate voice recognition and natural audio output. This solution enhances accessibility, reduces manual effort, and ensures faster dissemination of information. It is particularly useful in environments such as educational institutions, offices, hospitals, and public spaces, where quick and efficient communication is crucial. The system also provides scope for future enhancements, including multilingual support and mobile application integration. Overall, the Voice-Based Notice Board offers a smart, efficient, and user-friendly alternative to traditional notice systems by combining automation, cloud computing, and voice interaction technologies.

Design and Performance Evaluation of a Local Voltage Controller for Islanded AC Microgrids

Authors: Assistant Professor Bhupendra Deshmukh, Associate Professor Mohite Utkarsha Laxman, Assistant Professor Diksha M Ahire

Abstract: During islanded operation, AC microgrids operate without grid support, making voltage regulation a critical challenge due to load variations, intermittent renewable generation, and inverter-dominated dynamics. In such conditions, maintaining stable voltage becomes difficult without effective local control mechanisms. This paper presents a decentralized voltage control approach based on a PI-dominant PID controller applied at the primary control level. The proposed controller regulates the inverter output voltage to handle disturbances arising from load changes and renewable energy fluctuations, including photovoltaic and fuel cell sources. The control strategy is simple, does not require communication infrastructure, and is suitable for practical implementation. Simulation results obtained using MATLAB/Simulink demonstrate that the proposed method improves voltage stability, minimizes oscillations, and maintains acceptable performance under varying operating conditions.

DOI: https://doi.org/10.5281/zenodo.20807928

Demand Side Management in Smart Grids with Integrated Renewable Energy Sources: A Comprehensive Review

Authors: Research scholar Dinesh V Malkhede, Associate professor Dr. Prabhat Sharma

Abstract: Demand Side Management (DSM) has emerged as a critical component of within smart grid frameworks to optimize energy efficiency and mitigate peak load scenarios, and facilitate the integration of renewable energy sources. With the evolution of smart grids, advanced communication infrastructures, intelligent control algorithms, and dynamic pricing mechanisms have significantly transformed DSM strategies. This study explores demand side management by examining its key concepts, goals, and implementation practices, while highlighting pricing-based demand response, optimized appliance scheduling, and smart energy management systems. The review synthesizes recent research contributions covering heuristic, metaheuristic, and artificial intelligence–based approaches, including game theory, evolutionary algorithms, and deep reinforcement learning. The review places special focus on residential DSM, electric vehicle integration, and energy storage technologies, while also outlining major challenges, open research problems, and future research opportunities relevant to researchers and industry professionals.

DOI: https://doi.org/10.5281/zenodo.20808055

Load forecasting and Load Management in Smart Grids Using NSGA-II Optimized ANN Model

Authors: Nilesh.P.Dabe, Yogesh R. Patni, Deepak Kadam, Kulkarni Kirti S

Abstract: Precise prediction of residential power consumption, and effective management of load are important tasks in smart grid. The current research proposes a novel hybrid model of ANN with NSGA-II to solve the multi-objective optimization problems for smart grid operations. The model incorporates four important inputs to simultaneously predict forecast demand and load management reliability: time-of-day, temperature, consumer type, and historical load. The ANN model optimized by NSGA-II offers improved forecasting, resulting in the best fitness value of 855.176 kWh, and the resulting high correlation coefficient R = 0.97432 for the load forecasting. Meanwhile, the model also maintained a high level of load management reliability as present an best Fitness 86.7012 % and a correlation R = 0.93381. Pareto front analysis demonstrated a trade-off solution between forecast accuracy (855.928–855.934 kWh) and reliability (84.043% to 84.086%) and therefore it is flexible in advising grid operator. This NSGA-II-ANN hybrid approach has wide range of applications for real-time load prediction, and better resource allocation and control for increasing smart grid stability in dynamic operation condition.

DOI: https://doi.org/10.5281/zenodo.20808126

Smart Grids with Renewable Energy Uncertainty Management for Hybrid Generative AI–Enhanced Load Forecasting Model

Authors: Nilesh.P.Dabe, Yogesh R. Patni, Deepak Kadam, Kulkarni Kirti S

Abstract: Accurate electricity load forecasting is critical for maintaining stability, reliability, and cost efficiency in modern smart grids, especially with the growing integration of renewable energy sources. However, the inherent intermittency and uncertainty of renewables such as solar and wind introduce significant challenges for traditional forecasting models. This paper proposes a Hybrid Generative AI–Enhanced Load Forecasting Model that combines Generative Adversarial Networks (GANs) with deep learning architectures to improve prediction accuracy under varying renewable energy conditions. The generative component synthesizes high-variance energy patterns that capture extreme fluctuations, while the predictive module leverages a hybrid CNN–LSTM network for temporal–spatial learning. Experimental results on real-world datasets demonstrate substantial improvements, with reductions of 40.1% in MAE, 38.2% in RMSE, and enhanced robustness against high-uncertainty renewable inputs. The proposed model also reduces load–supply mismatch by 42.4% and energy imbalance cost by 41.3%, leading to more efficient power distribution and operational cost savings. These findings highlight the potential of Hybrid Generative AI to significantly enhance smart grid forecasting performance and support resilient, data-driven energy management strategies.

DOI: https://doi.org/10.5281/zenodo.20808188

An Eco-Smart Approach: Pervious Concrete Blocks with Partial Replacement by Plastic Aggregates

Authors: Assistant Professor Shekhar P Kale, Assistant Professor Vishal K Paithankar

Abstract: The paper addresses the dual environmental challenges of urban waterlogging and the accumulation of non-biodegradable plastic waste 1. This study investigates the feasibility of developing sustainable pervious concrete by partially replacing natural coarse aggregates with waste plastic aggregates at varying levels of 5%, 10%, 15%, and 20%. Experimental specimens, cast as 150 mm x 150 mm x 150 mm cubes using 10 mm aggregates and a water-cement ratio of 0.35, were subjected to rigorous testing for compressive strength, permeability, and workability after 14 days of curing. The results indicate that while increasing the plastic content leads to a reduction in compressive strength and a slight decrease in permeability due to modifications to the void structure, a replacement level of up to 10% offers an optimum balance, maintaining sufficient structural integrity for light-load applications. Ultimately, this research demonstrates that integrating plastic waste into pervious concrete not only aids in groundwater recharge by effectively reducing surface runoff but also provides a viable waste management solution for sustainable infrastructure development.

DOI: https://doi.org/10.5281/zenodo.20808288

Experimental Analysis of Minimization of Trap Efficiency of Dam Using Different Techniques: A Review

Authors: Assistant Professor Shekhar P Kale, Assistant Professor Vishal K Paithankar

Abstract: Trapping of sediments in rivers is done by various methods as is is tedious job; But still many researches have shown different techniques. By using artificial obstacles for collection of trap we can minimize transfer and deposition of trap in our reservoir. Along with obstacles some river training works found to be useful for collection and deposition of trap at particular location so that it will not get transferred close to the dam site. This research suggests the experiment analysis of trap collection in the river channel prior to dam site. Perennial rivers in which there is no chance to collect or remove the trap in dry period. It is quite possible for seasonal rivers therefore collection of trap in wet season and removal of it in dry season is quite possible in most of the states of India. Reservoir sedimentation has become one of the major problems facing water resources development projects in many countries around the world. However, only a limited number of studies has been reported in this field, particularly addressing the trap efficiency of reservoirs. The most important practical and critical problem related to the performance of reservoirs is the estimation of storage capacity loss due to sedimentation process.. A small-scaled laboratory model was set-up in representing a reservoir and a series of tests were conducted by varying inflow rate, inflow sediment concentration, reservoir capacity and outflow rate. The experimental results were compared with the available theories

DOI: https://doi.org/10.5281/zenodo.20808352

AI-Based Grammatical Error Correction System for Native Language

Authors: Gaurav Kankuse, Jay Deshmukh, Om Borse, N. D. Dhamale

Abstract: This project focuses on building a smart, easy- to-use Grammatical Error Correction (GEC) system for a native Indic language, specifically Marathi. The system leverages modern transformer-based AI models, such as IndicBERT and mBART, which are fine-tuned using local language data. The primary objective of the system is to identify and correct grammatical errors in sentences in real time. The proposed solution includes a simple web-based tool where users can input text, view suggested corrections along with brief explanations, and choose which changes to accept or reject. The study outlines the system design to automated text analysis. Preliminary observations indicate the feasibility of the proposed approach, with future work focusing on extensive experimental validation.

DOI: https://doi.org/10.5281/zenodo.20808637

Kumbh Connect: AI-Powered Solutions for Kumbh Mela

Authors: Tejas Rajendra Moule, Kunal Sanjay Patekar, Abhay Ramesh Mishra, Assistant Professor Ganesh Keshav Gaikwad

Abstract: The Kumbh Mela, one of the largest human congregations on Earth, presents significant challenges in crowd management, health response, and transportation logistics. Traditional management systems[1] often rely on manual surveillance and limited communication mechanisms, which are insufficient for real-time risk detection and decision-making. Kumbh Connect proposes a comprehensive AI-powered framework that integrates computer vision, predictive analytics, Internet of Things (IoT) sensors, and natural language processing to ensure safety, efficiency, and improved pilgrim experience. This paper surveys current research and technologies relevant to large-scale event management, identifies key challenges, and outlines potential directions for future development toward a more intelligent, connected, and secure Kumbh Mela environment.

DOI: https://doi.org/10.5281/zenodo.20808940

OmniLiftBot – An Autonomous Mecanum -Wheeled Robot For Smart Load Transport, Elevation, And Real-Time Weight Monitoring

Authors: Ms. Vaishnavi Kishor Patil, Ms. Rutuja Rajkumar Waghmare, Ms. Priyanka Ubhad, Mr. Ramgopal Sahu

Abstract: Material handling plays a crucial role in industrial automation, warehouse management, and logistics operations. Conventional transportation methods based on manual carts and trolleys require significant human effort, resulting in reduced operational efficiency, increased labor dependency, and limitations in confined working environments. To address these challenges, this paper presents OmniLiftBot, an autonomous Mecanum-wheeled robotic platform designed for smart load transportation, vertical load handling, and real-time payload monitoring. The proposed system is built around an ESP32 microcontroller and integrates Mecanum wheels for omnidirectional mobility, a scissor-lift mechanism for controlled elevation of loads, a load cell with HX711 module for weight measurement, and an ultrasonic sensor for obstacle detection. A Wi-Fi-based interface enables path selection and system control, allowing flexible operation in indoor environments. The combination of mobility, lifting, sensing, and monitoring functionalities within a single platform enhances the versatility of the system while reducing manual intervention. Experimental evaluation of the developed prototype demonstrates reliable navigation, effective lifting operation, accurate payload monitoring, and safe obstacle detection under controlled conditions. The proposed solution offers a compact, cost-effective, and scalable approach for modern material handling applications and can serve as a foundation for future intelligent warehouse automation systems.

Smart Border Surveillance System Using Audio & Visualai Sensors

Authors: Saurav khambe, Suchipriya Malge, Harshit Mishra, Ayushi Chinde, Sakshi Jadhav

Abstract: This paper presents an edge-based smart border surveillance system integrating multi-modal sensing with lightweight deep learning for real-time intrusion detection. The system combines a Raspberry Pi 5, PIR motion sensor, KY-037 acoustic sensor, and NoIR camera in an event-driven architecture. Motion or abnormal sound triggers visual analysis using a TensorFlow Lite–optimized YOLOv5 model deployed for on-device inference. Experimental evaluation across 10 controlled scenarios under daytime and low-light conditions achieved an overall detection accuracy of 80%, with precision and recall of 0.89 for human and vehicle detection. The measured end-to-end latency ranged from 1.6–1.9 s. Average CPU utilization during inference was 55–60%, with peak usage of 72%, and total power consumption measured 6–8 W during active operation. The decision-level sensor fusion approach reduced unnecessary visual processing and minimized false activations compared to continuous vision-based monitoring. The system operates entirely at the edge without cloud dependency, enabling low-latency and bandwidth-efficient deployment in remote border environments.

DOI: https://doi.org/10.5281/zenodo.20809718

Human Activity Recognition Using OpenCV

Authors: Swami Bhagwat, Shreyash Vidhate, Darshan Shinde, Professor G. K. Gaikwad

Abstract: Human Activity Recognition (HAR) focuses on automatically identifying human actions from video streams or sensor data using computer vision and machine learning techniques. With the rapid growth of intelligent healthcare, surveillance, and smart automation systems, HAR has become an important research area. This paper presents a redesigned and implementation-oriented study of a HAR system built using OpenCV and modern learning models. The work explains the complete pipeline including video acquisition, preprocessing, feature extraction, and activity classification. Instead of relying only on theoretical descriptions, the paper emphasizes a practical modular architecture and real-time considerations. The role of deep learning models combined with OpenCV preprocessing is discussed along with system challenges such as lighting varia- tion, occlusion, and computational cost. The proposed approach highlights how lightweight processing and hybrid models can support accurate and efficient recognition suitable for real-world deployment.

DOI: https://doi.org/10.5281/zenodo.20809909

Financial Literacy and Investment Decision Making Among Young Adults

Authors: Associate Professor Dr. Baby. M. S

Abstract: This study explores the causal connection between financial literacy (FL) and investment decision quality among urban Indian young adults aged 18 to 30 years. This mixed-methods study uses a structured survey sample of 450 participants and an experimental investment decision scenario involving 120 individuals to assess the influence of budgeting skills, risk assessment knowledge, compound interest awareness, and digital financial skills on decision making. We find a significant positive correlation (r = 0.72, p < 0.001) between FL score and prudence, defined in terms of diversification of portfolio, risk-return tradeoff, and non-speculative nature of investment decisions. Only 34% of individuals could understand compound interest, and 58% failed to define mutual funds. Comparing investment decision quality in four FL intervention groups, we find that gamified simulation was significantly more effective than traditional lecture-based instruction in improving decision quality by 41%.

DOI: https://doi.org/10.5281/zenodo.20811288

AURA: An LLM-Driven Voice Interface For Intelligent Desktop Automation And Human–Computer Interaction

Authors: Mayuresh More, Piyush Punchmukhe , Shantanu Wagh , Rajashree kumbhar

Abstract: Recent developments in conversational Artificial Intelligence have ushered in a new era in the field of natural and easy man–computer interaction. Traditional desktop interfaces are sometimes cumbersome to navigate and operate using the keyboard, potentially making them less accessible and less efficient to use. This paper introduces AURA, an intelligent voice-driven desktop assistant that combines the capabilities of speech recognition, intent understanding using the large language model (LLM), desktop automation, and adaptive voice feedback into a single system. The system uses wake word detection, speech recognition, context-based intent understanding and automatic command execution to allow for hands-free interaction with the desktop. AURA can be used to manage applications, navigate websites, manipulate text, retrieve information and provide conversational assistance using natural language commands. The proposed architecture is realized in Python, with the voice processing and intent analysis modules, command execution, and user interaction management being modularized. Experimental assessment shows that the system interprets the commands accurately, responds quickly to the user and is more user-friendly than traditional command-based systems. The results demonstrate the promise of voice assistants that are powered by LLM for intuitive and inclusive computing experiences. Key Words: Large Language Models, Voice Assistants, Desktop Automation, Human–Computer Interaction, Speech Recognition, Conversational AI, Accessibility.

Green Artificial Intelligence for Energy-Efficient Computing Systems

Authors: Assistant Professor Mr Devendra Kumar Pandey, Assistant Professor Dr. Swarna Surekha

Abstract: However, the growing problem of the size of deep learning models has brought the issue of energy use, in which a single large transformer model can produce over 500 metric tons of CO₂ equivalent when training. We, in this work, propose the first green awareness framework, named GreenAI-Framework, that alters the precision level of a given model, making it sparse, and scheduling its computations on low-carbon energy sources using the carbon intensity signal. There are three proposed algorithms in our proposed framework, and these are as follows: (1) Adaptive Precision Scaling (APS) with the use of reinforcement learning to decrease the number of FLOPS between 40% to 60% with no accuracy cost, (2) Energy Aware Early Exiting (EAEE) to exit from low confidence inference requests, and (3) Carbon-Aware Task Scheduling (CATS) for executing non-urgent tasks in low-carbon energy slots. Experimental analysis demonstrates that our framework helps reduce energy use by 47.3%, having only 0.9% loss in accuracy for ResNet-50, BERT, and GPT-2 on GPU clusters.

DOI: https://doi.org/10.5281/zenodo.20811568

Hybrid AI Frameworks for Stock Market Prediction and Portfolio Optimization

Authors: Research Scholar Namrata Ramrao Pawar, Dr. Ganesh R. Teltumbade

Abstract: Precise stock prediction and efficient portfolio optimization are still problematic in practice because of the non-stationarity, volatility, and complexity of the financial time series. In this study, we present a Hybrid Artificial Intelligence Framework (HAIF), which is the combination of three different frameworks: (1) a Graph Neural Network (GNN) with an attention layer for modelling relationships between stock prices; (2) a Transformer with convolutional layers for predicting price movements in different periods ahead; and (3) a Deep Reinforcement Learning (DRL) model called Proximal Policy Optimization for managing transactions and balancing the portfolio in different conditions. Based on 5 years of daily S&P 500 time series from 2020 to 2025 with 50 constituent stocks, our model obtains Sharpe ratio = 1.84, annual return = 28.4%, and maximum drawdown = -11.2% while outperforming

DOI: https://doi.org/10.5281/zenodo.20811838

Comparitive Analysis of Earthquake Design of Steel Structure Static Vs Dynamic Anaysis

Authors: Vikash, Mrs Sheela Malik, Mr Atul Dubey

Abstract: Earthquakes are one of the most devastating forces on the planet. The seismic waves that travel through the ground can demolish buildings, kill people, and cost billions of dollars in damage and restoration. According to the National Earthquake Information Centre, there are over 20,000 earthquakes every year on average, including 16 major disasters. The damage was caused by the collapse of buildings with people inside, as in previous earthquakes, prompting the development of earthquake-resistant constructions. Constructions intended to withstand earthquakes are known as earthquake-resistant structures. While no structure can be completely safe from earthquake damage, earthquake-resistant construction aims to build structures that perform better than their conventional equivalents during seismic activity. Building rules state that earthquake-resistant constructions must be able to withstand the greatest earthquake with a reasonable chance of occurring at their site. In this paper we are marking comparison between Static and dynamic analysis. It is concluded from paper Dynamic analysis is economical as compared to static analysis.

DOI: http://doi.org/10.5281/zenodo.20817202

Design And Development Of A Smart Society Maintenance Management System

Authors: Priyanka Gonnade, Santosh Selokar

Abstract: The Smart Society Management System is a comprehensive web-based application designed to address the growing needs and complexities of modern residential society management. Developed using Python and the Flask framework, the system seeks to overcome the limitations of traditional, manual processes that often result in inefficiencies, miscommunication, and lack of transparency in daily society operations.The principal aim of the project is to automate and streamline critical tasks such as maintenance billing and tracking, complaint registration and resolution, user management, and administrative communication. The application features robust, role-based modules: an Admin Module that empowers society managers to securely log in, generate and assign monthly maintenance bills, view payment statuses, manage user data, and publish important notices; and a User Module that enables residents to access their maintenance dues, view payment histories, download receipts, submit complaints, and receive instant updates from the administration. A key innovation is the real-time tracking dashboard, providing both admins and residents with up-to-the-minute information on payment status and outstanding balances. The integration of WhatsApp API for automated message delivery further enhances communication by allowing the administration to send payment receipts and critical notifications directly to residents’ phones, minimizing information gaps and manual efforts. Security is prioritized through a custom login system with initial credentials managed by the admin and a password reset feature, ensuring data privacy and personal control for users.Initial testing with actual residents has yielded positive feedback, highlighting the application’s user-friendly interface and practical utility. The use of an open-source SQLite3 database paired with cloud hosting delivers secure, reliable, and scalable storage of all transactions and user activities. By digitizing the entire workflow, the system eliminates paperwork, reduces errors, and supports transparency and accountability.

DOI: http://doi.org/10.5281/zenodo.20818177

Raspberry Pi Based Android App Controlled Digital Display Notice Board

Authors: Shukracharya S. Gore, Apeksha R. Malunjkar, Gauri S. Lonare, Manasi R. Mahajan

Abstract: Digital notice board systems have emerged as an efficient alternative to traditional paper-based information dissemination methods. By enabling real-time message updates, such systems improve communication efficiency in educational institution, offices and public spaces. This project presents the design and implementation of a Raspberry Pi based Android application controlled digital display notice board that allows wireless transmission of notices using Bluetooth communication. The proposed system integrates an Android application with voice-to-text functionality, enabling users to input messages through speech, which are then converted into text and transmitted to the Raspberry Pi. Despite the advantages of digital notice boards, challenges such as reliable wireless communication, real-time data processing, system scalability and secure access control must be addressed. Bluetooth-based communication, while cost-effective and suitable for offline operation, is limited by range and device pairing constraints. Additionally, handling dynamic message updates and ensuring smooth system performance require efficient software design and robust hardware integration. The Raspberry Pi zero 2W saves as the central processing unit, leveraging Python-based scripts to manage incoming data and display operations through an HDMI-connects screen. Despite these challenges, addressing them through improved modelling techniques, automated data collection, and interdisciplinary collaboration can significantly enhance the effectiveness of software testing and support more informed decision-making.

DOI: http://doi.org/10.5281/zenodo.20825638

Dielectric Characteristics Influenced By Moisture Of Various Soil Textures At X-band Microwave Frequencies

Authors: Rajendra S. Dhake, Vinod S. Khairnar, Rajkumar D. Rajkuvar, Sanika Hingalkar

Abstract: The dielectric response of soils is influenced by multiple factors including electromagnetic frequency, volumetric moisture content, soil composition, internal geometry of components and electrochemical interactions. These effects were characterized using the infinite sample technique to determine the real (ε/) and imaginary (ε//) parameters of the complex permittivity (ε*) of soils with different moisture contents. Measurements were conducted using an X-band microwave test bench operating at 9.44 GHz in the TE10 mode with a crystal detector and a slotted section. This configuration enables precise determination of dielectric parameters for bulk soil specimens. The observed behavior of ε/ and ε// shows an initially modest rise with increasing moisture followed by a pronounced increase at higher water contents, reflecting the strong influence of water on dielectric properties at microwave frequencies. Derived quantities such as a.c. electrical conductivity and dielectric relaxation times were also extracted from the permittivity data. The results demonstrate significant alterations in the electrical properties between dry and moist soils, with notable dependence on soil texture. These observations aid in interpreting ground-penetrating radar signatures and in the calibration of both active and passive microwave remote sensing systems.

DOI: http://doi.org/10.5281/zenodo.20825704

Design and Implementation of Real-Time Obstacle Detection and Automatic Braking for Collision Prevention

Authors: Gowda Yashvi Manjunath, Ranjitha H, Impana P.S, Assistant Professor Chaithra K

Abstract: Road safety is a very important problem in today’s transportation systems. Rear-end collisions are among the most frequent and dangerous accidents on highways and city streets. The paper proposes a MATLAB based simulation of an intelligent obstacle detection and autonomous lane changing system for collision avoidance. We consider a scenario where an ego vehicle drives on a two-lane road and continuously observes a slower vehicle in front and a fast-approaching vehicle in the adjacent lane. The system features front obstacle detection, blind spot monitoring, adaptive speed control, and smooth lane-change execution based on a rule-based decision-making algorithm. When the vehicle in front enters the pre-set detection range of 45 m, the system checks the side lane for safety before beginning an overtaking maneuver. In case of a detected speed-risk vehicle in the blind spot zone, the lane change is delayed, and a warning alert is issued. Simulation results show successful collision avoidance, safe gap maintenance, and smooth overtaking operations, validating the effectiveness of the system as a simplified Advanced Driver Assistance System (ADAS) prototype. Index Terms — Collision prevention, lane change, blind spot detection, ADAS, V2V communication, obstacle detection, MATLAB simulation.

Mathematical Optimization of Personalized Alternative Medicine Interventions for Holistic Healthcare

Authors: Assistant Professor Dilip Badrinarayan Soni, Dr. Hariom Singh Tomar

Abstract: Personalized alternative medicine holds substantial promise for holistic healthcare; however, systematic optimization of multi-herb, multi-target interventions is still an open problem in terms of computational difficulties associated with combinatorics, nonlinearity, and individual differences. This paper develops a comprehensive mathematical optimization approach to personalized alternative medicine interventions by combining three approaches: evolutionary algorithms to optimize prescription of herbs, reinforcement learning to adapt the therapy, and Bayesian multidimensional hierarchical models to characterize patients’ responses to the medication. The effectiveness of the proposed optimization framework is validated through experimental analysis utilizing clinical records from traditional Chinese medicine (n=5,216). It is found that the optimized prescriptions with the use of evolutionary algorithms result in 28.5% higher effectiveness than the conventional methods (95% CI: 18.7-37.3%).

DOI: https://doi.org/10.5281/zenodo.20828820

Fuzzy Logic and Mathematical Decision-Making Models for Integrative Alternative Healthcare Systems

Authors: Assistant Professor Dilip Badrinarayan Soni, Dr. Hariom Singh Tomar

Abstract: An inevitable issue with integrative alternative healthcare systems (IAHS) is that although clinical reasoning is fuzzy, qualitative, and dependent on the practitioner’s expertise, current evidence-based requirements mandate a precise and quantifiable approach. This paper proposes a new framework for decision making within IAHS using fuzzy logic by considering the fuzzy nature of Ayurvedic doshas, TCM meridians, and constitutional types within naturopathy. In this regard, we propose a hierarchical structure of fuzzy inference systems (FISs) containing 27 rules that map qualitative input statements (“Vata moderately high,” “Qi slightly weak”) to recommendations of therapy along with an MCDM process using the Fuzzy TOPSIS algorithm for treatment prioritization. Applied to clinical information from 180 patients suffering from metabolic syndrome, our system yields a consensus with an expert panel of 86.4% (κ=0.82), while decreasing variability in prescription by 58% in comparison with unassisted practitioners.

DOI: https://doi.org/10.5281/zenodo.20829144

Explainable AI For Financial Decision Systems: Improving Transparency And Trust In AI-Driven Finance

Authors: Krishna Prisad Bajgai, Dr. Bhojraj Ghimire, Niraj Kumar Shah, Netra Prasad Joshi

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) technologies are increasingly applied in financial institutions for credit scoring, fraud detection, algorithmic trading, and risk management. Although these techniques offer high predictive performance, many models operate as complex “black-box” systems whose decision-making processes are difficult to interpret. This lack of transparency creates challenges related to trust, fairness, and regulatory compliance. Explainable Artificial Intelligence (XAI) aims to provide transparency and interpretability to AI-based models by offering explanations for their predictions. This paper explores the role of explainable AI in financial decision systems, focusing on its applications in credit risk assessment, fraud detection, and financial forecasting. The study reviews existing explainability techniques such as SHAP, LIME, and interpretable models, and proposes a conceptual framework for integrating explainable AI into financial decision-making systems. The findings highlight that integrating explainability mechanisms improves trust, transparency, and regulatory compliance while maintaining model performance. The paper concludes with future research directions for developing trustworthy AI-driven financial systems.

DOI: http://doi.org/10.5281/zenodo.20837802

Application Of Bradford’s Law To Artificial Intelligence Research In Indian Medical Healthcare

Authors: Dr. Praveen B. Hulloli, Dr.Venugopal Jalihal

Abstract: This research focuses on determining the applicability of Bradford’s Law of Scattering within the corpus of Indian medical research on Artificial Intelligence (AI) in healthcare. The study analyzing 9,774 papers that amassed 123,043 citations, the data was retrieved from the Web of Science citation database covering the period from 2005 to 2024. The study examined annual research output trends, researcher communication preferences, and the productivity of journals. The results show that year 2023 recorded the highest volume of publications, totaling 2104 (21.53%. The year 2021 demonstrated the peak research influence, as measured by total citations 26356 (21.42%) and an h-index of 69. Furthermore, journal impact was not solely linked to publication volume. Despite publishing fewer papers than volume leaders, the highly specialized “Expert Systems with Applications” demonstrated superior quality, achieving the highest Average Citations per Paper (ACPP) of 36.03. The Bradford’s analysis strongly confirmed the law’s applicability, with the core zone consisting of 1.90% of journals contributing 33.22% of the literature. This clear stratification, supported by a negligible negative error (0.49%), confirms the concentration of key AI medical literature in a small core of highly productive journals.

DOI: http://doi.org/10.5281/zenodo.20837913

Container Housing as Green Building Concept for Ews Housing Scheme in Context of India (Literature Review)

Authors: Atul Dubey, Vikash, Ashish Juneja

Abstract: The paper is a study on the use of ISO shipping containers to build sustainable Urban Economical weaker section housing society for Slum living in Metropolitan cities in India. As per world bank report nearly 35.2 % of urban population of India living in Slums (2018). In Japan, China and Vietnam the use of container housing solved their housing problems. Shigeru Ban , a house hold name in architecture in Japan has designed high end homes, biennials and museums by using containers. Room heating is a primary concern of using container as housing material because steel boxes are good heat conductors. But by using passive or active cooling technology we provide insulation. Also it leaves a low carbon foot print too. As per right to shelter in U. P. Avas Vikas Parishad v. friends coop. Housing society limited, the right to shelter has been fundamental right to residence secured under article 19(1)e and the right to life guaranteed under article 21.The state has to provide facilities and opportunities to build houses to standardize life of poor people. Container housing is a new trend of green construction technology, also it is made up of a heavy, good engineering property steel which is corrosion free and has adequate life to use as shelter. Average cost of shipping container houses are ranging from 5-15 lakh INR. India uses about 4 to 5 % of total shipping containers used in the world. This paper covers the literature review, methodology adopted for research, challenges and opportunity to use container as housing material.

DOI: http://doi.org/10.5281/zenodo.20838604

The Explainable AI Paradox: When Transparency Improves Decision Quality And When It Creates Overconfidence

Authors: Dr. Harsha Sammangi, Poloju Pravalika

Abstract: Explainable artificial intelligence (XAI) is widely promoted as a remedy for algorithmic opacity, premised on the assumption that revealing a model’s reasoning improves human oversight and decision quality. This study investigates the Explainable AI Paradox: the possibility that explanations simultaneously increase trust, reliance, and adoption while also producing overconfidence in flawed models — improving decision quality when models are valid but amplifying errors when models are not. Using a controlled experiment with 921 managers making pricing, lending, or inventory decisions assisted by AI systems of deliberately varied validity, participants were randomly assigned to one of five explanation conditions: no explanation, feature-importance, counterfactual, uncertainty-aware, or a combined feature-importance-plus-uncertainty condition. The study measured decision accuracy, confidence calibration, reliance behavior, override justification quality, and simulated financial outcomes. Results show that feature-importance explanations increased reliance (+9.8 percentage points, p < .001) and confidence (+0.61 scale points, p < .001) relative to no explanation, but produced the largest overconfidence increase (+0.084, p < .001) and the only negative financial outcome effect (–0.14 SD, p < .01) among all explanation types — concentrated specifically in the flawed-model conditions, where a significant Explanation × Model-Quality interaction (β = 0.047 to 0.089 across outcomes, all p < .001) confirms that feature-importance explanations’ benefits accrue under valid models while their overconfidence costs accrue under flawed models. Uncertainty-aware explanations, by contrast, improved calibration (–0.058, p < .001), reduced overconfidence (–0.047, p < .001), and produced the only significant positive financial outcome (+0.29 SD, p < .001) relative to no explanation. A twelve-stage design intervention pilot demonstrates that combining five calibration-oriented design principles — feature reliability tagging, confidence-first ordering, disagreement prompts, active verification nudges, and explanation-accuracy feedback — reduces the Overconfidence Index by 83% (from 0.084 to 0.014, p < .001) relative to unmanaged feature-importance explanations. Thematic analysis of 40 participant interviews identifies six mechanisms underlying these patterns, including a ‘plausibility heuristic substitution’ through which surface-level explanation coherence substitutes for independent verification. The paper contributes a theory of the Explainable AI Paradox to behavioral information systems research, identifies model-quality and explanation-type interactions as the central moderating mechanism, and provides a five-level maturity roadmap and design decision framework for deploying explainable, uncertainty-aware managerial AI systems.

DOI: http://doi.org/10.5281/zenodo.20843984

AI-Driven Green Computing For Energy-Efficient Data Centers: An Intelligent And Sustainable Framework

Authors: Sonali Vidhate, Fuldeore Pritee, Jagtap Vaishnavi, Khairnar Vishakha, Aruba Kudai, Safa Madoo

Abstract: The rapid expansion of cloud computing, artificial intelligence (AI), and data-intensive applications has significantly increased the energy consumption of data centers, making sustainability a critical concern. Conventional energy optimization techniques such as virtualization, Dynamic Voltage and Frequency Scaling (DVFS), and static cooling mechanisms provide limited adaptability to modern, dynamic workloads. This research paper presents a comprehensive analysis of AI-driven green computing approaches for improving energy efficiency in data centers. Using insights from existing literature, this work proposes an intelligent framework that integrates machine learning, reinforcement learning, and predictive analytics to optimize workload distribution, cooling systems, and energy demand forecasting in real time. The proposed approach aims to reduce energy consumption, minimize carbon emissions, and improve Power Usage Effectiveness (PUE) while maintaining system performance. Additionally, novel innovations such as carbon-aware scheduling and renewable-energy-aware AI optimization are discussed to enhance sustainability. The findings indicate that AI-based energy management can achieve significant energy savings and support the development of future-ready green data centers.

DOI: http://doi.org/10.5281/zenodo.20849513

NLP Chatbot For Patient Triage: A Hybrid Transformer-Based Conversational Framework For Ethical And Safe Healthcare Assistance

Authors: Vishal Rathod, B. Rohith Patel, Deepika Borgoankar

Abstract: The increasing strain on healthcare systems across the globe has made patient triage an essential procedure to guarantee prompt and efficient medical care.Conventional man- ual triage techniques are constrained by concerns with scale, subjectivity, and human availability.Intelligent, automated sys- tems that can help with early patient triage have been made possible by recent developments in conversational AI and Natu- ral Language Processing (NLP).The development of NLP-based healthcare chatbots for patient triage is covered in this review paper, with a focus on ethical design, safety, and technological robustness.These systems may be able to comprehend natural symptom descriptions, offer non-diagnostic therapy recommen- dations, and improve access to healthcare by combining frame- works like Rasa with transformer-based language models (BERT, DistilBERT).The article analyzes previous research, discusses current research trends and limits, and investigates future options for implementing conversational triage systems that are safe, intelligible, and context-aware.

DOI: http://doi.org/10.5281/zenodo.20849583

Advances In Fractional-Order Modeling: A Review Of Applications In Medicine, Epidemiology And System Optimization

Authors: Ms. Sneha Dattatray Pekhale, Dr. Jatin Majithia

Abstract: Traditional integer-order differential equations are increasingly recognized for their limitations in capturing the non-local, memory-dependent and hereditary properties inherent in complex biological and physical systems. This review provides a comprehensive synthesis of recent research into the application of fractional-order derivatives—including the standard Caputo, Caputo-Fabrizio, Atangana-Baleanu and generalized ψ-Caputo operators—across diverse scientific domains. In epidemiology, these models have proven superior to classical approaches for analyzing the transmission dynamics of diseases such as Tuberculosis, COVID-19 and Dengue fever with some models achieving a 28.6% reduction in predictive error by accounting for specific population behaviors and environmental factors. In oncology, fractional modeling has refined the simulation of radiotherapy and chemotherapy by integrating vital radiobiological factors like cell repair and repopulation, leading to more precise treatment protocols. Beyond medicine, the sources demonstrate the utility of fractional calculus in modeling ecological food chain interactions, world population growth and USA GDP rates as well as optimizing multi-agent systems and gradient descent algorithms. By employing rigorous qualitative analyses (e.g.fixed-point theory) and advanced numerical schemes (e.g.Adams-Bash forth-Moulton method), these studies establish that fractional-order derivatives provide a more flexible and realistic framework for capturing the complexities of real-world phenomena. This review underscores the transformative potential of fractional calculus in enhancing predictive accuracy for public health management and socio-economic forecasting.

DOI: http://doi.org/10.5281/zenodo.20854700

A Hybrid Deep Learning And Machine Learning Framework For Enhanced Brain Tumor Detection In MRI Using MobileNetV3 Features

Authors: Mr.Sachin .S.Bhosale, Dr. Anand Singh Rajawat, Dr.P.R.Bhaldare

Abstract: This study demonstrate the hybrid framework model that combine Machine Learning (ML) and Deep Learning (DL) techniques for the detection of brain tumor on MRI scan dataset. We employ MobileNetV3 for deep feature extraction via transfer learning, followed by classification using Logistic Regression (LR), SVM, Random Forest, KNN, and XGBoost. Experimental results demonstrate that Logistic Regression paired with MobileNet features achieved superior performance (Accuracy: 95.02%, Precision: 94.78%, Recall: 94.53%, F1-score: 94.58%), outperforming more complex classifiers. This indicates that MobileNet-derived features create a nearly linearly separable representation, positioning LR as an efficient and effective tool for automated, accurate brain tumor diagnosis, thereby augmenting clinical decision-making.

DOI: http://doi.org/10.5281/zenodo.20855379

Thermal Stress And Power Quality Impacts During Transformer Energization

Authors: Sanjay B. Amrutkar, Dr. Dolly Thankanchan

Abstract: Transformer energization is commonly accompanied by severe inrush currents that may lead to protection maloperation, thermal stress, and power quality degradation. This paper presents a comprehensive comparative investigation of transformer inrush current mitigation using voltage ramping and closed-loop flux linkage control strategies. A nonlinear transformer model incorporating magnetic saturation and core losses is developed to evaluate peak inrush current, inrush ratio, thermal stress expressed through the i^2 t index, and control effort under multiple energization conditions. Simulation results demonstrate that the uncompensated case exhibits a peak inrush current of 83.37 A, corresponding to an inrush ratio of 16.47 and significant thermal stress. Voltage ramping effectively limits the peak inrush current to 7.15 A, achieving an inrush ratio of 1.41 and reducing the i^2 t energy by approximately 97%. The flux control strategy, while requiring higher injected voltage and control energy, maintains inrush currents below 16.3 A under ideal conditions and demonstrates strong robustness against residual flux and unfavorable switching angles, with peak inrush currents of 14.69 A and 7.94 A, respectively. Total harmonic distortion values approach 100% for all cases due to the non-periodic and transient nature of inrush current, indicating that THD is not a reliable metric during transformer energization. The results highlight the trade-off between mitigation effectiveness and control effort, and confirm the superior robustness of flux-based control under practical energization uncertainties.

DOI: http://doi.org/10.5281/zenodo.20855803

Comparative Analysis Of Advanced Bridge Inspection Practices And Bridge Management Systems: Insights From India, The United States, And China

Authors: Abhijit Madhavrao Thakare, Chaitanya Mishra, Abhijit M. Thakare

Abstract: Bridges are essential components of transportation infrastructure, requiring effective inspection and management to ensure safety and durability. This study presents a comparative analysis of bridge inspection practices and Bridge Management Systems (BMS) in India, the United States, and China. It evaluates key aspects such as inspection methodologies, rating systems, inspector qualifications, and the use of advanced technologies. The results indicate that the United States follows a standardized and data-driven approach, India adopts a centralized and evolving system, and China demonstrates a technology-driven framework with real-time monitoring and predictive maintenance. The study highlights the growing role of advanced tools such as Structural Health Monitoring and non-destructive testing in improving inspection efficiency. It concludes that adopting modern technologies and standardized practices can significantly enhance bridge management systems.

DOI: http://doi.org/10.5281/zenodo.20855894

Ultra High Performance Concrete’s Mechanical Prperties At Elevated Temperature: A Review

Authors: Vishal Paithankar, Shekhar Kale

Abstract: Ultra High Performance Concrete (UHPC) is known for its exceptional mechanical strength and durability, yet its effectiveness is notably compromised when subjected to high temperatures. This study investigates the temperature- dependent mechanical properties of UHPC, with emphasis on residual compressive and tensile strengths. The influence of critical parameters such as steel fiber content, polypropylene fiber dosage, water-to-binder ratio, and supplementary cementitious materials is systematically analyzed. Experimental findings from existing literature indicate that increasing temperature leads to strength degradation due to microcracking, matrix densification loss, and fiber–matrix debonding. The inclusion of polypropylene fibers is found to mitigate explosive spalling by enhancing vapor pressure release. Furthermore, artificial neural network models are explored to predict residual mechanical properties of UHPC under thermal exposure. The outcomes contribute to a better understanding of UHPC behavior in fire-prone structural applications.

DOI: http://doi.org/10.5281/zenodo.20855989

EmotionSync: A Real-Time Emotion-Aware Conversational AI Companion With Photorealistic 3D Avatar And Semantic Memory

Authors: Gitesh Patil, Sakshi Mahajan, Shloka Shetty, Samruddhi Nevse, Dr. Arati R. Deshpande

Abstract: Conversational AI companions operating in emo-tionally sensitive and therapeutic contexts require the joint integration of speech understanding, affective reasoning, and photorealistic visual feedback — capabilities that existing systems address only in isolation, and largely through cloud-dependent infrastructure that introduces recurring costs and privacy con-cerns. Although recent advances in large language models and neural speech synthesis have improved the quality of automated dialogue, current systems lack the structural coupling between emotion recognition, semantic memory, and avatar-driven fa-cial expressiveness necessary for naturalistic human-computer interaction. This paper presents EmotionSync, a locally-hosted conversational AI companion capable of performing end-to-end affective interaction while maintaining real-time responsiveness. The proposed system integrates faster-Whisper-based speech-to-text transcription, Wav2Vec2 speech emotion recognition, retrieval-augmented generation over a ChromaDB vector store, locally-served LLaMA 3.1 language model inference, Microsoft Edge neural text-to-speech synthesis, and NVIDIA Audio2Face 3D blendshape-driven avatar animation within a unified Web-Socket streaming pipeline. By enforcing phrase-boundary audio chunking and performance.now()-anchored blendshape dispatch, the framework ensures frame-accurate lip synchronization and emotionally coherent response generation. The proposed frame-work contributes toward practical, privacy-preserving affective AI companions suitable for therapeutic, educational, and social interaction applications.

32-Bit Vedic Alu with Low Power Mode

Authors: Sushma P S Assistant Professor, Chiranthan M Y, Jayanth K M, D P Rajashekar, Suresh B

Abstract: Power consumption and computational speed are important factors in modern digital systems. This project presents a 32-Bit Vedic ALU with Low Power Mode using System Verilog. The design employs the Urdhva Tiryakbhyam algorithm for fast multiplication and incorporates operand isolation and clock gating techniques to reduce power consumption. The ALU performs arithmetic, logical, and shift operations efficiently while maintaining high performance. The proposed system provides a high-speed, reliable, and power-efficient solution for embedded systems and processor applications.

Product Line Profitability & Margin Performance Analysis For Nassau Candy Distributor

Authors: Tosif Raza Mansoori

Abstract: In the modern business environment, organizations generate large volumes of transactional data that contain valuable information regarding profitability, operational efficiency, product performance, and market behavior. However, extracting meaningful insights from raw datasets remains a significant challenge. Business Intelligence (BI) and Data Analytics techniques provide effective solutions by transforming data into actionable information that supports strategic decision-making. This research presents a comprehensive Product Line Profitability and Margin Performance Analysis Dashboard developed for Nassau Candy Distributor. The primary objective of this project is to evaluate the profitability of product lines, analyze revenue distribution, identify high-performing products, assess regional performance, and provide business recommendations through data visualization. The dashboard was developed using Python and Streamlit, while Pandas, NumPy, Matplotlib, and Seaborn were utilized for data preprocessing, statistical analysis, and visualization. Several analytical techniques including Key Performance Indicator (KPI) evaluation, profitability analysis, division-wise performance assessment, revenue analysis, and Pareto Analysis were implemented to uncover business insights. The developed dashboard enables stakeholders to monitor revenue, profit, cost, margin percentage, and product performance through interactive visualizations. The findings demonstrate that a limited number of products contribute significantly to overall profitability, confirming the applicability of the Pareto Principle in business analytics. The proposed solution provides a scalable and user-friendly analytical framework that assists management in making informed business decisions related to pricing, inventory planning, product portfolio optimization, and strategic growth initiatives.

Adaptive Commerce Intelligence Framework For RealTime Product Value Forecasting Using Hybrid Predictive Learning Models

Authors: Balla Revathi, Dhavala Shilpa, Yerrapatruni Jagadeesh Kumar

Abstract: Accurate product pricing has become a critical requirement for modern e-commerce platforms due to rapidly changing market conditions, customer preferences, competitor strategies, and fluctuating product demand. Traditional pricing methods often rely on static rules and historical analysis, making them ineffective in responding to real-time market dynamics. To address these challenges, this project proposes an intelligent framework called Adaptive Commerce Intelligence Framework for Real-Time Product Value Forecasting Using Hybrid Predictive Learning Models, which integrates machine learning techniques with business intelligence analytics to support intelligent pricing decisions and real-time product value forecasting.The proposed system collects and analyzes various pricing-related parameters, including product base cost, competitor pricing, sales volume, stock availability, customer ratings, reviews, and market trends. Individual machine learning algorithms such as Linear Regression, Random Forest, Support Vector Machine (SVM), and XGBoost are initially trained and evaluated independently to assess their forecasting capabilities. These models are then combined into a Hybrid Predictive Learning Model that leverages the strengths of each algorithm to improve prediction accuracy, forecasting stability, and pricing adaptability.Random Forest and XGBoost effectively identify complex market patterns and pricing trends, while SVM captures non-linear relationships among pricing factors. Linear Regression contributes to understanding pricing dependencies and improving model consistency. The framework also incorporates real-time analytics, competitor monitoring, historical prediction tracking, interactive dashboards, and MySQL-based data management to enhance business intelligence and decision-making capabilities.Experimental analysis demonstrates that the proposed hybrid framework provides more accurate and reliable pricing forecasts compared to standalone machine learning approaches. By integrating predictive learning with adaptive commerce analytics, the system enables dynamic pricing optimization, improves market responsiveness, supports revenue growth, and enhances competitiveness in modern digital commerce environments.

DOI: http://doi.org/

Disaster Vision: An Intelligent Neural-XGBoost Architecture For Predictive Disaster Analytics

Authors: Pilla Rushitha, Puppala Pradeep, Yerrapatruni Jagadeesh Kumar

Abstract: Natural disasters such as floods, earthquakes, cyclones, droughts, landslides, and wildfires continue to pose significant threats to human life, infrastructure, and environmental sustainability. The growing complexity of climate patterns and environmental changes has increased the need for intelligent disaster prediction systems capable of providing accurate and timely forecasts. This project presents a Neural-XGBoost Hybrid Framework for Disaster Prediction and Management that integrates deep learning-based feature extraction with the robust classification capability of Extreme Gradient Boosting (XGBoost). The proposed approach utilizes disaster-related environmental and meteorological data, including rainfall, temperature, humidity, wind speed, and atmospheric conditions, to identify potential disaster events. Data preprocessing techniques such as cleaning, normalization, and feature selection are employed to enhance data quality and model performance. The neural network component automatically learns complex patterns and hidden relationships within the dataset, while XGBoost performs efficient multi-class disaster classification. Experimental evaluation demonstrates that the hybrid framework achieves superior prediction accuracy, improved generalization capability, and reduced overfitting when compared with conventional machine learning approaches. The system supports disaster preparedness, risk assessment, resource planning, and early warning mechanisms, enabling authorities to make informed decisions and minimize disaster-related losses. The proposed framework offers a scalable, reliable, and data-driven solution for modern disaster management applications.

DOI: http://doi.org/

Vehicle Theft Protection

Authors: Mrs. Vidyashree B.P Assistant Professor, Manjunath A J, Pradeep Nagavath, Shreyank S D, Poorna Chandra Thejaswi M D

Abstract: Vehicle theft remains a significant concern worldwide, especially in urban areas where vehicle density is high and traditional security systems are often insufficient. This project presents a cost-effective and intelligent Vehicle Theft Protection System that enhances vehicle security through biometric authentication and real-time user intervention using GSM communication. The core of the system is built around the Arduino UNO microcontroller, interfaced with a fingerprint sensor module (R305S), a GSM module (SIM800L), and a relay module to control the ignition system. Authorized users register their fingerprints in the system memory. Upon an unauthorized access attempt, the system sends an SMS alert to the vehicle owner, who can remotely allow or deny engine start. The proposed system provides a high-speed, reliable, and cost-effective solution for automotive embedded security applications.

A Hybrid Deep Learning Framework For Multi-Class Image Recognition Using Smart Vision Fusion Architecture

Authors: Simhachalam Patnana, S.Sudeer Kumar, Y. Jagadesh Kumar

Abstract: Automatic image recognition has become a fundamental component of modern intelligent systems, finding applications in areas such as food recognition, healthcare imaging, smart surveillance, object detection, and visual analytics. However, traditional image classification techniques often face challenges due to image noise, class imbalance, varying lighting conditions, complex backgrounds, and diverse visual patterns, which reduce classification accuracy and prediction reliability. To address these challenges, this project proposes a Smart Vision Fusion Architecture for Multi-Class Image Recognition (SVFA-MCIR), an intelligent hybrid framework that combines deep learning and machine learning techniques for efficient multi-class image classification.The proposed framework incorporates image preprocessing, enhancement, augmentation, and feature optimization techniques to improve dataset quality and model performance. Existing image recognition models such as CNN, EfficientNet + XGBoost, and DenseNet + XGBoost are initially evaluated to analyze their classification capabilities. To further enhance recognition accuracy and classification stability, the proposed system integrates ResNet50 and XGBoost into a unified hybrid architecture. ResNet50 is utilized to extract high-level visual features and complex image representations, while XGBoost performs optimized multi-class classification using the extracted deep feature vectors.Experimental results demonstrate that the proposed SVFA-MCIR framework achieves superior performance in terms of recognition accuracy, prediction robustness, feature learning capability, and computational efficiency when compared with existing approaches. The framework provides a scalable, adaptive, and intelligent solution for modern image recognition applications and contributes to the advancement of smart vision systems through accurate and reliable multi-class image classification.

DOI: http://doi.org/

ClimateXAI: An Explainable Hybrid Deep Learning Framework For Climate Trend Analysis And Extreme Weather Prediction

Authors: Bala Sundara Rao Kimmoju, Y.Jagadeesh Kumar, P. Pradeep

Abstract: Climate change has significantly increased the occurrence of extreme weather events such as floods, cyclones, droughts, heatwaves, and heavy rainfall, creating a strong need for accurate and reliable forecasting systems. Traditional climate prediction methods often fail to effectively capture the complex spatial and temporal relationships present in large-scale climate data and generally lack interpretability. This project proposes an Explainable Hybrid Deep Learning Framework for Climate Trend Analysis and Extreme Weather Prediction that integrates Convolutional Neural Networks (CNN) for spatial feature extraction, Long Short-Term Memory (LSTM) networks for temporal sequence learning, and an Attention Mechanism for identifying important climatic features. To enhance transparency and trustworthiness, Explainable Artificial Intelligence (XAI) techniques such as SHAP and Grad-CAM are incorporated into the framework. The system utilizes climate parameters including temperature, humidity, rainfall, wind speed, atmospheric pressure, cloud cover, and satellite imagery collected from multiple sources. Data preprocessing techniques such as normalization, missing value handling, and feature engineering are applied to improve data quality and model performance. The hybrid CNN-LSTM architecture effectively learns spatiotemporal climate patterns, enabling accurate climate trend analysis and extreme weather forecasting. Experimental results demonstrate improved prediction accuracy, reduced false alarm rates, and better interpretability compared to traditional forecasting approaches. The proposed framework supports real-time climate monitoring and provides reliable, transparent, and efficient forecasting solutions for disaster management, agriculture, environmental monitoring, and public safety applications.

DOI: http://doi.org/

Rfid Based Door Lock System

Authors: Sushma P S Assistant Professor, Srujan H S, Vybhav Gowda S, Sumukh Kashyap S, Ajay R Shetty

Abstract: Security and access control are important requirements in modern homes, offices, and institutions. This project presents an RFID and Fingerprint-Based Door Lock System using Arduino Uno. The design employs RFID technology and biometric fingerprint authentication to provide dual-layer security against unauthorized access. The system verifies both the RFID tag and fingerprint before activating a servo motor to unlock the door. An LCD display and buzzer provide real-time status messages and alerts during operation. The proposed system provides a secure, reliable, and user-friendly solution for access control applications in residential, commercial, and institutional environments.

5G NR Link Simulation for UAVs with Beamforming Design for Drone-to-Base Station Link

Authors: Associate Professor Dr.Revanesh M, Punyashree B s, Punyashree T, Srujana H P, Yogitha A

Abstract: The rapid growth of Unmanned Aerial Vehicles (UAVs) in applications such as surveillance, delivery, public safety, and remote sensing demands highly reliable, low-latency wireless communication. Fifth-generation (5G) New Radio (NR) technology, with its support for Massive MIMO, millimeter-wave bands, and intelligent beamforming, offers a promising framework for enabling robust, high-throughput aerial connectivity. 5G Toolbox. The study includes modeling UAV mobility profiles, implementing an A2G channel model with Doppler effects, and designing an adaptive beamforming strategy to track the UAV in real time. Key performance metrics such as Signal-to-Noise Ratio (SNR), Reference Signal Received Power (RSRP), Bit Error Rate (BER), and throughput are evaluated under varying mobility and altitude conditions. The results demonstrate how beamforming significantly improves link stability and signal strength in high-mobility UAV communication scenarios.

Greenwashing Intelligence Systems: Detecting ESG Narrative-Performance Gaps With Multimodal AI

Authors: Rakesh Dondapati

Abstract: Corporate environmental, social, and governance (ESG) disclosures increasingly rely on persuasive sustainability narratives, yet investors, regulators, and civil society organizations often lack scalable tools to distinguish genuine environmental performance from rhetorical positioning. This study develops and validates a Greenwashing Intelligence System (GIS) that integrates six data modalities — ESG narrative text, verified emissions data, satellite and remote-sensing indicators, controversy and incident records, financial disclosures, and supply-chain risk signals — to construct two independent indices: a Narrative Ambition Score (NAS), derived from transformer-based analysis of sustainability disclosure text, and a Performance Index (PI), derived from verified and independently observable environmental performance data. The difference between these indices, the Greenwashing Gap Score (GGS = NAS – PI), is computed for a global panel of 4,642 public firms across five regions and six sectors over a 2019–2026 observation period. Firms are classified into four quadrants: Aligned Leaders (high NAS, high PI, 23.5% of sample), Greenwashing Risk (high NAS, low PI, 16.0%), Quiet Achievers (low NAS, high PI, 13.3%), and Disengaged (low NAS, low PI, 31.7%). Regression results show that GGS significantly predicts negative cumulative abnormal returns around disclosure events (β = –0.041, p < .001), elevated 24-month litigation risk (β = 0.0021, p < .001), and negative media sentiment shifts (β = –0.0089, p < .001), with these relationships substantially amplified when satellite-reported divergence (SRD) is high (GGS × SRD interaction significant across all outcomes, p < .001) — indicating that externally verifiable narrative-performance gaps carry the largest market and reputational consequences. Sector analysis reveals the largest gaps in Energy and Materials sectors, particularly for Scope 3 emissions claims. A validation study comparing GIS classifications against a 180-member expert panel shows substantial agreement (Cohen’s κ = 0.65–0.78 across classification dimensions). A two-year disclosure-change pilot demonstrates that sharing GIS reports with firms reduces subsequent GGS, with the largest reductions (–9.7 points) among Greenwashing Risk firms receiving publicly benchmarked reports. The paper contributes the GIS architecture, the NAS/PI/GGS measurement framework, and a five-level ESG assurance maturity roadmap to ESG analytics, accounting information systems, and AI governance research, demonstrating that multimodal AI can operationalize sustainability assurance at scale.

DOI: http://doi.org/10.5281/zenodo.20920915

Current Practice in Cost Estimating and Cost Control in Tendering and Bidding Process in Highway Construction

Authors: Gawai Santosh Bhaskar, Shashikant B. Dhobale

Abstract: The process of developing a comprehensive project cost estimate is critical for a project to be adjudged successful on completion. Projects’ costing is one of the most critical and most widely used project management tools. The complex nature of Projects and the inherent uncertainty of the financial performance of construction projects, development funding, and the monitoring and controlling of costs and schedules make exact budget needs impossible to forecast accurately. This same characteristic also makes projects to deviate from plans. The main object of this paper is to identify the factors affecting the accuracy of project cost estimation, determine the various methods of carrying out project cost estimation in construction projects within INDIA. The study is motivated by the inability of most construction professionals to arrive at a tentative and reliable project cost estimate in project realization which has created obvious problems of project cost overrun and subsequent abandonment. The study sampled the opinion of fifty-three selected project professionals who had worked on related construction outfits in INDIA. An objective realization instrument developed using eighteen (18) factors identified in the literature as possible factors affecting the accuracy of project cost estimation were ranked based on a Likert four-point scale. The score of respondents to the factors were analyzed using descriptive and inferential statistics, mean score value and factor analytical approach as the major tool.

Oversharing Culture: A Study on How Social Media Habit Increase Vulnerability

Authors: Anuradha Muttamwar, Devashri Ghotekar, Damini Mishra

Abstract: In the contemporary digital landscape, social media has become an integral part of daily communication for billions of users worldwide. While these platforms facilitate connectivity and self-expression, the growing trend of oversharing personal information has created unprecedented cybersecurity and privacy risks. Users increasingly disclose sensitive information such as location details, financial data, personal relationships, and health conditions, often without fully comprehending the potential consequences. This research presents a comprehensive study on oversharing culture, examining how habitual social media usage patterns intensify individual vulnerability to identity theft, social engineering attacks, data breaches, and psychological manipulation. The study integrates behavioral analysis, cybersecurity assessment frameworks, and vulnerability evaluation metrics to understand the mechanisms driving oversharing behavior and its security implications. Through survey-based analysis and comparative study of social media platforms, we examine the psychological motivations behind excessive self-disclosure, including the role of social validation through likes and comments, platform design strategies, and individual personality traits. The research demonstrates that approximately 93% of users who overshare personal information face significant privacy and security risks, making vulnerability assessment and user education critical priorities. The proposed framework employs data analysis techniques, behavioral pattern recognition, and machine learning algorithms to identify vulnerability indicators and predict susceptibility to cyber threats. The visualization layer presents findings through interactive dashboards and heat maps, enabling users and security professionals to understand oversharing risks and implement protective measures. Our findings indicate that comprehensive awareness programs, behavioral intervention strategies, and platform-level privacy controls can significantly reduce vulnerability when combined with individual digital literacy initiatives.

DOI: https://doi.org/10.5281/zenodo.20923714

A Comprehensive Study on Artificial Intelligence Techniques for Sustainable Precision Agriculture

Authors: Mayuri Dongre, Harsh Upase, Krushnakant Shinde

Abstract: Artificial Intelligence (AI) has emerged as a transformative technology in modern agriculture, enabling sustainable and data-driven farming practices through precision agriculture techniques. This research paper presents a comprehensive study of AI-based technologies and their applications in sustainable precision agriculture. The study explores the integration of Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), computer vision, robotics, drones, and sensor-based systems for improving agricultural productivity, resource optimization, and environmental sustainability. AI techniques are increasingly used for crop prediction, disease detection, soil analysis, irrigation management, yield forecasting, weed identification, and climate monitoring, helping farmers make accurate and timely decisions. The paper also highlights how precision agriculture minimizes the excessive use of water, fertilizers, and pesticides while enhancing crop quality and reducing environmental impact. Furthermore, the study examines recent advancements, real-world applications, challenges, and limitations. AI adoption in agriculture, including high implementation costs, lack of technical knowledge, data availability issues, and infrastructure constraints in rural areas. Precision agriculture harnesses data-driven techniques to optimize crop production, resource use, and sustainability. However, low-income countries like Bangladesh face a short- age of localized, high-quality datasets that reflect regional agroclimatic conditions and cropping practices.

DOI: https://doi.org/10.5281/zenodo.20923886

Design and Implementation of a Distributed Scalable Web System for Intelligent Skin Disease Diagnosis Using Node.js Framework

Authors: Anuradha Muttamwar, Bhumika Balpande, Shantanu Gawai

Abstract: Skin diseases are a major health concern worldwide, but getting an appointment with a dermatologist can be tough, especially in rural areas. That’s why we’ve created a web-based system that uses artificial intelligence to help diagnose skin conditions. Our system is built using the Node.js framework and combines a powerful image classification model with a user-friendly website. Here’s how it works: users upload pictures of their skin through a simple interface, and our system uses a special kind of neural network called a Convolutional Neural Network (CNN) to analyze the image and make a prediction. We’ve trained our model using a technique called transfer learning, which allows it to learn from existing knowledge and apply it to new situations. Our model can accurately diagnose five common skin conditions: eczema, acne, psoriasis, dermatophytosis, and benign nevi. We’ve designed our system to be fast and efficient, even when lots of people are using it at the same time. Our tests show that it can handle up to 100 users simultaneously without slowing down, and it can give results in under a second. We’re excited about the potential of our system to provide a low-cost, accessible way for people to get a preliminary diagnosis and take the first step towards getting treatment. Our system is made up of three main parts: a website that users interact with, a backend server that handles the image analysis, and a database that stores all the information.

DOI: https://doi.org/10.5281/zenodo.20924037

Enhancing Fake News Detection through Optimized Feature Engineering and Supervised Machine Learning

Authors: Anuradha Muttamwar, Esha Dorkhande, Vaibhavi Meshram

Abstract: The exponential proliferation of digital media in the modern era has created an environment where mis- and disinformation as well as “fake news” can spread uncontrollably, leading to challenges to public discourse, political trust and integrity. In this paper we present a detailed research approach toward fake news detection through efficient feature engineering and the use of supervised machine learning. We use a dataset composed of 5,000 current news articles (2,537 real, 2,463 fake news) and conduct an in-depth research regarding the performance of TF-IDF with n-grams. We build and train a Multinomial Naive Bayes model and attain excellent classification accuracy. Furthermore, we investigate the importance of text preprocessing such as stop word removal, stemming and lemmatization. Our model achieves a final accuracy of 93.6%, while also achieving scores for precision, recall and F1 greater than 0.92. When comparing with baseline models, the presented method with enhanced feature engineering shows excellent results. We then developed a web based system with the help of Flask that allows real time fake news detection and confidence. It will establish a reusable, light and scalable pipeline to automate fake news detection in real world applications.

DOI: https://doi.org/10.5281/zenodo.20924573

Beyond the Surface Web: An Analytical Study of Deep Web and Dark Web Threat Ecosystems

Authors: Deepa Barethiya, Himanshu Praveen Dethekar, Bhavesh Tembhurkar

Abstract: The dark web constitutes a stratified, operationally sophisticated cybercrime ecosystem whose threat dynamics are shaped by layered anonymity infrastructure, AI-augmented criminal tooling, and resilient financial obfuscation mechanisms. While existing literature provides valuable but fragmented analysis of individual components, few studies integrate these elements within a unified analytical framework. This paper addresses that gap through a hybrid analytical survey approach, advancing four primary contributions: (1) a six-dimension taxonomic model differentiating surface web, deep web, and dark web environments; (2) a Five-Layer Dark Web Threat Ecosystem Model characterising the functional architecture of criminal infrastructure; (3) a structured capability taxonomy of AI-augmented criminal tools (Dark LLMs); and (4) a proposed Cyber Threat Intelligence (CTI) extraction pipeline for dark web environments. Drawing on peer-reviewed literature spanning 2020–2025, operational intelligence from Europol IOCTA, Chainalysis Crypto Crime Reports, and FBI IC3 data, and documented threat actor behaviour, the paper analyses ransomware-as-a-service dynamics, cryptocurrency financial obfuscation, law enforcement response limitations, and post-Tor architectural evolution. Persistent research gaps in multilingual CTI extraction, post-Tor forensic methodology, and AI-threat detection are identified, with a structured research agenda proposed.

DOI: https://doi.org/10.5281/zenodo.20925387

AI-Based Prediction of Turbofan Engine Life

Authors: Deepa Barethiya, Kshitij Moon, Dipak Meshram

Abstract: Accurate Remaining Useful Life (RUL) prediction for turbofan engines is critical for implementing effective condition-based maintenance strategies, enhancing operational safety, and reducing maintenance costs. Traditional predictive maintenance approaches often struggle with the non-linear, time-dependent characteristics of engine degradation. This paper presents a data-driven prognostic model utilizing a Long Short-Term Memory (LSTM) neural network to predict the RUL of turbofan engines based on sensor-derived operational data. The model is trained and validated on the NASA C-MAPSS dataset, which contains run-to-failure data for multiple turbofan engines. The proposed methodology involves preprocessing raw sensor data, creating sequential inputs using a sliding window approach, and training a two-layer LSTM architecture designed to learn complex temporal degradation patterns. Model performance is evaluated using standard regression metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² score. The resulting model demonstrates robust predictive capabilities and is deployed in a Flask-based web application, offering a practical tool for real-world CBM systems and highlighting the efficacy of deep learning for industrial prognostics.

DOI: https://doi.org/10.5281/zenodo.20925860

A Comparative Study of Performance and Scalability in Java vs. ASP.NET Enterprise Web Application Frameworks

Authors: Assistant Professor Deepa Barethiya, Sakshi Jibhkate, Samiksha Daronde

Abstract: This paper compares Java-based frameworks and ASP.NET Core for web applications used by companies. It looks at how they work and how well they handle a large number of users. The study checks things like how long it takes for the application to respond, how much work it can handle, how much of the computer’s brain it uses and how much memory it needs when a lot of people are using it at the same time. They ran tests to see what would happen if many people used the application. The results show that ASP.NET Core is really good at responding and using resources wisely. Java-based frameworks are good at handling a lot of users and working with computers at the same time. This study tells us what is good and what is not so good about Java-based frameworks and ASP.NET Core. It helps people choose the tools to build big web applications for companies.

DOI: https://doi.org/10.5281/zenodo.20926193

Beyond Accuracy: A Decision-Oriented, Profit-Aware Framework for Crop Recommendation Using Ensemble Learning and Economic Analysis

Authors: Deepa Barethiya, Dhanashri Pannase, Gangasagar Kashyap

Abstract: Ensemble machine learning has pushed crop recommendation accuracy past 99% on standard soil-weather benchmarks — yet this milestone conceals a troubling gap. Systems built around Random Forest, XGBoost, and gradient boosting produce ranked crop labels while leaving the economic viability of each suggestion entirely unexamined. A farmer told “grow rice with 99% confidence” still does not know whether that choice will leave a positive margin after seed, fertiliser, and irrigation costs. This paper proposes a decision-oriented framework that moves beyond the accuracy plateau by coupling a soft-voting ensemble with per-crop yield regressors and a configurable economic layer that estimates expected profit. Where conventional pipelines terminate at a suitability label, the proposed architecture extends the output to a Risk-Adjusted Expected Profit, mathematically formulated as E[Π_c ]_(risk-adjusted)=P_ensemble (c│X) Π_c, where P_ensemble (c│X)is the Ensemble Suitability Probability and Π_c=((Y_c ) ̂(X)×P_(market,c)×1000)-Total Cost_cis the Nominal Net Profit. This coupling mathematically discounts the apparent value of high-risk crops by their probability of soil-weather failure — a correction absent from every reviewed system. To illustrate the theoretical decision dynamics of this framework, we construct a conceptual walkthrough across 200 hypothetical soil-weather scenarios derived from standard agricultural benchmarks. This analysis suggests that the agronomically top-ranked crop and the economically top-ranked crop diverge in roughly 46% of cases — a finding that, if borne out in empirical deployment, would have direct implications for farm-level income planning. A conceptual Streamlit dashboard design is also proposed, embedding real-time what-if sliders and SHAP-based feature attributions to make the system transparent to extension workers and farming cooperatives. The central argument of this paper is simple: a classifier that ignores profit is only half a tool. This framework proposes the other half.

DOI: https://doi.org/10.5281/zenodo.20927114

A 180-kWp Grid-Connected Rooftop PV System For Energy Security In Higher-Education Institutions: Long-Term Performance And Financial Robustness At Shivaji University, Kolhapur

Authors: Amit C. Kamble, Himmat T. Jadhav

Abstract: Energy security and tariff volatility are growing concerns for Indian higher-education institutions (HEIs) due to rising digital infrastructure, cooling loads, and escalating electricity prices. This paper presents a multi-year, bill-validated assessment of a 180.18 kWp grid-connected rooftop PV system in- stalled at Shivaji University, Kolhapur (SUK). Beyond reporting measured performance (average generation ≈283,824 kWh/yr; CUF ≈18%), the study introduces a Performance Stability Index (PSI) and a Tariff Resilience Index (TRI) to quantify interannual energy stability and financial robustness under adverse tariff scenarios. A 25-year discounted-cash-flow model, incorporating real tariff evolution, yields an IRR of 18.4%, NPV of about INR 517 lakh, and payback of ∼5.3 years. Annual CO2 avoidance is estimated at ∼233 tCO2/yr using the CEA grid factor. A benchmarking framework situates the system against Indian HEI PV case studies, and a replication pathway is outlined for campus-scale deployment. The results demonstrate that rooftop PV can significantly enhance HEI energy security while support- ing national solar and NEP-2020 sustainability goals.

DOI: http://doi.org/10.5281/zenodo.20927527

AI in Clinical Decision-Making: Ethical Challenges in Disease-Based Treatment Selection

Authors: Deepa Barethiya, Himani Shirpurkar, Drushti Dharmik

Abstract: Artificial Intelligence (AI) is increasingly integrated into clinical decision-making, particularly in disease-based treatment selection. AI systems promise efficiency, predictive accuracy, and personalized care by analyzing large datasets and recommending tailored therapies. However, these benefits are accompanied by ethical challenges that must be addressed before widespread adoption. Issues of transparency, bias, accountability, privacy, and patient autonomy are consistently reported in recent literature [1][5]. This paper synthesizes findings from 20 peer-reviewed studies published between 2023 and 2026, offering a systematic review of ethical concerns and governance strategies. By combining thematic analysis with case studies in oncology, cardiology, infectious disease, and neurology, we propose a framework for ethically responsible AI deployment in healthcare.

DOI: https://doi.org/10.5281/zenodo.20927818

The Societal Impact of Artificial Intelligence on Job Displacement and Re-Skilling Initiatives

Authors: Deepa Barethiya, Ankita Vairagade, Harshal Kathalkar

Abstract: The role that Artificial Intelligence plays in changing the way people work around the world is really big. Artificial Intelligence makes things more efficient. Creates new jobs but it also makes people worry about losing their jobs and having to be more flexible at work. This paper looks at how Artificial Intelligence’s affecting people’s jobs and it uses surveys and reviews of what other people have written to do this. The results show that there is a difference between how worried people are about losing their jobs and how much they are doing to learn new things because things, like money and time get in the way. Artificial Intelligence is making it really important for people to learn skills it is not just something people can do if they want to it is something people have to do.

DOI: https://doi.org/10.5281/zenodo.20928264

Machine Learning Model for Predicting Heart Disease Risk Using Clinical Data

Authors: Deepa Barethiya, Deepak Vinod Chouksey, Ankur Sanjeev Khurpadi

Abstract: Cardiovascular diseases remain the leading cause of mortality worldwide, accounting for approximately 17.9 million deaths annually according to the World Health Organization. Early detection and accurate risk assessment of heart disease are critical for effective clinical intervention and improved patient outcomes. Traditional diagnostic methods often depend heavily on subjective clinical judgment, which can be inconsistent and time-consuming. This research proposes a Machine Learning-based predictive system that leverages clinical data to assess the risk of heart disease with high accuracy. The proposed system employs multiple classification algorithms including Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost, and evaluates their performance on the UCI Cleveland Heart Disease dataset. Feature selection techniques such as correlation analysis and Recursive Feature Elimination (RFE) are used to identify the most significant clinical predictors. The proposed ensemble model achieves an accuracy of 91.8%, sensitivity of 93.2%, and specificity of 90.4%, outperforming individual classifiers. The results demonstrate that machine learning can serve as a reliable and scalable decision-support tool for cardiologists and general physicians in early heart disease diagnosis.

DOI: https://doi.org/10.5281/zenodo.20929505

Exploring Behavioural Patterns in Transaction Data: A Data-Driven Study

Authors: Mayuri Dongre, Arshiya Sahare, Sarang Dumbhare

Abstract: In the age of digitalisation, a lot of transaction information is produced online, and it is significant to understand customer behaviour and market trends. This paper aims at examining the behavioural patterns in transaction data based on a data-driven approach. The information is gathered using web scraping on Flipkart, primarily in the electronic products categories of mobiles, headphones, smart watches, speakers, accessories with the help of Selenium WebDriver and Python. The obtained data is saved in the CSV format and processed with Python libraries, such as Pandas and NumPy, that allow cleaning data, eliminating duplicates, missing values, and categorizing products. Additional analysis is conducted to establish customer preferences, expenditure trends and product demand trends. The end results are presented in visual representations in the form of dashboards and reports to aid in improved business decision-making. This research assists in interpreting the behaviour of transactions and is useful in the data-driven strategies.

DOI: https://doi.org/10.5281/zenodo.20929867

Multi-Task CNN-Based Pet Listing Engine For Fraud Prevention In Online Pet Adoption Platforms

Authors: Ayush Wankhede, Ajinkya Patil, Partth Thombre, Mohit Patil, Mahesh Korade

Abstract: Online pet adoption platforms face significant chal- lenges with fraudulent listings and attribute misrepresentation, eroding user trust. This paper presents a complete pet adoption system integrating CNN-based image verification to authenti- cate listing attributes before publication. Transfer learning with EfficientNet-B0 is applied to 110,425 images spanning 712 breed classes across dogs, cats, and birds. A two-stage training strategy first trains the classification head with frozen base layers, achiev- ing 84.7% validation accuracy, then fine-tunes the top 40 layers to reach 89.3% validation accuracy. The verification pipeline combines breed confidence, color confidence, and prediction certainty into a normalized trust score (VScore, range 0–100). Server-side scoring with an 85-point threshold prevents client manipulation while achieving 98.0% fraud detection accuracy. A one-way privacy gateway protects adopter identities, and automated digital adoption certificates with unique certification IDs formalize successful adoptions. Experimental validation on 128 verification requests demonstrates an 84.4% acceptance rate, 1.8 s average processing time, and only 1.6% false positive rate.

DOI: http://doi.org/10.5281/zenodo.20930615

Autonomous Threat Detection And Elimination System

Authors: Vidya Deshmukh, Samradnyi Patil, Akshada Veer, Jayada Talharkar, Tahareen begampalli

Abstract: Modern security environments, particularly on the battlefield, demand autonomous systems capable of real-time threat detection and neutralization without relying on human intervention. This paper presents the Autonomous Threat Detec-tion and Elimination System (ATDES), an integrated hardware-software platform designed to detect enemy armored threats — specifically tanks — using a vision-based AI detection pipeline and respond autonomously through a servo-controlled targeting and firing mechanism. The system leverages a Raspberry Pi 3B+ as the central processing unit, integrating a 2- megapixel camera for visual acquisition, an IR transceiver pair for friend-or-foe (IFF) identification, an RF receiver for enemy signal detection, and a servo-mounted firing mechanism for threat neutralization. A lightweight deep learning model is deployed on-device for real-time tank detection from camera frames, achieving sub-50 ms inference latency at a resolution of 480×640 pixels. IR-based IFF communication ensures that allied units are correctly identified and excluded from targeting, minimizing the risk of fratricide. Blynk IoT cloud integration enables remote monitoring and event logging. The system operates off-grid using a battery and solar power combination, enabling continuous 24×7 autonomous surveillance. Simulation results confirm consistent real-time de-tection with high confidence scores, demonstrating the feasibility of deploying edge AI for autonomous military threat response. The proposed system contributes a cost-effective, scalable, and intelligent prototype for next- generation autonomous defense systems.

Secret Chat Room With AI Summarization System

Authors: Jayshree Pansare, Karan Singh, Affan Ali Sayyed, Rushikesh Langhi, Prathamesh Dive

Abstract: The rapid expansion of digital communication platforms has significantly increased the need for secure and efficient messaging systems. Modern users rely heavily on chat-based applications for academic collaboration, professional coordination, and personal communication. However, traditional messaging systems often fail to provide an optimal balance between data security and efficient information management. While some platforms emphasize usability, they frequently compromise on privacy, whereas others focus on encryption but lack intelligent tools to manage large volumes of conversational data. This research presents a Secret Chat Room with AI Summarization System, a web-based platform designed to address both security and usability challenges. The system integrates end-to-end encryption using AES and RSA algorithms to ensure confidentiality and protect messages from unauthorized access. Additionally, it employs WebSocket-based real-time communication to enable low-latency and efficient message exchange between users. A key contribution of this work is the integration of an AI-based summarization module that utilizes transformer-based model Gemini Summarization. This module processes chat histories and generates concise summaries, allowing users to quickly understand lengthy discussions without manually reviewing all messages. This feature significantly reduces information overload and enhances productivity. The system follows a modular architecture consisting of authentication, encryption, messaging, and AI components. Experimental observations indicate that the system achieves efficient performance with minimal latency while maintaining strong security standards. The proposed solution is suitable for applications in education, enterprise communication, and collaborative environments.

DOI: http://doi.org/10.5281/zenodo.20937546

Design And Implementation Of A Smart Healthcare System For Disaster Management And Mitigation System: A Case Study For Lusaka, Zambia, Africa.

Authors: Mwinilombe Joseph Mushabati, Dr. Sampa Nkonde

Abstract: This study investigates the design and implementation of a Smart Healthcare System (SHS) integrated into a Disaster Management and Mitigation System (DMMS), using Lusaka, Zambia as a case study. The increasing frequency of disasters such as cholera outbreaks, floods, and the COVID-19 pandemic have demonstrated the limitations of conventional healthcare systems in responding effectively. The SHS aims to enhance real-time data collection, health monitoring, early warning systems, and coordinated emergency response. Through qualitative methodology, data were collected from healthcare professionals, ICT experts, and disaster management personnel. Findings show that a well-integrated SHS can significantly improve response time, resource allocation, and resilience during disasters. The research contributes to local and continental knowledge on digital health innovations in disaster-prone regions.

DOI: http://doi.org/10.5281/zenodo.20953794

Emotional Resonance in Visual Art: A Blind Comparative Study of AI-Generated and Human-Created Artworks

Authors: Deepa Barethiya, Pratik Gajbhiye, Siddhi Lokhande

Abstract: This paper explores the extent to which artificial intelligence (AI) systems, increasingly capable of creating visual artworks indistinguishable from human-created ones, are part of the broader conversation about creativity and the role of AI in the creative process. Expanding on existing research that considers AI creativity as a whole construct, this paper focuses on a component-based approach to creativity, examining it as a series of discrete components. An empirical analysis is also presented to compare AI-created and human-created artworks with respect to the most important factors traditionally associated with human creativity: emotional depth, intentionality, originality, awareness of context, and experiential authenticity. A quantitative approach was taken using a survey-based methodology, in which a series of artworks were evaluated using a structured Likert-scale survey. The results were analyzed using comparative statistics to determine performance differences between AI-created and human-created artworks across each creativity component. The results show that AI-created artworks exhibit uneven creative performance, with higher visual originality and significant shortcomings in emotional depth and intentionality compared to human-created artworks. These results suggest that creativity is a multidimensional construct and that current AI systems have difficulties in recreating several core components of human creativity. This paper contributes to the existing literature on AI and creativity by providing a structured approach to evaluating AI-created artworks beyond superficial visual aesthetics and highlighting implications for the role of AI as a creative tool versus an autonomous artist.

DOI: https://doi.org/10.5281/zenodo.20954101

Influence of Artificial Intelligence on Problem-Solving Ability and Confidence of Beginner Programmers

Authors: Deepa Barethiya, Prajwal Suklal Bankar

Abstract: Artificial Intelligence (AI), particularly Generative AI (GenAI) tools such as ChatGPT, has significantly influenced programming education by providing instant code generation, debugging support, and conceptual explanations. These tools are increasingly used by beginner programmers to assist in learning and problem-solving tasks. While AI has the potential to enhance learning efficiency and boost learner confidence through immediate feedback, concerns remain regarding its impact on independent thinking and long-term skill development.This study investigates the influence of AI tools on the problem-solving ability and confidence of beginner programmers. The research examines how learners interact with AI-assisted systems, how frequently they rely on generated solutions, and how such usage affects their understanding of programming concepts. Data was collected through a survey-based analysis of beginner programmers using AI-assisted tools. The findings indicate that AI tools can improve problem-solving efficiency and significantly enhance learner confidence by reducing frustration and providing instant support. However, excessive reliance on AI-generated solutions may limit the development of critical thinking and independent problem-solving skills. The study highlights the importance of balanced AI integration in programming education. This research contributes to the growing field of computing education by providing insights into both the benefits and limitations of AI-assisted learning. It also offers recommendations for educators to design effective learning strategies that leverage AI tools while preserving core problem-solving abilities.

DOI: https://doi.org/10.5281/zenodo.20954427

Machine Learning-Based Detection of Obfuscated Malware in Secure Computing Environments

Authors: Deepa Barethiya, Kajal Lanjewar, Damini Mondhe

Abstract: Malware — malicious software — represents one of the most pervasive and rapidly evolving threats in modern cybersecurity. Traditional signature-based detection systems, while effective against known threats, are fundamentally inadequate against polymorphic, metamorphic, and zero-day malware variants. This paper presents a comprehensive study and implementation of a machine-learning-based malware detection framework that overcomes the limitations of conventional approaches. The proposed system employs static analysis (PE header features, API call sequences, n-gram byte patterns), dynamic analysis (system call traces, network behaviors), and hybrid analysis to extract discriminative feature sets. Several supervised classification algorithms — including Random Forest, Support Vector Machine (SVM), Gradient Boosting (XGBoost), and a custom Convolutional Neural Network (CNN) — are evaluated on the EMBER 2018 and VirusShare benchmark datasets. Experimental results demonstrate that the ensemble model achieves a detection accuracy of 98.7%, a false-positive rate below 0.4%, and an average inference time of 12 ms, outperforming state-of-the-art baselines by a significant margin. The paper further discusses real-time deployment considerations, adversarial robustness, and future research directions.

DOI: https://doi.org/10.5281/zenodo.20954815

Supervised Machine Learning for Early DDoS Attack Detection

Authors: Mayuri Dongre, Tanmay Lanjewar, Vedant Chaple

Abstract: With the rapid expansion of internet-based applications, cloud services, and digital communication platforms, cybersecurity threats have become increasingly complex and harmful. Among these threats, Distributed Denial of Service (DDoS) attacks are considered one of the most disruptive network-based attacks because they overwhelm targeted servers or networks with excessive traffic, causing downtime, service interruption, and financial loss. Traditional security mechanisms such as firewalls and rule-based intrusion detection systems often fail to detect evolving DDoS attack patterns in their early stages. This research focuses on applying supervised machine learning techniques for early DDoS attack detection by analyzing network traffic behavior and classifying malicious activities. The proposed system performs data preprocessing, feature extraction, traffic analysis, model training, and attack classification using supervised learning algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, and K-Nearest Neighbors (KNN). The study aims to improve detection accuracy, reduce false alarms, and strengthen real-time cybersecurity monitoring. Results indicate that supervised learning models provide reliable performance in identifying suspicious traffic patterns and can significantly enhance proactive defense mechanisms in network infrastructures.

DOI: https://doi.org/10.5281/zenodo.20955167

Optimizing Recommendation Systems in Social Media: Techniques, Challenges, and Future Directions

Authors: Professor Mayuri Dongre, Aniket Manoj Singh, Deepak Albankar

Abstract: With the exponential growth of user-generated con-tent on social media platforms, recommendation systems have become the primary mechanism for content curation, user engagement, and personalized information delivery. Traditional recommendation approaches, such as collaborative filtering and content-based filtering, increasingly struggle with inherent limi-tations, including data sparsity, cold-start issues, and the highly dynamic, multimodal nature of modern social media networks. This paper provides a comprehensive analysis of contemporary optimization techniques designed to enhance the precision, scala-bility, and diversity of social media recommendation engines. We systematically review the integration of deep learning architec-tures, Graph Neural Networks (GNNs) for structural relationship mapping, and advanced embedding strategies. Furthermore, we investigate critical operational challenges, including algorithmic bias, real-time computational latency, and data privacy regula-tions. Finally, this study outlines pivotal future research direc-tions, highlighting the paradigm shift toward Large Language Model (LLM) integration and autonomous agentic workflows to build next-generation, context-aware, and explainable recommen-dation frameworks.

DOI: https://doi.org/10.5281/zenodo.20955477

Implementation of College Management System Using Salesforce CRM

Authors: Mayuri Dongre, Kalyani Parihar

Abstract: This paper describes the design and implementation of a CMS system using the Salesforce CRM platform. Our goal is to automate current processes for educational institutions with a cloud-based, user-friendly system replacing manual, and paper-based procedures. It has four most important modules: Student Module, Fee Management Module, Teacher/Faculty Module and Admin Module. These modules modernize academic processes, reducing operational costs, minimize data redundancy, and enhancing efficiency. Unlike the traditional data warehouse, where problems arise with storage systems and access to remote data usually takes time as well, this system utilizes Salesforce cloud infrastructure. The appropriate communication and streamlined processes are the key steps to attracting and retaining more students, as well as staying competitive; therefore, an adequate use of a CRM system can support taking advantage of these success factors. The paper proposes a comparative analysis of existing leading CRM systems in the field of higher education, a summarization of the benefits and the need for their deployment.

DOI: https://doi.org/10.5281/zenodo.20955858

Designing an Interactive Learning Management Platform to Strengthen Learner Engagement in Higher Education

Authors: Mayuri Dongre, Hrutuja Meshram, Vidhi Ugale

Abstract: Digital transformation is really changing the way we learn in education. This is why Learning Management Systems are so important now. Even though a lot of schools are using Learning Management Systems they often do not keep students because the content is not interactive and it is not personalized for each student. This paper is about a kind of Learning Management Platform that we call Interactive Learning Management Platform. The Interactive Learning Management Platform uses four ideas to make learning more engaging for students: combining different ways of teaching, making the content fit each student’s needs, using games to make learning fun for students, analysing how students learn. We based our ideas for the Interactive Learning Management Platform on what other researchers have found and, on theories that are well established. We think that our Interactive Learning Management Platform can really help students stay engaged when they are learning online. We talk about how each part of our Interactive Learning Management Platform’s based on research and how all these parts work together to help students. Our goal is to help students behave think and feel in a way that makes them want to learn. At the end we discuss how to make our Interactive Learning Management Platform a reality and what we need to do to test it.

DOI: https://doi.org/10.5281/zenodo.20956129

A Machine Learning Approach for Identification and Analysis of Fraudulent Voice Communication Calls

Authors: Professor Mayuri Dongre, Saurabh Bhoyar, Sanskar Karnewar

Abstract: Fraudulent voice calls have become a prominent cyber threat in the contemporary telecommunication environment as the usage of online banking, UPI transactions, mobile wallets, and instant messaging services becomes widespread. The perpetrators of cybercrime resort to fraudulent activities such as voice calls, phishing attacks, OTP manipulation, lottery scams, insurance scams, loan scams, and identity deception. The consequences include substantial monetary damage and grave security vulnerabilities. Existing techniques for spam detection in phone calls depend upon manual reporting, blacklisting, and basic rules-based filtering algorithms. However, these methods prove ineffective against newly emerging and evolving forms of fraud, particularly when the perpetrator changes their phone number and employs advanced social engineering techniques. Therefore, there is a need to develop an efficient and automated fraud detection system. In this paper, we propose a machine learning-based method to detect and analyze fraudulent phone calls. Using the following indicators for call behavior analysis, duration of a call, frequency of calls, suspicious phrases, time of calls, and voice pattern recognition, our approach is intended to identify and classify every call as either fraudulent or legitimate. For better prediction and detection, such machine learning models as Naive Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM) will be applied.

DOI: https://doi.org/10.5281/zenodo.20956340

ToxiShield: A Next-Generation Intelligent Framework for Toxic Comment Detection Using Machine Learning and Natural Language Processing

Authors: Mr. Appalla Yazna Surya Sai Kiran, Miss. Savarapu Suhasini

Abstract: The rapid growth of social media platforms and online communication has significantly increased the volume of user-generated content, creating new challenges in identifying toxic language, hate speech, cyberbullying, and abusive comments. These harmful interactions negatively affect online communities, user well-being, and digital safety, highlighting the need for intelligent and automated content moderation systems. This paper presents ToxiShield, a next-generation intelligent framework for toxic comment detection that integrates Machine Learning (ML) and Natural Language Processing (NLP) techniques to accurately classify online comments as toxic or non-toxic. The proposed framework employs comprehensive text preprocessing, including tokenization, stop-word removal, text normalization, lemmatization, and feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF) and word embedding techniques to generate meaningful textual representations. To evaluate the effectiveness of the proposed framework, multiple classification algorithms, including Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, and Convolutional Neural Networks (CNN), are implemented and comparatively analysed using performance metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that deep learning-based models, particularly CNN, achieve superior performance in identifying complex contextual toxicity patterns compared with traditional machine learning methods. The proposed ToxiShield framework provides an efficient, scalable, and intelligent solution for automated online content moderation, contributing to safer digital communication environments and promoting respectful interactions across social media platforms and online communities.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.472

Shadow AI And Competitive Advantage: The Hidden Risks Of Unmanaged Enterprise AI Adoption

Authors: Rakesh Dondapati

Abstract: The rapid diffusion of generative AI tools and autonomous agents has generated a pervasive and largely ungoverned organizational phenomenon: shadow AI, whereby employees and teams deploy AI capabilities outside formal information technology governance and procurement processes. While shadow AI may generate local productivity improvements and serve as an incubator for grassroots innovation, it simultaneously exposes organizations to compounding risks across data security, regulatory compliance, intellectual property control, and operational integrity domains. This study investigates the dual character of shadow AI — as both an organizational threat and an innovation catalyst — and examines the conditions under which adaptive governance structures enable firms to convert unauthorized AI experimentation into sanctioned strategic capability. Drawing on a multi-source dataset comprising IT leader survey responses, employee-level AI usage telemetry, security incident reports, patent disclosures, and longitudinal firm performance data from 487 firms across seven industry sectors (2022–2026), the study develops and validates the Shadow AI Prevalence Index (SAPI) and the Governance Adaptiveness Score (GAS). Structural equation models demonstrate that SAPI is positively associated with risk exposure (β = 0.48, p < .001) but that governance adaptiveness significantly moderates this relationship (interaction β = –0.27, p < .001), and independently predicts innovation output (β = 0.41, p < .001) and organizational resilience (β = 0.48, p < .001). Six inductively derived qualitative themes from 48 executive interviews illuminate the mechanisms linking governance adaptiveness to shadow AI outcomes. The study advances a theory of adaptive AI governance, provides the first large-scale empirical examination of the shadow AI prevalence-performance relationship, and delivers a practical Shadow-to-Sanctioned AI conversion framework for enterprise practitioners. Findings indicate that the critical governance imperative is not the elimination of shadow AI — which is both practically infeasible and strategically self-defeating — but its structured transformation from hidden organizational risk into visible competitive capability.

DOI: http://doi.org/10.5281/zenodo.20958484

An Intelligent Machine Learning Framework for Cyber Attack Detection in Secure UAV Communication Networks

Authors: Miss. Kathula Lakshmi, Miss. Savarapu Suhasini

Abstract: The rapid adoption of Unmanned Aerial Vehicles (UAVs) in applications such as surveillance, precision agriculture, disaster response, logistics, and intelligent transportation has significantly increased the demand for secure and reliable communication networks. However, the wireless nature of UAV communication exposes these systems to a wide range of cyber threats, including GPS spoofing, data injection, denial-of-service (DoS), and network intrusion attacks, which can compromise mission integrity and operational safety. To address these security challenges, this paper presents a machine learning-based cyber attack detection framework for UAV communication networks. The proposed framework employs comprehensive data preprocessing, feature engineering, and intelligent classification techniques to analyze UAV telemetry data, communication signals, and operational parameters for identifying malicious activities. Multiple machine learning models are utilized to distinguish normal UAV behavior from cyber attack scenarios through behavioral pattern analysis and anomaly detection. The framework is evaluated using standard performance metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, to assess its detection capability and reliability. Experimental results demonstrate that the proposed framework effectively detects various cyber attacks with high detection accuracy, low false positive rates, and efficient response time. By integrating intelligent machine learning algorithms into UAV cybersecurity, the proposed approach enhances communication security, improves system resilience, and supports the development of reliable and autonomous drone operations in dynamic network environments.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.474

An AI-Driven Fire Detection Framework Using Convolutional Neural Networks for Smart Safety Monitoring

Authors: Mr. Suryaashokkumar Siriki, Miss. Savarapu Suhasini

Abstract: Rapid and accurate fire detection is essential for minimizing human casualties, reducing property damage, and enabling timely emergency response. Conventional fire detection systems primarily depend on smoke, heat, and gas sensors, which often experience delayed response, high false alarm rates, and limited effectiveness in complex or large-scale environments. Recent advances in deep learning and computer vision have enabled intelligent visual monitoring systems capable of identifying fire incidents directly from surveillance imagery. This paper presents a deep learning-based intelligent fire detection and early warning framework that employs Convolutional Neural Networks (CNNs) to automatically classify surveillance images into fire and non-fire categories. The proposed framework utilizes a comprehensive image preprocessing pipeline, including resizing, normalization, and data augmentation techniques such as rotation, scaling, zooming, and horizontal flipping to improve model robustness and generalization. Training optimization strategies, including Early Stopping and ReduceLROnPlateau, are incorporated to enhance learning stability and prevent overfitting. The performance of the proposed CNN model is compared with conventional machine learning algorithms, including Logistic Regression, K-Nearest Neighbors (KNN), and AdaBoost, using evaluation metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC analysis. Experimental results demonstrate that the CNN-based framework achieves superior classification performance by effectively learning complex visual characteristics of flames and smoke while maintaining high detection accuracy and a low false alarm rate. The system further integrates an automated alert mechanism that instantly generates notifications upon fire detection, supporting rapid emergency intervention. The proposed framework provides an intelligent, scalable, and cost-effective solution for real-time fire monitoring and can be effectively deployed in smart buildings, industrial facilities, public infrastructures, and smart city surveillance systems to strengthen fire safety management and disaster prevention.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.475

An Intelligent Hybrid Transfer Learning Framework for Automated Food Image Classification

Authors: Mr. Boddu Pavan Kumar, Miss. Savarapu Suhasini

Abstract: The increasing demand for intelligent dietary assessment and nutrition monitoring has accelerated research in automated food image classification systems. However, accurately identifying food categories remains challenging due to significant variations in appearance, illumination, background complexity, and similarities among visually related food items. Conventional machine learning techniques, which rely on handcrafted feature extraction, often fail to capture the intricate visual characteristics required for robust food recognition. To overcome these limitations, this paper presents a hybrid food image classification framework that integrates transfer learning-based feature extraction with machine learning classifiers. Pre-trained deep learning architectures, including EfficientNet, DenseNet, and MobileNet, are employed to learn rich and discriminative visual representations from food images without requiring extensive model training. The extracted deep features are subsequently processed using advanced machine learning algorithms such as Random Forest and XGBoost to perform accurate food category prediction. This hybrid strategy effectively combines the representational strength of deep neural networks with the computational efficiency and interpretability of classical machine learning methods. Experimental evaluation demonstrates that the proposed framework achieves superior classification accuracy, precision, recall, and F1-score compared with conventional image classification approaches. Furthermore, the model exhibits improved robustness when handling diverse food images captured under varying environmental conditions. The proposed framework has significant potential for practical deployment in applications such as intelligent nutrition monitoring, automated calorie estimation, healthcare support systems, and smart dietary recommendation platforms, contributing to the development of reliable AI-driven food analysis solutions.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.476

Next-Generation Heart Disease Prediction Using Quantum Machine Learning: A Comparative Evaluation

Authors: Mr. Chokkakula Chaitanya, Miss. Savarapu Suhasini

Abstract: Heart disease is one of the leading causes of mortality worldwide, making early and accurate diagnosis essential. This study proposes a next-generation heart disease prediction framework using Quantum Machine Learning (QML) and presents a comparative evaluation with traditional machine learning approaches. A clinical heart disease dataset containing attributes such as age, gender, blood pressure, cholesterol level, and heart rate is pre-processed, balanced, and divided into training and testing sets. Traditional algorithms, including Logistic Regression, Support Vector Machine (SVM), Naïve Bayes, Decision Tree, K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA), are compared with Quantum Machine Learning models for disease prediction. The models are evaluated using 5-fold cross-validation with accuracy, precision, recall, F1-score, and ROC-AUC as performance metrics. Results show that Logistic Regression and Linear Discriminant Analysis achieve the best performance among classical models, while Quantum Machine Learning demonstrates competitive prediction capability with improved feature representation. The proposed framework highlights the potential of QML as a scalable and intelligent solution for next-generation heart disease prediction and clinical decision support.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.477

A Robust Ensemble Learning Framework for Automated Credit Risk Prediction

Authors: Miss. Tatipaka Pooja, Miss. Savarapu Suhasini

Abstract: Accurate credit risk assessment is essential for financial institutions to minimize loan defaults and support effective lending decisions. Conventional loan evaluation processes largely depend on manual analysis of customer financial information, making them time-consuming, inconsistent, and susceptible to human bias. With the rapid advancement of machine learning, intelligent prediction models have emerged as efficient solutions for automating credit risk evaluation and improving decision-making accuracy. This paper presents an intelligent credit risk prediction framework that utilizes machine learning algorithms to classify loan applicants based on their probability of loan repayment or default. The proposed framework analyzes customer financial and demographic attributes, including credit history, checking account status, employment status, loan amount, loan duration, and applicant age. Data preprocessing techniques such as missing value handling, outlier removal, categorical feature encoding, and feature scaling are employed to enhance data quality before model training. Multiple machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), Naïve Bayes, Multi-Layer Perceptron (MLP), and a Stacking Ensemble model, are implemented and comparatively evaluated using performance metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC analysis. Experimental results indicate that the ensemble learning approach consistently outperforms individual classifiers by achieving higher prediction accuracy and improved generalization capability. The proposed framework provides a reliable, scalable, and data-driven solution for intelligent credit risk assessment, enabling financial institutions to improve loan approval decisions, reduce financial losses, and strengthen overall credit risk management.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.478

A Robust Machine Learning Approach for Real-Time Cloud Vulnerability Detection and Threat Mitigation

Authors: Miss. Pemmanaboyina Durga Devi, Miss. Savarapu Suhasini

Abstract: Cloud computing has become the foundation of modern digital services by providing scalable, flexible, and cost-effective computing resources for organizations across various domains. Despite its widespread adoption, the increasing complexity of cloud infrastructures has introduced numerous security challenges, including unauthorized access, insecure configurations, application vulnerabilities, distributed denial-of-service (DDoS) attacks, and abnormal network activities. Conventional cloud security mechanisms primarily rely on rule-based detection techniques, which often struggle to identify sophisticated and previously unknown cyber threats in dynamic cloud environments. To overcome these limitations, this paper proposes an intelligent machine learning-based framework for cloud vulnerability detection and threat prevention. The proposed framework analyzes security-related information collected from system logs, network traffic records, cloud service activities, and vulnerability reports to identify malicious behavior and potential security risks. Comprehensive data preprocessing and feature engineering techniques are employed to improve data quality before training multiple machine learning models, including Decision Tree, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Isolation Forest. The effectiveness of these algorithms is evaluated using performance metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC analysis. Experimental results demonstrate that the Random Forest model achieves superior detection performance by accurately identifying cloud vulnerabilities while maintaining a low false alarm rate. The proposed framework enables real-time threat monitoring, intelligent anomaly detection, and adaptive security analysis, thereby improving the resilience, reliability, and overall protection of distributed cloud infrastructures against evolving cyber threats.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.479

Leadoverse: An AI-Powered Multi-Channel Lead Scoring and Management Platform

Authors: Atharv Nitin Gore, Rushikesh Vijay Kolhe, Samarth Suresh Gaikwad, Professor Snehal Phate

Abstract: This paper proposes an AI-powered lead management platform designed to optimize sales pipeline efficiency through a Hybrid Machine Learning Classifier. The system incorporates multi-channel lead capture, XGBoost-based lead scoring, BERT-driven intent detection, lead deduplication, and CRM synchronization to enable real-time qualification and conversion of leads. With an F1 Score of 87% and AUC-ROC of 93%, it ensures a reliable and data-driven pipeline management experience. The system supports various lead sources including web forms, social media, email, and API integrations, making it highly adaptable for B2B and B2C enterprises. Ethical considerations are addressed through strong privacy safeguards, JWT-based authentication, and GDPR-compliant data management. Additionally, it minimizes manual sales effort, reduces lead response time by 89%, and enhances conversion rates by 79%. This solution establishes a reliable framework for secure, automated, and scalable lead management in digital marketing and sales operations. By leveraging advanced AI techniques such as XGBoost scoring, BERT intent detection, and fuzzy deduplication, the system effectively prioritizes high-value prospects to maximize pipeline conversion.

DOI: https://doi.org/10.5281/zenodo.20962801

Intelligent MRI-Based Brain Tumor Detection and Classification Using Deep Learning Techniques

Authors: Jyoti Gahora, Bhanu Pratap Singh

Abstract: Brain tumors are among the most critical neurological disorders that require early and accurate diagnosis for effective treatment and improved patient survival. Magnetic Resonance Imaging (MRI) is widely used for brain tumor diagnosis because of its superior soft tissue visualization capability. However, manual tumor detection and classification are time-consuming and highly dependent on radiologists’ expertise. To overcome these limitations, this research proposes an intelligent MRI-based brain tumor detection and classification system using deep learning techniques. The proposed framework integrates preprocessing, segmentation, feature extraction, deep learning classification, and performance evaluation into a unified automated system. Initially, MRI images undergo preprocessing steps such as artifact removal, noise reduction, intensity normalization, and bias field correction to improve image quality. Segmentation techniques including thresholding, region growing, and watershed algorithms are then applied to isolate tumor regions from healthy brain tissues. Histogram-based, texture-based, and shape-based features are extracted to improve discriminative learning. The EfficientNetB3 deep learning model is employed for tumor and non-tumor classification due to its efficient feature learning and lightweight architecture. Hyperparameter tuning techniques such as optimized learning rate, batch size, dropout regularization, and data augmentation are used to improve classification performance and reduce overfitting. The proposed model achieves high performance with improved accuracy, precision, recall, and F1-score compared to existing approaches. Experimental results demonstrate that the proposed framework provides accurate and reliable brain tumor detection with enhanced segmentation and classification capability. The system also supports intelligent clinical decision-making and has the potential for future real-time healthcare applications.

Enhancing Cybersecurity Through Machine Learning and Explainable AI-Based Intrusion Detection

Authors: Prakash Gahora, Bhanu Pratap Singh

Abstract: The rapid growth of digital communication, cloud computing, Internet of Things (IoT), and smart infrastructures has significantly increased cybersecurity threats and network vulnerabilities. Traditional intrusion detection systems (IDS) often struggle to detect sophisticated and evolving cyber-attacks due to their dependence on static rule-based mechanisms. To address these limitations, Machine Learning (ML) and Explainable Artificial Intelligence (XAI) have emerged as promising solutions for intelligent and adaptive intrusion detection. This research explores the integration of ML and XAI techniques in intrusion detection systems to improve attack detection accuracy, transparency, and real-time threat response. The study reviews various machine learning approaches, including supervised learning, deep learning, reinforcement learning, and federated learning methods used in modern IDS frameworks. Additionally, the role of explainable AI in enhancing trust, interpretability, and decision-making within cybersecurity systems is examined. The proposed approach emphasizes intelligent threat detection, reduced false alarm rates, and improved adaptability in IoT, industrial, and distributed computing environments. The findings indicate that AI-driven IDS frameworks provide efficient and scalable cybersecurity solutions capable of addressing emerging cyber threats while ensuring transparency and reliability in security operations.

A Review of an Intelligent Deep Learning Framework for Violence Detection and Criminal Activity Identification in Smart Surveillance Systems

Authors: Shivam Namdev, Bhanu Pratap Singh

Abstract: The rapid increase in urbanization, public security challenges, and criminal activities has accelerated the development of intelligent surveillance systems for real-time violence detection and criminal activity identification. Traditional surveillance systems often depend heavily on manual monitoring, which limits detection efficiency, increases response time, and reduces reliability in complex environments. Recent advancements in deep learning, machine learning, computer vision, sensor networks, and predictive analytics have significantly improved automated surveillance capabilities for public safety management. This review presents an intelligent deep learning framework for violence detection and criminal activity identification in smart surveillance systems by analyzing recent developments in convolutional neural networks (CNNs), 3D-CNNs, ConvLSTM architectures, transfer learning, optimization techniques, and sensor-based monitoring systems. The framework integrates video analytics, spatiotemporal feature extraction, facial recognition, object detection, anomaly detection, and predictive threat analysis into a unified intelligent surveillance ecosystem. Furthermore, the study highlights the role of real-time monitoring, smart city technologies, and intelligent decision-support systems in improving public security operations. The review indicates that deep learning-based surveillance frameworks significantly improve violence detection accuracy, reduce false alarms, enhance predictive threat identification, and support automated emergency response systems in modern smart environments.

Random Forest and Personality-Based Skill Analysis

Authors: Rushikesh Falke, Vishal Bagal, Pranav Bartakke

Abstract: Choosing the right career path is a critical decision for students and often requires personalized guidance based on their interests, skills, and abilities. This paper proposes an Intelligent Questionnaire-Based Career Path Recommendation System that utilizes the Random Forest machine learning algorithm to recommend suitable career options. The system collects user responses through a structured questionnaire covering personality traits, technical skills, academic interests, aptitude, and career preferences. The collected data are processed and analyzed using a trained Random Forest model to predict the most appropriate career path. In addition to career recommendations, the system provides guidance on relevant skills and learning resources to enhance career readiness. A web-based interface enables users to complete the assessment and receive recommendations instantly. The proposed approach improves the accuracy and personalization of career guidance compared with traditional counseling methods. The experimental results demonstrate that the system provides reliable recommendations and supports students in making informed career decisions. The proposed framework is scalable and can be extended with real-time job market data and advanced AI techniques in future work.

DOI: https://doi.org/10.5281/zenodo.21031283

Comparative Soil Structure Interaction Performance of Geopolymer and Conventional Foundations under Cyclic and Impact Loading Using Advanced Numerical Modeling

Authors: Kester Nwinuazor Neemana, Victor Dugbor

Abstract: The interaction between soil and structure (SSI) is a key factor in determination of the dynamic response of foundation systems under cyclic and impact loading. However, most of the previous studies concentrated on the conventional concrete foundations and the effects of other sustainable materials are rarely studied under complex loading condition. Further, few research has focused on the interaction between cyclic and impact loading in a nonlinear-SSI model. The aim of this study is to overcome these shortcomings by developing an advanced nonlinear numerical model for comparing the SSI performance of geopolymer and conventional foundations under combined cyclic and impact loading. The model incorporated soil stiffness degradation, damping characteristics of the soil materials and introduces a novel Damage Accumulation Index (DAI) to quantify progressive deterioration. Using MATLAB simulation approach, transient and steady state dynamic responses were captured in time domain analysis. The results shows that geopolymer foundations outperform the conventional foundations in all the important parameters. In particular, the peak displacement was reduced by ~4.69% while the reduction in velocity and acceleration responses was ~7.62% and the stiffness degradation was ~6.54%, respectively. Moreover, geopolymer foundations have energy dissipation capacity of about 7.35% higher. The proposed DAI model also shows that the cumulative damage was reduced by ~27.33%. These results verify a better damping and better stiffness retention capacity and a better resistance to dynamic loading effects of geopolymer foundations. The study confirms that geopolymer foundation offers a promising sustainable alternative for infrastructure subjected to cyclic, impact, and seismic loading conditions.

DOI: https://doi.org/10.5281/zenodo.21032123

Developing Kameshwar Mahadev Temple Into A Regional Tourist Destination: Planning, Infrastructure, And Promotion Strategies

Authors: Ruchi Gandhi

Abstract: Religious tourism is one of the most important and significant sectors of the Indian tourism industry. It plays a major role in contributing to economic growth, employment generation, infrastructure development, cultural preservation, and regional development. Gujarat is the one of the states from India which has rich religious heritage and some of them are known worldwide such as Somnath, Dwarka, Ambaji, Dakor and Palitana. However, some other religious destinations are underdevelopment though they have tourism potential. One such destination is Kameshwar Mahadev Temple, situated on the bank of the Ambika River in Gadat village, Navsari District, Gujarat. The main purpose of this research is to investigate the potential for sustainable development of Kameshwar Mahadev Temple as a regional religious tourism destination. The study evaluates the temple’s historical significance, geographical setting, tourism resources, visitor characteristics, existing infrastructure, environmental attributes, and socio-economic context. Furthermore, it examines opportunities and constraints associated with tourism development through SWOT analysis and sustainable tourism assessment frameworks. The research uses the mixed-method approach which is based on secondary data, demographic analysis, tourism statistics, infrastructure assessment, policy review, and qualitative evaluation. Findings indicate that Kameshwar Mahadev Temple possesses significant strengths including religious importance, strategic accessibility, natural landscapes, cultural heritage, and an established visitor base. Nevertheless, deficiencies in tourism infrastructure, accommodation facilities, sanitation, destination marketing, and community participation continue to constrain its development potential. This study introduces a master plan for tourism that combines better roads and facilities, environmental protection, community involvement, smart marketing, and teamwork among local authorities. The findings show that focusing on sustainable tourism can turn the Kameshwar Mahadev Temple into a major regional pilgrimage site. This development will boost the local economy and create jobs for residents while fully protecting the surrounding natural resources.

DOI: http://doi.org/10.5281/zenodo.21033316

Secure Predictive Analytics in Industrial IoT Using Hybrid Deep Learning

Authors: Mr. Kuldeep, Associate Professor Dr. Pramod Kumar

Abstract: The Industrial Internet of Things (IIoT) has transformed industrial ecosystems by enabling real-time monitoring, automation, and data-driven decision-making. Deep learning techniques have emerged as powerful tools for predictive analytics, supporting applications such as anomaly detection, fault diagnosis, and predictive maintenance. However, centralized deep learning approaches introduce significant security and privacy risks, including data leakage, adversarial attacks, and model poisoning. This research proposes a secure hybrid deep learning framework integrating CNN-LSTM with ANFIS, along with federated learning, blockchain, and differential privacy to ensure secure, privacy-preserving, and explainable predictive analytics in IIoT environments. The framework enhances prediction accuracy while maintaining data confidentiality, robustness, and real-time performance.

DOI: https://doi.org/10.5281/zenodo.21060033

A Multi-Agent Smart Autonomous System for Adaptive Student Profiling in Personalized Learning

Authors: Anjali Kapoor, Dr Mridula

Abstract: In order to facilitate dynamic customization in contemporary learning environments, this study introduces a Smart Autonomous System for Adaptive Student Profiling. To create constantly changing student profiles, the suggested architecture incorporates a variety of educational data sources, such as academic achievement, behavioral patterns, cognitive traits, environmental data, social interactions, and emotional indications. To handle missing data, the system uses KNN-based imputation Convolutional Neural Networks (CNNs) for emotion identification, Principal Component Analysis (PCA) for feature reduction, and XG Boost for academic risk prediction and student profile. A Deep Q-Network (DQN)-based reinforcement learning method that modifies suggestions and interventions based on learner needs enables autonomous decision-making. A hybrid recommendation engine also facilitates optimum learning paths and individualized material distribution. Real-time profiling, ongoing monitoring, proactive intervention, and adaptive feedback mechanisms are made possible by the framework’s implementation within a multi-agent architecture. Learner engagement, academic achievement, and early detection of at-risk kids have all improved, according to experimental evaluation utilizing benchmark educational datasets. The findings show that the suggested approach is reliable, scalable, and successful in facilitating intelligent, customized learning environments.

DOI: https://doi.org/10.5281/zenodo.21063202

Privacy-Aware Medical Image Analysis

Authors: Lavish Kumar, Mohd Aamish, Murad Aalam, Himanshu Kumar Thakur, Ashwani Dubey, Dr. Raj Kumar

Abstract: The use of artificial intelligence (AI) technology for medical image analysis has gained significant importance in contemporary health care systems. AI models assist physicians in diagnosing diseases based on medical images like x-rays, MRIs, CT scans, and ultrasound scans with great precision and fast diagnosis. Nonetheless, medical datasets involve critical and sensitive data about patients, thus raising serious concerns regarding privacy protection in the context of AI applications. The exposure or unauthorized access of medical data can result in severe ethical and legal problems [2], [9], [16]. In this paper, we present a privacy-aware medical image analysis system based on the implementation of convolutional neural networks (CNN), PyTorch framework, Streamlit toolkit, and Differential Privacy technology. CNN is used to extract the features of the medical images automatically and classify the underlying diseases. PyTorch is used for developing the proposed model efficiently, and Streamlit provides a user-friendly interface for physicians. Moreover, the training process is implemented based on differential privacy in order to maintain the privacy of the data. [4], [5]. The proposed framework can support hospitals, diagnostic centers, and telemedicine systems in secure healthcare applications [6], [20].

DOI: http://doi.org/10.5281/zenodo.21063384

Analysis Of Cosmological Constant In The Bianchi Type 1 With Cosmological Model

Authors: Dr R.K.Dubey, Mohd Wahid Mansury

Abstract: This Paper analyzed the effects of the cosmological constantΛ in the context of the Bianchi Type I cosmological model. The Bianchi Type I model represents an anisotropic but spatially flat universe, where expansion rates can differ along three spatial directions.This study analyzes the effects of the spatial directions with respect to axes . The cosmological, plays a significant role in the universe expansion. This work aims to understand how influences the expansion, energy density, and anisotropy of the universe. Einstein’s field equations with variable cosmological constant if considered in the presence of a perfect fluid for a Bianchi type I universe by assuming that the cosmological term is proportional to the square of the Hubble parameter. The variation law for vacuum density was recently proposed by many researches on the basis of the quantum field estimation in a curved expanding background. The cosmological term tends asymptotically to a genuine cosmological constant and the model tends to a de-Sitter universe. More obtained some new results by using a slightly different method from that of other researchers obtained the result that the present universe is accelerating with a large fraction of cosmological density in the form of a cosmological term.

Premenopausal Manifestations of Calcium Deficiency Among Indian Women: A Contemporary Evidence-Based Analysis

Authors: Professor V.Abirami

Abstract: Calcium deficiency is a major but underrecognized nutritional concern among Indian women, particularly during the premenopausal transition. Emerging evidence indicates that bone mineral loss and biochemical deficiencies begin before menopause. This review synthesizes recent literature (2020–2025) on calcium intake, associated symptoms, and risk factors among Indian women aged 35–50 years. Available data suggest that a large proportion of women consume inadequate calcium, often compounded by widespread vitamin D deficiency. Clinical manifestations during premenopause are frequently subtle, including musculoskeletal discomfort, fatigue, and mood disturbances, contributing to underdiagnosis. Early screening and preventive interventions are critical to reduce long-term skeletal morbidity.

An Embedding Governance Ensures Recoverability and Reduces Risks in AI Pipelines

Authors: Associate Professor Dr. Surender Singh

Abstract: Artificial Intelligence (AI) systems have become essential across industries, supporting decision-making, automation, healthcare, finance, and cybersecurity. However, the increasing complexity of AI pipelines introduces significant risks related to data integrity, model drift, security vulnerabilities, regulatory compliance, and operational failures. Embedding governance within AI pipelines provides a structured framework to ensure accountability, transparency, recoverability, and resilience. This paper examines governance mechanisms integrated throughout the AI lifecycle and demonstrates how embedding governance enhances system recovery while minimizing operational, ethical, and security risks. The study proposes a governance-driven AI pipeline architecture incorporating continuous monitoring, version control, audit trails, explainability, and automated rollback mechanisms. The findings indicate that governance significantly improves reliability, trustworthiness, and regulatory compliance while reducing downtime and model-related failures.

DOI: https://doi.org/10.5281/zenodo.21096755

Design and Finite Element Analysis of Lightweight Composite Automotive Body Under Frontal and Rear Impact Conditions

Authors: Kamatam Munna Kiran, Associate Professor Dr. S. Solomon Raj

Abstract: The increasing demand for lightweight, safe, and fuel-efficient vehicles has driven structural optimisation of automotive body frames. A passenger vehicle body shell was designed in SolidWorks and analysed in ANSYS Workbench 2024 R1 under frontal and rear impact at 60, 80, and 100 km/h using five material configurations: ABS, structural steel, Carbon Fiber Reinforced Polymer (CFRP), Glass Fiber Reinforced Polymer (GFRP), and a hybrid CFRP+GFRP laminate. Performance metrics — total deformation, equivalent stress, equivalent strain, and factor of safety (FOS) were extracted for each scenario. Modal analysis extracted the first six natural frequencies and mode shapes. Results show CFRP achieves superior crashworthiness (FOS > 2.40 at all speeds) and highest natural frequencies (86.38–153.62 Hz), while the hybrid composite nearly replicates CFRP performance at reduced cost. ABS is structurally unsuitable and steel approaches failure at 100 km/h. The hybrid CFRP+GFRP laminate is the optimal lightweight alternative to conventional steel for passenger car body shell applications.

DOI: https://doi.org/10.5281/zenodo.21193036

Privacy- Preserving Personalized Pathway Recommendation In Kenya’s Competence-Based Education Using Federated Learning, Cosine Similarity And Random Forest.

Authors: Brian Levi Okimaru, Betty Mayeku, Humphrey Juma kilwake

Abstract: The transition from junior to senior school under Kenya's Competency-Based Education (CBE) requires learners to select academic pathways that align with their competencies and interests. This transition presents a challenge because pathway selection requires personalized guidance while ensuring the privacy of sensitive student information. Existing educational recommender systems predominantly rely on centralized data processing, exposing learner data to privacy risks and limiting the secure exchange of information across institutions. This study proposes a privacy-preserving personalized pathway recommender system that integrates federated learning, cosine similarity, and Random Forest to support academic pathway recommendation without sharing raw student data. Cosine similarity was employed to model learner competency profiles and measure their alignment with predefined pathway requirements. The resulting similarity scores were incorporated into a Random Forest classifier through feature engineering to improve pathway prediction accuracy. A horizontal federated learning framework enabled multiple schools to collaboratively train the recommendation model by exchanging only model updates while retaining student records locally. The proposed model was evaluated using accuracy, precision, recall, and F1-score. Experimental results showed that integrating cosine similarity with Random Forest improved pathway classification performance, while the federated recommender system achieved an accuracy of 86.54%, outperforming the centralized recommender approach while preserving student privacy. The proposed framework provides an effective and privacy-preserving decision-support tool for personalized academic pathway recommendation within Kenya's Competency-Based Education. The study demonstrates that integrating federated learning with content-based filtering and machine learning can simultaneously enhance recommendation accuracy, personalization, and data privacy in educational environments.

DOI: http://doi.org/10.5281/zenodo.21126397

Effectiveness of Combined Inspiratory Muscle Training and Peripheral Progressive Resistance Exercise on Respiratory Function and Functional Capacity in Active Smokers: A Pre-Post Experimental Study

Authors: Professor B. R. Shaalini

Abstract: Background: Chronic cigarette smoking induces systemic pathophysiological changes, leading to respiratory muscle deconditioning, impaired pulmonary ventilation, and peripheral muscle fatigue. While Inspiratory Muscle Training (IMT) targets central ventilatory drive, progressive resistance training addresses systemic deconditioning. Aim: To evaluate the combined effectiveness of Inspiratory Muscle Training and DeLorme progressive resistance exercise on respiratory muscle strength, pulmonary function, and functional capacity in active smokers. Methods: This pre-post experimental study enrolled 30 active young adult smokers (aged 20–40 years; mean smoking history: 5.4 ± 1.8 pack-years). Participants underwent a structured 6-week intervention consisting of targeted IMT using a threshold resistance device (40–60% of Maximal Inspiratory Pressure [MIP], 20 min/day, 5 days/week) and DeLorme progressive resistance exercise focused on the bilateral quadriceps femoris muscle groups (3 sets of 10 repetitions at 50%, 75%, and 100% of 10-Repetition Maximum [10RM], 5 days/week). Outcome measures included MIP, spirometric parameters (FEV1, FVC, MVV), functional capacity via the Six-Minute Walk Test (6MWT), and exertional dyspnea via the Borg CR10 Scale. Pre- and post-intervention data were analyzed using a paired t-test. Results: Following the 6-week training protocol, participants demonstrated statistically significant improvements across all primary and secondary parameters (p < 0.001). MIP increased from 68.4 ± 7.2 cmH2O to 84.6 ± 6.8 cmH2O, and 6MWT distance improved by a mean of 74.2 meters. Exertional dyspnea on the Borg scale decreased significantly from 5.8 ± 1.1 to 3.2 ± 0.9. Conclusion: Integrating IMT with DeLorme progressive peripheral resistance exercise significantly enhances respiratory muscle strength, functional exercise tolerance, and ventilatory efficiency in active smokers. This dual-component approach addresses both central respiratory limitations and peripheral skeletal muscle deconditioning.

Short Term Electricity Price Forecasting Using Hybrid Deep Learning and Feature Selection Techniques

Authors: Manjesh Kumar, Assistant Professor Jaya Shukla, Professor Rajnish Bhasker

Abstract: Short-term electric price prediction is important in deregulated power markets and operations as well as planning processes as it aids in the bidding process, risk management and demand response programs. The growing infiltration of renewable energy sources, as well as switching variability of the loads, and market uncertainties, has brought about high nonlinearity and volatility in the electricity price dynamics, which restrain the applicability of traditional forecasting techniques. In solving such challenges, this paper suggests a hybrid deep learning forecasting structure combined with efficient feature selection mechanism to predict short-term price of electricity. The advanced feature selection methods are used in the proposed approach to determine the most informative market, demand, and generation-related variables and to lower the dimensions, as well as to remove redundant information. A hybrid deep learning model, which is a combination of the positive attributes of sequential and nonlinear learning structures, is subsequently trained exploiting the chosen features to absorb intricate temporal variations and price surges. An evaluation of the model by real-world data of the electricity market and a comparison with the traditional statistical methods and individual machine learning are conducted. The simulation outcomes prove that the suggested hybrid structure is more accurate in predictions, more robust, and converges faster, which is indicated by the lower error indicators like MAE, RMSE, and MAPE. In addition, the feature selection step will increase the interpretability and the computational efficiency of models without affecting prediction accuracy. The results attest to the fact that the suggested approach is highly applicable when it comes to short-term electricity price prediction in highly volatile and renewable-based power markets.

Synthetic Aperture Radar into Comprehensive Colorized Images Using Deep Learning Model

Authors: Sneha Kanawade, Dr. Suvarna Patil, Siddhi Kadu, Aniruddha Ojha, Aryan Sahu, Indra Pratap Singh Rajawat

Abstract: Synthetic Aperture Radar is vital remote sensing technology, offering all-weather, day-and- night imaging ca-pabilities. However, its inherent grayscale nature, along with speckle noise, presents significant challenges for interpretation by non-specialists. This review addresses recent advancements in applying deep learning to SAR colorization, a technique aimed at enhancing visual interpretability of these images while preserving unique radiometric properties. The primary motivation is to bridge the gap between complex radar data, intuitive visual analysis, thereby broadening its application in fields like disaster management, environmental monitoring. Major themes covered include critical distinction between grayscale colorization, SAR-tooptical translation, evolution of methodologies from tradi-tional regression to advanced deep learning models, lack of standardized evaluation protocols that has hindered progress. Existing technologies often involve convolutional neural networks, Generative Adversarial Networks (GANs). This review high-lights a proposed methodology centered on conditional GAN within a complete benchmarking protocol utilizing synthetically generated ground truth via intensity-high saturation (IHS) fusion. Key features of this approach include an end-to-end supervised learning framework, use of domain-specific evaluation metrics (Q4, NRMSE, SAM). This advancement holds significant impli-cations for real-time disaster response, contributes to Sustainable Development Goals (SDGs) such as ”Sustainable Cities and Com-munities”, ”Climate Action” by making critical environmental data more accessible, actionable.

Dos Attack Detection Using Edge Machine Learning

Authors: OM Kute, Yuvraj Narwade, T.B. Faruki

Abstract: Denial of Service (DoS) attacks are one of the most common cyber threats that disrupt network services by overwhelming systems with malicious traffic. Traditional cloud-based detection methods often experience higher latency and increased bandwidth usage, making them less effective for real-time protection. The DoS Attack Detection System Using Edge Machine Learning introduces an intelligent approach that detects malicious network traffic directly at edge devices before it reaches the central server. By leveraging Edge Computing, Machine Learning, and real-time traffic analysis, the system identifies abnormal network behavior with low latency and improved accuracy. This approach reduces server overload, enhances network security, and ensures continuous availability of services while providing a scalable and efficient solution for modern IoT and edge-enabled environments.

A Bibliographic Analytical Assessment of ICT Pedagogical Infrastructures and Attitudinal Moderation in Secondary Classrooms

Authors: Divya Krishnan Maniyeri, Professor (Dr) Parshuram Dhaked

Abstract: This bibliographic analytical paper systematically evaluates the theoretical and empirical literature surrounding the integration of Information and Communication Technology (ICT) in secondary school systems, with specific reference to the Indian classroom context. Positioned within the scholarly mandates of the National Education Policy (NEP) 2020 and institutional research criteria at Sabarmati University, this analysis explores the structural mechanisms through which technological treatments influence cognitive academic outcomes and learner dispositions. Historically, educational technology research has been limited by a reliance on un-moderated, direct-effect correlational models, leaving an empirical gap regarding the unique, conditional variables that determine localized instructional success. This paper traces the evolution of educational technology literature from early psychological frameworks to contemporary digital-age learning models, culminating in a thematic assessment of the "no significant difference phenomenon." By synthesizing global meta-analyses alongside domestic quasi-experimental trials, this paper maps the research gaps that justify an experimental investigation into the moderating role of student attitudes within an Aptitude-Treatment Interaction (ATI) framework. The analysis concludes that sustainable technology implementation requires moving past simple device provisioning to prioritize the psychological readiness and attitudinal architecture of the individual learner.

Evolutionary Dimensionality Reduction for Structured Heart-Disease Classification: Balancing Predictive Performance, Clinical Input Burden and Global Transparency

Authors: Research Scholar Rakesh Kumar Khillan, Associate Professor Dr. Abhinav Shukla

Abstract: Background: In clinical machine learning, the task of feature selection is frequently stated as a step toward increased accuracy, but a smaller model can be just as useful as it can help to ease the burden of input and give a better global picture of the model. This study compared the performance-compactness trade-off between a full feature Random Forest and a Genetic Algorithm (GA) selected Random Forest in terms of their performance in binary classification of the recorded heart disease status. Data: A public structured dataset with 918 instances, 11 features and a binary target HeartDisease was used. The full-featured Random Forest employed all of the predictors. The binary chromosomes, population size of 20, number of generations of 10, tournament selection, two-point crossover, bit-flip mutation and fitness function of 20-fold Random Forest accuracy are used in a wrapper GA. A subset of 7 predictors was selected and compared to the full-feature model via 10 replications of 20-fold stratified cross-validation. Accuracy, precision, sensitivity, F1-score, ROC-AUC, predictor count and cross-validated permutation importance were measured. Results: The best repeated internal accuracy (87.11% ± 5.06%) and ROC-AUC (0.9285 ± 0.0396) was obtained by the full-feature Random Forest method. The GA-selected model reduced the predictor set from 11 to 7 (36.36%) and achieved accuracy of 83.67% ± 5.45% and ROC-AUC of 0.9075 ± 0.0439. The mean difference of accuracy between the two models in the paired accuracy was −3.44 percentage points in favor of the full-feature model. The largest mean decreases in validation ROC-AUC following permutation was from ST_Slope, followed by ChestPainType and Oldpeak. Conclusions: The evidence was not sufficient to support the assumption which led to the improvement of predictive accuracy through evolutionary features selection. On the contrary, GA has come up with a small, clinically identifiable prototype that had less intraclass discrimination. Thus, the full-featured versus the compact configuration are used in different ways: to maximize predictive performance versus to minimize both user input and global predictor transparency. Prior to clinical-use claims, the features should be fully nested and be externally validated.

DOI: https://doi.org/10.5281/zenodo.21187688

Early Social Interaction in Infancy and Developmental Outcomes: Distinguishing Influence from Causation in Autism-Like Presentations

Authors: Dr.Pavithra Lakshminarasimhan

Abstract: Early infancy is a critical period for brain development, where social interaction plays a foundational role in shaping communication, emotional regulation, and cognitive growth. With increasing shifts toward nuclear family systems and digital engagement, concerns have emerged regarding reduced caregiver-infant interaction. This paper explores the relationship between early social deprivation and developmental outcomes, particularly behaviours resembling autism. While autism is a neurodevelopmental condition with strong genetic underpinnings, this paper emphasises that environmental factors may influence developmental expression without causing autism, often leading to delays or autism-like presentations.

DOI: https://doi.org/10.5281/zenodo.21192947

Optimizing Effect Of Venturi Size On Integration Of Hybridized Drip And Sprinlkler Irrigation System

Authors: Dr. Lakshmi Kunhikrishnan, Dr.J.Elanchezhian, Dr.K.Karnavel, Mrs.P.Aruna, Dr.V.V.Rajasegharan, Mr. Dhianeshwar, J

Abstract: A proper irrigation system is considered as the back bone of agriculture. To provide a channelized irrigation system various irrigation setups and techniques are targeted to meet different yields and for different purposes. Cheyyur has two major types of crops cultivated in two different seasons with two different irrigation setups. The irrigation setups need to be changed whenever the crops are changed. These difficulties not only cause wastage of water but also decreases the overall yield of the crops. To improve the improvement facilities a hybridized setup was modelled fabricated and implements in and around the parts of Cheyyur district to improve their irrigation abilities. The proposed project was accepted by the department of science and technology. The hybridized model was designed and fabricated. Optimization of the flow of the suction pressure was conducted and simulation has been carried out using Genetic Algorithm and performance was analysed using Fminconalgorithm in MATLAB to hybridized and integrate the two different irrigation system. Modelling studies were carried out to improve the suction and the control flow rate of 986 litres per hour to 70 Litres per hours to the agricultural field. This applied optimization technique proves to improve the yield of the crops.

DOI: http://doi.org/10.5281/zenodo.21188506

CapitalSense AI: Intelligent Startup Investment and Profit Forecasting

Authors: Bandaru Udayasree

Abstract: The rapid growth of e-commerce startups has created significant opportunities for innovation and economic development; however, a large proportion of these ventures fail due to inadequate financial planning and uncertain profitability. Accurate estimation of start-up capital requirements and early prediction of business profitability are therefore essential for entrepreneurs, investors, and financial institutions. This research presents a machine learning-based framework for estimating start-up capital and predicting the profitability of e-commerce startups using historical business and financial data. The proposed system analyzes critical parameters such as funding amount, investment history, operational expenses, revenue projections, market trends, and business characteristics to identify patterns associated with successful and profitable ventures. Multiple machine learning algorithms, including Decision Tree, Random Forest, Gradient Boosting, Logistic Regression, and Multi-Layer Perceptron (MLP), are trained and evaluated to determine the most effective prediction model. Data preprocessing techniques such as feature selection, handling missing values, and normalization are applied to improve model performance and reliability. Experimental results demonstrate that the proposed framework achieves high prediction accuracy, enabling data-driven decision-making for startup planning and investment evaluation. The developed system provides an intelligent decision support tool that assists entrepreneurs in estimating initial capital requirements, assessing business profitability, minimizing financial risk, and improving the likelihood of long-term business success in the competitive e-commerce ecosystem.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.503