IJSRET Volume 12 Issue 3, May-Jun-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.

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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.

DOI:

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

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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.

DOI: http://doi.org/

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}

DOI: http://doi.org/

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.

DOI: http://doi.org/

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.

DOI: http://doi.org/

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

Design And Development Of A Sales Performance Dashboard For The BFSI Sector Using Advanced Data Visualization Tools

Authors: Rahul Praful Gaonkar, Shubham Kashinath Jadhav, Dr. Jasbir Kaur, Prof. Suraj Kanal, Prof. Sandhya Thakkar

Abstract: The Banking, Financial Services, and Insurance (BFSI) sector operates within a highly competitive and data-heavy ecosystem. Organizations generate vast amounts of trans-actional, customer, and sales data daily. However, extracting actionable insights from this raw data to drive sales performance and strategic decision-making remains a critical challenge.This paper outlines the design, architectural framework, and implementation of a comprehensive Sales Performance Dash-board tailored specifically for the BFSI sector using modern business intelligence and data visualization tools like Power BI. The proposed system integrates diverse data streams including customer relationship management (CRM) records, core banking transaction logs, and insurance policy sales pipelines into a unified data repository via an optimized Extract, Transform, and Load (ETL) pipeline.By applying rigorous data modeling and structural design principles, the dashboard provides intuitive, real-time, and gran-ular tracking of critical key performance indicators (KPIs) such as cross-selling conversion rates, regional revenue distribution, agent productivity matrices, and product-wise profit margins. The implementation demonstrates how interactive analytics and structured visual layouts eliminate information silos, decrease operational reporting latencies, and empower branch managers and executives to make rapid, data-backed strategic choices.

DOI: http://doi.org/10.5281/zenodo.20621705

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: Dr. Raja Sekhar Koduru, Saranya P K

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

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, Dr. Dinesh A. Dinesh , Samarpita Roy

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