Daily Archives: November 12, 2025

Deep Learning -Based Suicidal Thought Detecion From Social Media With IOT-Enabled Alert System

Authors: J. Jenitta, Amulya N, Keerthi Kandakur, Nithin N, Shashank G

Abstract: As the online socialization process advances, more and more individuals are reporting their emotional conditions online and in many instances showing symptoms of misery and suicidal tendencies, which present significant obstacles to monitoring mental health. Conventional techniques of detecting such content are often untimely and ineffective and it is based on this need that automated and real time detection is important. In this paper, the author suggests a deep learning-based suicide risk prediction system based on linguistic data obtained on social media. The model achieves a high degree of accuracy in differentiating between texts that are suicidal and those that are not by learning the contextual semantics using bidirectional long short-term memory (Bi-LSTM) networks. The system is coupled with an IoT-based alert system to make sure that timely intervention is applied to prevent suicidal intent by sending alarms to caregivers or other interested parties whenever any suicidal intent is identified. The results of the testing show that the proposed method performs better in comparison to the traditional machine learning classifiers that are measured in terms of precision, recall, and F1-score. This combination related to the higher-order NLP techniques and the use of IoT-driven indicators offers a scalable and productive approach to stopping destructive online behavior. This article highlights the role of artificial intelligence and IoT in the improvement of digital mental health support systems.

DOI: http://doi.org/10.5281/zenodo.17590356

Automatic Cruise Control System for Car

Authors: Dr. S. P. Munot, Pratiksha Gudade, Rutuja Ikhe, Vaibhavi Kandare

Abstract: The Automatic Cruise Control (ACC) System using Raspberry Pi 4 is a smart, cost-effective prototype designed to enhance vehicle safety and comfort through intelligent automation. Powered by Raspberry Pi 4, it integrates multiple sensors—ultrasonic for obstacle detection, gyroscope for stability, GPS and GSM for tracking and communication, and a camera with face recognition for secure vehicle access. The system automatically maintains and adjusts vehicle speed using an L298 motor driver and DC motor based on real- time sensor data, while an OLED display and buzzer provide instant feedback and alerts. This project effectively demonstrates how embedded technology can simulate adaptive cruise control, offering a scalable and efficient step toward future autonomous driving system.

A Study On The Procurement Strategies Adopted By The Hospitals In Kolkata: An Analysis By Using AI Based Tolls

Authors: Dr. Pramit Das, Mr. Prasmit Das, Ms. Subhasree Ray

Abstract: Healthcare procurement in India encompasses a complex interplay between cost containment, transparency, and quality improvement. This research examines procurement mechanisms in public and private hospitals, highlighting the transition from price-based purchasing toward value-based, sustainable, and technology-driven strategies. This paper analyses purchase and revenue data from 2022 to 2025 in the healthcare sector, with special attention to implants and consumables. Analytical methods employed include descriptive statistics, variance analysis, correlation studies, and regression modelling. The findings highlight notable growth in purchases and revenue, identify leading product categories, and examine the interplay between inflation and purchase behaviour.

Smart Attendance System Using Location

Authors: Swarupa Chavare, Madhuri Dhole, Sneha Bankar, Prajakta Dhaygude, Harshda Bhongale, I.T.Mukerjee

Abstract: Attendance for the organization is important in both academic and professional institutions, to keep discipline and track performance. Conventional practices such as manual roll calls or use of biometric scanners are highly time-consuming, error pro and susceptible to proxy attendance through multiple means. This paper proposes a Smart Attendance system with the help of GPS technology as well as the geofencing concept which does marking attendance automatically. As student’s smart phone entered pre-defined classroom region, system recorded the attendance of the student. The students are supported by a mobile app while attendance data is stored and processed in a cloud based backend. It also has a Defaulter List Module which computes percent attendance automatically and lists the respective students who does not attend by given cut-off (i.e. less than 75%). It enhances accuracy and transparency, minimizes manual labor, while enhancing rigour. Practical realization demonstrates that the system is capable of effectively avoiding proxy attendance and realizing real-time attendance management with reliability.

Buddy: A Python-Based Mood-Aware Chatbot For Personalized Movie, Music, And Motivation Recommendations”

Authors: Aditya Kamble, ,Jayash Chavan, Siddharth Gaikwad, Vaishnavi Dardige, Professor I.T. Mukharjee

Abstract: In recent years, conversational artificial intelligence (AI) has transitioned from simple command-based systems to emotionally responsive digital companions capable of contextual understanding and human-like interaction. However, most available chatbot architectures depend on large-scale machine learning models and cloud-based computation, making them complex, data-intensive, and inaccessible for lightweight or educational applications. To address these challenges, this research presents Buddy, a modular, Python-based conversational chatbot designed to deliver personalized entertainment and motivational support using emotion cues and real-time API integrations. The proposed framework emphasizes simplicity, scalability, and emotional adaptability without requiring extensive natural language processing infrastructure. Buddy functions as a context-aware conversational assistant capable of recognizing user moods and providing tailored responses in the form of movie, music, weather, and motivational recommendations. The system integrates multiple public APIs — including TMDb for film suggestions, Spotify for mood-aligned music, ZenQuotes for motivational content, and OpenWeatherMap for contextual awareness — enabling multi-domain interaction. A rule-based mood detection engine identifies user sentiment from keywords such as “sad,” “happy,” or “bored,” while modular functions handle data fetching and formatting. This architecture creates a seamless conversational loop where Buddy adapts to user feedback, learns preferences in real time, and maintains an empathetic conversational tone. Experimental evaluation demonstrates that Buddy achieves an average response latency of 1.25 seconds, with a keyword detection accuracy of 95% across varied moods and network conditions. The framework exhibits low computational overhead (<100 MB memory usage) and functions efficiently on standard hardware without GPU support. Furthermore, its modular structure allows integration with advanced AI components such as sentiment classifiers or edge-deployed models in future versions. Compared to existing AI chatbots, Buddy provides a balance between human-like engagement, data privacy, and system transparency, highlighting how modular API-driven design can democratize access to emotionally intelligent AI systems. This study establishes Buddy as a sustainable, privacy-preserving, and adaptable conversational framework that bridges the gap between rule-based logic and emotionally aware digital companionship — paving the way for next-generation lightweight AI assistants suitable for education, mental wellness, and personalized entertainment ecosystems.

CENT-Advancing Banking System Using Face Recognition

Authors: Harshvardhan Patil, Dhruv Agrawal, SuryaPratap Singh Solanki, Parth Patel

Abstract: This research paper explores the development of a next-generation banking system that integrates biometric authentication, specifically facial recognition, to ensure robust security and a seamless user experience. The project, titled CENT (Centralized Enhanced Neural- banking Technology), combines machine learning models, backend services, frontend design, and database integration to simulate a secure and scalable financial application. Built using technologies like OpenCV and TensorFlow, the system features a comprehensive pipeline including face detection, user registration, account management, and transaction processing, all accessible through a modern web interface. This paper elaborates on the project’s architecture, system requirements, design methodology, implementation details, and real-world applications, providing a comprehensive overview of the CENT system.

DOI: http://doi.org/10.5281/zenodo.17597420

Smartfolio ( AI-based Portfolio Management System )

Authors: Cheenepalli Gowthami Priya, Shreya Itkapalle, Priya Prasad, Saginala Arifulla, Rishabh Tripathi, Dr. Nithya

Abstract: SmartFolio is a portfolio management software aimed to assist users in creation, management and. present individual portfolios effectively. The platform serves differ- ent professionals who include By offering lively customization of the portfolio, intel- ligent, investors, designers and freelancers. user experience, and content suggestions. Constructed on the basis of the latest web technologies. SmartFolio uses AI-based analytics to streamline the portfolio material, propose better options, and. enhance user engagement. The system incorporates the mechanisms of automated portfolio generation, reactive design templates, live-time data analytics, and safe cloud stor- age. Additionally, it enables multimedia support, interactive user in- terfaces and rich search capabilities into guarantee an enjoyable and uninterrupted experience. The project will seek to close the divide that exists between static. portfolio websites and intelligent automation through delivering a smart, adaptable and user- friendly. platform. It will use machine learning algorithms, real- time data processing, and cloud-based. architecture, SmartFolio allows professionals to sustain an effective and relevant digital. presence with little work. In this paper, the architecture, the pro- cess of development and the major points will be discussed. capabilities, and possible future improvements of SmartFolio, with a focus on how it can change. AI-based portfolio manage- ment. Keywords:Portfolio management system, Intelligent portfo- lio generation,Machine learning,Professional portfolio showcase, Interactive UX/UI de- sign,Freelancer portfolio,Investor portfo- lio,Designer portfolio.

Design And Implementation Of A Modern Expense Tracker Web Application Using MERN Stack

Authors: Marella Jaya Sri, Mounika Sai Sowjanya Kodi, Koppula Vasundhara, Gunji Sai Sasank

Abstract: Personal financial management has become in- creasingly crucial in today’s fast-paced world where individuals struggle to track their expenses manually. This research paper presents the design, development, and implementation of a comprehensive Expense Tracker web application built using the MERN (MongoDB, Express.js, React, Node.js) stack. The application provides users with an intuitive platform to monitor income and expenses, categorize transactions, set monthly budgets, and analyze spending patterns through interactive visualizations. Key features include JWT-based authentication, dynamic transaction filtering, category management, budget tracking with proactive alerts, and data visualization. The paper discusses the technology selection rationale, system architec- ture, implementation challenges, and performance evaluation. The application demonstrates how modern web technologies can be leveraged to create effective financial management tools that empower users to make informed financial decisions. Expense Tracker, MERN Stack, Financial Management, Budget Tracking, React, Node.js, MongoDB, Web Applica- tion

Ai Based Student Feedback Analysis System

Authors: Prajwal Sunil Saste, Om Santhosh Dhage, Aditya Arun Tathe, Prof. Rahane.D.A

Abstract: The swift advancement of AI in education has paved the way for more customized and flexible educational settings. This study introduces a student feedback analysis system powered by AI, which offers immediate, smart feedback to boost learning results. The system uses sentiment analysis to determine the emotional aspectof student communications and uses machine learning methods like decision trees, support vector machines (SVM), and deep learning models to assess participation, success, and emotional conditions. By merging cognitive and emotional understandings,the suggested system offers tailored, relevant feedback to help students conquer learning obstacles. Testing outcomes reveal enhanced student involvement, contentment, and general academic success, emphasizing the ability of AI to revolutionize contemporary education

Ai Based Student Feedback Analysis System

Authors: Prajwal Sunil Saste, Om Santhosh Dhage, Aditya Arun Tathe, Prof. Rahane.D.A

Abstract: The swift advancement of AI in education has paved the way for more customized and flexible educational settings. This study introduces a student feedback analysis system powered by AI, which offers immediate, smart feedback to boost learning results. The system uses sentiment analysis to determine the emotional aspectof student communications and uses machine learning methods like decision trees, support vector machines (SVM), and deep learning models to assess participation, success, and emotional conditions. By merging cognitive and emotional understandings,the suggested system offers tailored, relevant feedback to help students conquer learning obstacles. Testing outcomes reveal enhanced student involvement, contentment, and general academic success, emphasizing the ability of AI to revolutionize contemporary education

E-Library Management System

Authors: B.Parthiban, R.Sripadma

Abstract: Libraries have come a long way from the traditional manual processes to the modern digital solutions that we now have, changing forever how information is handled and accessed. This paper presents the design, construction and application of a Library Management System (LMS) integrated with various advanced capabilities like Chat Bot, Voice Recognition and GUI. This LMS is intended to automate library operations, help improve user experience as well as optimize resource management. The paper describes the problem definition, project aims, approach taken, and system level architecture along with future work directions.

DOI: https://doi.org/10.5281/zenodo.17607767

AI-Powered Forensic Image Suite For Authenticity Verification

Authors: Professor Shivani Karhale, Mr. Rohit Pawar, Ms. Sanskruti Marawade, Ms. Nandini Jadhav, Ms. Vaishnavi Pawaskar

Abstract: The rapid advancement of artificial intelligence, image editing tools, and generative models has made visual manipulation easier than ever. Altered images can influence legal investigations, journalism, social media, and political narratives, creating a critical need for automated authenticity verification systems. This research introduces an AI-Powered Forensic Image Suite integrating shadow analysis, image consistency detection, and metadata verification to identify tampered digital images. The system preprocesses images through resizing, normalization, and noise reduction, followed by shadow recognition using gradient-based and geometric estimation techniques. Image consistency is evaluated using structural similarity, lighting coherence, and texture uniformity checks. Metadata analysis extracts EXIF information to verify timestamps, camera signatures, and editing traces. Experiments conducted on a dataset of 500 real and manipulated images demonstrate high accuracy, with shadow detection (94%), consistency check (92%), and metadata validation (98%). The suite serves as a reliable tool for investigators, journalists, and forensic professionals, and provides a scalable foundation for advanced features such as deepfake detection, reverse image search, and error-level analysis.

College Event Management: A Survey of Analytics and Personalization

Authors: P. Shiva Sanakara Pandian, K. Sai varsha

Abstract: College Event Management System represents a comprehensive software solution designed to optimize and streamline the planning, organization, and management of events within college. This research project addresses the challenges encountered by academic institutions in coordinating and executing a diverse range of events, including conferences, seminars, cultural festivals, and sports tournaments, with a primary focus on enhancing efficiency, communication, and collaboration. The objective of this study is to explore the development and implementation of the College Event Management System, underscoring its potential to transform event management within educational institutions. By combining user insights, case studies, and in- depth analysis. The findings underscore the importance of modernized event management tools in promoting student engagement, fostering effective communication, and facilitating the successful execution of events within the college environment. Ultimately, this research project aims to provide valuable insights for academic institutions seeking to optimize their event management processes, thereby enhancing the overall campus experience.

Environmental Influence On Chicken Raised In Refused Dumpsites In The Zaria Metropolis

Authors: Umudi Ese Queen, Erienu Obruche Kennedy, Apuyor Kingsley Efe, Apuyor Stanley Ejohwomu, Onwugbuta Godpower Chukwuemeka, Eresanya Olanrewaju Isola, Ikechukwu Sampson Chikwe

Abstract: The research looked into how dumpsites affect the areas around them. They collected and tested the dust and heavy metals found in chickens raised near these waste sites during both dry and wet seasons. For three months, young chickens were fed solid waste and leachates from these sites, and then they were sacrificed for analysis. A standard method for testing dust and heavy metals was followed, as recommended by the World Health Organization (WHO). They used Atomic Adsorption Spectroscopy to find out how much heavy metal was present. The levels of Zn, Cd, Cu, Pb, and Hg in the dust varied by season, ranging from 1.40 (JK) to 210.60 (SA), BDL (CTR) to 3.74 (RA), 0.241 (KU) to 390.0 (JK), 2.26 (CTR) to 78.260 (SH), and BDL (CTR) to 25.69 (AJ). For the chicken samples, the heavy metal levels ranged from BDL (CTR) to 8.844 (JK), BDL (CTR) to 2.850 (BG), BDL (CTR) to 0.099 (BG), BDL (CTR) to 128.017 (NTC), and BDL (CTR) to 83.122 mg/kg (DD) for Zn, Pb, Cd, Cu, and Hg across different sites and seasons. Most of the metal levels in the chicken samples were below safe limits, but a few were not, indicating that people living near these dumpsites are affected. The Kaduna State Environmental Agency (KEPA) needs to work on reducing hazardous waste and provide better waste disposal options.

DOI: http://doi.org/10.5281/zenodo.17637261

Traffic Sign Recognition Using Multi-Task Deep Learning For Self-Driving Vehicles

Authors: Atharva Rajesh Gosavi

Abstract: raffic sign recognition (TSR) is a critical component of autonomous driving systems, enabling vehicles to understand and respond to road regulations in real time. Traditional TSR approaches typically separate classification and localization tasks, resulting in increased computational cost and reduced robustness in complex driving environments. This paper proposes a multi-task deep learning framework that performs simultaneous traffic sign detection, classification, and attribute prediction using a shared feature-extraction backbone. The model leverages multi-task learning to exploit interrelated features across tasks, improving overall accuracy while reducing inference time—an essential requirement for self-driving applications. Extensive experiments conducted on benchmark datasets such as GTSRB and GTSDB demonstrate that the proposed approach outperforms single-task baselines, achieving higher precision in both recognition and localization. The results show that multi-task learning enhances generalization under challenging conditions, including occlusion, varying illumination, and high-speed motion. This work highlights the potential of unified deep learning architectures to deliver efficient and reliable traffic sign recognition for next-generation autonomous vehicles.

Pseudo Irregular Fuzzy Soft Graphs

Authors: L.Subhalakshmi, Dr. N.R.Santhi Maheswari

Abstract: This paper deals with pseudo irregular fuzzy soft graphs. The definition of pseudo irregular graphs is introduced with some properties. The pseudo edge irregular fuzzy soft graphs are illustrated with examples. The properties of the defined graphs are studied. Highly, neighbourly, strongly pseudo irregular graphs are explained with examples. Also some pseudo edge irregular fuzzy soft graphs are illustrated. The relation between strongly pseudo irregular fuzzy soft graphs with highly and neighbourly pseudo irregular FSG is given. Results on total pseudo irregular FSG and total pseudo edge irregular FSG is examined.

Fuzzy PDE Models For Sustainable Resource Dynamics: An A-Cut And Robust Optimization Framework

Authors: Siddalingaswamy R, Yogeesh N, Rajathagiri D T, M. S. Sunitha, Jagadeesha K C

Abstract: This study develops a practical modeling pipeline to treat epistemic uncertainty in sustainability-focused partial differential equations governing environmental and urban systems. We represent imprecise forcings and parameters with fuzzy numbers (triangular/trapezoidal membership functions) and propagate uncertainty via α-level analysis: for each α, parameters are mapped to compact intervals and a deterministic diffusion–reaction problem is solved to yield envelopes of feasible states. The workflow integrates (i) fuzzy parameterization and α-cut computation, (ii) numerically stable parabolic solvers (implicit/Crank–Nicolson discretizations with Dirichlet boundaries), and (iii) a stylized robust multi-objective design that visualizes trade-offs between expected performance and sustainability risk. Two representative applications illustrate relevance: groundwater-style storage under uncertain recharge–demand balance and urban heat mitigation with uncertain material/forcing properties. Results include interpretable membership curves and α-cut bounds, α-dependent terminal profiles, time-evolution bands that communicate worst-plausible excursions, and Pareto fronts clarifying yield–risk compromise under policy intensity. A grid-refinement study indicates indicative second-order spatial convergence in the smooth-solution regime, supporting numerical consistency. Beyond these cases, the framework is modular and extensible to nonlinear physics, higher dimensions, and hybrid fuzzy–stochastic formulations, while remaining transparent for expert elicitation and decision support. Overall, the approach preserves uncertainty structure without imposing unwarranted probability models, providing decision-makers with conservative, policy-ready indicators for risk-aware planning in data-sparse contexts

DOI: http://doi.org/10.5281/zenodo.17617766

Leveraging Artificial Intelligence (AI) And Machine Learning (ML) In Women’s Access To Healthcare In Rural India

Authors: Hridayjyoti Deka, Dikshita Medhi

Abstract: Women’s healthcare necessitates comprehensive approaches to address unique and ubiquitous health related issues of women, which include nutritional, reproductive, mental, and chronic diseases. On the other hand, rural healthcare must overcome tangible and intangible barriers, including geographic isolation, inadequate physical and digital infrastructure, sociocultural resistance, a shortage of healthcare professionals, and policy paralysis. Artificial Intelligence and Machine Learning have revolutionized the healthcare paradigm through developments such as deep learning enabled medical imaging and diagnostics, predictive analytics, drug discovery, real time monitoring of disease surveillance, precision and personalised medicines, robotic surgery, robotic neurorehabilitation, etc. However, the benefits of these breakthroughs are mostly being received by the advanced societies; the greater rural masses still expect miracles of the trickle down effect. The healthcare issues and challenges pertaining to women are peculiar and need special focus from researchers, particularly in low resource settings like rural India. Nevertheless, the researchers have long started exploring Artificial Intelligence and Machine Learning based solutions for problems related to women’s healthcare and rural healthcare. In this article, focusing on the challenges and approaches, we review the state of the art of Artificial Intelligence and Machine Learning in women’s healthcare that carries significant potential for implementation in the rural healthcare system in India

DOI: http://doi.org/10.5281/zenodo.17617845

Enhancement Of Energy Efficiency For Decarbonization In The Indian Manufacturing Sector: A Review

Authors: D. Damodara Reddy, Soma Vivekananda, Vennam Gopala Krishna

Abstract: Primary energy consumption doubled since the 2000s, between 440 and 880 million TOE. It is projected to double within the next 20 years, reaching approximately 1,900 million TOE, and to reach 1,500 million TOE by 2030. It is anticipated to double over the next 20 years to around 1,900 million TOE, and by 2030, it will reach 1,500 million TOE. The manufacturing sector in India uses the most energy. Energy use in the global industrial sector accounts for one-third of total consumption, according to a review of energy analysis. So that effort has been made to improve the energy efficiency (EE) of the industry to enhance performance. Energy efficiency means using less energy to do an identical task while lowering energy costs and emissions. A key component of the all-encompassing plan to decarbonize industrial operations is energy efficiency. This research intends to investigate the most current systematic literature evaluations on energy efficiency in the industrial sector that were published between 2017 and 2023, taking into account this vast amount of information. The current study creates and establishes six distinct groups that reflect the state of the field’s research after conducting qualitative and topical evaluation: Energy Conservation and Innovation, Energy Diagnostics, Energy Monitoring, and Energy Optimization. It consists of the automated and comprehensive formulation of measures for energy efficiency utilizing energy efficiency analysis, broad and flexible modeling of consumption of energy at various production stages to determine technological efficiency possibilities, and the comprehensive evaluation and sorting method taking into account the relationships among methods.

DOI: http://doi.org/10.5281/zenodo.17618019

Social Media Monetization (Modern Advertising System)

Authors: Aaditya Deo, Shah Saurabh, Anurag Tiwari, Ishordev Chaudhary, Dr. Sneha Soni

Abstract: Traditional digital marketing frequently encounter high costs, authenticity gaps, and declining consumer trust. Simultaneously regular customers and small creators who organically promote products seldom receive benefits. This paper present a concise research version of the project “Social Media Monetization: A Modern Advertising System” which enable customers to earn cashback by promoting purchased product from partnered brands on Instagram. The system leverage user generated con- tent to provide brands with authentic, cost effective promotion while offering customers a transparent reward mechanism. We summarize the problem context, objectives, system methodology, modules, expected outcomes, and future directions strictly based on the original project report, and reserve space for illustrative figures aligning with the submitted work. Customer Based Advertising, Cashback Reward, Digital Marketing, Social Media Promotion, Instagram Marketing, Authenticity, Secure Payment Integration, Marketing Ana- lytics, User Engagement Tracking, AI Based Advertisement Optimization, Scalable Marketing Model.

Adoptly: A Cross-Platform Application For Streamlined Pet Adoption

Authors: Akshat Parekh, Jemit Patel, Deep Upadhyay, Jay Shah

Abstract: The increasing number of abandoned animals underlines the necessity of reliable and transparent adoption systems. Existing adoption practices often rely on scattered sources such as social media, which lack consistency, verification, and accountability. This paper presents Adoptly, a mobile-first application designed to streamline pet adoption by ensur- ing secure authentication, verified listings, and seamless communication between adopters and shelters. Built using React Native (Expo) for cross- platform compatibility, Clerk for user authentication, and Firebase for real-time storage and database services, Adoptly integrates features such as verified pet profiles, favorites, adoption requests, story sharing, and an in-app chat system. Developed under an agile methodology, the system reduces misinformation, enhances trust, and improves adoption efficiency compared to unstructured methods. Early testing demonstrates reliabil- ity in authentication, smooth navigation, and real-time synchronization. The findings highlight the potential of mobile-first systems to revolu- tionize adoption processes by bridging gaps of communication, trust, and scalability.

DeepLeaf: AI-Powered Plant Disease Detection And Prediction With Multilingual Support And PDF Report Generation_783

Authors: G Vishal, G K Sharath Kumar, Karthik N Ganiga,, Sanjay Kumar

Abstract: An important factor in a nation’s economic development is the agricultural sector. In particular, it provides millions of people in rural India with a vital source of income. One of the major issues influencing the agriculture industry is plant disease. Diseases can infect plants for a variety of reasons, such as synthetic fertilizers, outdated methods, environmental factors, etc., which affect farm productivity and thereby hurt the economy. Researchers have looked into a variety of AI and machine learning-based applications to identify plant diseases in order to address this problem.

Deep Leaf: AI-Powered Plant Disease Detection and Prediction with Multilingual Support and PDF Report Generation

Authors: G Vishal, G K Sharath Kumar, Karthik N Ganiga,, Sanjay Kumar, Prof. Merlin B

Abstract: An important factor in a nation’s economic development is the agricultural sector. In particular, it provides millions of people in rural India with a vital source of income. One of the major issues influencing the agriculture industry is plant disease. Diseases can infect plants for a variety of reasons, such as synthetic fertilizers, outdated methods, environmental factors, etc., which affect farm productivity and thereby hurt the economy. Researchers have looked into a variety of AI and machine learning-based applications to identify plant diseases in order to address this problem.

Energy-Efficient Data Center Optimization using AI Control

Authors: Logeshwaran V, Bhuvaneswari B

Abstract: In recent years, the exponential growth of dataintensive applications and cloud-based services has drastically increased energy consumption in data centers worldwide. This surge poses significant challenges, including high operational costs, carbon emissions, and sustainability concerns. To address these issues, this paper proposes a comprehensive AI-based framework for energyefficient data center optimization. The approach integrates intelligent control algorithms, predictive analytics, and adaptive resource allocation mechanisms that dynamically manage workloads, cooling systems, and server utilization. Using techniques such as machine learning (ML), reinforcement learning (RL), and neural network-based control, the proposed model achieves significant energy savings while maintaining service-level agreements (SLAs). The research demonstrates how AI- driven systems can autonomously predict server loads, optimize cooling parameters, and minimize idle energy consumption. The findings contribute to the design of sustainable, selfoptimizing data centers for the next generation of green computing infrastructure.

A Comprehensive Review On Canine Health And Welfare: Trends, Challenges, And Technological Advancements

Authors: Isha V Solanki, Preet Darji, Jainil Parmar, Manush Desai, Prof. Garima Sharma

Abstract: The paper addresses an in-depth review of over 60 peer-reviewed studies that deal with canine health and welfare and their proponents and cons on both conventional and emerging strategies. The review encompasses topics of behavioral assessment, vaccination, dental hygiene, population management, diagnosis of allergies and the introduction of artificial intelligence into veterinary medicine, aligning clinical science and technology and ethics. It is worth noting that it determines the vital areas of investigation of behavioral and dental diagnostics, issues of population affected by stray populations, and lack of standardiza- tion in the implementation of preventive care despite its evident usefulness. In contrast to the previous reviews, the given study is rather multi-dimensional, as it links welfare and innovation to each other. The paper can be reproducible because it adheres to PRISMA standards on the selection of studies. Conclusions will target both the veterinarians, the pet owners, researchers, and policymakers by promoting evidence-based models of care. In the perspective, the paper highlights the necessity of standard behaviors and tools, available AI-aided diagnosing, and more significant public education so as to drive international levels of canine well-being.

DOI: https://doi.org/10.5281/zenodo.17636958

Desktop Voice Assistant

Authors: Anish Mukhiya, Raushan Kumar Ram, Anuraj Chaudhary, Omkar Mahto

Abstract: Background: Personal Desktop Voice Assistants (PDVAs) represent a significant advancement in human-computer interaction, offering intuitive voice-based control of desktop environments. Meth- ods: This study employed Python programming language with integrated speech recognition, natural language processing, and text-to-speech technologies to develop an accessible PDVA. The system archi- tecture incorporates wake word detection, intent recognition, and task execution modules. Results: The developed PDVA successfully executes diverse commands including web searching, media control, system management, and information retrieval. The system demonstrates particular effectiveness for visually impaired users, providing multimodal feedback through synthesized speech and on-screen text. Conclu- sion: The research confirms that Python-based PDVAs can significantly enhance desktop accessibility while highlighting the importance of addressing privacy concerns and interaction limitations in future developments.

Study Report On Swift Kiosk

Authors: Arpita Sahu, Dr. Waris Patel

Abstract: The rapid digital transformation in the food and beverage industry has created a demand for efficient, customizable, and user-friendly ordering solutions. Tra- ditional restaurant operations often face challenges such as order inaccuracies, long wait times, dependency on labor, and inefficient payment systems. To ad- dress these issues, we propose Swift Kiosk, a digital kiosk application designed specifically for caf´es and restaurants. Built using the MEARN stack (MongoDB, Express.js, Angular, React, and Node.js), Swift Kiosk enhances the food ordering experience by allowing customers to customize their meals, process cashless trans- actions, and reduce dependency on manual labor. This innovation streamlines restaurant operations while delivering a seamless and engaging customer experi- ence. One of the core functionalities of Swift Kiosk is food customization, allowing users to personalize their meals and beverages with ease. Customers can select portion sizes, ingredients, dietary preferences (vegan, gluten-free, keto, etc.), and cooking methods, ensuring a tailor-made dining experience. The application pro- vides a real-time preview of the customized meal, ensuring accuracy before order placement. Swift Kiosk leverages MongoDB as the database to efficiently store and manage user preferences, order details, and restaurant menu configurations. With Node.js and Express.js powering the backend, the system ensures fast and scalable pro- cessing of customer orders, reducing wait times and improving order accuracy. To enhance user interaction, the application is designed using React.js for the frontend, offering a responsive and visually appealing interface. For businesses, an Angular-based admin panel allows restaurant owners to manage menus, track sales, and analyze customer behavior in real time. This combination of technolo- gies ensures a smooth and dynamic user experience. Swift Kiosk automates the order-taking process, reducing the reliance on hu- man staff and increasing efficiency. Customers can place their orders directly through the kiosk interface, which sends requests instantly to the restaurant’s kitchen system. The application supports multiple payment options, including UPI, digital wallets, credit/debit cards, and QR-based transactions, ensuring se- cure and hassle-free payments. By integrating Express.js and MongoDB, transaction data is stored securely, and real-time payment confirmations ensure a frictionless checkout process. This automated system eliminates long queues, minimizes order miscommunication, and enhances customer satisfaction. By digitizing order placement and payment, Swift Kiosk reduces labor depen- dency, allowing restaurant staff to focus more on food preparation and service rather than manual order management. The application also provides data ana- lytics using MongoDB’s aggregation framework, helping restaurant owners analyze customer preferences, order trends, and peak business hours. These insights assist in optimizing menu offerings, pricing strategies, and targeted promotions, enhanc- ing overall profitability. Swift Kiosk, powered by the MEARN stack, presents a modern, scalable, and efficient solution for caf´es and restaurants. By allowing customers to customize their food, automating the ordering and payment process, and reducing opera- tional inefficiencies, Swift Kiosk transforms traditional restaurant operations into a tech-driven, customer-centric model. As the food service industry continues to embrace digital transformation, Swift Kiosk stands as a pioneering step towards enhancing efficiency, reducing operational costs, and improving the overall dining experience.

DOI: http://doi.org/10.5281/zenodo.17637230

Healthsync: Smartcare Companion

Authors: Krishna Wable, Bhumika Raut, Radhika Ware, Achal Patil, Prof.Vrushali Channe

Abstract: Maintaining a healthy lifestyle often becomes diffi- cult due to generic advice and delayed health insights. Health- Sync: SmartCare Companion is an AI-driven system designed to offer personalized, adaptive, and culturally relevant healthcare support. It integrates BioMistral-7B for medical text understand- ing and ResNet for food and symptom image recognition. Using fuzzy logic with TOPSIS/AHP, the system estimates personalized daily nutrient and calorie needs, supported by an ontology-based food graph linking Indian dishes, nutrients, and diseases. With time-series tracking and reinforcement learning, HealthSync continuously refines recommendations based on user progress. Trained on Indian food and lifestyle datasets, it delivers accurate, real-time, and inclusive wellness guidance—transforming preven- tive healthcare into a smarter, more user-focused experience.

Developing an End-to-End Secure Chat Application

Authors: S. Surendar Raj, K. Sai varsha

Abstract: Chat applications have emerged as indispensable tools on smartphones, offering users the ability to exchange text messages, images, and files at no cost. However, ensuring the security of these communications is paramount. This paper proposes a secure chat application that implements End-to-End encryption, safeguarding private information exchanged between users and providing robust data protection. The application also addresses storage security concerns. By outlining a set of requirements for a secure chat application, this paper informs the design process. The proposed application is evaluated against these requirements and compared with existing popular alternatives to assess its security features. Furthermore, the application under goes rigorous.

Abusive and Hate Speech Detection in Social Media using Natural Language Processing

Authors: Praveen B, Sripadma R

Abstract: Social media platforms such as Facebook, Twitter, Instagram, and WhatsApp have emerged as primary channels for public communication, information sharing, and social interaction. However, the same platforms also serve as spaces where abusive expressions, offensive remarks, and hate speech are increasingly common. Hate speech may target individuals or groups based on factors such as religion, nationality, gender, ethnicity, or other identity characteristics, and can result in psychological harm, discrimination, and real-world conflict. Manual moderation of such continuously increasing online content is challenging, inconsistent, and time-consuming. Therefore, automated detection systems are needed to analyze and classify harmful language. This research proposes a Natural Language Processing based system that preprocesses text, extracts features using TF-IDF, and classifies content using Support Vector Machine (SVM). The results show that this approach effectively distinguishes between normal, abusive, and hate speech, making it suitable for real-time moderation in social media platforms.

Facial Expression Recognition For Mental Health

Authors: Dr. Radha Shirbhate, Zaidkhan Pathan, Aditya Gude, Vishal Joshi

Abstract: Mental health plays a vital role in determining overall well-being, productivity, and social interaction [1], [2]. However, diagnosing mental disorders like depression and anx- iety often relies on self-reporting and therapist observation, which may introduce subjectivity and delay treatment. This paper presents an AI-based facial expression recognition (FER) framework that analyzes human emotions from visual cues to assist early mental health assessment [3], [4]. The proposed system uses Convolutional Neural Networks (CNNs) trained on the FER-2013 dataset, combined with MediaPipe for real-time facial landmark extraction and OpenCV for image preprocessing. The model recognizes seven basic emotions: happy, sad, fear, anger, disgust, surprise, and neutral. Real-time video streams are processed, and the detected emotional states are visualized on a dashboard that can track emotion trends over time [5]. The system demonstrates promising performance with accuracy above 92% on validation data and real-time latency under 40 ms/frame [6]. The integration of FER technology into mental health analysis offers an innovative, non-invasive, and continuous monitoring tool that complements traditional clinical methods.

Ai in Cybersecurity: Threat Intelligence and Prediction

Authors: Mohammad Usman M, Bhuvaneswari B

Abstract: The rapid digitization of global infrastructure has led to an exponential increase in cyber threats. Traditional security mechanisms are proving inadequate against evolving, intelligent, and large-scale cyberattacks. Artificial Intelligence (AI) has emerged as a transformative tool in cybersecurity, enabling automated detection, proactive threat prediction, and adaptive defense mechanisms. This paper explores how AI enhances threat intelligence through predictive analytics, machine learning, and deep learning techniques. It also examines challenges related to data privacy, model interpretability, and adversarial attacks. The study concludes that AI-driven threat intelligence has immense potential in transforming cyber defense, provided that human oversight and ethical frameworks are maintained.

Enhancing Restaurant Efficiency with Swift Kiosk: An Electron.js based Interactive Ordering Platform

Authors: Warish Patel, Nana Yaw Duodu, Arpita Sahu, Raksha Choudhary, Sakshi Sharma, Achala Karn

Abstract: The food and beverage sector is undergoing rapid digital transformation, which has in- creased the need for flexible, efficient, and user-friendly ordering platforms. Conventional restaurant systems often struggle with problems like order mismatches, extended waiting times, heavy dependence on staff, and outdated payment methods. To overcome these challenges, we introduce Swift Kiosk, a digital kiosk solution tailored for caf´es and restaurants. Developed using Electron.js, Swift Kiosk en- hances the dining experience by offering customizable ordering, seamless UPI-based payments, and reduced reliance on manual operations. A key highlight of Swift Kiosk is its meal customization feature, where customers can choose portion sizes, ingredients, dietary options (such as vegan, keto, or gluten- free), and preferred cooking styles. The system displays a real-time preview of the customized dish, ensuring precision before order confirmation. Orders are then sent directly to the kitchen system, which minimizes delays and reduces the chance of human error. Additionally, secure UPI integration enables quick, cashless transactions for a smoother checkout process. By streamlining order-taking, payment, and analytics, Swift Kiosk helps restaurants lower labor re- querulents while delivering higher customer satisfaction. This technology-driven approach modernizes traditional restaurant operations, cutting costs, improving efficiency, and creating a customer-focused dining environment. As the industry continues to adopt digital innovations, Swift Kiosk stands out as a scalable, sustainable, and future-ready solution for restaurant management.

Selecting an Effective Microservices Decomposition Approach: A Decision Framework

Authors: Md. Abdul Momin, M.M. Musharaf Hussain, Md. Ezharul Islam

Abstract: This research presents a comprehensive exploration of diverse microservices decomposition techniques. This research identifies the sequential steps integral to each decomposition method through a meticulous study and analysis of multiple techniques. Moreover, the paper integrates insights gleaned from a select group of experts. These experts offer valuable perspectives on software characteristics and elucidate the types of example software ideally suited for distinct decomposition types. They also validate the time and cost implications associated with each decomposition technique. Drawing from these multifaceted insights, the paper culminates in creating an algorithm. This algorithm is intricately designed based on collective knowledge and discussions surrounding software traits, such as suitability, time, and cost considerations linked to various decomposition techniques. This algorithm helps developers choose the most effective decomposition approach for microservices.

DOI: https://doi.org/10.5281/zenodo.17645806

The Role of Artificial Intelligence in Indian Education: A Survey-Based Study

Authors: Kartavy Gupta

Abstract: Artificial Intelligence (AI) is rapidly transforming the Indian education system, offering personalized learning, improved accessibility, and efficient administration. This paper examines the role of AI in Indian education, combining secondary research with a small-scale student survey. The survey, conducted among 50 senior secondary students from urban and semi-urban schools in India, explored awareness, usage, and perceptions of AI in learning. Results indicate that while 82% of students use AI-powered tools for study support, only 38% receive any formal school-based AI education. The findings highlight both enthusiasm for AI and the need for structured training, equitable access, and ethical guidelines. The study concludes that AI can revolutionize Indian education if implemented inclusively and responsibly.

Blockchain For Secure Electronic Medical Records: A Review

Authors: Suraj Yadav, Girdhari Lal

Abstract: Electronic Medical Records contain extremely private medical data, electronic medical records are frequently shared between authorized parties. Maintaining the integrity and security of that procedure is still very difficult. Blockchain technology provides a promising basis for creating and systems that are resistant to tampering with EMR data management and sharing. The blockchain-driven strategy for safe EMR sharing between healthcare organizations and research institutes is covered in this paper. We offer a framework created in collaboration with Stony Brook University Hospital to support data exchange for cancer patient care scenarios. A The implementation of the prototype confirms that the suggested system improves data availability, increases privacy, and permits precise, role-based access control.

The Penetration Power Of Water Towards Stones: A New Approach

Authors: Dr. Rishu Agarwal

Abstract: Penetration power of water is a different quality that makes universal solvent much more useful. There are various factors affecting penetration of water including pressure, velocity, temperature, present minerals in water. It also depends on the surface of stone on which water is thrown. Present study is focused on study of various factors affecting penetration power of water.

Smart Shared Container Space Management Platform with AI-Based Container Matching and Real-Time Booking System

Authors: Sharayu Anap, Divyanshu Rajhans, Atharva Naik, Srividhya Achanta, Vedant Jadhav

Abstract: The global logistics and transportation industry faces persistent challenges related to inefficient container utilization, manual space booking, and communication delays among stakeholders. The proposed system, Smart Shared Container Space Management Platform with AI-Based Container Matching and Real-Time Booking System, addresses these issues through an integrated, cloud-enabled digital platform that connects Container Providers and Traders in a shared, intelligent ecosystem. The platform leverages Artificial Intelligence (AI) for smart container- space recommendations, enabling efficient matchmaking based on trader requirements such as size, cost, duration, and proximity. The system ensures seamless, real-time operations through automated booking workflows, live availability tracking, and AI-assisted approvals—monitored via a centralized Admin Dashboard. Developed using a modern web technology stack, the system employs Next.js / React for an interactive frontend, Supabase for structured data storage, and Firebase for authentication and real-time synchronization. The AI chatbot is powered by the OpenAI API (or a custom GPT endpoint), providing users with intelligent assistance for queries and bookings. Tailwind CSS and ShadCN UI deliver a clean, responsive, and modern interface, while payment modules such as Stripe or Razorpay enable secure transaction processing. By integrating AI intelligence, automation, and real-time cloud connectivity, the proposed platform significantly enhances container space utilization, reduces idle storage time, and improves coordination between traders and providers. This system sets the foundation for a next-generation logistics management ecosystem that is scalable, data-driven, and powered by intelligent automation.

“Teacher-AI Trust Dynamics: Understanding the Psychological Barriers Adoption of Artificial Intelligence (AI) in Education”

Authors: prof. Dharmaraj s kumbar

Abstract: Artificial Intelligence (AI) has definitely revolutionised the world of science and technological era. Specifically, the introduction of AI has taken place in a revolutionary shift in the field of education. AI’s capabilities are transforming education in the future, from automated assessment systems to personalised learning experiences. But even with this technological development, there is still an important issue with teachers and AI not working together. One of the greatest obstacles to fully deploying AI is this coordination gap. The complex interactions that result from integrating AI into the educational system are examined in this study report. It mostly concentrates on the difficulties in attaining harmony between educators, learners, and educational institutions. While AI has the ability to offer data-driven insights in the area of education, it currently falls short of meeting expectations of human interaction and educator’s experience-based judgements. Additionally, this study explores the different impacts of applying AI on teacher performance. AI has the ability, on the one hand, to expand educational chances, lessen administrative tasks, and enhance educational standards. It can improve productivity for educators. However, there are questions about how AI will impact educator classroom management and professional ability. This study looks at how AI is transforming the job of educators and how it affects student-teacher engagement. Finding a balance between artificial and human intelligence is essential for the future of education. In order to make full use of AI in education, this study examines strategies for strengthening collaboration between educators and technology.

MoleculAR: An Autonomous Agentic Framework for Novel Molecule Discovery via Stability Analysis and ChEMBL Cross-Referencing

Authors: Rajeshkumar S. A, Vishrut Nath Jha

Abstract: The rapid evolution of artificial intelligence in chem- istry has enabled autonomous systems capable of exploring vast chemical spaces and identifying novel compounds with potential pharmacological value. We introduce MoleculAR, an autonomous agentic framework that integrates molecular relationship discov- ery, quantum-level stability analysis, and cheminformatics-based novelty verification. Given a set of input molecules, MoleculAR predicts potential co-functional partners using hybrid structural and functional similarity analysis, followed by energetic and stability evaluation through quantum chemical computations. Subsequently, the system performs compound novelty verification via cross-referencing with the ChEMBL database. Molecules that are predicted to be both chemically stable and absent from ChEMBL are shortlisted for further investigation. By combin- ing agentic reasoning, computational chemistry, and database- driven validation, MoleculAR establishes a closed-loop discovery pipeline that enhances efficiency in de novo compound identifica- tion. Experimental evaluations demonstrate MoleculAR’s ability to autonomously identify stable and novel molecular candidates across diverse chemical classes.

Web-Based Strategic Performance Management System For President Ramon Magsaysay State University

Authors: Carl Angelo S. Pamplona, Menchie A. Dela Cruz

Abstract: The web-based strategic performance management system for President Ramon Magsaysay State University (PRMSU) was developed to provide a more streamlined and centralized way of handling performance management–related tasks. The main purpose of the study was to evaluate the developed system in terms of software quality (using ISO/IEC 25010:2011 metrics), level of acceptability, and level of readiness of PRMSU. The study also identified significant differences between the evaluations of PRMSU staff and supervisors on (a) software quality of the system, (b) acceptability of functionality and performance, and (c) readiness for implementation in terms of information system facility and technical personnel. Based on the respondents’ evaluation, the developed system’s software quality was “Excellent,” its level of acceptability was “Highly Acceptable,” and its readiness for implementation was “Very Ready.” There was no significant difference between staff and supervisor evaluations on software quality or on any of the measured domains (Functional Suitability, Performance Efficiency, Compatibility, Usability, Reliability, Security, Maintainability, and Portability). Finally, the researcher provided recommendations including full implementation of the system, periodic re-evaluation and maintenance, user training, and ongoing studies to align with evolving trends such as implementation of data analytics and artificial intelligence functionalities

DOI: http://doi.org/10.5281/zenodo.17657287

AI-Powered Trip Planner Using Retrieval-Augmented Generation (RAG) Models

Authors: Yash Sharma, Mr. Ritesh Kumar Chandel

Abstract: Travel planning requires synthesizing large and diverse datasets including destinations, transportation, budgets, accommodations, user preferences, and seasonal variations. Traditional tools depend on static rules and manual inputs, making them inefficient for personalized planning. This research introduces an AI-powered RAG-based trip planner that integrates vector retrieval with generative reasoning. The system mitigates hallucinations, enhances real-world grounding, and produces optimized, personalized itineraries.

Enhanced Thesis: RAG-Based Intelligent Expense Tracker

Authors: Saransh Khanna, Mr. Ritesh Kumar Chandel

Abstract: This research investigates the development of an intelligent expense tracking system powered by Retrieval-Augmented Generation (RAG). Traditional expense tracking tools rely on structured inputs and static categorizations, creating friction for users who log expenses in natural language. The proposed system uses a hybrid architecture of vector retrieval and generative reasoning to extract accurate financial insights from unstructured text, improving reliability while reducing hallucinations commonly seen in standalone LLMs.

“UNI Verse: A Unified Digital Platform for Student–Faculty Interaction and Academic Coordination”

Authors: Reeva Rawat, Ronit Roy, Vivek Sharma, Soham Andhyal, Dr. Ravi Rai Chaudhari

Abstract: The UNI Verse platform is designed to create a more intelligent, engaged campus and digital learning environment. It provides a single platform for all organizational, informational, and engagement needs of students and faculty to facilitate their learning and academic experience. It gives students a centralized location to learn about their academic engagement, course schedule, office hours of professors, assignment due dates, and upcoming campus activities. UNI Verse will even offer reminders, to help keep students informed about deadlines & updates on campus. In addition to the academic purpose, UNI Verse allows students and faculty to have more substantive communications with the real-time direct messaging and meeting scheduling options. It even provides tools to navigate campus or even journal or share notes digitally, all to foster collaboration with classmates and engage as a learning community outside and inside of the classroom. Ultimately, UNI verse is a hub and usable system for productive, engaged, interactive, and organized campus life.

DOI: https://doi.org/10.5281/zenodo.17657734

A Review On The Ethics Of AI In Facial Recognition Technology

Authors: Kashish Aggarwal, Mr.Vikas kumar

Abstract: One of the most powerful and ethically debatable technologies of the 21 st century are Facial Recognition Tech- nology (FRT), which is also driven by Artificial Intelligence (AI). FRT can be used to automate the identification and verification of individual persons under different conditions, such as law enforcement, border control, and digital authentication, by relying on machine learning and deep neural networks. Even though the technology has a positive impact on reducing safety and efficiency, it poses significant ethical issues concerning privacy, data protection, bias, consent, and responsibility. The paper is a thorough overview of the ethical aspects of AI-driven facial recognition, the benefits that it has, and the vulnerabilities of this technology. It studies the problem of algorithmic bias, data governance, and ethical dimensions of surveillance-based applications. Global regulatory reactions to the subject, including the European Union General Data Protection Regulation (GDPR), the suggested AI Act, and other upcoming data protection regulations in the United States and India are discussed to point out differences in regulation. In addition, the paper explains mitigation measures such as fairness-conscious algorithms, transparency, and privacy- sensitive methods to encourage the responsible use of AI. This research highlights the importance of balancing between innova- tion and accountability as a means of seeking to fulfill societal needs without infringing on human rights by critically analyzing and conducting case-based reviews that conclude that facial recognition can be used to the benefit of society without taking away the rights of the people.

Optimizing Hospital Resource Utilization Through Edge AI

Authors: Sagar Gupta, Vikas kumar

Abstract: Hospitals often face immense challenges related to resource utilization and managing these resources efficiently in light of increasing demands from patients and the volume of data. The use of traditional centralized healthcare computing systems introduces latency and inefficiencies related to real-time decision-making. This chapter reviews how Edge AI can transform hospital resource utilization. By processing data closer to its source using edge devices, Edge AI allows for real-time analytics, proactive resource allocation, and responsiveness of operations. This chapter details the current challenges in hospital resource management, the architecture of an Edge AI-driven resource management system, and also discusses the case studies for their implementation. Quantitative evaluation regarding improved performances such as reduced wait time for patients and improvement in the bed occupancy rate is discussed. Integration of Edge AI with IoT and other emerging technologies such as 5G and federated learning is also considered as future work. Our analysis further shows that Edge AI increases not only hospital efficiency but also better patient outcomes through intelligent and timely interventions.

Color Tuning In Eu2+ Doped Barium Silicate Nanophosphor: A Facile Combustion Synthesis For Display Device Applications

Authors: M. Venkataravanappa, K.N. Venkatachalaiah

Abstract: Combustion synthesis method was used to prepare Europium doped Barium silicate nanophosphors. The crystalline structure from PXRD profiles showed that the fabricated sample have orthorhombic phase [JCPDS Card No. 78-1371] with a face group Pb nm- 62, with no variation in the diffraction profiles because of the inclusion of the Eu2+ions. The images are regular and irregular shapes with smooth surface were observed from SEM. The photometric spectra were studied for optimized nanophosphor displays green emission at ~ 505 nm due to the presence of Eu2+ ions corresponds to 5D07F2 transition. The CIE arrangement was green spread, which are basically near to the standard characteristics and Correlated Color Temperature (CCT) was acquired 12236K. These outcomes showed that the fabricated NPs can be viably utilized as green color part in the fabrication of white light emitting diodes.

DOI: https://doi.org/10.5281/zenodo.17660524

Monitoring Of Bio Medical Devices Based On Electronics

Authors: Professor Kavita Singh

Abstract: The electronic monitoring of biomedical devices has revolutionized healthcare delivery by enabling real-time diagnostics, continuous physiological tracking, and rapid response mechanisms. This study reviews the technological advances underpinning electronic biomedical monitoring systems, describes key classes of devices, and discusses engineering challenges and prospects for future developments

EduHelm – Educationally Helping Mentor

Authors: Dhyanesh M, Dharshini S, Deepak P, Aisha Amna A, Mr. S. Dhinakaran

Abstract: EduHelm is an intelligent AI-driven career and learning platform designed to personalize and optimize student growth through dynamic learning paths, adaptive projects, and real-time skill tracking. The system leverages machine learning and large language models to recommend personalized roadmaps, micro-projects, and certification-based goals aligned with each user’s academic profile and aspirations. EduHelm integrates AI mentorship, progress analytics, and gamified challenges to foster continuous improvement and engagement. It also enables students to upload learning journals and research patterns, automatically analyzing them for feedback and improvement suggestions. Developed using a React frontend, Flask backend, and PostgreSQL database, with AI modules powered by OpenAI and Hugging Face, EduHelm delivers a seamless and intelligent experience that empowers learners to take control of their career growth in the digital age.

Traffic Control with Ambulance Sign and ANPR

Authors: Mr. Karthiban R, Tharun S, Naveen B, Yuvaraj S, Pragathiswaran G

Abstract: Urban site visitors congestion has come to be one of the most critical challenges confronted via cutting-edge towns. Emergency automobiles, wi-fi ambulances, enjoy frequent delays due to traffic wireless indicators and roadblocks, which may be deadly in lifestyles-threatening conditions. This paper proposes a smart visitors control device that mixes automatic wide variety Plate recognition (ANPR) and ambulance signal detection to make sure actual-time prioritization of ambulances. The proposed system makes use of digicam-primarily based vehicle identity, an IoT-enabled site visitors manage network, and adaptive sign timing to create a “inexperienced corridor” for emergency automobiles. Experimental simulations imply that the device can reduce ambulance waiting time at intersections via extra than 60%, improving the wi-fi of emergency reaction services. the combination of ANPR era permits automatic detection of ambulances without counting on guide systems or wireless tags, making it scalable and price-effective for clever city deployment.

Topic:Human–Robot Interaction: Current Trends and Applications

Authors: Sameer Agarwal, Vaibhav Kalukar

Abstract: Human–Robot Interaction (HRI) is an interdisciplinary field that examines how humans communicate, collaborate, and coexist with robotic systems. With major advancements in artificial intelligence, sensor technologies, and automation, robots are increasingly becoming interactive partners in healthcare, manufacturing, education, and personal assistance. This research investigates current trends in HRI, emphasizing the shift from traditional command-based systems to socially aware, adaptive, and collaborative robots. A qualitative review methodology is used to analyze studies published between 2015 and 2024, focusing on social robots, industrial cobots, healthcare and assistive robotics, and service-oriented systems. Findings reveal rapid growth in socially interactive robots, widespread adoption of collaborative robots in industry, and enhanced communication through AI-driven speech, gesture, and emotion recognition. Despite challenges such as limited emotional intelligence, ethical concerns, and high costs, the increasing integration of robots into human environments highlights significant potential for future development. The study concludes that HRI will play a crucial role in shaping intelligent, human-centric robotic systems, requiring continued research in transparency, trust, and ethical design.

DOI: https://doi.org/10.5281/zenodo.17670137

Data Journalism Practices In Indian News Media: Opportunities And Challenges

Authors: Mr. Mayank Arora, Nupur

Abstract: The rapid expansion of digital technologies has transformed the landscape of contemporary journalism, bringing data-driven reporting to the forefront of news production. In India, the rise of data journalism—an approach that integrates statistical analysis, visualization tools, and storytelling—has opened new possibilities for accuracy, depth, and transparency in media reporting. Yet, the adoption of data journalism remains uneven, complicated by structural issues within Indian newsrooms, limited technological expertise, and the pressures of fast-paced news cycles. This paper investigates how Indian news organizations understand, adopt, and implement data journalism practices. It explores the professional, infrastructural, and ethical challenges that constrain data-driven reporting, while also identifying opportunities created by digital literacy, open-data movements, and audience demand for evidence-based journalism. Through a review of scholarly literature, industry reports, and comparative perspectives, the study highlights how data journalism in India stands at a critical intersection of innovation and limitation. The paper argues that although data journalism has the potential to strengthen public discourse and democratic accountability, its growth depends on sustained investment in training, technological resources, and editorial vision. Ultimately, the study positions data journalism as an evolving journalistic paradigm that can contribute significantly to India’s media ecosystem if supported by a culture of transparency, collaboration, and professional development.

Modeling Human Emotion Dynamics Using Chaos Theory

Authors: Manisha Rai, Vishal Rai

Abstract: Human emotions exhibit complex, nonlinear behaviors that traditional linear psychological models fail to capture adequately. This paper proposes a comprehensive framework for understanding emotional dynamics through the lens of chaos theory and nonlinear dynamical systems. We examine how concepts such as strange attractors, bifurcations, Lyapunov exponents, and phase space representations can illuminate the intricate patterns underlying affective processes. Empirical evidence from heart rate variability studies, electroencephalographic analyses, and longitudinal mood assessments supports the view that healthy emotional functioning corresponds to a bounded chaotic regime characterized by optimal complexity and adaptability. We further explore how deviations from this regime—manifesting as either excessive rigidity or instability—may underlie various mood disorders including depression, anxiety, and bipolar disorder. The paper presents mathematical formulations for emotional phase space dynamics, discusses computational approaches for reconstructing emotional attractor landscapes from empirical data, and outlines applications in clinical psychology, affective computing, and personalized mental health interventions. Understanding emotions as emergent properties of complex dynamical systems offers profound implications for diagnosis, treatment, and the development of emotionally intelligent technologies.

AI Enable GPS Based Employee Attendance And Live Monitoring System With Real Time Alerts

Authors: Himanshu Kumar Rai, Harsh Goyal, Dr. Anshu Gupta

Abstract: This research proposes a Geo‑AI based smart attendance monitoring system that will integrate Artificial Intelligence (AI) with Global Positioning System (GPS) geofencing to provide accurate, contactless, and secure attendance tracking. The proposed system will address persistent challenges such as proxy attendance, location spoofing via mock‑GPS tools, and manual recording errors by combining AI‑based facial recognition (with liveness analysis) and geolocation verification. A mobile front end will enable frictionless check‑ins, while a cloud back end will ensure secure storage, auditability, and real‑time analytics. We detail the proposed architecture, algorithms, and implementation plan that will use a React Native application, Node.js/Express services, MongoDB storage, and Python micro‑services for inference and anomaly detection. If successfully implemented, in a controlled deployment across two office sites the system is expected to achieve 98% attendance‑marking accuracy, is projected to reduce administrative overhead by approximately 60%, and is anticipated to deliver alert latencies below five seconds. We will compare our approach with RFID, fingerprint biometrics, and GPS‑only mobile apps, and we will report an ablation analysis to quantify the benefits of liveness checks and geofence validation.’

DOI: http://doi.org/10.5281/zenodo.17679353

Smartguard: Emergency Response System for Portable Device

Authors: Sabitha.K, Mohan Sundar.M, Santhosh. A. S, Sri Raghav. B. V, Supriya.A

Abstract: The Vehicle Accident Detection and Notification App is a mobile safety tool designed to identify vehicle accidents and automatically notify emergency services and family members. Utilizing the smartphone’s accelerometer and gyroscope sensors, the app tracks sudden impacts, sharp turns, or rapid stops that suggest a potential collision. Upon detecting unusual movement, the GPS feature activates to pinpoint the exact location, ensuring that emergency responders can get to the scene quickly, even if the user is unconscious. The app automatically sends a comprehensive alert message that includes the user’s information, timestamp, and precise coordinates to registered contacts. To minimize false alarms, it incorporates a brief confirmation timer that enables the user to cancel the alert. Additional functionalities such as a manual SOS button, cloud-based data storage, and automated calling improve reliability. Overall, the app provides an efficient, affordable way to enhance road safety and decrease fatalities.

Gesture Mouse controller

Authors: Dr.P.Guhan, Mr.C.Barath

Abstract: Gesture-controlled computers and laptops have recently become increasingly popular, with Leap Motion technology leading this innovation. This technique enables users to control certain system functions simply by moving their hands in front of a camera. Compared to traditional slides or overhead projectors, computer-based presentations offer greater interactivity through audio, video, and programmable elements, though they can be more complex to use. As technology continues to evolve, finding new and affordable ways to interact with computers has become essential, especially since touchscreens are not feasible for all applications. To address this, a virtual mouse system based on object tracking and hand gestures is proposed as an alternative to physical mice and touch interfaces. The system employs computer vision techniques using Python and OpenCV, with a webcam detecting hand movements through HSV color segmentation. Users can wear colored caps or tapes on their fingers to move the cursor and perform actions like left-click, right-click, and double-click. The camera feed is processed in real time and displayed on the screen, allowing smooth, contactless interaction.

DOI: https://doi.org/10.5281/zenodo.17680123

Real Time Object Detection using YOLOv8

Authors: Mr.S. Sathish, Ms. A. Sangeetha

Abstract: Object detection is a key area in computer vision with wide-ranging applications such as autonomous driving, surveillance, and augmented reality. YOLOv8, an advanced version of the YOLO series, stands out for its high accuracy and real-time performance. This study focuses on the analysis and implementation of YOLOv8 for real-time object detection, emphasizing its architecture that employs a deep neural network to perform a single forward pass for predicting bounding boxes and class probabilities simultaneously. The model’s main components—backbone network, detection layers, and anchor boxes—work together to achieve fast and efficient detection. Practical aspects such as model optimization, GPU acceleration, and post-processing are also explored to enhance speed and accuracy. Experiments conducted on benchmark datasets and real-world data demonstrate YOLOv8’s effectiveness, proving it to be a robust and adaptable solution for real-time object detection tasks. This research contributes to the advancement of computer vision and provides practical insights for deploying YOLOv8-based detection systems across multiple domains.

DOI: https://doi.org/10.5281/zenodo.17680246

A Novel Transformer Model With Multiple Instances Learning For Diabetic Retinopathy Classification

Authors: Mr. S. Kaushik Raj, Mrs. B. Shyamala Devi

Abstract: Diabetic retinopathy (DR) is one of the major causes of vision loss worldwide, making early and reliable detection extremely important. This work presents an advanced transformer-driven framework combined with a Multiple Instance Learning (MIL) strategy to classify DR using retinal fundus images. The transformer model effectively learns long-range relationships and contextual patterns, while the MIL approach analyzes image patches to highlight clinically significant areas. Together, this hybrid system delivers strong feature representation and stable classification performance, even with variations in image quality and resolution. Trained on a large and diverse dataset, the proposed model achieves higher sensitivity and specificity than many existing deep learning techniques. The system is designed to assist eye-care professionals by enabling accurate, timely assessments and providing a scalable solution suitable for extensive DR screening initiatives.

DOI: https://doi.org/10.5281/zenodo.17680366

Smart LV Conductor Protection System

Authors: Priyanka K, Sasiprabha V, Shobaa P, Shreelinganaathan M, Srimukhi TG

Abstract: Low‑voltage distribution lines are widely used in residential and semi‑urban areas, where accidental conductor breakage or unintended grounding can pose severe safety hazards, result in prolonged power outages, and damage equipment. Current detection methods rely heavily on manual handling or high‑cost protection systems, resulting in delayed responses and increased operational risks. To address these gaps, this work proposes a cost‑effective Smart LV Fault Detection System capable of identifying conductor breakage and earth faults in real time. The system integrates voltage and current sensing units with an ESP32 controller that continuously analyses line behaviour, recognises abnormalities, and triggers immediate alerts. Visual and audio warnings are activated on-spot, while remote fault notifications are delivered via GSM‑based communication. A solar‑powered backup ensures uninterrupted operation during power failures. Experimental tests show more than 95% detection accuracy with a response time under one second. The proposed solution is lightweight, scalable, and suitable for both rural and urban power networks, providing improved safety, reduced downtime, and faster maintenance response.

Deployment Of Quantum Computing For Optimized Disaster Response Planning

Authors: V.Gomathi priya

Abstract: Disaster management has transformed from a reactive emergency-centred practice to a proactive, technology-driven discipline focused on minimizing the impact of both natural and human-induced hazards. This research highlights the rising frequency and severity of disasters such as earthquakes, floods, and cyclones, emphasizing the demand for integrated, systematic, and multi-sectoral strategies. The study examines how contemporary frameworks now prioritize preparedness through scenario planning, prevention, and capacity-building, supplemented by robust early-warning systems and risk reduction measures. The role of advanced technology—including decision support systems powered by artificial intelligence, geographic information systems, and, more recently, quantum computing—is analysed for its effectiveness in rapid data processing, predictive modelling, and optimized resource allocation. The research also reviews key disaster events from the past five years to illustrate ongoing challenges and responses, demonstrating the need for resilient infrastructure and coordinated recovery operations. Findings suggest that modern disaster management not only saves lives and reduces economic loss during crises but also promotes societal resilience and sustainable development through continuous innovation, capacity enhancement, and collaborative governance. This evolution ensures that disaster management remains a critical element for safeguarding communities in an era of mounting environmental and technological risk.

Explainable Artificial Intelligence Based Early-Stage Detection of Liver Cancer

Authors: Rohit.N, Dr.R. Kannadasan

Abstract: In human anatomy, the liver has a special feature called regeneration, and this feature helps the liver to grow back even after a large part of its organ is removed. Like regeneration, it helps maintain bile salts, protein synthesis, and detoxification. Irregular eating habits, sleep, and alcohol consumption increase liver function and cause various diseases such as fatty liver and other liver problems. Hepatocellular carcinoma is one of the liver diseases, and it is caused by the abnormal growth of cells in the liver. In such cases, liver regeneration is possible if the disease is identified at an early stage. To support this early identification, several research works have been carried out using both artificial intelligence and deep learning techniques. Therefore, this paper proposed an automated approach to identify liver cancer at an early stage through a segmentation and classification using deep learning techniques. The early-stage identification becomes possible with the dual stage of segmentation using U-NET and XAI. The U-NET helps to segment the image through its various texture properties of the image. Then, the XAI is used to analyze the individual regions of the image. This special feature of this approach is that it uses descriptive AI for classification, and this helps in identifying critical regions through heat maps and saliency maps. This technique was tested on a computed tomography dataset of liver images and its performance was evaluated in terms of precision, accuracy, recall and F1-score.

DOI: https://doi.org/10.5281/zenodo.17696117

Contrast Enhancement Method For MRI And X-ray Images Based On A Modified Entropy Curve

Authors: Shashikant, Vinay Saxena

Abstract: MRI and X-ray images are widely used for detecting various prevalent diseases, and accurate diagnosis heavily depends on image quality. Often, images suffer from low contrast due to factors such as uneven or insufficient illumination, mo- tion of the subject or imaging device, and device-related imperfections, some- times accompanied by noise or artifacts. In such cases, an effective contrast enhancement algorithm that improves visibility without amplifying noise or ar- tifacts becomes crucial. In this work, we propose a simple yet efficient contrast enhancement method for medical images, based on dividing a modified entropy curve into two sub-entropy curves and combining it with homomorphic filtering. The proposed approach enhances image contrast while preserving important details and minimizing noise amplification. The effectiveness of the method is demonstrated through comprehensive qualitative and quantitative evaluations, highlighting its potential for improving medical image analysis.

DOI: http://doi.org/10.5281/zenodo.17697301

Moving Object Detected System

Authors: Ms. Haripriya, Vishva P, Varunkumar V.

Abstract: The Moving Object Detection System is a project that is based on vision and is aimed at the real-time identification and tracking of moving objects by applying the processing of images. The system can either analyze live video streams or recorded footage in order to detect the motion by the method of comparing the differences between two consecutive frames. The system that is executed on Python together with the OpenCV library performs background subtraction, frame differencing, and contour detection to find and draw the moving objects accurately. The technology has great potential in fields like surveillance, traffic management, and automation of security systems. The system is capable of detecting motion with high efficiency, requiring very little computational resources, and at the same time it is very easy to integrate with IoT and alert systems for added functionality. The system in question represents an efficient solution that can be applied in various contexts not only for motion detection but also for its cost-effectiveness and versatility. Moreover, the proposed system can handle indoor and outdoor setup variations like light changes and background noises through the use of filtering and thresholding techniques. Thus, it can already be a low-budget, real-time, and powerful solution for motion tracking in smart surveillance and safety applications. Further improvements can introduce the application of deep learning for the classification of detected objects and the provision of cloud-based alert systems that would further extend its range of use across various domains.

‘A Study Of Conflict Management Techniques In Large Organizational Settings’

Authors: Ms. Pooja Jagatnarayan Dixit

Abstract: Conflict is an unavoidable component of organizational life, particularly in large organizational settings characterized by interdependence, complex hierarchies, and resource competition. Effective conflict management is essential for sustaining productivity, fostering collaboration, and promoting positive workplace relationships. This study examines conflict management techniques used in large organizations, with a specific focus on how the choice of technique influences employee satisfaction and organizational performance outcomes. A mixed-methods approach involving literature synthesis and analysis of a representative dataset was used. Findings highlight that collaborative and compromising approaches tend to correlate most strongly with improved employee satisfaction and performance outcomes, while avoidance and competitive approaches exhibit weaker or inconsistent effects. The study provides implications for managers and organizational leaders regarding selecting appropriate conflict management strategies, and suggests future directions for organizational research on adaptive conflict intervention models.

Assessment Of Land Encroachment In Kwara State Polytechnic Permanent Site Using A Geographic Information Approach

Authors: Fashagba, I, Asonibare, R. O, Babatunde, K, Ajadi, B. S

Abstract: Land encroachment has become a major challenge affecting land administration and institutional expansion in Nigeria, particularly in peri-urban areas. This study assesses the pattern, extent, and progression of land encroachment on the permanent site of Kwara State Polytechnic, Ilorin, from 2004 to 2024 using aerial drone imagery and GIS techniques. The objectives were to: (i) identify areas encroached upon by surrounding settlements, (ii) determine the proportion of land currently occupied by the institution, and (iii) visualize encroachment trends through maps and imagery. Primary data were collected using a DJI Phantom 4 Pro drone, and the imagery obtained was processed into digital, detailed, topographic, and perimeter maps. Results show rapid and continuous expansion of settlements such as Ara, Ajia, Magaji, Budo-Oba, Yerima, Dangiwa, Akuo, and others, with several fusing into larger settlement clusters. Encroachment is most severe along the southern and eastern axes of the Polytechnic. Less than one-third of the acquired institutional land remains undeveloped, creating opportunities for illegal occupation. The study recommends the construction of a perimeter fence, government-led relocation of encroaching settlements, and the provision of institutional accommodation through public-private partnerships.

Role of AI in Autonomous Vehicle Decision Making

Authors: Mayuri R. Tone, Mohammad Abdul Razzaq, Syed Abdur Rasheed, Saud Ahamed

Abstract: Artificial Intelligence (AI) has become the cornerstone of autonomous vehicle (AV) technology, enabling self-driving systems to make complex, real-time decisions with minimal human intervention. By integrating machine learning, deep neural networks, computer vision, and sensor fusion, AI allows vehicles to interpret their surroundings, predict potential hazards, and plan safe and efficient routes. Decision-making in AVs relies on continuous data analysis from LiDAR, radar, cameras, and GPS to assess dynamic traffic conditions and respond adaptively to unpredictable environments. AI algorithms learn from vast datasets to improve accuracy, reliability, and safety, ensuring context-aware and ethical decision processes. This paper explores the pivotal role of AI in enhancing the perception, reasoning, and decision-making capabilities of autonomous vehicles, highlighting current advancements, challenges, and the potential impact of intelligent systems on the future of transportation.

DOI: https://doi.org/10.5281/zenodo.17708497

Machine Learning Based System For Optimal Crop Recommendation

Authors: Yash Pratap Singh, Tanya Dwivedi

Abstract: Agriculture plays a major role in the economy and livelihood of many people, especially in developing countries. Farmers often face difficulties in choosing the correct crop because soil nutrients, weather conditions, and rainfall vary from place to place. Choosing the wrong crop can reduce yield and lead to financial loss. To solve this problem, a machine learning based crop recommendation system can be used. This system analyzes soil features such as Nitrogen (N), Phosphorus (P), Potassium (K), pH value, and environmental factors like temperature, rainfall, and humidity. Based on these inputs, the system suggests the most suitable crop for cultivation. In this research, different machine learning algorithms are studied, and Random Forest is selected theoretically because it provides high accuracy and stable performance. The main aim of this study is to support farmers in making better decisions, reduce risk, and improve productivity. The proposed approach is simple, understandable, and can be further developed into a mobile or web application for real-world use.

DOI: http://doi.org/10.5281/zenodo.17708787

Optimizing Energy Efficiency In Data Centers Through Nuclear Power Integration

Authors: Girish Kishor Ingavale

Abstract: The substantial expansion of hyperscale data centers, driven by exponential growth in cloud computing, artificial intelligence, and distributed computing architectures, has created a critical energy crisis characterized by unsustainable power consumption patterns and substantial carbon emissions. Conventional energy infrastructure, encompassing fossil fuel generation and intermittent renewable sources, demonstrates fundamental inadequacies in satisfying the stringent requirements for continuous baseload power, grid stability, and cost predictability demanded by contemporary data center operations. These deficiencies manifest through supply volatility, carbon intensity concerns, escalating transmission costs, and the inherent inability of renewable portfolios to guarantee uninterrupted power delivery without extensive energy storage systems. Nuclear energy presents a strategically viable solution, characterized by exceptional capacity factors exceeding 90%, negligible greenhouse gas emissions during operation, and energy density several orders of magnitude superior to alternative generation technologies. This article provides a rigorous examination of nuclear power integration strategies for data center infrastructure optimization, emphasizing quantitative improvements in energy efficiency metrics, decarbonization outcomes, and operational resilience. Through systematic comparative analysis employing established performance indicators and lifecycle assessment methodologies, this investigation substantiates the transformative potential of nuclear power adoption in enterprise-scale computing facilities. Principal findings demonstrate that nuclear-powered data centers achieve carbon emission reductions of 92-98% relative to coal-fired generation and 85-90% compared to natural gas combined-cycle plants. Economic analysis reveals levelized cost of energy (LCOE) reductions of 25-40% over 30-year operational horizons, accounting for capital expenditure amortization, fuel costs, and decommissioning provisions. Operational metrics indicate sustained power availability factors of 99.97%, representing a 15-20% improvement over grid-dependent configurations subject to transmission constraints and generation intermittency. Integration of nuclear baseload capacity with advanced power distribution architectures yields Power Usage Effectiveness (PUE) improvements of 35-45%, attributable to elimination of redundant uninterruptible power supply (UPS) systems and optimization of thermal management infrastructure. Small Modular Reactor (SMR) technologies and fourth-generation microreactor designs demonstrate applicability to distributed data center architectures, offering scalable deployment models ranging from 1 MWe to 300 MWe capacity with enhanced passive safety systems and reduced physical footprints. The substantial capital requirements for nuclear infrastructure development, estimated at $5,000-$8,000 per installed kilowatt for SMR deployments, are economically justified through comprehensive total cost of ownership (TCO) analysis incorporating energy price stability, carbon compliance costs, and operational expenditure reductions over multi-decade asset lifecycles. Regulatory frameworks governing nuclear facility licensing, operational oversight, and decommissioning obligations are examined within the context of data center deployment scenarios, identifying pathways for streamlined approval processes and public-private partnership structures. This research advances the academic discourse on sustainable computing infrastructure by providing evidence supporting nuclear power adoption as an essential component of decarbonization strategies for the information technology sector.

DOI: http://doi.org/10.5281/zenodo.17711318

Legal Aid Chatbot

Authors: Indu Shinde, Sarvada Anvekar, Purva Nargide, Shravni Shikhre, Shantu Pujari, Professor P.S.Pandhre

Abstract: Access to legal information and assistance remains a major challenge for many individuals due to high costs, lack of awareness, and geographical barriers. With the rapid growth of Artificial Intelligence (AI) and Natural Language Processing (NLP), chatbots have become an effective tool for improving access to information and services. This research paper presents the design, development, and evaluation of a Legal Aid Chatbot that provides preliminary legal guidance to users in a simple and accessible manner. The system uses NLP techniques, machine learning models, and a structured legal knowledge base to understand user queries and generate meaningful responses.

A Novel Performance-Optimized Chaotic Mapping Technique for Secure and Compressed Image Transmission

Authors: Dr. Latha H R

Abstract: Secure communication and computing is the vital requirement of the day as global networks and information systems are expanding like the big bang theory of the universe. People have started treating information as an asset. The information asset needs to be secured from attacks. Everything in the world is being upgraded to electronic communication and this requires protection against data fraud. Information has chosen different media like text, image, audio, video and multimedia for its existence. Cryptography is the science which provides techniques for securing information over network. Network security is the process of taking physical and software measures to protect underlying infrastructure. This paper introduces cryptography, chaotic cryptography, its computational power in image security. It proposes new sealion algorithm to increase the computational power of cryptographic algorithms. It also verifies the efficiency of proposed algorithm against benchmarks set for the security of images over network.

DOI: http://doi.org/10.5281/zenodo.17720340

Productivity And Carbon Footprint Analysis Of Organic Vs. Conventional Agroforestry Systems

Authors: Chidanandamurthy G

Abstract: This study compares productivity and carbon performance of organic agroforestry (ORG-AF) and conventional agroforestry (CON-AF) using paired plots under similar soil and climatic conditions. Six pairs of 0.25 ha plots were monitored for three years. System productivity was calculated as the sum of all marketable crop and tree products per hectare, while carbon stocks were derived from tree and crop biomass and soil organic carbon (0-30″ ” cm). Life cycle inventories of all inputs and field operations were compiled to estimate greenhouse gas emissions and carbon footprints per hectare and per kilogram of product. CON-AF achieved higher system yields (mean 5,808″ ” kgha^(-1)) than ORG-AF (mean 5,017 kgha^(-1)), a difference of about 16%. In contrast, tree biomass increment was greater in organic plots (3.55tha^(-1) yr^(-1)) than in conventional plots ( 2.55tha^(-1) yr^(-1)), and soil carbon increased faster in ORG-AF (0.43tCha-1yr^(-1)) than in CON-AF (0.16tCha^(-1) yr^(-1)). Total annual carbon stock change averaged 2.09tCha^(-1) yr^(-1) in ORG-AF and 1.36tCha^(-1) yr^(-1) in CON-AF. Area-based carbon footprints were 2,950 and 4,150″ ” kgCO_2-eq ha^(-1) yr^(-1) for organic and conventional systems, respectively, while product-based footprints were 0.59 and 0.71″ ” kgCO_2-eq kg^(-1). Both systems acted as net carbon sinks, but net carbon balance was much higher in ORG-AF (4.7vs.0.8tCO_2-eq ha^(-1) yr^(-1)). The results show that organic agroforestry can maintain high productivity while substantially improving carbon efficiency and climate mitigation potential.

DOI: http://doi.org/10.5281/zenodo.17720645

Interpreting The Urban Black Box: A Spatio – Temporal XAI Framework For Causal Feature Attribution In Smart City Prediction Models

Authors: Husna Sultana, Irfan Ahmed, Shivani

Abstract: Interpreting the Urban Black Box, the proliferation of sensors and Internet of Things (IoT) infrastructure in Smart Cities has enabled the development of highly accurate Spatio-Temporal Data Mining models, often relying on deep learning architectures like Graph Neural Networks (GNNs), for tasks such as traffic prediction, crime forecasting, and resource management. Despite their high predictive performance, these models remain “black boxes,” hindering their adoption by urban planners and emergency services who require transparency and justification for critical operational decisions. This lack of interpretability poses significant challenges to accountability, auditability, and public trust. This paper addresses the critical need for Explainable AI (XAI) in the urban domain by proposing a novel Spatio-Temporal XAI (ST-XAI) Framework designed for Causal Feature Attribution. Our framework leverages a modified version of SHapley Additive exPlanations (SHAP) combined with the inherent spatial and temporal structure of the data to provide granular, instance-based explanations. The proposed methodology focuses on Temporal Attribution: Quantifying the specific influence of various look-back time windows (e.g., data from the last hour vs. data from 24 hours ago) on the current prediction. Spatial Attribution: Identifying and weighting the contributing influence of specific geographic nodes, links, or neighboring zones within the network structure. Causal Inference: Moving beyond mere correlation by prioritizing features that exhibit a strong, temporally preceding impact, providing a more actionable justification for the prediction. We demonstrate the ST-XAI Framework on a smart traffic prediction model, showing how it successfully translates opaque deep learning outputs into clear, human-understandable narratives. The results illustrate that our framework not only validates model efficacy but also acts as a vital debugging tool for city engineers, transforming black-box predictions into accountable and actionable urban intelligence.

DOI: http://doi.org/10.5281/zenodo.17720787

AI-Based Career Advisor

Authors: Radhika Kulkarni, Tejal Mungase

Abstract: The current job market introduces major difficulties in effectively connecting skilled candidates with suitable employment opportunities. This paper proposes an AI-powered career advisory system using Natural Language Processing (NLP), Machine Learning (ML), and Large Language Models (LLMs) to automate career guidance. The proposed system unites four core modules: semantic-based resume parsing using transformer models, intelligent job matching using BERT embeddings, complete skill gap analysis with customized learning recommendations, and AI-driven resume optimization for Applicant Tracking System (ATS) compatibility. The system implements a three-tier architecture with React.js frontend, FastAPI backend, and a hybrid database layer. This Stage 1 paper presents problem identification, literature survey, system architecture, and methodology. The framework addresses critical gaps in career advisory services through conceptual understanding, customized guidance, intelligent automation, and access to professional career counseling.

SENTIMENT 360

Authors: Srujan Gatla, Teja, Surya, Shashi Kumar

Abstract: Sentiment analysis on social media is a natural language processing technique used to extract subjective information and opinions from user-generated content on various social media platforms, such as Twitter, Facebook, and Instagram. The goal of this project is to perform sentiment analysis on social media data related to a particular topic or brand, such as a product launch or a social issue. Social media data will be collected using relevant APIs or web scraping tools and preprocessed by cleaning and filtering out irrelevant or spammy content. A sentiment analysis model, such as a lexicon-based or machine learning model, will be applied to classify the sentiment of the content as positive, negative, or neutral. Results will be visualized and analysed using various techniques and tools, such as word clouds, bar charts, and time series analysis, to gain insights and make data-driven decisions based on public opinion. Challenges in social media sentiment analysis include the use of slang, emojis, and hashtags, as well as the need to handle multilingual con tent. Sentiment analysis on social media can be a powerful tool for understanding public opinion, customer satisfaction, and brand reputation, and making data-driven deci- sions in various domains, such as marketing, politics, and social sciences.

Advanced Cooling Systems for Nuclear-Powered Data Centers

Authors: Girish Kishor Ingavale

Abstract: The demand for computational power in nuclear-powered data centers requires effective thermal management. Traditional cooling methods are inadequate for the heat generated in these environments. This article examines advanced cooling systems for nuclear-powered data centers, focusing on energy efficiency, safety, and performance. Analyzed technologies include liquid cooling, immersion cooling, free cooling, and hybrid systems. Findings show these systems reduce energy consumption by up to 50%, improve PUE by 20-35%, and enhance computational performance by 15-20%. They also reduce server failure rates and improve reliability. Initial investment is offset by long-term energy cost savings and reduced maintenance. This article highlights the importance of advanced cooling systems for sustainable and efficient operation of nuclear-powered data centers.

DOI: https://doi.org/10.5281/zenodo.17731414

Invest Wise – Ai Driven Investment Portfolio Recommendation System Based On Risk Profiling And Market Analytics

Authors: Ayush Sadanand Bhuyar, Bhakti Kiran Kumar Yete, Jayendrasin Rathod, Jayendrasin Rathod, Mr. Yatin Shukla

Abstract: The growth of retail participation in financial markets has created a strong need for intelligent, transparent and easy-to-use investment advisory tools. Beginner investors in particular often struggle to understand their own risk-bearing capacity and to select a suitable mix of equity, bonds and cash instruments. This paper presents INVESTWISE, an AI‑driven investment portfolio recommendation system that models user risk profiles using questionnaire responses and combines them with live market fundamentals such as P/E ratio, beta, dividend yield and sector information. The backend is designed using a hybrid MongoDB and relational database approach, while the frontend delivers a modern web dashboard that visualises allocations, recommended stocks and market movers. Experimental evaluation on simulated user profiles and live NSE data demonstrates that the system can generate consistent, risk-aligned portfolios with low response time, making it suitable for real‑time decision support.

Smart Gaming Supervision System

Authors: Goutami Bankapure, Rakshita Giri, Nandini Khadakhade, Sneha Teli, Aishwarya shengar, pallavi pandhare

Abstract: The rapid growth of digital gaming has led to increasingly complex behavioral challenges, particularly among adolescents and young adults. Excessive gameplay, exposure to toxic communication, and unhealthy engagement patterns continue to raise concerns regarding digital well-being. Existing monitoring tools typically offer only partial solutions, such as parental controls or time-restriction features, and lack the ability to analyze user behavior holistically. To address these gaps, this research presents the Smart Gaming Supervision System, an integrated AI-driven framework designed to promote healthier gaming habits while reducing abusive interactions. The system combines real-time gameplay duration monitoring, multilingual text toxicity detection, voice-based abusive speech recognition, motivational prompt generation, and behavior-based reward mechanisms. Leveraging state-of-the-art technologies such as XLM-R transformer models for text analysis, Whisper-based speech-to-text pipelines, and a rule-based behavioral engine supported by SQLite storage, the system continuously evaluates player behavior across multiple channels. Real-time alerts, warnings, and positive reinforcement are generated to encourage self-regulation and promote responsible gaming. Experimental evaluation demonstrates that the system achieves high accuracy in toxicity detection, effective time-limit enforcement, and improved user engagement through positive reinforcement techniques. The proposed solution highlights the potential of combining machine learning, psychology-driven reward systems, and digital wellness principles to create a comprehensive, scalable, and user-centric gaming supervision platform. This work contributes a novel and practical approach toward fostering safe, balanced, and respectful digital gaming environments.

AI in Everyday Life: How Artificial Intelligence Shapes Modern Applications

Authors: Ishaan Iyer, Abhijeet Anand Aiwale

Abstract: From voice assistants and recommendation systems to navigation tools and personalized learning, AI shapes how people live, work, and interact with technology. This rapidly growing area of research has transformed AI from a concept of the future to a technology that permeates almost every aspect of human life. This paper will discuss the integration of AI into everyday applications, identify common areas where students interact with AI unknowingly, and examine its positive impacts and potential ethical concerns. This study is based on a survey among first-year engineering students and a review of secondary literature. The results show that while AI enhances convenience and efficiency, the awareness of its mechanisms and ethical issues remains limited.

DOI: https://doi.org/10.5281/zenodo.17733691

A Review Of XGBoost And Supervised Learning Approaches For Crop Recommendation Using Soil Composition Data

Authors: Harshal Patel, Jitendra Shrivastav, Kamlesh Patidar

Abstract: The integration of machine learning into agriculture has shown great promise in improving decision-making, particularly in crop recommendation systems. This review focuses on XGBoost and supervised learning approaches for crop recommendation based on soil composition analysis. Soil properties such as pH, nutrient levels, moisture, and texture play a crucial role in determining crop suitability and yield. However, many existing models fail to adequately capture the complex interactions among these variables or account for regional soil variability. By examining current supervised learning methods and the growing application of XGBoost, this paper highlights their strengths, limitations, and potential for enhancing prediction accuracy. Furthermore, it identifies key research gaps, including the scarcity of diverse soil–crop datasets and the need for models that can adapt across geographical regions and climates. The review concludes that integrating advanced supervised learning with robust soil data can significantly optimize crop recommendations, promoting sustainable and precise agricultural practices.

Comparative Analysis Of Deep Learning Models For Brain Tumor Detection With Superior Performance Of InceptionResNetV2

Authors: Pratik Pandey, Nagendra Patel

Abstract: Brain tumor detection through magnetic resonance imaging (MRI) plays a crucial role in early diagnosis and treatment planning. This study presents a comparative analysis of five deep learning models—CNN, ResNet50, U-Net, YOLOv7, and InceptionResNetV2 for accurate classification of brain tumors. The dataset was preprocessed with cleaning, augmentation, and normalization before training and evaluation. Performance was measured using accuracy, precision, recall, and F1-score. Results demonstrate that all models achieved strong outcomes, but InceptionResNetV2 significantly outperformed others, reaching nearly 100% across all metrics. This superior performance highlights its effectiveness in minimizing false positives and false negatives, thereby offering a robust tool for clinical applications. The findings emphasize the importance of advanced deep learning architectures in medical imaging for reliable tumor detection.

Trip Crafters-Crafting Experience For Travelers

Authors: Harsh Manvani, Niraj Shrimali, Sahil Ahir, Ved Patel

Abstract: The tourism sector is changing quickly as digital tools be- come more common, but many travelers still struggle to plan trips that are both convenient and affordable. A major problem is the absence of a single platform that can offer clear, reliable, and budget-focused itineraries. Although there is plenty of information available on travel websites and search engines, it is scattered and often difficult to com- pare, which makes the planning process tiring and confusing.Another issue appears once tourists reach their destination. Local transport frequently becomes a challenge, especially for first-time visitors who may end up paying high prices or relying only on taxis and ride-hailing services. This also reduces their chances of exploring places more freely and experiencing the local culture. In many tourist locations, two-wheeler rentals are actually a more convenient and enjoyable way to move around, yet they are not well integrated into existing travel systems.To overcome these problems, this study proposes an AI-based platform that combines personalized trip planning with a peer-to-peer bike rental service. The system uses machine learning to create tailored itineraries by collecting and organizing travel data from multiple sources. At the same time, it offers a safe and easy-to-use marketplace where travelers can rent two-wheelers from local owners. By bringing together artificial intelligence and community-driven mobility options, the platform aims to make travel more accessible, reduce instances of overcharging, and improve the overall experience for tourists.

DOI: http://doi.org/10.5281/zenodo.17746212

World Trip Deal

Authors: Radhika Marathe, Jagruti Patil, Vraj Patel, Nakka Narendar

Abstract: The travel and tourism industry is rapidly transi- tioning toward digital ecosystems that provide seamless user ex- periences. However, most travel applications remain fragmented, requiring customers to access different platforms for hotels, packages, transfers, and visa services. World Trip Deal resolves this by offering an integrated web platform that centralizes all essential travel-related modules. The system is built using Angular, Node.js, Express, Firebase, and MongoDB, ensuring scalability, responsiveness, and security. This paper presents the system architecture, methodology, workflow, implementation details, and performance evaluation of the platform.

Beyond Quiet Quitting: Naked Resignation in The Digital Workforce Of Pune’s IT Industry

Authors: Dr. Prabodhini B Wakhare, Bhagyashree S. Borhade, Professor Dr. Shivaji Borhade

Abstract: This study investigates the emerging trend of naked resignation among the digital workforce with a focus on IT professionals in Pune (India). Naked resignation refers to employees leaving abruptly without prior communication, gradual disengagement or attempts at negotiation. Utilising a mixed-methods approach the research gathered responses from IT employees and HR professionals to examine how factors such as techno-stress, burnout, emotional fatigue, workplace relationships and organisational trust influence resignation intentions. The findings reveal that emotional fatigue is the most significant factor followed by burnout and techno-stress. Participants noted a diminished trust in leadership and a sense of organisational disconnect as contributing factors to sudden resignations. Qualitative responses indicate that employees view naked resignation not as an impulsive decision but as a necessary reaction to psychological strain and declining workplace well-being. The study concludes that resignation is increasingly seen as a means of setting boundaries rather than a response to isolated incidents. The results underscore the need for improved communication channels, mental health resources and employee-centered leadership practices. This research sheds light on the evolving behaviours in the post-pandemic workforce and adds to the literature on employee retention, digital work culture and mental well-being in technology-driven organisations.

AI-Driven Smart Farming System

Authors: Smita Tanvade, Jyoti Sathe, Niha Kudchikar, Samruddhi Patil, Mrs. P.D Nasalapure

Abstract: Smart farming integrates advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and data analytics to improve the efficiency, productivity, and sustainability of agricultural processes. This paper presents an AI-driven smart farming system that uses real-time soil data to help farmers make accurate and effective decisions. The system collects essential soil parameters such as moisture, pH, temperature, electrical conductivity, and nutrient levels through IoT sensors and analyzes them using machine learning algorithms. Based on this analysis, the system predicts the most suitable crop for the existing soil condition and also recommends the appropriate fertilizers needed to improve soil health and support optimal crop growth. By reducing manual guesswork, minimizing resource wastage, and offering clear data-based guidance, the proposed system helps farmers increase productivity, reduce costs, and adopt sustainable farming practices. Cloud integration and an easy-to-use interface ensure that the solution remains accessible, scalable, and suitable for different types of farms. Overall, this AI-driven system provides a practical and efficient approach to modern agriculture, enabling higher yield, better soil management, and smarter decision-making.

Appraisal of Groundwater Quality, A Case study of Panruti Block, Cuddalore District.

Authors: P. Ramamoorthy

Abstract: The study area Panruti Block has chosen to assess the groundwater quality. There are 15 groundwater samples were collected and analyzed for various physical and chemical parameters such as pH, TDS, EC, Ca, Mg, Na, K, Cl, NO3,SO4,Fl, etc. The groundwater samples are compared with the WHO standards. The quality of ground water in the study area is fresh to Brackish, slightly basic in nature, Salinity nature.

Simulation And Analysis Of A Cascaded 4-bit Digital Comparator Architecture Extended To 16-bit Using MATLAB/Simulink

Authors: Mr Sandeep Tandon

Abstract: Digital comparators are fundamental building blocks in arithmetic and control processing units, forming an essential component of ALUs, microprocessors, DSP modules, and embedded logic devices. Traditional 4-bit comparators offer a limited operational range, and real-time digital systems require scalable multi-bit comparison architectures. This paper presents a modular, cascaded comparator design methodology that extends a basic 4-bit comparator to 8-bit and 16-bit architectures using MATLAB/Simulink. The work proposes a structured cascading mechanism using hierarchical decision logic (GT, EQ, LT propagation) and evaluates performance through extensive simulation-based analysis. Functional verification, simulation timing behavior, switching activity estimation, and scalability evaluation are conducted. Results demonstrate that the proposed comparator architecture maintains accuracy, modularity, and linear scalability, making it suitable for integration into FPGA, ASIC, and real-time embedded systems.

Design And Implementation Of An Intelligent Career Guidance Ecosystem With ATS Optimization And Personality-Based Recommendation Engine

Authors: Ankit Verma, Anmol Soni, Arnav Singh, Adnan Khan, Ahtesham Farooqi

Abstract: This paper presents AI Career Navigator, a com- prehensive AI-driven career guidance platform integrating ten essential modules: career recommendation, resume analysis, job search aggregation, interview preparation, cover letter genera- tion, skill gap analysis, career trend analytics, learning resources, conversational AI chatbot, and LinkedIn profile optimization. The system employs a hybrid methodology combining NLP- based resume intelligence, psychometric profiling using the Big Five Personality Model, supervised machine learning for career prediction, and ATS-based scoring mechanisms. The frontend ar- chitecture leverages React with TypeScript, enhanced by Tailwind CSS, ShadCN UI, and Framer Motion, while the backend imple- ments advanced AI/NLP/ML algorithms for semantic analysis, personality classification, and intelligent career-profile matching. The modular architecture incorporates domain-skill mapping, career-interest correlation, and real-time trend analytics with adaptive feedback loops to generate context-aware recommenda- tions. Comparative evaluation demonstrates that AI Career Navi- gator significantly outperforms conventional systems by providing data-driven insights, holistic skill assessment, and personalized recommendations, thereby enhancing graduate employability and reducing career mismatch rates observed in traditional models.

DOI: http://doi.org/10.5281/zenodo.17761603

Identifying and Analyzing the Barriers to Adopting Green Concepts in The Sri Lankan Construction Industry

Authors: G S P Gunasekara

Abstract: In The Sri Lankan construction industry has a central position in the national development, instead of being one of the worst agents of environmental destruction due to energy consumption and production of waste and carbon dioxide. As response, strategies to reduce the environmental impact of building operations have come into the limelight in the form of green concepts and sustainable building practices in the world. Although sustainability programs have been increased and structures have been designed like the GREENSL Rating System, there is still little uptake of green practices in Sri Lanka. This paper will seek to establish and discuss the challenges to the adoption of green construction concepts in the Sri Lankan context. Based on a qualitative content analysis of twenty academic and institutional sources on local repositories, such as the University of Moratuwa, General Sir John Kotelawala Defence University, and the Green Building Council of Sri Lanka, the research synthesizes research results on the economic, institutional, technical, and cultural levels. Findings indicate that the initial high costs, lax regulatory implementation, low technical capacity and deep-rooted behavioural resistance are all complex and interdependent webs of constraints. The lack of financial incentives, poor awareness and disjointed policy frameworks are the forces that reinforce these barriers. The paper suggests a conceptual framework that demonstrates the relationship between these barriers and provides policy, industry, and research solutions to promote integrated solutions. It concludes that a rational system of national policy, coupled with specific capacity building and financial solutions, is essential in achieving mainstreaming of sustainable construction and long-term environmental resilience in Sri Lanka.

Integrated Risk Model Combining Financial, OSH, and ESG Factors for Modern Corporations

Authors: Dr. Priyanka

Abstract: Corporations today operate in environments marked by financial volatility, sustainability pressures, rising workplace safety expectations, and increased scrutiny from regulators and stakeholders. Traditional risk-management frameworks—which focus mainly on financial indicators—are no longer sufficient. This study proposes a comprehensive Integrated Risk Model (IRM) that merges Financial Risk, Occupational Safety & Health (OSH) Risk, and Environmental, Social & Governance (ESG) Risk into a single evaluative structure. Using comparative analysis, survey data, secondary financial records, and regression modelling, the paper examines how OSH and ESG performance influence financial resilience and overall corporate stability. Findings suggest that firms with strong OSH and ESG systems consistently experience fewer disruptions, lower costs, enhanced productivity, and significantly higher financial resilience scores. The research demonstrates that integrating OSH and ESG risk indicators into financial risk management leads to more accurate predictions of long-term corporate performance.

DOI: https://doi.org/10.5281/zenodo.17775670

The Impact Of Artificial Intelligence On Human Resource Efficiency: Enhancing Teachers’ Performance In Educational Institutions_819

Authors: Mohamad Siraj

Abstract: This study examines the impact of artificial intelligence (AI) on human resource efficiency among secondary teachers in international schools. While AI is increasingly promoted as a means to reduce teacher workload and enhance productivity, empirical evidence from school settings—particularly international schools—remains limited. The research focuses on how AI is used in teachers’ work, how it affects perceived workload and efficiency, and how organisational conditions shape these effects. A quantitative, cross-sectional survey design was employed. Data were collected from 150 secondary teachers working in 18 international schools, using a structured online questionnaire. The instrument captured AI usage patterns, perceptions of AI (perceived usefulness, perceived ease of use, AI anxiety, autonomy), HR-efficiency outcomes (perceived administrative workload, instructional efficiency, overall efficiency, job satisfaction) and organisational factors (leadership support, training and infrastructure). Descriptive statistics, reliability and factor analyses, correlations and multiple regression models were used to analyse the data. Findings indicate that AI is widely used for lesson planning, resource creation and assessment, but less so for administrative work and rarely for pastoral care or live classroom interaction. Teachers generally perceive AI as useful and moderately easy to use, yet administrative workload remains high. Perceived usefulness and actual AI usage are strong positive predictors of instructional and overall efficiency, and are associated with somewhat lower perceived administrative workload. AI anxiety is linked to higher workload and lower efficiency. Organisational support—through leadership, training and clear policies—consistently amplifies positive outcomes and reduces anxiety. The study concludes that AI currently offers incremental rather than transformative efficiency gains. Its contribution to human resource efficiency and teacher well-being depends on strategic, task-focused implementation and supportive organisational conditions, rather than on technology alone. Recommendations are offered for school leaders, HR practitioners and teachers, alongside directions for future research on AI, workload and sustainability in international education.

DOI: http://doi.org/10.5281/zenodo.17785987

Development of a Low Cost 3D Printed Myoelectric Hand using EMG and ECG Signal Fusion

Authors: Ayush Kumar, Abhendra Pratap Singh, Uma Gautam, Nandini Sharma

Abstract: For amputees in underdeveloped nations, the high expenses and complexity of commercial upper-limb prosthetics continue to be major obstacle to accessibility. The design, development, and testing of an affordable, 3D printed bionic hand with a dual-sensor interface is presented in this study. This system incorporates Electrocardiography(ECG) as a secondary control modality for improved stability and mode switching, in the contrast to standard myoelectric systems that only use Electromyography (EMG) and are vulnerable to motion artifacts and false triggers. Autodesk Fusion 360 was used to design the mechanical structure, which was then made of polylactic acid (PLA) and has a tendon-driven actuation mechanism controlled by SG90 servo motors. Band-pass filtering and threshold-based algorithms are used by the control logic, which is implemented on an Arduino Uno, to handle biosignals in real time. The ECG signal successfully serves as a safety interlock, and experimental results show a system latency of about 190ms and a strong object grabbing capacity. The combination of multimodal biosignals with additive manufacturing can produce a dependable, accessible, and useful prosthetic solution, as evidence by the fact that the entire fabrication cost was kept under 10000 INR.

DOI: https://doi.org/10.5281/zenodo.17786605

Parental Control Time Lock App_569

Authors: Hitanshu Bodana, Ronak Singh, Krish Patel

Abstract: The increasing immersion of children in digital ecosystems—mobile applications, virtual environments, and gamified digital spaces—has amplified concerns around excessive screen time, addictive applications, and exposure to harmful content. Traditional parental control systems rely on rigid blocking mechanisms, often creating resistance among children and lacking nuanced, interactive behaviour regulation. This paper presents a Parental Control Time Lock App, a collaborative digital parenting framework integrating real-time monitoring, application-level time budgeting, and OTP-based conditional unlocking. Developed using Kotlin (Android) and a Spring Boot backend, the system enforces usage limits and enables parents to remotely authorize temporary access by providing a secure one-time password. The application incorporates behavioural design elements to promote healthy usage rather than punitive restriction. Testing conducted across 20 families demonstrated a 32% reduction in unregulated screen time and high parental satisfaction. The study contributes to the domains of digital wellbeing, child safety, and human–computer interaction by proposing a hybrid control model that balances autonomy, security, and trust.

Predictive Analytics In Big Data

Authors: Dr. C.K. Gomathy, Swaminathan S, Rohith Reddy S, Monishkumar V

Abstract: The exponential growth of data generated from social media, IoT devices, enterprise systems, online transactions, and cloud platforms has transformed big data analytics into a critical domain for modern organizations. Predictive analytics, a major branch of data analytics, leverages statistical models, machine learning algorithms, and AI-driven techniques to forecast future events and uncover hidden patterns within large-scale datasets. Traditional analytical approaches are increasingly inadequate for handling the velocity, variety, and volume of modern data environments. With advancements in distributed computing frameworks such as Hadoop, Spark, and cloud-native analytics systems, predictive analytics has become a powerful enabler for data-driven decision-making. This paper explores the principles of predictive analytics in big data environments, examining its methodologies, architectures, machine learning techniques, and industry applications. A detailed literature survey highlights developments from 2015–2025, focusing on model optimization, scalable processing, and domain-specific predictive frameworks. The methodology outlines an end-to-end predictive analytics pipeline, including data ingestion, preprocessing, model training, evaluation, and deployment. Implementation details demonstrate how predictive models can be integrated into distributed systems using containerized microservices and scalable cloud architectures. Experimental results confirm the effectiveness of the model in supporting real-time predictions, trend analysis, and intelligent automation. The findings emphasize predictive analytics as a foundational tool across sectors such as finance, healthcare, retail, manufacturing, and cybersecurity.

DOI: http://doi.org/10.5281/zenodo.17798573

Federated Learning In Cloud–Edge Environments For Privacy-Preserving Cognitive Computing

Authors: Swaminathan S, Rohith Reddy S, Dr. R. Prema, Assistant Professor

Abstract: Federated learning (FL) enables collaborative machine learning without transferring raw data to a central server, thereby ensuring privacy and security. When integrated with cloud–edge environments, FL enhances cognitive computing by enabling real-time, decentralized intelligence. This paper explores the architecture, opportunities, applications, and challenges of federated learning for privacy-preserving cognitive systems. It highlights how cloud–edge collaboration improves data security, latency, scalability, and model performance while addressing integration barriers, communication overhead, and ethical concerns.

DOI: https://doi.org/10.5281/zenodo.17799449

Evaluation of Green Plant Extracts as Corrosion Inhibitors for Mild Steel in Acidic Medium

Authors: Dr. P.Gowsalya, M.Revathi, K.Palanisamy, S.Saranya

Abstract: The corrosion inhibition efficiency of Aster chinensis extract on mild steel in 1 M HCl was examined using weight loss and electrochemical techniques. The extract significantly reduced the corrosion rate, and inhibition performance increased with increasing inhibitor concentration. Polarization studies confirmed a mixed- type inhibition behaviour, while EIS results showed higher charge transfer resistance, indicating strong adsorption of phytochemical constituents on the steel surface. Adsorption obeyed the Langmuir isotherm, suggesting monolayer formation. Surface characterization by FT-IR and SEM supported the formation of a protective film on the metal surface. The extract also facilitated the reduction of Ag⁺ to Ag⁰ nanoparticles, confirming its dual function as a green corrosion inhibitor and an effective reducing agent. Overall, Aster chinensis demonstrates excellent potential as an eco-friendly and sustainable corrosion inhibitor.

DOI: https://doi.org/10.5281/zenodo.17800101

Advancing Human–Computer Interaction Through Cognitive Computing And Natural Language Processing

Authors: VD Sasank, Vishnuvel Ragavan K E C, Dr. R. Prema

Abstract: Human–Computer Interaction (HCI) is rapidly transitioning from conventional interfaces to intelligent, context-sensitive systems driven by Cognitive Computing and Natural Language Processing (NLP). Traditional input–output interactions lack the capability to understand user intent, emotions, and behavioural patterns. Cognitive computing enables machines to simulate human mental processes such as perception, reasoning, and learning, while NLP supports natural communication through speech and text. This paper presents an integrated cognitive–NLP architecture for adaptive and human-centred interaction. A detailed literature review highlights existing HCI limitations, including lack of emotional understanding, multilingual constraints, system bias, and poor contextual reasoning. A proposed hybrid model is introduced, combining behavioural sensing, cognitive modelling, semantic processing, sentiment analysis, and feedback-driven learning. Applications in healthcare, accessibility, virtual assistants, smart environments, and education are examined. The paper concludes with challenges in ethics, privacy, and data bias, followed by future advancements such as emotion-aware agents, multilingual cognition, and real-time brain–computer interfaces.

DOI: https://doi.org/10.5281/zenodo.17800823

Implementing Blue-Green Infrastructure In GIS For Climate Impact: A Case Study Of Chennai, Tamil Nadu, India.

Authors: Raegan Alex. A, Jyothi Gupta

Abstract: Aim: This paper reviews urban expansion through space and time in Chennai city (a megapolis of India) based on GIS technology etc, and investigates ways to integrate blue-green infrastructure, i.e., wetlands, riverscapes, greenscapes into the development for climate adaptation, resilient cities & sustainable growth. Research Question: What can GIS show us about Chennai’s ancient land use changes and urban expansion? What are the possibilities of climate-proofing resilience and promoting sustainable urban development with blue-green infrastructure? Methodology: Ten research papers were analyzed, and core concepts were determined as well as gaps identified. The Shapefile/GIS data was obtained and studied to observe the pattern of city growth. For identifying potential areas for BGI and urban resilience interventions, the land use/land cover changes were mapped. Results: GIS based analysis identified speedy urbanization in and around OMR and GST Road at the cost of wetlands, agricultural land, and open spaces. Map of vegetation, water bodies showed areas to reclaim for restoration. The rivers, sewers, sumps and metro lines became avenues for blue-green infrastructure projects. Zones for planning interventions were determined using highway networks and administrative boundaries. A mapping of priority areas for flooding, urban cooling and sustainable development was conducted. Conclusion and Limitation: Urbanization of Chennai has shrunk green and blue spaces, a situation that emphasizes the urgency for incorporating blue-green infrastructure to improve climate resilience. Challenges that people faced around the country included access to up-to-date ward boundaries, transforming shapefiles and high prices of datasets. Sensitve areas for restoration and management had been successfully mapped if imperfectly served by maps

DOI: http://doi.org/10.5281/zenodo.17878089

A Comprehensive Review Of Advances In Smart Agriculture

Authors: Rachna Chandra, Manoj Mittal, Anmol Dobriyal, Suneet Bhalla, Utsav

Abstract: We analysed the peer-reviewed literature published between 2021 and 2025 focusing on smart agriculture techniques which integrates advanced technologies such as the Internet of Things (IoT), wireless sensor networks (WSN), machine learning, artificial intelligence (AI), unmanned aerial vehicles (UAVs), remote sensing, edge–fog–cloud computing, and renewable energy systems to enhance farming efficiency and sustainability. Recent research shows significant progress in precision irrigation, automated monitoring, and data driven decision support systems that optimize water usage and improve crop yield. This paper presents a comprehensive review of forty research papers focused on smart agriculture techniques, highlighting IoT-based soil moisture monitoring, LoRaWAN enabled long-range communication, machine learning-based disease detection, UAV assisted irrigation planning, and solar powered intelligent water systems. The findings show that integrating smart technologies enables resource optimization, climate resilience, and scalable automation across diverse agricultural scenarios. The study contributes a consolidated view of recent advancements to support the development of sustainable agriculture.

Big Data In Healthcare

Authors: Dr. C.K. Gomathy, VD Sasank, R Srishreya

Abstract: Big Data in healthcare leverages advanced analytics on massive, heterogeneous datasets (electronic health records, medical images, genomics, wearable sensor streams, etc.) to improve patient outcomes and operational efficiency. Traditional healthcare IT systems cannot cope with the volume, velocity, and variety of these data. Modern distributed platforms (Hadoop, Spark, cloud) and AI methods (machine learning, deep learning) are therefore crucial for enabling real-time predictive modeling and trend analysis in medicine. This paper reviews recent (2015–2025) developments in healthcare-focused Big Data analytics, including architectures, algorithms, and applications. A comprehensive end-to-end methodology is proposed, comprising data ingestion, preprocessing, distributed model training, and deployment via containerized services. We describe the implementation of a prototype healthcare analytics system and present experimental results demonstrating its scalability and accuracy for real-time patient risk prediction. The findings underscore that Big Data analytics has become a foundational tool in healthcare, enabling evidence-based clinical decision support, disease surveillance, and personalized medicine.

DOI: http://doi.org/10.5281/zenodo.17813444

Exploring The Role Of Artificial Intelligence In Agriculture: Innovations, Challenges, And Future Prospects

Authors: Dr. Goldi Soni, Reddi Rishitha, Karri AmruthaVarshini

Abstract: The review highlights the significant impact of artificial intelligence (AI) on agriculture, showcasing its ability to enhance efficiency, productivity, and sustainability in response to global food scarcity and a rising population. Key applications of AI include advanced soil, crop, weed, and disease management, facilitated by technologies such as automated irrigation, drones, and data analytics. Despite these advancements, challenges such as uneven distribution of mechanization, the necessity for big data, complexities in soil treatment, pest control issues, and gaps in farmers’ technological knowledge hinder widespread adoption. The integration of AI with vertical farming presents a promising solution for urban land and water scarcity by significantly improving crop monitoring and yield predictions through machine learning and IoT technologies. As the agricultural sector faces pressure to increase production by 70% by 2050 amid limited resources and climate change, AI emerges as a crucial element in developing expert systems for crop management and enhancing overall economic efficiency while supporting sustainable farming practices. Nevertheless, significant hurdles remain, including high implementation costs, privacy concerns, and the need for interdisciplinary collaboration to develop holistic AI applications that consider economic, social, and environmental impacts alongside ethical implications. Overall, AI is positioned as a transformative force that could revolutionize agriculture, addressing core challenges and paving the way for smarter, more sustainable farming methods.

Big Data Computing: Architectures, Technologies, And Future Perspectives

Authors: Dr. C K Gomathy, Vishnuvel Ragavan K E C, Ghiridharan S

Abstract: Big Data computing has become a cornerstone technology driving digital transformation across industries. This paper provides a comprehensive exploration of Big Data computing paradigms, architectural frameworks, processing technologies, and contemporary challenges. We examine the evolution from traditional data warehousing to modern cloud-native architectures, analyze key processing frameworks including Apache Spark, Hadoop, Flink, and Kafka, and discuss real-time analytics capabilities. Furthermore, this paper addresses critical challenges including data privacy, security, scalability, and regulatory compliance, while highlighting emerging trends such as AI-ML integration, federated learning, and edge computing. Our findings demonstrate that hybrid approaches combining on-premise and cloud solutions are becoming mainstream, with approximately 65% of enterprises adopting Hadoop and Spark in tandem. This research concludes by identifying future research directions necessary to address emerging complexities in distributed data systems and regulatory landscapes.

DOI: http://doi.org/10.5281/zenodo.17838338

Digital Transformation Of Human Resource Management: A Conceptual Framework For Enhancing Organizational Performance In Small And Medium-Sized Enterprises

Authors: Buddhika YPAS

Abstract: This conceptual paper discusses the role of HR digitalization in the performance of SMEs in the context of agility, efficiency, and innovation. Combining the Resource-Based View, Dynamic Capabilities Theory and Technology Acceptance Model, the framework defines the HR digitalization as a strategic resource that converts human capital into competitive advantage. The research hypothesizes eight research propositions which connect HR digitalization with performance results via mediating variables of employee engagement and organizational agility and are modulated by digital leadership, resource limitation, and institutional contexts. The results provide a theoretical understanding and practical recommendations to SME leaders and policymakers to use digital HR systems to benefit their sustainable growth and competitiveness in the digital economy.

Mapping Sustainability: Evaluating Channapatna’s Green Spaces, Water Bodies, And Mobility Networks

Authors: Mohammed Khan, Jyoti Gupta

Abstract: This study conducts a geospatial analysis of Channapatna’s urban fabric, focusing on the spatial distribution and interrelationship of green spaces, water bodies (blue infrastructure), and transportation networks. Leveraging Google Maps and other mapping tools, the paper identifies the placement and accessibility of parks, urban lakes, river systems, and transit corridors within the town. Findings reveal a landscape shaped by both ecological assets—such as Shettahalli and Kudlur lakes—and robust connectivity via road and rail, highlighting critical roles in urban quality, economic activity, and environmental sustainability. This research presents a comprehensive geospatial analysis of Channapatna’s green spaces, water bodies, and transportation infrastructure, using Google Maps and other spatial mapping tools to generate a nuanced urban profile. The study systematically maps the distribution and accessibility of public parks, open areas, lakes, and rivers, assessing their impact on land use, environmental quality, and urban well-being. Through NDVI and Air Quality Index analysis, the research highlights disparities in green space allocation, emphasizing their role in city resilience, ecological health, and recreation. The examination of Channapatna’s blue infrastructure uncovers significant deterioration: key water bodies like Shettahalli and Kudlur Lakes, once lifelines for agriculture and community use, now face acute pollution and encroachment. Extensive sewage inflow, lack of Underground Drainage (UGD) systems, encroachment, and unregulated dumping threaten water quality, agricultural productivity, and public health. The study reviews recent policy interventions and ongoing planning efforts—including proposals for a dedicated Sewage Treatment Plant (STP) and expansion of UGD—framing these within the broader context of sustainable urban management.

DOI: http://doi.org/10.5281/zenodo.17876819

Implementing Single Image Denoising Diffusion Model For Image Editing And Synthesis

Authors: Priyadharshini P, M.Gayathri

Abstract: This research paper presents a comprehensive implementation and evaluation of the Single Image Denoising Diffusion Model (SinDDM) for sophisticated image editing and synthesis tasks using only a single training image. Unlike conventional diffusion-based generative models that rely on extensive datasets, SinDDM employs an innovative multi-scale training strategy to learn hierarchical priors from a single input image. The model supports a wide range of image manipulation tasks, including artistic style transfer, semantic image harmonization, region-of-interest (ROI) guided editing, and CLIP-based text-guided content generation. Experimental results demonstrate that SinDDM consistently produces coherent, high-quality, and semantically aligned outputs without requiring extensive training data or pre-trained encoders, making it particularly suitable for personalized applications and data-efficient computational scenarios. This paper provides detailed architectural insights, implementation methodologies, comparative analysis, and potential applications of the proposed framework

DOI: http://doi.org/10.5281/zenodo.17862616

The Implementation of Online Learning: Its Effect on Students’ Learning in Essu, Borongan Eastern Samar

Authors: Judy Ann O. Gagate, Dolly Ann A. Lupido, Professor Jayson D. Magalona

Abstract: This study examined the implementation of online learning and its effect on students’ learning at Eastern Samar State University (ESSU), Borongan Campus. Using a descriptive-correlational research design, the study investigated how online learning platforms, communication mechanisms, digital resources, and instructional strategies influenced students’ academic performance, comprehension, motivation, engagement, and overall learning satisfaction. A total of 150 undergraduate students participated by answering a validated researcher-made questionnaire administered through Google Forms. Findings revealed that students generally perceived online learning positively, noting that learning platforms were accessible, instructors provided clear guidance, and learning materials were sufficient. Results also showed that online learning contributed to improved digital literacy, independent learning skills, and time management. However, students reported challenges such as intermittent internet connectivity, device limitations, and reduced interaction with instructors. Statistical analysis confirmed a significant relationship between online learning implementation and students’ learning outcomes. The study concludes that while online learning is effective and beneficial, its success depends greatly on the quality of instructional delivery, technological access, and continuous institutional support. Recommendations were formulated to further enhance online learning implementation in ESSU

RESUMESYNC: AI Resume Builder With Integrated Real Time Chat

Authors: Unnati Raikwal, Abhishek Kumar, Subrata Sahana

Abstract: The increasing reliance on ATS in the hiring process demands that job seekers prepare ATS-compatible resumes. Unfortunately, most applicants lack technical insight into how to format their resumes in accord with ATS automated filtering requirements. To address this challenge, we propose an AI- driven resume builder endowed with real-time chat and smart enhancement capabilities. The system provides integrations with Gemini AI for real-time suggestions, ImageKit for background removal and image optimization, and MongoDB for structured storage of resume data. Users can start with templates, upload pre-existing files, edit the content of their resumes, and enhance phrasing with AI-powered augmentation. [7] This solution saves time while avoiding common ATS-compatibility problems in the creation of professional resumes. Experimental results showed improved keyword alignment, structural consistency, and clarity of content compared to traditional resume builders, which will, in turn, enhance the chances of success in job applications.

DOI: https://doi.org/10.5281/zenodo.17864311

Machine Learning In Biomedical Image Segmentation: A Technical Review

Authors: Suraj Kumar, Mr. Vaibhav Singh Sekhawat

Abstract: The automation of anatomical and pathological region identification in clinical imaging has become a cornerstone of modern diagnostics. This review presents a systematic exploration of machine learning paradigms—from classical statistical models to cutting-edge foundation architectures—and their role in transforming segmentation accuracy, speed, and generalizability. We dissect foundational techniques such as kernel-based classifiers, ensemble tree models, and probabilistic graphical frameworks, contrasting them with deep learning systems including convolutional, recurrent, and transformer-based networks. Performance metrics from 2022–2025 benchmarks are synthesized across MRI, CT, ultrasound, and pathology datasets. We address persistent barriers—annotation scarcity, class imbalance, domain shift, and computational overhead—and evaluate mitigation strategies like transfer learning, synthetic data generation, and prompt-driven inference. A dedicated section introduces 2020–2025 breakthroughs: vision transformers, large-scale pre-trained models (e.g., MedSAM), diffusion-based synthesis, and hybrid neuro-symbolic systems. The convergence of these innovations signals a paradigm shift toward universal, data-efficient, and clinically deployable segmentation.

Green Is The New White: Sustainability Transformation In The Lifestyle & Beauty FMCG Sector

Authors: Aqsa Khalid

Abstract: Sustainability has emerged as a central driver of strategic transformation within the beauty and lifestyle segment of the fast-moving consumer goods (FMCG) industry. The expression “Green is the New White” captures the sector’s movement away from conventional, resource-intensive production practices toward environmentally responsible, ethically governed, and transparently communicated business models. Drawing on secondary data from international sustainability frameworks, peer-reviewed studies, market intelligence reports, and corporate disclosures, this research employs thematic analysis to identify three dominant patterns: Sustainable Product and Packaging Innovation, the Growth of Green Consumerism, and Regulatory–Reputational Pressures. The findings demonstrate that sustainability now underpins brand reputation, competitive advantage, and long-term sectoral resilience. The study concludes that beauty and lifestyle FMCG companies must embed environmental stewardship throughout the value chain to remain relevant in an evolving global marketplace.

Haptic Feedback Shoes For Navigation

Authors: Jai Gupta, Shreya Upadhyaya, Dr. H S Guruprasad

Abstract: This paper introduces a smart shoe that helps people move around inside buildings using gentle vibrations. Instead of relying on GPS or online maps, it tracks steps and direction with the phone’s built-in motion sensors. The method used is called pedestrian dead reckoning, which figures out position based on movement patterns. A matching app made with Flutter holds custom digital floor plans for different places indoors. Users can plan paths or get guided directions straight from their phone. Commands are sent wirelessly to small computers in each shoe using Wi-Fi signals. These tiny controllers then turn on one of two vibrating pads per foot – indicating turns or when they’ve reached the spot. The setup offers a complete, standalone way to navigate – ideal for indoor demos, restricted areas, or studies helping people with vision loss. Tests show it guides users step by step with precision while giving steady touch-based alerts on the go, proving that wearable navigation using only PDR can work reliably, no outside systems needed.

The Predominant Liverworts Collected From Jageshwar Region Of Almora, Uttarakhand

Authors: Rahul Jaiswar, Abhishek Kumar Sharma, Meena Rai

Abstract: The present study focuses on the morphological identification of dominant liverwort genera in the Kumaon hills of Uttarakhand, India, with particular emphasis on sporophyte characters for accu-rate taxonomic resolution. Field investigations were conducted in the Jageshwar region of Almora district and its adjoining areas up to Jageshwar Dham, a moist temperate zone characterized by mixed broad-leaved forests, shaded rock surfaces, and anthropogenically influenced temple com-plexes. These varied habitats form a mosaic of microenvironments favourable for the establish-ment of thalloid liverworts. During the survey, members of the families Aytoniaceae, Marchantiaceae, and Targioniaceae were recorded across soil, rock, and wall substrates. The liv-erwort flora documented comprised five species belonging to the genera Plagiochasma, Targio-nia, and Marchantia. Species of Plagiochasma and Targionia formed extensive patches on ex-posed to semi-shaded soil and rocky slopes, whereas Marchantia species were predominant in persistently moist, partially shaded habitats. These distributional patterns indicate clear ecological preferences among the dominant taxa within the study area. Overall, the investigation highlights the rich representation of complex thalloid liverworts in the Jageshwar landscape and underscores the significance of habitat heterogeneity in shaping bryophytic diversity in the mid-altitude Ku-maon Himalaya.

DOI: http://doi.org/10.5281/zenodo.17879162

A Generative AI And LLM-Driven Data Fabric Architecture For Real-Time CRM Intelligence And Predictive Sales Forecasting In Salesforce Ecosystems

Authors: Priya Nair, Vikram Chauhan, Anika Deshpande, Vasudev Sharma

Abstract: Real time customer relationship management intelligence continues to evolve as organizations rely on advanced analytics to drive sales planning, revenue optimization, and customer engagement decisions. This study addresses persistent challenges related to data fragmentation, inconsistent contextualization of CRM information, and the limited adaptability of conventional predictive models within Salesforce environments. The research introduces a generative AI and large language model driven data fabric architecture designed to unify distributed CRM assets, automate semantic enrichment, and enhance predictive sales forecasting accuracy. A mixed methodological approach was adopted, combining architectural modeling, data flow simulation, and empirical evaluation using historical opportunity data, customer interaction logs, and multichannel engagement records. Findings indicate that the proposed model improves context aware forecasting precision, reduces data preparation overhead, and increases interpretability for frontline sales teams by enabling narrative style insights generated through domain tuned language models. The framework demonstrates the potential to streamline CRM operations, enhance cross system interoperability, and support adaptive decision making by integrating knowledge graphs and LLM based reasoning into the Salesforce ecosystem. The study contributes an extensible reference architecture for enterprise CRM analytics and offers a pathway for organizations seeking to modernize sales intelligence processes. The results hold significance for both practitioners and researchers by proving that next generation AI enabled data fabrics can meaningfully strengthen forecasting reliability, reduce operational friction, and support scalable data governance strategies across complex CRM landscapes.

DOI: http://doi.org/10.5281/zenodo.17894776

IoT-Augmented Healthcare Monitoring Using Hybrid Deep Learning Pipelines And Cloud-Native Event Stream Processing

Authors: Buya Lekha, Pramani Kota, Nallireddy Anu, Vasudev Sharma

Abstract: Advances in sensor miniaturization, pervasive connectivity, and scalable cloud architectures have accelerated the adoption of Internet-of-Things solutions in healthcare, enabling continuous physiological monitoring, early disease detection, and remote clinical interventions. Yet, the complexity of heterogeneous sensor data, variable patient contexts, and unpredictable network conditions still limit reliability and predictive accuracy in real-world deployments. This study develops a hybrid deep-learning pipeline that integrates convolutional neural networks, bidirectional recurrent architectures, and attention-based temporal encoders with cloud-native event stream processing to enable real-time interpretation of multimodal physiological signals. The research examines how edge-assisted inference, micro-batch stream analytics, and distributed message brokers collectively enhance detection latency, anomaly classification, and model robustness. A mixed-method methodology combines simulation-driven performance evaluation with empirical analysis of IoT device logs and consumable EHR-derived datasets. Results demonstrate significant improvements in prediction accuracy, event-processing throughput, alert precision, and resilience against noisy sensor streams. The findings highlight the potential of hybrid AI pipelines to strengthen remote patient monitoring, chronic disease management, and population-health surveillance while addressing operational barriers tied to privacy, scalability, and interoperability.

DOI: https://doi.org/10.5281/zenodo.17920642

A LLM-Powered Semantic Automation Engine For Enterprise Reporting, Knowledge Extraction, And Data Lifecycle Governance

Authors: Rohan Mehta, Arvind Sethi, Nisha Kulkarni, Vasudev Sharma

Abstract: Enterprises operating in complex digital ecosystems face accelerating growth in data volume, reporting demands, and governance obligations. Traditional rule-based automation remains insufficient for interpreting ambiguous business semantics, harmonizing heterogeneous information assets, or sustaining consistent reporting logic across distributed platforms. This study introduces a large language model powered semantic automation engine designed to unify enterprise reporting, knowledge extraction, and end-to-end data lifecycle governance. The research focuses on the central challenge of operationalizing generative models, retrieval-augmented reasoning, and dynamic semantic alignment to automate high-stakes analytical and compliance workflows while maintaining auditability, accuracy, and policy adherence. Using a mixed methodological approach that combines empirical prototyping, workflow instrumentation, and qualitative validation with enterprise architects, the study develops a layered architecture integrating semantic parsers, governance ontologies, vector-indexed knowledge repositories, and automated lineage reasoning. Findings show that LLM-driven inference strengthens metadata completeness, reduces manual reconciliation cycles, enhances cross-system reporting consistency, and improves lifecycle visibility from ingestion to archival. The study contributes a scalable framework for semantic automation, a reference ontology for enterprise reporting logic, and a set of design principles supporting trustworthy, context-aware automation across data-intensive environments.

DOI: http://doi.org/10.5281/zenodo.17895633

Causal AI Driven Workforce Outcome Modeling Using SAP SuccessFactors, SAP Analytics Cloud, And Multi Source HR Signals

Authors: Vikram Chauhan, Anika Deshpande, Priya Nair, Vasudev Sharma

Abstract: Understanding the drivers of workforce outcomes requires analytical methods capable of distinguishing correlation from true causal influence. Traditional predictive models commonly used in HR systems can forecast attrition, performance, or engagement shifts, yet they offer limited visibility into the underlying mechanisms that produce these changes. This paper introduces a causal AI approach that integrates SAP SuccessFactors operational data, SAP Analytics Cloud workforce metrics, and diverse multi source HR signals to estimate the effects of organizational interventions on measurable employee outcomes. The proposed framework combines structural causal modeling, treatment effect estimation, mediation analysis, and counterfactual reasoning to evaluate how learning pathways, compensation adjustments, managerial behaviors, mobility opportunities, and work environment conditions contribute to changes in performance, retention, and development trajectories. A unified data architecture harmonizes information from SuccessFactors modules with analytical layers in SAP Analytics Cloud to construct causal ready datasets that isolate confounders and quantify both direct and indirect effects. Empirical evaluation across representative HR scenarios demonstrates that causal models provide more actionable insight than conventional predictive methods by clarifying which interventions meaningfully influence workforce outcomes and under what conditions. The study argues that embedding causal AI within enterprise HR ecosystems supports evidence informed decision making, strengthens workforce planning accuracy, and enhances the strategic value of people analytics in complex organizational environments.

DOI: http://doi.org/10.5281/zenodo.17895700

Criminal Liability For Actions Using Deepfake Technologies That Cause Serious Consequences

Authors: Nadiia Kudriashova, Alexander Mirza

Abstract: In recent years, generative artificial intelligence has gained traction, resulting in incredibly realistic synthetic multimedia content that can disseminate misinformation and mislead society. Deepfakes pose serious national security vulnerabilities since they enable sophisticated disinformation operations, foreign meddling, financial crime, and the erosion of faith in institutions. Deepfake detection and legal prosecution became an important agenda for contemporary nation-states. However, serious consequences of deepfakes for national security are still are not properly realized by legislative and regulatory establishment even in the countries of Five Eyes Alliance, known for its advanced cybersecurity awareness and policies. With this in mind, the article makes an attempt of integrating technological and legal domains of combating deepfake technology usage which causes serious consequences, within a single analytical model. Based on a combination of descriptive and exploratory research design, involving comprehensive literature review and semi-structured interviews with the experts across the fields of cybersecurity, machine learning, digital forensics, law, and ethics in the countries of Five Eyes Alliance (sample size 12 participants), the article outlines current landscape of deepfakes creation and detection technologies, as well as institutional awareness and legislative environment in the field of deepfakes law prosecution. The findings allowed making conclusion about scattered landscape of deepfakes identification and, at the same time, the evident lack of legal instruments to prevent deepfakes danger for national security even in the most developed countries, recently especially concerned with national security issues. The integration of findings allowed summarizing the essence of deepfakes serious consequences and developing integrative analytical model, based on Agile Security paradigm, implying predictive analysis of deepfake technology evolutive implications and options of appropriate criminal liability. The novelty of study lies in ‘organic’ combining of technological and legal planes of combating security danger of deepfakes, and the suggested integrative analytical model, based on Agile Security paradigm can become a starting point for further studies and developments in the field.

DOI: http://doi.org/10.5281/zenodo.17894064

AI Tool for Early Detection of Brain Related Diseases

Authors: Priti shivaji Birajdar, Ambika Ganesh Kshirsagar, Shravani Hanumant Raut, Harshada Machindra Raykar

Abstract: Early detection of brain-related diseases plays a crucial role in improving treatment outcomes, reducing mortality, and enhancing the quality of patient care. However, traditional diagnostic methods—such as manual MRI/CT scan interpretation and neurological assessments—are time- consuming, error-prone, and highly dependent on specialist expertise. To address these limitations, this study presents an Artificial Intelligence (AI)-based tool designed for the early detection and classification of multiple brain disorders, including brain tumors, stroke indicators, Alzheimer’s disease patterns, and abnormal EEG activity. The proposed system integrates advanced deep learning techniques, including Convolutional Neural Networks (CNNs), hybrid feature extraction, and medical imaging analytics, to automatically identify subtle abnormalities that may be overlooked by human observation. A comprehensive dataset comprising MRI scans, CT images, and EEG signal recordings was used to train and validate the model. The images were preprocessed using noise reduction, skull stripping, normalization, and region-of-interest extraction to improve diagnostic accuracy. The model was trained using supervised learning and evaluated using performance metrics such as accuracy, sensitivity, specificity, precision, recall, and F1-score. Experimental results demonstrate that the AI tool achieves high accuracy in early-stage detection, outperforming conventional diagnostic methods and providing faster, consistent, and automated analysis. The system holds significant potential for use in hospitals, rural clinics, telemedicine platforms, and large-scale screening programs. It can support neurologists by acting as a decision- support tool, reduce diagnostic delays, and contribute to improved patient outcomes. Future work will focus on expanding the dataset, integrating real-time monitoring, and enhancing the system’s capability to detect additional neurological disorders using multimodal data. Overall, the proposed AI tool demonstrates that artificial intelligence can be a transformative technology in the field of brain disease diagnosis and early prediction.

Emerging Trends In Metaverse

Authors: Aqsa Almas Sheikh, Eram Shamim Ur Rehman Khan, Naseem Husain, Krishna Prasad Pal

Abstract: The concept of metaverse represents an innovative change in digital interaction by combining virtual reality and expanded reality with a common experience. There is an integrated virtual common space created by combining virtually expanded physical reality with normal physical virtual reality. This content examines various aspects of metaverse, including underlying technologies (such as blockchain and AI) and applications in a variety of fields such as entertainment, education and business. It also examines the possibilities of new social and business forms, such as issues of privacy and digital justice, and the impact of businesses on society. A combination of findings from research and practice should provide this content with a better understanding of the impact of metabar on future digital ecosystems and community interventions. This is a rapidly evolving digital limit that transforms physical and virtual reality into an immersive, interactive, and persistent environment. For progress in Virtual Reality ( VR), Augmented Reality (AR), Blockchain and Artificial Intelligence (AI), users can create contacts, work, play and handles in the 3D room. It promises transformative impacts in a variety of sectors, including education, healthcare, entertainment, real estate, and long-distance work. This article examines the fundamental technologies behind metaverse, their potential socioeconomic implications, privacy, data security, digital identity and interoperability challenges. Metaverse can still interact with digital systems and cooperation during the development stage, but it shows the next important development in the use of the Internet.

Architectural Integration Of A BioBERT-Based Symptom Triage And Specialist Recommendation Engine

Authors: Mohammad Zaid Khan, Dr. Arvind Jaiswal

Abstract: The rapid growth of digital health platforms has created an urgent need for intelligent clinical decision-support tools that can interpret patient-reported symptoms and streamline care navigation. This work presents an enhanced architecture for MediTrack, a healthcare management platform, through the integration of a BioBERT-powered symptom triage and specialist recommendation engine. Leveraging domain-specific language representations, the system processes free-text symptom descriptions, identifies likely clinical categories, and recommends appropriate medical specialties with improved accuracy and contextual relevance. The proposed architecture combines natural-language preprocessing pipelines, BioBERT inference modules, probabilistic triage scoring, and a rule-augmented recommendation layer. Furthermore, the integration design emphasizes scalability, interoperability with existing MediTrack services, and compliance with healthcare data-protection standards. Experimental evaluation using benchmark clinical-symptom datasets demonstrates significant gains in classification performance and user-experience efficiency. This enhancement positions MediTrack as a more responsive, intelligent, and patient-centric digital health orchestration platform.

An IoT-Based Advanced Health Monitoring Technique Using MAX30100 Sensor For Reliable Healthcare Data Management

Authors: Manvir Kaur, Gurpreet Singh, Varuna Tyagi

Abstract: The rapid advancement of digital technologies has transformed healthcare, demanding intelligent and automated health monitoring systems. Traditional healthcare infrastructures often face challenges such as limited medical staff, delayed diagnosis, and lack of real-time monitoring. This research proposes an Internet of Things (IoT)-based advanced health monitoring system using the MAX30100 sensor for continuous measurement of heart rate (HR) and blood oxygen saturation (SpO₂). The system leverages microcontrollers such as Arduino and ESP32 for sensor interfacing and wireless data transmission to cloud platforms for real-time visualization, processing, and storage. Signal processing techniques, including noise filtering, peak detection, and smoothing, ensure accurate measurement. The proposed system addresses limitations of existing commercial devices by providing a low-cost, reliable, and scalable solution for remote patient monitoring, early disease detection, and data-driven healthcare management.

Design and Implementation of Novel Hybrid Wireless Electric Vehicle Charging Station using Integrated Solar-Grid Management System

Authors: B.SathiyaSivam, S.Sriram

Abstract: This paper presents a novel smart wireless electric vehicle (EV) charging station that integrates solar photovoltaic (PV) and piezoelectric road-energy harvesting with a smart-grid-connected common DC bus architecture. The proposed system employs bidirectional Wireless Power Transfer (WPT) for Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) operation and an intelligent Energy Management System (EMS) for real-time power optimization. Maximum Power Point Tracking (MPPT), adaptive load balancing, and predictive renewable forecasting enhance overall energy efficiency. Advanced coil-alignment sensors enable high coupling efficiency, while a bidirectional DC/AC interface ensures stable grid interaction. The system aims to provide eco-friendly, contactless, and efficient EV charging suitable for smart cities, highways, and autonomous transportation networks.

Motor Car Hub: A MERN-Based ERP System for Multi-Brand Vehicle Workshops

Authors: Aakib Beg, Prof. Geeta Santhosh HOD

Abstract: The rapid digital transformation of the automobile service industry has highlighted the inefficiencies of traditional workshop management methods, especially in multi-brand environments. Manual billing, fragmented inventory tracking, and inconsistent labor pricing frequently lead to revenue losses and customer dissatisfaction. Motor Car Hub is developed as a comprehensive Enterprise Resource Planning (ERP) system built on the MERN stack—MongoDB, Express.js, React.js, and Node.js—to address these challenges. Untitled document (2)This study expands on the original system architecture, presenting a deeper analysis of module interactions, database workflows, performance benchmarks, and the measurable operational improvements achieved in real-world simulations. The enhanced paper discusses MERN-driven scalability, the significance of schema flexibility, automated GST-compliant billing, technician performance tracking, inventory forecasting, and role-based access governance. The system shows an 80% reduction in invoice processing time, complete elimination of manual billing discrepancies, and substantial gains in accountability. These expanded insights provide strong evidence that MERN-based ERP solutions can revolutionize automotive workshop management, making operations more transparent, accurate, and data-driven.

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Enhancing Email Verifier and Domain System: Architectural Integration of AI-Driven Email Validation, Domain Intelligence, and Risk Scoring Engine

Authors: Abhinay Gour, Prof. Geeta Santosh

Abstract: In the evolving landscape of digital communication, ensuring the authenticity and reliability of email addresses and domains is critical for maintaining security, optimizing deliverability, and mitigating fraud. This paper presents an integrated architecture for an AI-driven email verification and domain intelligence system, combining real-time validation, domain reputation assessment, and a dynamic risk scoring engine. The proposed system leverages machine learning algorithms to detect syntactic anomalies, validate domain existence, and assess historical engagement patterns, while incorporating threat intelligence to evaluate potential risks such as phishing, spam, and disposable addresses. By unifying these components, the architecture not only enhances email deliverability but also provides actionable insights for cybersecurity and marketing strategies. Experimental results demonstrate that the AI-enhanced approach significantly outperforms traditional rule-based verification methods in accuracy, response time, and risk detection, offering a scalable solution for organizations requiring robust email and domain trustworthiness assessment.

Development of a Full-Stack Social Media Application Using Spring Boot, React.js, and Cloudinary Multiauthor

Authors: Sujay Dey, Shrey Jaiswal, E Hemasabari

Abstract: The rapid growth of social media platforms has transformed digital communication, content sharing, and online collaboration. This project presents the development of a full-stack social media application using Spring Boot for the backend, React.js for the frontend, and Cloudinary for cloud-based media storage and management. The system is designed to provide core social networking features such as user authentication, profile management, post creation, image and video uploads, likes, comments, and real-time interaction. Spring Boot is utilized to build secure and scalable RESTful APIs, ensuring efficient handling of business logic and database operations. React.js enables the creation of a responsive and dynamic user interface, enhancing user experience through component-based architecture and state management. Cloudinary is integrated to handle media uploads, storage, and optimization, reducing server load and improving performance. Security mechanisms such as JWT-based authentication and role-based access control are implemented to protect user data and ensure authorized access. The proposed application demonstrates how modern full-stack technologies can be effectively integrated to build a scalable, secure, and user-friendly social media platform. This project highlights practical implementation strategies and serves as a foundation for further enhancements such as real-time notifications, chat functionality, and advanced analytics.

A Comprehensive Review of Global Groundwater Quality, Hydrochemistry, and Health Risk Assessments

Authors: Nivashini N

Abstract: Groundwater serves as a primary source of drinking and irrigation water across the world, particularly in developing regions where surface water is scarce. However, increasing anthropogenic activities, geogenic influences, and climatic variability have resulted in deteriorating groundwater quality. This review synthesizes findings from ten recent peer-reviewed studies from Africa, Asia, and the Middle East. The selected studies evaluate heavy metal contamination, hydrochemical processes, water quality index (WQI), multivariate statistical assessments, GIS-based mapping, spatiotemporal variability, and human health risks. Results reveal widespread contamination by arsenic, lead, cadmium, nitrate, fluoride, and other ions, with significant non-carcinogenic and carcinogenic health impacts. Hydrochemical analyses indicate that water–rock interactions, ion exchange, and anthropogenic pollution play dominant roles. This review emphasizes the need for integrated groundwater monitoring, sustainable management approaches, and advanced spatial–temporal tools to ensure groundwater safety.

DOI: https://doi.org/10.5281/zenodo.17934323

CarbonNet – AI Carbon Emission

Authors: Rutuja Toggi, Akanksha Kawade, Shraddha Deshmukh, Ulka Nikalje, Vaishnavi Hippargi, Professor N.J.Shaikh

Abstract: Digital technologies such as cloud platforms, online video streaming, internet browsing, and IoT devices significantly contribute to global carbon emissions, yet traditional carbon calculators often ignore these digital footprint s. This project introduces an AI-driven Carbon Emission Monitoring Security System that tracks carbon output from digital activities while ensuring cybersecurity. The system leverages Flutter, Spring Boot, MySQL, Python ML, and a React.js dashboard to monitor activities, detect anomalies, score threats, and generate explainable AI reports. Security features include JWT authentication, MFA, refresh tokens, and RBAC. The AI models automatically retrain with new feedback, providing real-time alerts, analytics dashboards, and secure file scanning.

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On Some Combinatorial Action of Direct Product of Symmetric Groups S6 on A6 Sets

Authors: Salihu Aliyu Lawan, Shuaibu Garba Ngulde, Babagana Ibrahim Bukar

Abstract: In this paper, we study some action of S6 on A6. With Particular cases for n 2, 3, …,. and provide new combinatorial and structural insight into direct product actions of symmetric groups. Groups, Algorithms and Programming software (GAP) have been used to compute the elements of stabilizer S6. Orbit-stabiliser theorem and Cauchy-Frobenius lemma were applied to determine the number of S6(x)-orbits and their corresponding length respectively. We established that the action is transitive, faithful and imprimitive for n ≥ 2. Further results include explicit descriptions of point stabilizers, computation of orbit sizes using the Orbit–Stabilizer Theorem. We also established kernel of the action S6 on A6, and the construction of associated suborbitalni graphs and Upper bounds for the diameter of the resulting graphs are obtained, we the generalized the action (Sn)k for any n ≥ 2 on Cartesian products of k sets.

DOI: https://doi.org/10.5281/zenodo.17937347

Wastewater Stabilization Techniques: A Comprehensive Review

Authors: Manuela christy dany S

Abstract: Wastewater stabilization is one of the primary methods of environmental engineering that can protect public health and preserve aquatic ecosystems. In this regard, increasing wastewater generation due to urbanization, industrialization, and population growth has increased the need for cost-effective, sustainable, and efficient treatment technologies. Wastewater stabilization techniques undertake the reduction of organic matter, nutrients, pathogens, and toxic substances through biological, chemical, and physical processes. Among these technologies, WSPs, sludge stabilization methods, and integrated hybrid systems have shown high efficiency, especially in developing countries and rural regions. This review paper covers a critical and comprehensive synthesis of recent studies on the various technologies of wastewater stabilization. Major topics that will be covered in this review include fundamental aspects of stabilization, design and operational issues of WSPs, stabilization techniques of sludge, hybrid and decentralized systems, and more recent studies on modeling optimization, and AI applications. Much emphasis is placed on the environmental, economical, and public health impacts, along with the shortcoming of the existing systems. Also, resource recovery, energy-neutral treatment, and climate-resilient design are some of the emerging trends pointed out in this review. This paper intends to provide an in-depth understanding among students, researchers, and practitioners regarding the different stabilization technologies of wastewaters and their future research directions towards sustainable wastewater management.

DOI: https://doi.org/10.5281/zenodo.17935085

The Impact Of Social Media On Assamese Culture: An Analytical Discussion

Authors: Dr. Arati Basumatary

Abstract: Social media has become a special part of society. In the present age, social media is considered the best medium for communication across the world. The availability of the internet has made social media popular with the facility of instant exchange. Especially platforms like Facebook, WhatsApp, Instagram, YouTube etc. have facilitated communication, photo-video sharing from anywhere. From the new generation to the older generation, people are now attracted to and experienced with the use of such social media. It can be said that social media is a suitable platform not only for entertainment but also for education, business etc. The widespread use and popularity of social media is also observed in Assamese culture. Assamese songs-dances, food etc, the original cultural elements have been able to be presented on the world stage through social media. However, in this context, it can be assumed that the authenticity and values of Assamese traditional culture are somewhat hindered. This proposed research paper will also discuss the impact of social media on Assamese culture, including both positive and some negative impacts.

Predicting Student Dropout Using Enhanced Boosting Algorithms: A Comparative Study With ADVXGBoost

Authors: Mridulaxika, Gurpreet Singh, Varuna Tyagi

Abstract: Student dropout is a persistent challenge in higher education, leading to academic, financial, and institutional losses. Accurate early prediction of at-risk students can significantly improve retention through timely interventions. This paper presents a comparative analysis of three ensemble-based machine learning models AdaBoost, Gradient Boosting Machine (GBM), and a proposed Advanced Extreme Gradient Boosting (ADVXGBoost) algorithm for predicting student dropout risk. The models were evaluated using a dataset of 5,000 student records containing demographic, academic, and behavioral attributes. Performance was assessed using 10-fold stratified cross-validation in the WEKA Explorer environment. Experimental results demonstrate that ADVXGBoost outperforms AdaBoost and GBM, achieving the highest accuracy of 90.76%, the lowest error rates, and balanced class-wise prediction. The findings confirm the effectiveness of enhanced boosting techniques for reliable student dropout prediction and decision-support systems in educational institutions.

Predictive Reliability Engineering For Real-Time Event Streaming Pipelines Using Multi-Modal Deep Learning Models

Authors: Srinivasa Chakravarthy Seethala

Abstract: Real time event streaming pipelines form the operational backbone of modern digital platforms, supporting continuous data ingestion, processing, and delivery across cloud native and distributed environments. Despite their importance, reliability management in such pipelines remains largely reactive, relying on threshold based monitoring and post failure diagnostics that are insufficient for preventing cascading disruptions. This study addresses the problem of anticipating reliability degradation in real time event streaming systems by proposing a predictive reliability engineering framework grounded in multi modal deep learning. The primary objective is to enable early identification of failure precursors by jointly analyzing heterogeneous telemetry sources, including system metrics, execution logs, distributed traces, and event level metadata. A mixed method research approach is adopted, combining quantitative modeling of historical incident data with qualitative architectural analysis of streaming platforms to inform model design and integration. The proposed framework employs temporal and representation learning techniques to fuse multi modal signals and generate probabilistic reliability risk scores ahead of observable failures. Experimental evaluation across representative streaming workloads demonstrates improved failure prediction accuracy, longer warning lead times, and reduced false alert rates compared to single source monitoring baselines. The findings highlight the innovation of multi modal fusion for reliability prediction and its implications for proactive operational decision making. From an academic perspective, the study advances reliability engineering by introducing predictive, data driven models tailored to real time pipelines. From an industry standpoint, the framework supports more resilient event driven architectures through earlier intervention, reduced downtime, and improved service continuity, reinforcing the strategic value of intelligent reliability management in high availability systems.

DOI: http://doi.org/10.5281/zenodo.17938555

An AI-Driven Compliance Intelligence Platform For Continuous Monitoring And Automated Risk Assessment In Regulated CRM And ERP Systems

Authors: Srinivasa Chakravarthy Seethala

Abstract: This study proposes an AI driven compliance intelligence platform designed to enable continuous monitoring and automated risk assessment within highly regulated CRM and ERP environments. Organizations operating in finance, healthcare, public sector, and other compliance intensive domains increasingly rely on complex enterprise platforms where regulatory obligations evolve faster than traditional audit and control mechanisms can adapt. The research addresses the limitations of static compliance models by introducing an architecture that integrates machine learning, natural language processing, and policy aware analytics to interpret regulatory requirements, monitor transactional and configuration level signals, and dynamically assess compliance risk in real time. A mixed methodological approach is adopted, combining conceptual system design with simulated enterprise data flows and scenario based evaluations across common regulatory regimes such as data protection, financial controls, and access governance. The findings demonstrate that AI driven compliance intelligence can significantly improve early risk detection, reduce manual audit effort, and enhance traceability across CRM and ERP processes by continuously correlating system behavior with regulatory intent. The platform introduces adaptive risk scoring, automated control validation, and explainable compliance insights that support both operational teams and governance stakeholders. From a strategic perspective, the study contributes to a forward looking compliance paradigm that shifts organizations from periodic, reactive audits toward proactive and continuous assurance models. Academically, the research extends existing literature on enterprise governance by formalizing compliance intelligence as a scalable, data driven capability embedded within enterprise software ecosystems.

DOI: http://doi.org/10.5281/zenodo.17938605

ML-Enhanced SQL And NoSQL Query Optimization For High-Volume Big Data Processing In Financial And Healthcare Applications

Authors: Srinivasa Chakravarthy Seethala

Abstract: This research paper investigates the role of machine learning enhanced query optimization techniques in improving the performance, scalability, and reliability of SQL and NoSQL databases used for high volume big data processing in financial and healthcare applications. Traditional rule based and cost based query optimizers often struggle to adapt to dynamic workloads, heterogeneous data distributions, and rapidly evolving access patterns that characterize modern financial transactions and healthcare data ecosystems. This study addresses the research problem of how adaptive machine learning driven optimization models can overcome these limitations by learning from historical query execution patterns, system telemetry, and workload characteristics. A mixed methodology is adopted, combining conceptual framework design, algorithmic modeling, and comparative performance analysis across representative SQL and NoSQL environments handling large scale transactional and analytical workloads. The findings demonstrate that machine learning enhanced optimizers significantly reduce query latency, improve resource utilization efficiency, and enhance workload predictability under peak data volumes compared to traditional optimization approaches. The paper highlights key innovations such as predictive cost modeling, adaptive index selection, and real time execution plan refinement driven by supervised and reinforcement learning techniques. From an academic perspective, this research contributes to the evolving discourse on intelligent data management systems by extending optimization theory into data driven adaptive architectures. From an industry standpoint, the results provide actionable insights for designing resilient, high performance data platforms capable of supporting mission critical financial and healthcare operations where accuracy, compliance, and responsiveness are paramount.

DOI: http://doi.org/10.5281/zenodo.17938651

Smart Dress Recommendation System

Authors: Sharmila P, Harini S, Harini D, Harini RV, Jothivarshini S, Karthi S

Abstract: In recent years, personalized fashion recommendation systems have gained significant importance due to the growing demand for customized user experiences in online shopping platforms. This project presents a Smart Dress Recommendation System that provides personalized clothing suggestions based on individual user preferences and physical attributes. The system employs a rule-based recommendation approach, where users are guided through a structured questionnaire to collect essential information such as occasion, gender, budget, style preferences, body measurements, body shape, and skin tone. The body measurement module analyzes user inputs to classify body types such as pear, rectangle, apple, or hourglass, while the skin tone module allows users to select their skin color from a predefined palette. Based on these parameters, a set of predefined rules is applied to recommend suitable dresses that enhance the user’s appearance and meet their personal preferences. The system ensures that fashion suggestions are practical, affordable, and visually appealing by considering budget constraints and occasion-specific requirements.

Laser Technology And Its Uses In Various Fields: An Overview

Authors: Dr Hari Gangadhar Kale

Abstract: Many aspects of life have benefited from laser technology which is regarded as one of the most significant technologies of the 20th century. These days laser technology is valuable in all manufacturing fields and offers a number of unique advantages including the production of mechanical tools and machines. Laser technology has steadily taken over and dominated the mechanical market particularly in the areas of material handling and metal parts because of its advanced cleaning capabilities fine welding lines powerful etching strokes high power operation and precise distance measurement capability. The benefits this business receives from laser cutting technology.

Big Data Analytics for Predicting Urban Crowd Flow Using Digital Footprint Signals

Authors: Dr.C.K Gomathy, Ananth Lakshmi ss, Lakshmi A

Abstract: Urban areas are becoming increasingly congested as populations grow and public spaces experience unpredictable fluctuations in foot traffic. This constant movement creates challenges for city planners, traffic authorities, and public safety teams who require reliable, real-time information to manage crowds efficiently. This research investigates the use of Big Data Analytics for predicting urban crowd flow by analyzing digital footprint signals generated through everyday human interaction with technology. These signals include smartphone GPS activity, Wi-Fi hotspot connections, public sensor logs, transport card swipes, and metadata from CCTV systems. By integrating these diverse and continuous data streams, the study proposes a multi-layered predictive framework capable of detecting mobility patterns, forecasting future crowd density, and supporting city-level decision-making. Through machine learning and deep learning models, the framework processes large-scale movement data and produces highly accurate predictions. The findings demonstrate that Big Data-driven analysis significantly enhances crowd-flow forecasting accuracy, improves safety management, supports effective traffic control, and strengthens urban planning strategies for smart cities.

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Numerical Simulation And Analysis Of The Mass Attenuation Coefficient, Half-value Layer, And Mean Free Path Of X-rays At 30 KeV In Fe, Ag, Sn, Pt, Au, And Pb Using XCOM And FASST: Comparison Study By Matlab -2014

Authors: Wafaa N. Jasim, Faten N. Jasim

Abstract: To characterize and analyze the capability of elements to attenuate X-rays, a number of important physical indicators were calculated, namely the mass attenuation coefficient µ/ρ, the half-value layer HVL, and the free path rate MFP, as they play a role in describing the nature of the material that can be used as a protection method in medical centers and laboratories related to dealing with X-rays, using two methods. The first is using the XCOM program, and the second method is the FFAST tool. They were applied to the elements Fe, Ag, Sn, Pt, Au, and Pb at an energy of 30 KeV, in order to evaluate the effectiveness of each element in medical and industrial applications related to radiation protection. We obtained good agreement between the two methods, and the Matlab program was relied upon in the calculations and drawings that show the relationship and agreement between them.

DOI: https://doi.org/10.5281/zenodo.17949497

Heart Health Prediction System Using Machine Learning

Authors: Vikram S Tigadi, Yallaling R Dalawayi, Rajesh S Meti,, Rajguru M Hiremath, Professor Pooja C Shindhe

Abstract: Heart disease remains one of the leading causes of death throughout the world, and early detection is the key to improved patient outcomes. This paper introduces a Decision Support Heart Health Prediction System (DSHHPS) developed using machine learning techniques to help diagnose critical clinical and demographical data including age, BP level, cholesterol level, glucose level and other vital medical signs. The processed data is further sanitized using pre-cleaning, preprocessing and selection of features to make it reliable and accurate. Several different machine learning models are tested and compared The system evaluates many clinical information such as age, sex, blood pressure, cholesterol level, the results of the resting ECG reading, the type of chest pain and the amount of sugar in their bloodstream along with other important health readings. Rigor: The dataset is subjected to various cleaning, preprocessing and feature selection processes to remove inconsistencies and error prior to training the model. A number of machine learning models are experimented and compared to select the best one, which produces the most accurate predictions.

Enhancing Student College Management System: Architectural Integration of Intelligent Academic Automation, Centralized Student Information Management, and Data-Driven Performance Analytics

Authors: Akshay Bhangade, Dr. Pushpa Pathak

Abstract: The rapid expansion of higher education institutions has intensified the need for efficient, intelligent, and scalable student management solutions. Traditional college management systems often suffer from fragmented data handling, limited automation, and insufficient analytical capabilities, leading to administrative inefficiencies and suboptimal academic decision-making. This paper presents an enhanced Student College Management System that integrates intelligent academic automation, centralized student information management, and data-driven performance analytics within a unified architectural framework. The proposed system leverages automation to streamline core academic processes such as admissions, course registration, attendance tracking, assessment management, and result processing, thereby reducing manual intervention and operational errors. A centralized database architecture ensures secure, consistent, and real-time access to comprehensive student records across departments. Furthermore, advanced analytics modules utilize historical and real-time data to evaluate student performance, identify learning patterns, predict academic risks, and support evidence-based decision-making for faculty and administrators. The system architecture emphasizes modularity, scalability, and interoperability, enabling seamless integration with existing institutional platforms and future technological enhancements. By combining intelligent automation with robust analytics, the proposed solution enhances administrative efficiency, improves academic monitoring, and supports personalized student development. This integrated approach contributes to improved institutional governance, better learning outcomes, and a data-driven academic ecosystem aligned with modern higher education requirements.

An AI-Driven, Explainable Machine Learning Framework For Early Disease Prediction In Healthcare

Authors: Sreehari K B, Deepakumar M

Abstract: Early disease prediction is a crucial aspect of modern healthcare systems, as it enables timely medical intervention, improves patient survival rates, and reduces long-term healthcare costs. Many chronic and life-threatening diseases such as diabetes, cardiovascular disorders, cancer, and neurological conditions develop gradually and often remain asymptomatic during their early stages. Traditional diagnostic approaches, which rely on clinical rules, physician experience, and fixed statistical thresholds, are often inadequate for detecting these early-stage disease patterns. and neurological disorders progress slowly over time and are often diagnosed only at advanced stages. Late diagnosis significantly reduces treatment effectiveness and increases mortality rates. With the growing global disease burden and aging population, early detection has become a priority in modern healthcare systems. Advancements in healthcare digitization have led to the availability of large-scale medical data, including Electronic Health Records (EHRs), laboratory reports, and medical imaging. These datasets provide valuable insights into patient health patterns and disease progression, enabling the development of predictive models for early diagnosis. With the rapid digitization of healthcare, vast amounts of medical data are generated through Electronic Health Records (EHRs), laboratory test reports, diagnostic imaging, and wearable health devices. This has created opportunities for Artificial Intelligence (AI) and Machine Learning (ML) techniques to analyze complex and high- dimensional medical data efficiently. Existing AI- based disease prediction systems have demonstrated improved accuracy compared to conventional methods; however, many of these systems suffer from limitations such as reliance on single-modal data, centralized data storage, poor generalization across healthcare institutions, severe class imbalance, and lack of interpretability. This project proposes an AI-based early disease prediction framework that addresses these limitations through the integration of multimodal clinical data, privacy-aware learning mechanisms, imbalance-sensitive training strategies, and explainable AI techniques. The proposed system learns complex patterns from longitudinal patient data and generates calibrated risk scores to support early diagnosis and preventive care. By improving transparency, robustness, and clinical trust, the proposed framework aims to provide an effective and scalable solution for early disease prediction in real-world healthcare environments.

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Review Paper on Low-Cost Indoor Air Quality in Residential and Institutional Buildings

Authors: Darshana N V

Abstract: This review paper explores low-cost methods and technologies for monitoring and enhancing indoor air quality (IAQ) in residential and institutional buildings. It highlights the importance of maintaining healthy IAQ for occupant health, comfort, and productivity, especially amid growing urbanization and environmental concerns. The paper systematically examines affordable sensor technologies, measurement approaches, and intervention strategies that provide effective IAQ management without significant financial investment. By evaluating recent innovations and practical applications, this review offers a comprehensive overview of accessible solutions aimed at improving indoor environments, supporting occupant well-being, and advancing sustainable building practices.

DOI: https://doi.org/10.5281/zenodo.17959352

The Role of Digital Marketing in Transforming Business Practices in India: A Qualitative Study

Authors: Sagar Shivaji Thakare

Abstract: This qualitative research paper explores the transformative impact of digital marketing on business practices in India. With rapid advancements in internet penetration, smartphone usage, and social media engagement, Indian businesses—both large and small—have increasingly adopted digital marketing tools to reach wider audiences. The study draws upon qualitative insights from existing literature, expert interviews, and case studies to understand how businesses leverage digital platforms for branding, customer engagement, and sales growth. The findings reveal that digital marketing enhances competitiveness, fosters innovation, and supports customer-centric strategies. However, challenges such as digital literacy, regulatory issues, and high competition remain significant. The paper concludes with recommendations to improve digital marketing adoption, emphasizing education, government support, and local innovation.

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Review paper on Concrete with Partial Replacement of Fine Aggregate by Copper Slag

Authors: R Swetha

Abstract: Concrete construction largely depends on natural river sand as fine aggregate. However, continuous extraction of river sand has resulted in serious environmental issues such as riverbed erosion, groundwater depletion, and ecological imbalance. At the same time, copper industries generate large quantities of copper slag as an industrial waste, which poses disposal and environmental challenges. This experimental study focuses on evaluating the suitability of copper slag as a partial replacement for fine aggregate in concrete. Concrete mixes were prepared by replacing river sand with copper slag at proportions of 0%, 10%, 20%, 30%, 40%, and 50% by weight. The fresh properties, strength characteristics, and selected durability parameters of concrete were studied. The results show that concrete containing copper slag exhibits improved strength and durability up to an optimum replacement level of about 20–30%. Beyond this level, a reduction in strength was observed. The study concludes that copper slag can be effectively utilized as an alternative fine aggregate, contributing to sustainable and eco-friendly concrete construction.

DOI: https://doi.org/10.5281/zenodo.17961373

Removal of Pharmaceuticals and Personal Care Products from Water and Wastewater

Authors: Meenu Bose

Abstract: Pharmaceuticals and personal care products (PPCPs) have been among the emerging contaminants of water and wastewater systems in the recent years. These substances have found their way into the environment on a continuous basis as they are widely used and they are not fully eliminated in the traditional treatment procedures. Although in extremely low levels, their long-term occurrence and biological effects may be hazardous to aquatic ecosystems and human health. This review presents the key contributors to PPCPs, their presence in water bodies, physicochemical behaviour, detection methods, and the treatment technologies. Special attention is paid to adsorption processes, the use of advanced oxidation processes, membrane-based treatment, and combined treatment procedures. Further problems and gaps in research and directions are also presented to promote better water management practices.

DOI: http://doi.org/10.5281/zenodo.17961994

A Comprehensive Review Of Biosorption For Fluoride Removal From Industrial Effluents

Authors: Nikitha B

Abstract: Fluoride-rich industrial effluents originating from sectors such as metal smelting, fertilizer production, glass manufacturing, and battery industries pose significant environmental and public-health concerns due to the risk of dental and skeletal fluorosis. Precipitation, ion-exchange, and membrane filtration are examples of conventional treatment techniques that are less appropriate for large-volume or variable-composition industrial wastewaters due to their high operating costs, sludge production, fouling, or poor selectivity. Biosorption, which uses natural, waste-derived, or biologically modified materials, has become a viable, affordable, and sustainable method of removing fluoride. This review offers an extensive review of biosorbents reported for fluoride remediation, including engineered bio-chars, metal-loaded bio-composites, raw biomass (plant fibers, algae, and agricultural waste), and advanced hybrid materials like MOF-based bio-adsorbents. The majority of research reports Langmuir-type monolayer adsorption behavior, with pseudo-second-order kinetics suggesting chemisorption, particularly for biosorbents impregnated with metals. High removal efficiencies (up to ~90%) and significant adsorption capacities have been attained under ideal laboratory conditions, but problems still exist, including limited regeneration data, narrow effective pH ranges, high adsorbent modification costs, and a lack of validation using actual industrial effluents containing competing ions. This review identifies these knowledge gaps and makes recommendations for future research, such as mechanistic evaluation under actual wastewater matrices, pilot-scale continuous-flow studies, hybrid treatment processes, and regeneration optimization. As long as future studies concentrate on scalability, long-term stability, and practical application, biosorption has great potential as a cost-effective and ecologically friendly technique for fluoride removal.

DOI: https://doi.org/10.5281/zenodo.17962912

KhetSetGO: Machine Finding Application

Authors: Himanshu Kaspate, Tejas More, Atharv Sanas, Pruthviraj Sarade, Vijay Mohite

Abstract: Indian agriculture increasingly relies on mechanization to improve productivity; however, access to agricultural machinery remains a major challenge for small and marginal farmers due to high purchase costs, maintenance expenses, and unorganized rental practices. In recent years, several agricultural equipment rental and machine finding systems have been proposed to address these challenges through web and mobile-based platforms. This survey paper reviews and analyzes existing agricultural machinery rental systems, focusing on their architecture, functionalities, and technological approaches. The study examines key features such as equipment listing, location-based search, online booking, scheduling, payment mechanisms, and user feedback systems. A comparative analysis of existing research reveals significant limitations, including lack of real-time availability tracking, limited support for rural connectivity constraints, low digital literacy consideration, and absence of intelligent machine discovery mechanisms. Based on the identified research gaps and insights from the literature, this paper highlights the need for a centralized, farmer-friendly, and scalable machine finding platform. The survey provides the foundation for KhetSetGo, a proposed digital machine finding and rental application aimed at improving equipment accessibility, reducing operational costs, enhancing resource utilization, and supporting sustainable digital transformation in Indian agriculture.

Web Application Security Headers: A Comprehensive Analysis of Their Role in Mitigating Modern Web Threat

Authors: Harsh Parashar, Kartik Sharma, Rehansh Mohta

Abstract: The increasing reliance on web-based systems for critical operations—such as financial transactions, healthcare data management, government services, and e-commerce—has augmented the need for reliable web security mechanisms. Modern cyberattacks increasingly exploit browser vulnerabilities rather than server-side weaknesses. According to recent research, over 72% of web- based attacks target insecure browser environments through injection, manipulation, session hijacking, or redirection techniques. This creates a significant attack surface where traditional backend security mechanisms are insufficient. This research paper provides a deep analysis of HTTP security headers, an often overlooked yet highly effective method of browser protection. When properly implemented, security headers can reduce the likelihood of browser-based attacks by more than 70%. The headers examined include Content- Security-Policy (CSP), Strict-Transport-Security (HSTS), X-Frame-Options, Referrer-Policy, X- Content-Type-Options, and Permissions-Policy. Each header's function, implementation method, security impact, and limitations are investigated. The research further incorporates threat modeling using the STRIDE framework, comparative analysis of websites with and without headers, real-world case studies, and AI-based automation concepts for detecting missing headers. The findings indicate that security headers are both low-cost and highly impactful, making them one of the most practical defenses for modern web applications. The paper concludes by proposing future AI-driven methodologies that can automatically analyze, predict, and configure optimal security headers.

2, 6−BIS (Benzimidazol−2−YL) Pyrazine, ITS N−Methylated Derivative Reactions with Some Acids and Iron (II) Salts

Authors: Dr. Ishwar Singh

Abstract: The NMR, IR and Electronic spectra studies on biologically active complexes of iron (II) have been reported. The bands observed and discussed assuming the molecule under CS point group symmetry. The electronic study in nujol phase has been calculated. The IR spectral studies of this compound have been discussed.

DOI: https://doi.org/10.5281/zenodo.17974049

Survey Paper on UniTasker: A Role-Based Academic Task Management System

Authors: Praful Madne, Yash Nanwatkaer, Aary Mahadik, Agrim Khanna, Poonam Chavan

Abstract: The increasing digitalization of academic environments has created the need for efficient task management systems that support assignment distribution, submission tracking, and faculty–student interaction. UniTasker is a role-based task management application designed for diploma-level educational institutions. It provides dedicated interfaces for faculty and students, enabling assignment creation, submission, grading, and automated reminders. Although the current version focuses on assignment workflows, UniTasker is designed to support future extensions such as integrated attendance tracking. This survey paper reviews existing academic task management solutions, identifies gaps in current systems, and evaluates how UniTasker addresses these gaps through its architecture and features.

A Study On Resampling Methods For Handling Imbalanced Dataset

Authors: Akshay Jajpuriya, Dr. Vandan Tewari

Abstract: Occurrence of imbalanced classes in a dataset is one of the major issues in machine learning, as such datasets often yield biased models in favor of the majority classes in- stead of the minority ones. This paper discusses several tech- niques developed by previous researchers that help manage imbalanced datasets. This paper offers a study of resampling strategies aimed at balancing the distribution of classes using techniques such as oversampling, undersampling, and hybrid approaches. The main goal is to help researchers and prac- titioners choose appropriate strategies based on dataset char- acteristics and model requirements. By highlighting trends and real world applications, this survey serves as a guide for effectively tackling data imbalance problem.

DOI: http://doi.org/10.5281/zenodo.17975684

FlowBeat: Gesture based control technique for intelligent music interaction system

Authors: Siddhi Pawar, Anuradha Raut, Tanuja Suryawanshi, Shravani Wadghare

Abstract: Gesture-controlled interfaces have emerged as an intuitive alternative to conventional touch and voice-based music control. Gesture recognition is becoming more and more popular for hands-free media interaction thanks to developments in computer vision and machine learning. This study describes a gesture-controlled music player system that can control media features like play, pause, next/previous track, and volume adjustment by interpreting real-time hand gestures. The project aims to deliver a touchless user experience, offering intuitive control of digital media via simple hand movements detected through a webcam.

AI Based Analysis Of Labor Shortage And Workforce Development Using Intelligent Modelling

Authors: V. R. Deshmukh, S. M. Waysal

Abstract: The recruitment of skilled labor in the construction sector has consistently posed significant challenges. There exists a notable disparity between the high demand for specific labor skills from employers and the limited availability of such skills within the local construction workforce. This imbalance results in skill shortages that adversely affect construction projects, leading to issues such as substandard workmanship, diminished efficiency, reduced effectiveness, project delays, defects, the need for rework, material wastage, increased costs, and postponed project completions. This study presents a comprehensive analysis of the current labor skills and shortages within the construction market. It identifies the underlying factors contributing to these shortages and ranks them based on the opinions of respondents, thereby highlighting the significance of these factors. Furthermore, it aims to enhance awareness regarding professional skills in the industry and offers recommendations for their improvement to benefit the market. The unprecedented economic growth accompanied by widespread urbanization along with foreign direct investment has fueled a demand for skilled workers in India. The task of workforce development in India faces the changing realities of globalization and competitiveness, on one hand, and the need for inclusive growth on the other.

DOI: https://doi.org/10.5281/zenodo.17976315

Identification And Evaluation Of Safety Factors In Construction Industry Using Fuzzy Reasoning Technique

Authors: S. D. Phad, M. C. Aher

Abstract: Modern construction projects, characterized by their complexity and uniqueness, are inherently susceptible to various risks. These risks represent uncertain events that may arise during the project's life cycle, potentially influencing its objectives either positively or negatively. Positive risks are referred to as opportunities, while negative risks are identified as threats. To effectively harness these opportunities and mitigate threats, the implementation of Risk Management is essential. A novel theoretical framework known as Fuzzy theory enhances the risk management process. This approach not only addresses the challenges posed by inaccurate data but also provides a structured methodology for drawing conclusions. The application of Fuzzy theory is exemplified through a risk assessment of an ongoing residential construction project. By employing a systematic fuzzy model, expert judgment values are transformed into precise metrics. This paper focuses on the application of fuzzy logic models within the construction sector and highlights the benefits of this approach compared to traditional risk assessment methods.

DOI: http://doi.org/10.5281/zenodo.17976370

Enhancing Slope Stability: A Study On Strip Footing With And Without Micro Piles

Authors: R.T. Jagtap, Dr. Pavan N. Ghumare, I. J. Bathe, V.S. Wagh

Abstract: The stability of strip foundations situated at naturally occurring site conditions, particularly in proximity to soil slopes characterized by low bearing capacity, constitutes a significant challenge for engineers. The current investigation elucidates the behavior of strip foundations positioned adjacent to soil slopes composed of soft soil with inadequate bearing capacity. Among the various methodologies employed for assessing soil stability, the implementation of micro piles is utilized to enhance the load-bearing capacity of the foundation and improve the soil's bearing capacity in the immediate vicinity of the foundation. Utilizing the finite element software Plaxis 2D, the interaction between the strip footing and the earth slope can be quantified across multiple parameters, including horizontal displacements, vertical displacements, and effective stresses.

DOI: http://doi.org/10.5281/zenodo.17976445

Design Of Dual Media Water Filter Purifier Using Clay Pot

Authors: Patil Prashant Shivajirao, Thakare Abhijit M, Manoj R Avhad, Patil Sachin Sopan

Abstract: Low cost water treatment devices for rural households like filters, RO and UV based water purifiers are developed by many industries but these devices suffer from problems like filter clogging, periodic replacement of filters, wastage of water (in case of RO) and unavailability of electricity in rural areas making them costlier. Due to above reason RO, UV purifier are not affordable to rural areas. The objective of our project is to deal with improvisation in existing filter design and remove the flaws in existing filtration models at economical level. The study deals with the purification of water using naturally available material using copper mesh as a disinfectant and provide effective and economical water purifier for rural area. Earthen pots are used for the purification of water made from different proportions of Clay and Saw dust (50:50, 60:40, 70:30). Lake and well water was treated as it is normally used in rural areas. Various drinking water test carried on samples before and after the treatment of water showed that the filters were capable to treat water as per the drinking water standards. Maximum turbidity and TDS was removed in 1st model (50:50) up to 89% and 72.35% respectively for lake water and 78% and 67.47% respectively for well water As copper was used as a disinfectant, no trace of E-coli was found in the treated water for all three models. The results showed that Pot Filter (50:50 model) can be effectively used as a low cost filtration device in rural areas.

DOI: http://doi.org/10.5281/zenodo.17976521

Power Generation By Burning Dry Waste By Using Solar Heating Panels And TEG

Authors: Dwarkesh A. Saykhedkar, Ajit S. Pawar, Nikhil S. Kothawal, Abhilash A.Netake

Abstract: The concept of metaverse represents an innovative change in digital interaction by combining virtual reality and expanded reality with a common experience. There is an integrated virtual common space created by combining virtually expanded physical reality with normal physical virtual reality. This content examines various aspects of metaverse, including underlying technologies (such as blockchain and AI) and applications in a variety of fields such as entertainment, education and business. It also examines the possibilities of new social and business forms, such as issues of privacy and digital justice, and the impact of businesses on society. A combination of findings from research and practice should provide this content with a better understanding of the impact of metabar on future digital ecosystems and community interventions. This is a rapidly evolving digital limit that transforms physical and virtual reality into an immersive, interactive, and persistent environment. For progress in Virtual Reality ( VR), Augmented Reality (AR), Blockchain and Artificial Intelligence (AI), users can create contacts, work, play and handles in the 3D room. It promises transformative impacts in a variety of sectors, including education, healthcare, entertainment, real estate, and long-distance work. This article examines the fundamental technologies behind metaverse, their potential socioeconomic implications, privacy, data security, digital identity and interoperability challenges. Metaverse can still interact with digital systems and cooperation during the development stage, but it shows the next important development in the use of the Internet.

DOI: https://doi.org/10.5281/zenodo.17976583

Development Of A 5-DOF Prosthetic Hand With ROS 2 For Real-Time Adaptive Control

Authors: Rasika M. Chandramore, Awishkar Gawli, Harshada Gaikwad, Niraj Khankar, Ketan Thorat

Abstract: The Task of Prosthetic Hand with It fixates on the development of a cost-efficacious prosthetic arm controlled utilizing ROS2 (Robot Operating System 2) and Arduino Uno, designed to renovate rudimental hand functionalities for individuals with upper limb disabilities. The prosthetic arm employs five servo motors, each corresponding to an individual finger, to facilitate natural and precise hand forms of kineticism. The control system is built around ROS2 nodes that handle authentic-time communication and command execution. Commands are transmitted to the Arduino Uno, which engenders PWM (Pulse Width Modulation) signals to drive the servo motors. The hardware-software interface is implemented utilizing the Arduino IDE, with ROS2 providing a scalable framework for future enhancements. This modular and open-source approach ascertains that supplemental features, such as amended control algorithms and sensory feedback, can be incorporated in subsequent iterations. The prosthetic arm has been calibrated to perform essential tasks, such as prehending, holding, and relinquishing objects, demonstrating its potential as a practical assistive contrivance. It designate prosperous replication of rudimentary hand gestures with reliable motor performance and authentic-time responsiveness. The affordability and adaptability of the design make it a promising solution in the field of assistive technology. Future ameliorations may fixate on integrating haptic feedback and optimizing power consumption.

DOI: https://doi.org/10.5281/zenodo.17976653

Automation Framework For Landmine Detection Robotic Vehicle With GPS Positioning

Authors: Savita P. Deore, Shubham B. Gaikwad, Bhagyashri N. Gosavi, Siddhi N. Bagmar

Abstract: During combat, land mine detection is crucial, especially for the secure entry of armed vehicles into hostile territory. To reduce the chance of combat tank damage and to save the lives of defense troops, these armed vehicles including main battle tanks—are made to follow the path of a manually piloted pilot tank. The main goal of our landmine detection Within the defense zone, the robotic vehicle seeks to detect landmines over the largest feasible area. Landmine explosions have the potential to seriously hurt soldiers and release dangerous pollutants into the environment. In conflictare as, robots are typically used to detect these hazards before they explode. Landmine-detecting robots are crucial in this situation to protect military personnel's lives.

DOI: https://doi.org/10.5281/zenodo.17976700

Nextgen Voting System A Secure, Transparent, and Efficient Blockchain- Based Online Voting Platform

Authors: Kaustubh Nitin Salunke, Vinayak Amol Shewale, Anurag Sanjay Shigwan, Omkar Vinod Tate, Ms. Tejal Panmand

Abstract: Voting systems are the foundation of democratic decision-making, ensuring fair participation, representation, and legitimacy in elections. Traditional paper-based voting systems, although widely used, suffer from multiple limitations such as high operational costs, excessive manpower requirements, time-consuming vote counting, delayed result declaration, and vulnerability to fraudulent practices like ballot stuffing, impersonation, and vote tampering. These issues become more significant in large-scale or frequently conducted elections, including institutional and organizational elections. Electronic Voting Machines (EVMs) were introduced to address some of these challenges by improving efficiency and reducing manual errors. However, EVMs still operate in centralized environments, raising concerns related to transparency, verifiability, and trust. The lack of public audit mechanisms and dependence on election authorities for result validation often leads to skepticism among voters. Blockchain technology has emerged as a revolutionary solution capable of transforming traditional systems through decentralization, immutability, cryptographic security, and transparency. By maintaining a distributed and tamper-proof ledger, blockchain ensures that once a vote is recorded, it cannot be altered or deleted without network consensus. This survey paper presents the NEXTGEN VOTING SYSTEM, a blockchain-based online voting platform designed primarily for institutional elections such as college student council elections. The paper surveys existing voting technologies, analyzes their strengths and limitations, identifies research gaps, and proposes a secure, transparent, and scalable blockchain-based voting framework. The proposed system aims to enhance voter trust, ensure end-to- end verifiability, reduce election costs, and improve accessibility while maintaining voter privacy and ballot secrecy.

DOI:

Machine Learning Based Wildlife Intrusion Detector For Agricultural Areas

Authors: Suhani Ranjay Sinha, Vedika Sanjay, Sushant Ganesh Vidhate, Manisha Shinde

Abstract: The increasing human-leopard conflict in agricultural areas necessitates innovative solutions to prevent leopard intrusions and protect farms. This project proposes an intelligent wildlife intrusion detection system utilizing Raspberry Pi, GSM technology, and audio warning systems to deter leopards from entering farms. The system consists of two primary modules: leopard detection and deterrent mechanisms. Camera traps capture images of approaching animals, which are then processed using Convolutional Neural Networks (CNNs) to detect leopard presence. Upon detection, the system triggers a GSM alert to farmers and simultaneously activates speakers emitting loud, leopard-deterrent sounds. The audio warning system, designed to mimic natural threats, effectively scares leopards away from the farm perimeter. Integration of CNNs enables accurate leopard detection, while the GSM module ensures timely alerts to farmers. This cost-effective, IoT-based solution contributes to: 1. Enhanced farm security, 2. Reduced human-leopard conflict, 3. Decreased crop damage, 4. Increased farmer safety By leveraging AI-powered detection and audio deterrents, this system offers a promising solution for mitigating leopard intrusions, promoting coexistence between humans and wildlife, and ensuring sustainable agricultural practices.

DOI: https://doi.org/10.5281/zenodo.17985293

Intelligent Modelling For Multilingual Language Learning Chatbot

Authors: Saisha Hiray, Manisha Shinde, Priti Choudhari, Samarth Palve, Rushi Bagul

Abstract: In this era of rapid globalization and technological advancement, the ability to communicate in multiple languages has become a crucial skill. Traditional language learning methods often lack the interactivity and personalization needed to fully engage learners. This project addresses this challenge by developing a multilingual language learning chatbot robot that combines both software and hardware to offer an innovative, interactive language learning experience. The system is built around a Raspberry Pi, connected to a microphone and speaker, enabling real-time voice interactions with the chatbot. Leveraging Natural Language Processing (NLP), the chatbot can understand and respond in multiple languages, making it suitable for learners at various levels. Speech recognition allows the system to accurately interpret user input, while speech synthesis enables the chatbot to respond naturally, creating a conversational environment that mimics real-world language use. As learners interact with the chatbot, they engage in simulated dialogues that enhance their ability to speak and understand the language in realistic contexts. The chatbot offers a dynamic learning experience by adapting to different languages, providing users with the flexibility to switch between languages and practice multiple linguistic skills within the same session. Powered by the Raspberry Pi platform, the system is portable, affordable, and easy to deploy in various educational settings, from classrooms to home use. This project represents an innovative approach to language learning, combining AI-driven software with accessible hardware to provide a scalable, interactive tool for multilingual education.

DOI: https://doi.org/10.5281/zenodo.17985349

IoT-Based Real-Time Women Safety and Alert System

Authors: Sai Manish M S, Karunakar Reddy D, Prajwal H, Varun Tej R, Mrs. Trisha V S

Abstract: Women’s safety remains a significant concern, particularly during emergency situations where victims may be unable to manually operate their mobile phones. Many existing safety applications fail to provide timely assistance under such circumstances. To address this limitation, this paper presents an IoT-based real-time women safety and alert system that enables automatic emergency detection and communication. The system integrates GPS, GSM, and a microcontroller to continuously monitor the user’s condition through a heartbeat sensor and a panic button for manual activation. Once a distress situation is detected, the system sends an SOS message containing the user’s live location to pre-registered contacts and updates the IoT dashboard for real-time monitoring. The proposed system enhances emergency response efficiency and provides a practical, affordable, and reliable solution for improving women’s safety.

The Metaverse Revolution: Unveiling Indias Socio-Economic Transformation

Authors: Mayuri Mahajan, P. D. Jadhav, Mehraj Khan

Abstract: The metaverse, an immersive a virtual world where people can live, work, shop, learn, and interact, is anticipated to revolutionize how we engage with technology and each other. By merging physical and digital realities through technologies like virtual reality (VR), augmented reality (AR), blockchain, and artificial intelligence (AI), the metaverse offers unprecedented opportunities for connection, communication, and collaboration across boundaries. This paper investigates the potential effects of the metaverse in India, focusing on socio-economic impacts, technological advancements, and regulatory challenges. It explores how the metaverse could reshape critical sectors such as education, healthcare, business, and entertainment. By analyzing current trends and projecting future developments, the study highlights both the opportunities and challenges in India's journey toward embracing this transformative technology.

DOI: https://doi.org/10.5281/zenodo.17985389

Early Detection Of Dementia Using Deep Learning And Image Processing

Authors: Bhavesh Avinash Gadekar, Neha Sanjay Gaikwad, Swayam Vijay Bhosale, Prasad Ambadas Bidgar, Ganesh K Gaikwad

Abstract: Dementia diagnosis is a critical challenge in neurology, often relying on time-intensive and subjective manual analysis of MRI scans. This research proposes a novel hybrid AI-based system for early and accurate dementia detection, combining traditional neuroimaging techniques with advanced deep learning models. The system employs a hybrid 2D-3D pipeline that integrates slice-based 2D convolutional models with volumetric 3D CNN architectures, ensuring a balance between computational efficiency and spatial pattern recognition. The 2D models focus on extracting detailed features from individual MRI slices, while the 3D models capture spatial relationships across the entire brain volume. Additionally, clinical metrics such as cognitive scores are integrated with the MRI data to enhance diagnostic accuracy. Attention mechanisms and Grad-CAM visualizations improve model interpretability by highlighting critical brain regions, addressing the need for transparent AI-driven clinical tools. This hybrid approach significantly improves diagnostic accuracy, generalizability, and explainability compared to conventional methods. The system classifies scans into Strongly Demented, Mildly emented, or Non-Demented categories, providing actionable insights for clinicians. By bridging AI with neuroimaging and multimodal data integration, the proposed system aims to revolutionize dementia detection, enabling earlier intervention and improved patient outcomes.

DOI: https://doi.org/10.5281/zenodo.17985501

AI Based Autonomous Dynamic Job Market Analysis Platform

Authors: Chaitanya Madhav Mate, Sanil Nivrutti Shinde, Aakansha Ganesh Tambe, Niranjan Deepak Lahane, Kirti Patil

Abstract: This paper describes a "Dynamic Job Market Analysis Platform" that allows capturing real-time analytics and prediction of the employment fluctuations with the goal of benefiting universities students and employer. By utilizing machine learning models to forecast trends accurately, the platform fills this gap between student skill sets and the leading demands of the industry. It provides students with actionable insights to help them ensure their career paths are in line with market requirements, and it helps employers better understand trends in the workforce. The results highlight an accuracy of over 97% in prediction of the employment patterns, showcasing the ability of the platform to fuel data-based decision making. This project helps to improve employability and provides a better alignment between academia and market requirements by addressing issues, such as data imbalance and dynamic changes in the market. Future directions include expanding the scope of real-time data integration and refining prediction models for broader applicability.

DOI: https://doi.org/10.5281/zenodo.17985571

IOT Based Smart Farming For Crop Yield Prediction

Authors: Aaditya Said, Vivek Khalate, Shubham Sanap, Aayush Nahire, Deepali Suryawanshi

Abstract: Modern agriculture faces challenges such as soil degradation and suboptimal crop selection, leading to reduced productivity. The “Smart Farming-IOT based” project focuses on three key components: crop recommendation, fertilizer recommendation, and climate-based predictions. Using historical data, climate conditions, and soil characteristics, a machine learning model predicts the most suitable crops for a given area, ensuring optimal land use and increased yield. The project empowers farmers with real time insights to enhance productivity and support sustainable agriculture.

DOI: https://doi.org/10.5281/zenodo.17985636

AI-Enabled Feedback Management System For Enhancing Education

Authors: Sali Radha, Sali Radha, Bacchav Jayesh, Patil Harshal, Boraste Siddesh

Abstract: In today’s educational landscape, institutions increasingly recognize the value of student feedback for enhancing learning experiences. However, traditional methods like manual reviews and basic statistics often fail to capture the complex and complicated patterns within this feedback. Our project proposes a novel approach using Long Short-Term Memory (LSTM) algorithms to analyse student feedback and predict sentiment more effectively. LSTM’s strength in handling sequential data enables us to uncover deeper insights into student experiences and trends. This innovative method aims to transform feedback analysis into a comprehensive, data-driven evaluation tool, ultimately improving educational practices. Additionally, we implement a Generative Pre-trained Transformer (GPT) model to provide dynamic, tailored suggestions for student growth. By combining advanced machine learning techniques, our system not only analyses feedback but also offers actionable recommendations, fostering a more supportive and effective learning environment. This holistic approach aims to enhance both student outcomes and institutional practices.

DOI: https://doi.org/10.5281/zenodo.17985712

Ensuring Ethical Accountability On Personalized AI Tutor: Acampus.ai

Authors: Aryan Pate, Mohit Patil, Yadnesh Dalvi, Kanchan Birari, Ashutosh Kale

Abstract: Education changes dramatically today, with personal and adaptive learning experience needs at an all-time high. Amidst this dynamism, acampus.ai stands in the helm of revolutionary change with a product using artificial intelligence to fundamentally change the way people learn. The cutting-edge platform brings into every classroom personalized AI tutors with a customized educational experience tailored for each learner's specific needs. ACampus.ai is nothing less than an AI tutor system. So, it's much more than just a classroom approach. Instead of customized courses and real-time assistance on doubts, the teaching methodology adjusts dynamically to the learner's pace and style. It analyzes user behavior and learning patterns by finding out what needs to be done in relevance and alignment to the goal of the learner. It's either deep and comprehensive understanding about the subject or short answers to doubts-there is empowerment through a holistic and individualized experience provided by acampus.ai.

DOI: https://doi.org/10.5281/zenodo.17985744

The Analytical Review Of The Downsizing Reasons In The It Sector

Authors: Dr Meghna Sharma, Dr Namita Yash

Abstract: This research paper makes an effort to identify the different reasons and methods adopting the downsizing activities by the management in their organizations. The objective is fulfilled with the help of analysing the responses received against the questionnaire. The descriptive analysis has been done to work out the reasons to downsizing in a systematic manner. It’s the employee’s perspective by which the research can be concluded by providing the rational reasons to the layoffs. The reasons can be stated as cost factor, company policy format, performance criteria, company reorganization.

DOI: https://doi.org/10.5281/zenodo.17986288

Multilingual Chat Application

Authors: Durvesh Mohan Kavire, Swaraj Pradeep Khade, Saurav Deepak Patankar, Abhijeet Shivraj Waghmar, Ms. Tejal Panmand

Abstract: A Multilingual Chat Application is a communication platform designed to allow users from different linguistic backgrounds to interact seamlessly by integrating real-time messaging with automatic language translation capabilities. The application enables users to send and receive messages in their native language while the system transparently translates the content into the recipient’s preferred language, thereby eliminating language barriers and promoting effective communication. It leverages advanced natural language processing and machine translation technologies to ensure accurate, fast, and context-aware translations, while maintaining message privacy and data security. The system typically supports multiple languages, user authentication, message synchronization, and an intuitive user interface to enhance usability across diverse user groups. Such an application is particularly valuable in global business collaboration, online education, customer support services, healthcare communication, and social networking, where participants may not share a common language. By fostering inclusivity and improving accessibility, the multilingual chat application plays a significant role in connecting people worldwide, enhancing cross-cultural interaction, and supporting efficient information exchange in today’s globally interconnected digital environment.

Survey Paper On Xaanaax: A Digital Emergency Backup And Information Access System

Authors: Hemant Khatpe,, Ayush Pilane, Sahil Choudhari, Chaitanya Jam, Tejal Panmand

Abstract: The rapid dependence on smartphones for storing personal and professional information has introduced new risks during emergencies such as device loss, battery failure, or network unavailability. Xaanaax is a digital emergency backup and information access system designed to ensure uninterrupted availability of essential files, contacts, and notes anytime and anywhere. The platform offers secure, centralized cloud-based storage with 24/7 accessibility across devices. This survey paper reviews existing digital storage and backup solutions, identifies their limitations in emergency scenarios, and analyzes how Xaanaax addresses these gaps through its architecture, security mechanisms, and user-centric design.

Loan Utilization Tracking Via Mobile

Authors: Vedant Pawar, Hasnain Pathan, Sujal Thombare, Yash Kondake, Aarti Gohade

Abstract: Loan utilization tracking is an important element of responsible lending to ensure that borrowers utilize loan funds strictly for their intended purposes. Traditional monitoring techniques such as physical inspections, manual reporting, and post-disbursement audits are often slow, expensive, and prone to inaccuracies. With the rapid growth of mobile technology, digital finance platforms have transformed loan monitoring through mobile applications, GPS tagging, cloud-based dashboards, digital receipts, photo/video verification, USSD/SMS reporting, and AI-based analysis. This survey paper provides an in-depth review of existing mobile-based loan utilization tracking approaches, studies their benefits in reducing fraud, examines their effectiveness in improving transparency and repayment rates, and analyzes challenges such as privacy issues, digital literacy, and connectivity limitations. The paper also proposes a robust mobile-based framework for lenders to track loan utilization efficiently and discusses future innovations including blockchain, biometric authentication, and advanced analytics to strengthen monitoring systems.

Deepfake And AI-Scam Protection

Authors: Siddhi Ekawade, Apurva Jate, Arya Kamble, Sharvari Kate, Tejal Panmand

Abstract: We have seen an rapid increase in the Artificial Intelligence (AI) and Deepfake, due to that it has become very easy for online scams and frauds. It is good but when its in correct hands but they are misused in voice cloning, fake video impression, fraud message it has became a serious cybersecurity concern. This paper reviews different deepfake and AI-driven scams and examines the detection and protection methods. While the precautions are taken to keep the user safe from all scams they still face challenges. The paper focuses on the need of improved solutions along with the awareness measures. There are existing methods show results but struggles with advance deepfakes. Through all the complaints and reviews its seen there should be double verification or multi-layer security to secure the user. The study is mainly focused on analyzing how deepfakes are created using Generative Adversarial Networks (GANs) and previously published papers and reports. The current finding has shown that there is face limitation in identifying the advanced deepfakes. And to tackle this issue this paper highlights the need for combined approach to improve AI-based detection, system review, user awareness to reduce scams an increase the security of the device and realism.

Online Conferencing Application

Authors: Karthiban .R, Sabaripriya .S, Suganthi .R

Abstract: Online Conferencing Application is designed to be a communication platform in real time, which allows users to connect via video, audio, and chat from anywhere. The primary objective of this project is to build a system that is fast, secure, and user-friendly for online meetings, virtual classrooms, and remote collaboration.React.js is used to create the frontend, which gives a responsive and interactive user interface for smooth communication. Node.js is used for the backend, which handles server-side operations, user authentication, and data exchange in an efficient manner. In order to have an instant communication in real-time, Socket.io is brought in which makes it possible to have a live chat, notifications, and user synchronization without any waiting time. This set of modern technologies is what makes the system stable, with low latency, and scalable. To sum up, the Online Conferencing Application is an efficient, dependable, and userfriendly tool for virtual communication, which is in line with the increasing demand of online communication in this digital era. The Online Conferencing Application is a comprehensive web- based platform developed to enable users to conduct virtual meetings, conferences, and collaborative discussions in real time. With the increasing need for remote communication and online collaboration, this application provides an effective solution that integrates multiple technologies to deliver high-quality video, audio, and data transmission over the internet. The frontend of the application is built using React.js, which provides a responsive and interactive user interface for smooth navigation and dynamic updates. The backend is implemented with Node.js and Express.js, which handle API requests, authentication, and socket connections. The system uses Socket.IO to maintain low-latency bidirectional communication between the client and server, ensuring real-time synchronization of audio, video, and messages among all participants.

Determinants Of Cyber Incident Severity: Exploratory Evidence And Benchmark Prediction Of Financial Loss

Authors: Prajwal Chinchmalatpure, Suyash Chinchmalatpure, Sudeep Konde

Abstract: Cyber incidents vary significantly in the mechanisms, targets, and economic impacts they have, so it is significant to determine the factors which are connected to the severity and the economic effects of the incident. This paper is a data-driven analysis of world cybersecurity incidents, 2015-2024 (N=3000), focusing on contextual factors, e.g. country, type of attack, industry, source of attack, type of vulnerability, defensive mechanism, and operational outcomes, e.g. time to resolve the incident, victims, and financial loss (million USD). To surface patterns of incident occurrence and severity-linked outcomes, we describe the threat landscape in the first instance by exploratory distributional analysis, and in the second instance by relational analysis. We subsequently create a financial loss predictive pipeline by estimating a mixed data (categorical encoding and train/test analysis) predictor using common preprocessing of mixed data and a comprehensible baseline regression model. The combination of the empirical and benchmark results allows a clear point of departure to quantitative cyber risk profiling, showing which incident characteristics are too correlated with severity and defining a repeatable baseline to proceed with more detailed features and non-linear modelling methods in future studies.

EV Charger Sharing Platform

Authors: Megha Garud, Lalit Gaikwad, Prakash Mane, Ranjit Misal, Amey Phatak

Abstract: The rapid growth of Electric Vehicles (EVs) has increased the demand for efficient and accessible charging infrastructure. However, limited availability of public charging stations and uneven distribution create challenges for EV users. This paper proposes an EV Charger Sharing Platform that enables private charger owners to share their charging stations with EV users through a web-based application. The platform acts as a bridge between charger owners, EV users, and administrators by providing features such as charger listing, location-based search, booking, and usage management. Developed using HTML, CSS, and JavaScript, the system improves charger utilization, reduces charging wait time, and supports sustainable transportation. The proposed solution promotes a sharing economy model while enhancing convenience, cost efficiency, and scalability in EV charging infrastructure.

Where Are All The Men In The Morning ShowGender, Power, And The Reconfiguration Of Broadcast Television?

Authors: Ms Sujata Sinha

Abstract: This article questions the narrative displacement of men in The Morning Show (2019–present), Apple TV+ by using the lens of feminist television criticism and post-#MeToo cultural discourse. Although the series is usually understood as a timely investigation of workplace harassment and the corporate complicity, the author here is arguing that the most radical intervention of the series is the structural side-lining of male authority. The fall of Mitch Kessler is a metaphor for the decline of patriarchal charisma while those secondary male characters like Chip and Cory, as examples of marginal or alternative masculinities, do not bring male power back. However, the lack of men is not an indication that the patriarchy has been eradicated: the power of men is still there in the corporate boards that are faceless, legal frameworks, and financial imperatives, even if it is in its apparent invisibility. The series by focusing on women's agency, conflict, and solidarity in the newsroom, first, reconstructs broadcast journalism as a feminized space and, secondly, it allows the empowerment contradictions under neoliberal capitalism to be visible. This article, by comparing The Morning Show with contemporaneous texts like Bombshell, She Said, and Succession, is pointing out how televisual narratives are dealing with the afterlives of patriarchy in the wake of #MeToo.

Robot Shalu: A Low-cost, Multilingual, Social & Educational Humanoid Built From Recycled Materials

Authors: Dinesh Kunwar Patel

Abstract: This paper describes the design, software architecture, capabilities, and educational deployment of Robot Shalu, a low-cost, social and educational humanoid robot developed by a schoolteacher using largely recycled materials. Shalu demonstrates multilingual natural-language interaction (reported 47 languages), basic perception and memory, scripted and AI-assisted pedagogical interactions, and low-cost hardware solutions intended for real-world classroom integration in resource-constrained settings. We present the engineering choices, discuss human–robot interaction (HRI) considerations, review public reception and recognition, identify limitations, and propose rigorous evaluation protocols and next-step research to validate Shalu for broader academic acceptance. The paper aims to bridge maker-community innovation and formal scientific evaluation to support adoption of affordable humanoid educational agents.

Product Recommendation Systems For Online Platforms_729

Authors: Nimesh Agrawal, Mrs. Priyanka Bamne, Mr. Ranjeet Vishwakarma

Abstract: The exponential growth of e-commerce has led to an overwhelming abundance of products, making it challenging for consumers to find items that align with their preferences. Product recommendation systems have emerged as essential tools to enhance user experience by providing personalized suggestions. This paper delves into various recommendation methodologies, including collaborative filtering, content-based filtering, hybrid approaches, and deep learning techniques. It also explores the challenges faced in implementing these systems, such as scalability, cold-start problems, and data sparsity. Furthermore, the paper discusses evaluation metrics and real-world applications, providing insights into the effectiveness of different recommendation strategies.

DOI: http://doi.org/10.5281/zenodo.18046514

PhytoLink: Translating Plant Electrical Signals For Proactive Crop Stress Management

Authors: Rishi Kumar

Abstract: Timely detection of plant stress is a major challenge in agriculture, as conventional monitoring methods rely on visible symptoms that often appear after irreversible physiological damage has occurred. This delay contributes to significant crop losses, inefficient water use, and reduced agricultural sustainability. PhytoLink is a conceptual plant-monitoring system designed to address this limitation by translating plants’ internal electrical signals into early, actionable warnings. Plants generate distinct bioelectrical responses to stress factors such as water deficiency, disease onset, and environmental changes, which can be detected before external symptoms become visible. PhytoLink proposes a non-invasive bio-electronic interface that captures these electrical signals, processes them using signal analysis techniques, and delivers clear alerts to farmers and gardeners, enabling intervention more than 48 hours in advance. By shifting plant care from reactive to proactive management, PhytoLink has the potential to reduce crop losses by 30–50%, conserve water resources, and improve decision-making in precision agriculture. This paper presents the conceptual framework, working principle, applications, and future scope of PhytoLink as an innovative tool for sustainable and intelligent plant care.

DOI: http://doi.org/10.5281/zenodo.18045548

Code Insight Saas-Code Explanation Generator

Authors: Vipul Kanhere, Suraj Sonar, Atharva Awale, Pranav Shinde, Savita Biradar

Abstract: Software developers dedicate a substantial portion of their time to comprehending existing code, a challenge that intensifies as codebases grow in scale and complexity. Code Explanation Generators and Code Insight SaaS platforms have emerged as promising solutions, leveraging large language models to transform source code into accessible natural language explanations. This survey presents a comprehensive examination of code explanation technologies, tracing their evolution from traditional template-based and rule-based approaches through neural sequence models to contemporary LLM-powered systems. We establish a taxonomic framework for categorizing explanation tools across dimensions including target audience, explanation granularity, architectural approach, and deployment model. Our analysis encompasses commercial platforms, open-source implementations, IDE integrations, and lightweight web applications built on frameworks such as Streamlit that enable rapid development and free cloud deployment. The comparative analysis reveals significant consolidation around large language model approaches, with differentiation increasingly based on interface design, prompting strategies, and deployment architectures rather than fundamental algorithmic differences. Despite remarkable progress in explanation quality and accessibility, we identify persistent gaps including primitive granularity adaptation mechanisms, absent interpretability features for reliability assessment, inadequate privacy-preserving deployment options, limited contextual awareness beyond isolated code snippets, and evaluation methodologies that fail to capture developer-centric comprehension outcomes. Based on these findings, we propose future research directions encompassing improved evaluation frameworks grounded in task- based assessment, interpretable explanation generation with confidence indication, domain-specific adaptation for specialized contexts, and responsible deployment practices addressing privacy, accuracy, and equitable access. This survey provides structured guidance for researchers advancing code explanation capabilities and practitioners developing or adopting explanation tools.

Media Framing Of The 2025 Ladakh Violence: An Analysis Of Kashmir-Based Newspaper Coverage

Authors: Umar Manzoor Shah

Abstract: This study examined how four newspapers based in Kashmir portrayed the Ladakh violence in response to the region's demand for inclusion in the 6th Schedule of the Indian Constitution and the conferment of statehood. The conflict commenced on September 24, 2025, between local protesters and law enforcement in Leh, Ladakh. The Buddhists and Muslims in this region have collaboratively established an organisation advocating for Ladakh's elevation from a Union Territory to a full state, as well as the implementation of the Sixth Schedule of the Indian Constitution to safeguard their environment, land, and employment opportunities. Discussions between the Government of India and local leadership have persisted for several years; however, these negotiations reached an impasse on September 24 due to violence. Four citizens were fatally shot via police gunfire, and over 100 sustained injuries as the crowd escalated into violence during a protest demonstration in Leh's major market. The regime instituted a curfew and arrested environmentalist Sonam Wanchuk, a prominent advocate for the cause. A content analysis of four newspapers based in Kashmir was done to ascertain the overall pattern of coverage and the degree and existence of framing regarding this subject. The analysis encompassed the frame utilised, tonal variations and article count regarding the situation in Ladakh. One hundred seventy newspaper articles were extracted from archives and examined from September 1, 2025, to October 5, 2025. The study revealed that law and order frames were utilised more frequently than political and human frameworks. The coverage in regional newspapers of Kashmir was predominantly pro-government. The findings indicate a significant application of law and order, as well as administrative frameworks, in the reporting of violence and its aftermath.

DOI: http://doi.org/10.5281/zenodo.18045860

Detecting, Characterizing, And Mitigating Wildfire Threats In California: A Spatio-Temporal Study

Authors: Anees Ahmed Pinjari, Prashant Yelmar

Abstract: Wildfires have become one of the greatest and the most ongoing environmental hazards in the state of California, with a profound ecological loss, finances, and loss of life. Spatio-temporal dynamics of wildfire incidences are of great importance to the successful detection of the threat, mitigation planning, and allocation of resources. This paper is a Spatio-analytical analysis of wildfire threat in California based on incident-level data between 2013 and 2019. The analysis will incorporate time trends, spatial dispersion, fire intensity, duration, loss of life, and fire management efforts to recognize at risk areas and the changing nature of wildfires. Findings indicate that there was a strong increase in the severity and duration of wildfires in 20172018, with an excessively high proportion of acreage and deaths being agglomerated around a limited number of large events. Spatial analysis points to the areas of constant hotspots of wildfires in southeastern California, where the presence of fires correlates closely with population density and administrative fire management areas. The results also show that the efficiency of wildfire response increases after a severe fire season, as evidenced by diminished person deployment compared to the severity of the incidence in the following years. Revealing the essential spatial trends and temporal changes in the behaviour of wildfires, this investigation provides practical information to detect threats in time, mitigate them, and use the time as a policy to prevent wildfires. The suggested analytical framework is a data-based source of the improvement of wildfire preparedness and assisting in predictive and decision-support systems in the future.

Data Poison Detection Schemes For Distributed Machine Learning

Authors: Satyaki Adak

Abstract: Distributed Machine Learning (DML) enables efficient training over massive datasets by distributing computation across multiple nodes; however, it also increases vulnerability to data poisoning attacks, where adversaries inject malicious or mislabeled data to corrupt the learning process. Ensuring model integrity in such environments is a critical security challenge. This project classifies DML systems into basic-DML and semi-DML based on whether the central server participates in dataset training. For the basic-DML scenario, a novel cross-learning–based data poisoning detection scheme is proposed, where training results from distributed workers are compared through multiple training loops to identify anomalous behaviour. A mathematical model is developed to determine the optimal number of training loops that maximizes detection accuracy while minimizing overhead. For the semi-DML scenario, an enhanced poison detection mechanism is introduced by leveraging the central server’s computing resources, along with an optimal resource allocation strategy to reduce unnecessary computation. Experimental results demonstrate that the proposed schemes significantly improve model accuracy—up to 20% for Support Vector Machines and 60% for Logistic Regression in basic-DML—while reducing wasted resources by 20–100% in semi-DML. The proposed framework offers a general, efficient, and scalable defence against data poisoning attacks in distributed learning environments.

DOI: http://doi.org/10.5281/zenodo.18051593

Bridging The Future: 5G And Artificial Intelligence

Authors: Vaibhav Sinha, Abhishek Kumar Singh, Dr. Partap Singh

Abstract: The integration of 5G technology and Artificial Intelligence (AI) marks a transformative phase in digital communications and intelligent connectivity. As 5G networks offer unprecedented speed, ultra-low latency, and massive device connectivity, AI brings the intelligence required to optimize and automate 5G systems. This research paper critically examines how AI empowers 5G networks, explores key applications, discusses challenges, and highlights future prospects across industries. With supporting pictorial references, the paper presents a comprehensive, humanized view suitable for academic and professional audiences.

DOI: http://doi.org/10.5281/zenodo.18053277

Artificial Intelligence In Education: A Systematic Review Of Applications, Machine Learning Frameworks, And Predictive Analytics For Quality Enhancement_826

Authors: Dr. Rachna Rana, Er. Gundeep Kaur, Er. Manpreet Kaur, Mr. Sachin Sharma

Abstract: Artificial Intelligence (AI) is reshaping educational systems worldwide through personalized learning, predictive analytics, intelligent tutoring systems, automation, and institutional decision-support technologies. AI applications in education have transitioned from experimental prototypes to widely adopted tools used for assessment, student support, curriculum design, and governance. This paper presents a comprehensive analysis of the current landscape of AI in education, with emphasis on machine learning (ML) frameworks, learning analytics (LA), natural language processing (NLP), and predictive analytics used for monitoring academic quality assurance (QA). The paper synthesizes findings from recent empirical and conceptual studies, discusses the system-level implications of AI-enabled educational data mining, and identifies ethical, pedagogical, and institutional challenges that influence adoption. A section is dedicated to the integration of AI-driven predictive models into QA processes, including early warning systems, risk-prediction algorithms, and data-driven continuous-improvement frameworks. The paper concludes with recommendations for responsible AI deployment, future research trajectories, and policy considerations.

DOI: http://doi.org/10.5281/zenodo.18081433

A Study On Extraction Of Apigenin Flavonoid From Parsley Plants: A Natural Synergy For Cancer Prevention And Therapy _224

Authors: Ujjwal Kumar, Ritik Kumar, Kavita kumari, Nitika Vats

Abstract: Apigenin is a low-toxicity flavonoid with several beneficial bioactivities. It is a secondary metabolite bioflavonoid that shows pharmacological activities such as antibacterial, anticancer, antidiarrheal, antiemetic, and hemostatic effects. Apigenin is extracted from different parts of parsley plants by using the Soxhlet Extraction method. Parsley (Petroselinum crispum), a flowering species of the Apiaceae family, is native to Greece, Morocco, and the former Yugoslavia. In traditional medicine, parsley has been used as a carminative, gastrotonic, diuretic, urinary tract antiseptic, anti-urolithiasis, antidote, and anti-inflammatory agent. It is also used for treating dermal diseases, amenorrhea, dysmenorrhea, gastrointestinal disorders, hypertension, cardiac and urinary diseases, otitis, cold, and diabetes. Apigenin is a phytochemical that occurs along with tannins, alkaloids, triterpenoids, steroids, flavonoids, and saponins. Rich in parsley, celery, celeriac, and chamomile tea, apigenin has health-promoting properties, including use as a natural sleep aid, antidiabetic, and anticancer compound. This flavonoid induces relaxation as it binds to brain receptors that promote sleep. The main aim of this paper is to extract apigenin flavonoid from parsley plant parts. Parsley leaves and flowers are abundant in phenolic compounds, present as aglycones (flavones and flavonols) and glycosides. In this study, apigenin was extracted from parsley leaves using Soxhlet extraction, followed by hydrolysis and recrystallization. A combination of apigenin and lecithin was also synthesized using a solvent method. Several extraction parameters were tested to evaluate yield, with Soxhlet extraction 5.5 h, 65 °C, solid-to-solvent ratio 8:400 as the reference and the purification done by colume chromotography. UV-Visible analysis confirmed that the structure of apigenin remained stable after extraction and purification.

DOI: https://doi.org/10.5281/zenodo.18058776

AI In Cancer Treatment: Revolutionizing Genomics

Authors: A. Mohamed Sikkander, Joel J. P. C. Rodrigues, Manoharan Meena

Abstract: Artificial Intelligence (AI), also referred to as machine learning (ML) or deep learning (DL), is rapidly revolutionizing cancer treatment by using genomic information for improving diagnosis, prognosis, treatment decision, and drug discovery. Being a result of genetic and molecular changes, it is important to understand cancer’s genomic patterns and profiles. In conventional genomic analyses, common methodologies fail to handle high-dimensional genomic data produced from next-generation sequencing (NGS) and multi-omics platforms; on the other hand, AI approaches excel in detecting intricate patterns from large genomic datasets. This AI system trained from a large public genomic database such as ‘The Cancer Genome Atlas (TCGA),’ ‘Genomic Data Commons (GDC),’ or generally from the ‘Catalogue of Somatic Mutations in Cancer (COSMIC)’ has already facilitated accurate classifications of cancer subtypes and their treatment predictions or discovery of effective biomarkers for treatment of cancer subtypes that are accurate to a great extent. Deep learning from somatic mutation sequences showed an accuracy of approximately 0.98 for clinical biomarkers such as microsatellite instability (MSI), which is a considerably high improvement over other existing methodology. Integration of AI with multi-omics genomic, transcriptomic, proteomic data types further helps to increase efficiency of predictions regarding patient outcomes. Though AI is a revolution in genomic study thereby bringing a revolution in cancer treatment approaches following a detailed precise treatment decision of cancer treatment from an individual’s genomic study followed by inducing a global revolution in cancer treatment true to precision medicine practices around the world.

DOI: http://doi.org/10.5281/zenodo.18066233

Study On The Seismic Behavior Of Plan Irregular Buildings With Base Isolation In Seismic Zone V

Authors: Shubhangi Sondhiya, Deepesh Malviya

Abstract: These seismic risks are considered the prime cause for concern in seismic zones and earthquake-prone areas around the world. Over time, a sequence of earthquake motions with different seismic intensity has been used to conduct the investigation and analyze the structural dynamics. Analyses considering the effect of isolated structures showed that the isolators restrict the lateral loads transmitted to the structure, which, in turn, has the tendency to reduce the sizes of building components. In this study, design, operation, testing, and applicability of base isolation are analyzed in detail as per Indian Standards. Base isolation has been found to be one of the popular design approaches in recent times. A building structure is taken as a case study model for this study, and contemporary design tools are also applied for the analysis. Conclusions are drawn from the results obtained. We'll discuss the probable advantages of base isolation over the conventional dynamic analysis. This chapter deals with the design details of the models and step followed to design the building. This study is conducted on a G+11-story building located on soft soil. The structure is designed as a college building (with plan irregularity) situated in Seismic Zone V and analyzed using ETABS 2022. For the analysis, two models are considered: Model M-1, which has a fixed base, and Model M-2, which incorporates a base isolator. The comparative analysis between the fixed-base model (M–1) and the base-isolated model (M–2) clearly demonstrates the effectiveness of base isolation in improving seismic performance. The base-isolated structure shows reduced base shear, displacement, and overturning moments by approximately 25–30%, indicating enhanced stability and safety. Although story drift slightly increases due to controlled base movement, this behavior helps in dissipating seismic energy and reducing damage to the superstructure. Overall, base isolation significantly enhances structural resilience, minimizes earthquake-induced forces, and provides an efficient and reliable solution for earthquake-resistant design in multi-storey buildings. Overall, the comparative study clearly demonstrates that the implementation of base isolation considerably enhances the seismic performance of structures. It reduces base shear, displacement, and overturning moments while allowing controlled drift, thereby ensuring improved safety, flexibility, and durability. These outcomes confirm that base isolation is a highly effective and reliable strategy for seismic risk mitigation in multi-storey buildings.

DOI: http://doi.org/10.5281/zenodo.18066513

AI-Driven Fraud Detection Systems: Enhancing Security in Real-Time Card-Based Transactions Using Deep Learning and Agentic AI

Authors: Aadhithyan K, Pranauv Raaj N

Abstract: Card-based transactions and modern digital payment systems face sophisticated and rapidly evolving security threats, necessitating advanced fraud detection methods. Traditional approaches, often reliant on fixed rules and descriptive analytics, are slow to adapt to new fraud schemes and struggle with the volume of real-time transactions. This presentation analyzes the effectiveness of AI-driven fraud detection, specifically focusing on the integration of Real-Time Analytics, Deep Learning (DL), and Agentic AI systems to enhance security and prevent financial losses. The study highlights that DL models, such as hybrid Recurrent Neural Networks (RNNs) combined with attention mechanisms, offer superior performance by modeling sequential data and addressing challenges like data imbalance. Furthermore, adopting the Deep Learning–Sector–Governance (DLSG) framework is crucial, as it ensures that technical innovations are aligned with sector-specific constraints and regulatory requirements, such as the need for explainability and data privacy. The synthesis of these technologies provides a proactive, adaptive solution to safeguard complex financial ecosystems.

Emotion Aware Ai and Productivity Cycle

Authors: Gayatri dhasade, Soham vishe, Soham Bhintade, Darshan Bhamare, Prof.Poonam Chavan

Abstract: An academic event management system is A digital platform designed to plan, organize, manage and monitor academic and non-academic events held at educational institutions. College Event Management System is a digital platform designed to plan, organize, manage, and monitor academic and non-academic events conducted in educational institutions. The Colleges regularly organize seminars, workshops, cultural programs, sports events, technical competitions and guest lectures. competitions, and guest lectures. Handling these events manually using paperwork or scattered communications often results in inefficiency, communication issues, and data loss. Managing these events manually using paperwork or scattered communication often leads to inefficiency, miscommunication, and data loss. The proposed system provides a centralized solution to manage event creation, registration, scheduling, notifications and reporting. notifications, and reporting. It allows students, faculty and event coordinators to interact through a single platform, improving transparency and coordination. enables students, faculty, and event coordinators to interact through a single platform, improving transparency and coordination. By automating event workflows, the system reduces administrative workload, ensures timely communication and improves overall event execution. communication, and enhances overall event execution.

Edge-Level Load Distribution In IOT Using Random Forest Techniques

Authors: Semran Ojha, Professor Rahul Patidar, Professor Jayshree Boaddh

Abstract: The rapid expansion of the Internet of Things (IoT) has created significant challenges in managing computation and data processing efficiently. As billions of interconnected devices generate massive workloads, traditional cloud infrastructures experience latency and performance bottlenecks. To address these limitations, this research introduces an intelligent edge-level load distribution model using Random Forest techniques. The proposed system leverages edge computing to process tasks closer to data sources, thereby reducing dependency on centralized cloud servers. The Random Forest algorithm is utilized to learn from historical task patterns and predict optimal job scheduling sequences. This is integrated with a wolf optimization strategy that dynamically adjusts load distribution across heterogeneous edge nodes without prior training requirements. Experimental evaluation conducted using MATLAB demonstrates that the proposed model effectively minimizes makespan by 0.79% and enhances edge utilization by 16.25% compared to the existing Preference-Based Stable Matching (PBSM) model. These improvements confirm that machine learning- driven edge load balancing can significantly improve resource allocation, task completion time, and overall network efficiency in large-scale IoT environments.

Autonomous Failure Diagnosis & Self- Healing In Large-Scale Cloud Systems Using Self-Reflective Agentic AI

Authors: Dharunika G, Jack Robin J

Abstract: The increasing scale and dynamic complexity of modern cloud computing environments pose significant challenges for ensuring system reliability and availability, making traditional manual fault diagnosis and recovery insufficient. This paper explores advanced methodologies, contrasting established statistical monitoring techniques with emerging Artificial Intelligence (AI)-driven autonomous self-healing frameworks designed to manage faults, minimize downtime, and optimize resource utilization. Early methods employed correlation analysis using Canonical Correlation Analysis (CCA) and Exponentially Weighted Moving Average (EWMA) control charts for anomaly detection, followed by feature selection using ReliefF and SVM-RFE for problem location. The evolution toward AI-driven solutions leverages machine learning (ML), deep learning (DL), and reinforcement learning (RL) to achieve automated failure prediction and recovery. Recent advancements feature hybrid AI models and self- reflective multi-agent systems (SR-MACHA) that demonstrate enhanced accuracy and self- optimization capabilities, achieving significant reductions in Mean Time to Repair (MTTR) and improving system resilience. However, challenges remain regarding the scalability, computational cost of training complex models, and ensuring real-time performance in dynamic, heterogeneous cloud infrastructures.

AI-Driven Crop Disease Detection Systems: Enhancing Agricultural Productivity Through Real-Time Leaf Image Analysis Using Deep Learning

Authors: Sukesh G, Sanjay S

Abstract: Early detection of crop diseases is a critical requirement for ensuring agricultural productivity, food security, and economic stability for farmers. Crop diseases caused by fungi, bacteria, viruses, and pests often spread rapidly and remain unnoticed during their initial stages, leading to severe yield loss and financial damage. Traditional crop disease detection methods rely on manual inspection by farmers or agricultural experts, which is time-consuming, subjective, and often inaccurate due to human limitations and environmental variations. Moreover, expert support is not always accessible to farmers in rural and remote areas. With the rapid advancement of machine learning and image processing technologies, automated crop disease detection systems have gained significant attention in recent years. Leaf images contain rich visual information such as color variation, texture patterns, and shape irregularities that can be effectively analyzed using computer vision techniques. This paper presents an automated crop disease detection framework using machine learning and image processing techniques to identify plant diseases at an early stage. The proposed system involves image preprocessing, feature extraction, and classification using both traditional machine learning algorithms and deep learning models. The system aims to reduce crop loss, minimize excessive pesticide usage, and assist farmers in making timely and informed decisions. Experimental evaluation demonstrates that the proposed approach achieves improved accuracy and reliability, making it suitable for real-world agricultural applications.

Smart Healthcare Systems: Enhancing Healthcare Delivery Through Ai-Driven Medical Image Analysis and Intelligent Decision Support

Authors: Nithish Kumar R, Gokul Kanna Sm

Abstract: Smart Healthcare represents a transformative shift in modern medical systems by integrating artificial intelligence (AI), machine learning (ML), deep learning, and Internet of Things (IoT) technologies into healthcare delivery. Early disease detection, accurate diagnosis, and personalized treatment remain critical challenges in healthcare systems worldwide. Traditional healthcare practices largely rely on manual diagnosis, clinician expertise, and time-consuming diagnostic procedures, which may lead to delayed detection, human error, and increased healthcare costs. With the rapid growth of AI and medical imaging technologies, automated disease detection and health monitoring systems have gained significant attention. Medical images such as X-rays, MRI scans, CT scans, ultrasound images, and skin lesion images contain rich visual information that can be effectively analyzed using machine learning and deep learning techniques. This paper presents an intelligent Smart Healthcare framework that utilizes AI-driven medical image analysis for early disease detection and clinical decision support. The proposed system includes image acquisition, preprocessing, feature extraction, disease classification, and result visualization. Experimental studies indicate that AI-based healthcare systems significantly improve diagnostic accuracy, reduce workload on healthcare professionals, and enhance patient outcomes. The system aims to support early diagnosis, reduce medical errors, optimize treatment planning, and promote efficient and patient-centric healthcare services.

Operations Research as a Quantitative Framework for Managerial Decision-Making: Concepts, Models, and Evolving Applications

Authors: Nitish Kumar Bharadwaj

Abstract: Decision-making in contemporary organizations is increasingly complex due to technological advances, volatile markets, and uncertainty in social, political, and economic environments. Relying solely on intuition or experience often leads to inefficient allocation of scarce resources and costly managerial errors. Operations Research (OR) provides a scientific and quantitative framework that supports rational decision-making through modeling, analysis, and optimization. This paper reviews the historical evolution of OR, clarifies conceptual foundations, and explains the processes through which OR transforms real-world problems into structured decision models. Different classes of models, deterministic, probabilistic, static, dynamic, descriptive, and normative are examined with reference to their applicability and limitations. The paper further highlights implementation challenges and discusses how computing advances have broadened the scope of OR in domains such as supply chains, healthcare, energy, and public policy. The study contributes by presenting a synthesized framework that links classical OR principles with contemporary decision environments, emphasizing how quantitative modeling can improve managerial effectiveness and support evidence-based decisions. Recommendations for future research and practice are also discussed.

DOI: https://doi.org/10.5281/zenodo.18081819

MO-NAS: Multi-Objective Neural Architecture Search Using NSGA-II

Authors: Rayapudi Gautam Kumar

Abstract: MO-NAS: Multi-Objective Neural Architecture Search Using NSGA-IIThis paper presents MO-NAS, a production-ready framework for automatically discovering optimal neural network architectures using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization ap‐ proach. Unlike traditional Neural Architecture Search (NAS) methods that optimize for a single objective (typically accuracy), MO-NAS simultaneously optimizes multiple competing objectives including accuracy, computational cost (FLOPs), model size (parameters), inference latency, and memory footprint. The framework supports multiple data modalities including image, text, sequence, and tabular data, making it a universal NAS solution. We incorporate advanced techniques such as zero-cost proxies for rapid evaluation, Bayesian guidance for search efficiency, and weight sharing to reduce training costs. Our approach produces a Pareto-optimal front of architectures, allowing practitioners to select the best trade-off for their specific deployment constraints.

Cloud-Native Intelligent Healthcare Data Management Framework

Authors: Deeksha M, Subhiksha N

Abstract: The rapid growth of healthcare data generated by electronic health records, medical imaging systems, wearable sensors, and telemedicine platforms has created unprecedented challenges for healthcare data management. Conventional on-premise infrastructures are increasingly unable to support the scalability, interoperability, and analytical intelligence required by modern healthcare ecosystems. Cloud computing has emerged as a promising alternative; however, its adoption in healthcare remains limited due to concerns regarding data security, regulatory compliance, interoperability, and performance reliability. This paper proposes a cloud-native intelligent healthcare data management framework that integrates secure data ingestion, standards-based interoperability, artificial intelligence–driven analytics, and automated compliance governance within a hybrid or multi-cloud environment. The framework is designed to support heterogeneous healthcare data sources while maintaining privacy, regulatory adherence, and real-time responsiveness. A detailed architectural design, data flow model, security mechanisms, and use-case-driven analysis are presented. The proposed solution demonstrates how cloud-native principles can enable scalable, secure, and intelligent healthcare data management suitable for next-generation digital health systems.

Self-Healing Cloud Infrastructure Using Digital Immune Systems

Authors: Shrihari.G, Abilash.R

Abstract: Modern cloud infrastructures host large numbers of distributed services and microservices, where failures and attacks can propagate rapidly across virtual machines, containers, and orchestration layers. In this setting, static, signature-driven defenses are insufficient to maintain availability and resilience. Inspired by the biological immune system (BIS), this paper presents a self-healing cloud infrastructure framework that applies second-generation Digital Immune System (DIS) principles to detect, contain, and recover from process-level anomalies in real time. The approach treats cloud nodes and services as components of a larger artificial organism, embedding immune-like agents throughout the stack rather than relying solely on perimeter defense. At the core of the framework is a biologically plausible, multi-layered cellular signalling architecture for process anomaly detection. Building on Matzinger’s Danger Theory, the system moves beyond simple self/non-self discrimination by combining “danger signals” such as abnormal syscall patterns, privilege escalation attempts, and volatile resource usage with “safe signals” derived from stable workload and performance baselines. Specialized artificial cell populations—Dendritic Cells (aDCs), T-Helper Cells (T_H), and B-Cells—are instantiated as distributed agents within a cloud-aware middleware. aDCs aggregate local evidence on each node, T_H cells perform distributed consensus across nodes and services, and B-Cells maintain memory detectors that rapidly recognize previously observed attack strategies. These immune agents communicate over a virtual cytokine bus, enabling spatial-temporal correlation of signals across containers, virtual machines, and availability zones. When coordinated danger levels exceed adaptive thresholds, the framework triggers self-healing actions such as throttling or isolating compromised containers, rolling back affected service instances, or re-provisioning clean replicas through the underlying orchestration platform. Evaluation on syscall-level datasets and realistic exploit scenarios indicates that the proposed DIS-based controller can distinguish normal from attack behaviour with high accuracy while imposing minimal overhead, and that its coordinated responses significantly reduce both time-to-detection and time-to-recovery compared to baseline policies. The work demonstrates that biologically inspired, multi-agent immunity can provide a practical foundation for self-healing cloud infrastructure capable of adapting alongside evolving threats.

Face Recognition Voting System

Authors: Roshni.S, Sudarshana.K

Abstract: Face recognition technology has emerged as a powerful biometric solution capable of enhancing the security and efficiency of modern voting systems. Traditional voting mechanisms, including paper ballots and manual electronic verification, face numerous challenges such as voter impersonation, multiple voting, long verification times, and susceptibility to human error. In recent years, the rapid advancement of artificial intelligence and machine learning has enabled more accurate and scalable facial recognition systems, making them suitable for large-scale applications such as elections. This paper presents an in-depth study of a face recognition–based voting system, discussing its conceptual design, system architecture, methodology, security mechanisms, performance considerations, advantages, limitations, ethical implications, and future scope. The study concludes that while face recognition technology has significant potential to improve election integrity and voter convenience, successful implementation requires robust privacy protection, legal frameworks, and public trus.

Operational Graph Patterns For Continuity And Fulfillment In Large Enterprises: A Field-Based Reference Architecture

Authors: Mallesh Miryala

Abstract: Large organizations run on operational data that changes every hour: people join and leave, locations are renamed, incidents unfold, and responsibilities shift. In practice, the hardest part is not storing records; it is keeping the records consistent enough that policy decisions and workflows remain trustworthy. This paper proposes a practical design pattern called the policy- aware operational graph. The pattern treats people, organizational units, locations, requests, and tasks as a connected graph with explicit ownership and audit history. It combines three ideas that are often built separately: identity lifecycle management, rule-driven routing, and cross-system transaction safety. The design is informed by field experience maintaining a continuity platform at a large public university and building high-volume fulfillment workflows at a national telecom. The paper contributes a reference architecture, a repeatable identity hygiene loop for key contacts, and an efficient duplicate-detection method that routes only uncertain cases to human review. A small reference implementation is provided to demonstrate how blocking keys and union-find can scale to large datasets without excessive memory or quadratic comparisons.

Early Alzheimer’s Disease Prediction Using Machine Learning And Deep Learning Algorithms.

Authors: Ms.Dhanushni.N, Ms.Vivisha Catherin.P

Abstract: Alzheimer’s disease (AD) is a pressing global issue, It’s known as the severe neuron disease. They Mainly damages the Brain cells, which leads to permanent lose of memory which is also called dementia. Many people die due to this disease every year because it is not curable but the early detection can prevent from spreading. Alzheimer’s are most commonly found in the elder peoples or from the age of (60 and above). It requires an efficient and automated system which can detect the disease and classify it in the basis of Alzheimer’s stages like Mild Demented(MD), Moderate Demented(MOD), Non Demented(ND), Very Mild Demented(VMD). For the prediction we use Machine learning and deep learning Algorithm’s like convolutional neural networks for imaging data(CNNs), Random forest and Gradient Boosting(XGBoost / LightGBM), Support Vector Machines(SVM) Which is much more efficient from the preexisting models of the Alzheimer Detection. Of relying on methods, like CNNs and SVM for our model design like Random Forest and XGBoost do typically with fixed structures and manual feature selection processes; we take a different approach thats more intricate and advanced by utilizing transfer learning through the InceptionV3 network already trained on ImageNet for its robust feature extraction abilities. To boost our models effectiveness in handling datasets adequately; we integrate various data augmentation methods such as adjusting image angles and proportions along, with mirroring techniques. Address the issue of class distribution by adjusting the weights for classes to focus more on identifying cases of Alzheimers disease accurately. In addition, to this adjustment in class weighting strategy consider implementing techniques like dropout regularization method and early stopping along with model checkpoint mechanism to prevent the model from learning noise and improve generalization. This holistic strategy leads to a model that's proficient in reducing both positives and false negatives which is crucial, in accurate medical diagnosis.

Early Alzheimer’s Disease Prediction Using Machine Learning And Deep Learning Algorithms.

Authors: Ms.Dhanushni.N, Ms.Vivisha Catherin.P

Abstract: Alzheimer’s disease (AD) is a pressing global issue, It’s known as the severe neuron disease. They Mainly damages the Brain cells, which leads to permanent lose of memory which is also called dementia. Many people die due to this disease every year because it is not curable but the early detection can prevent from spreading. Alzheimer’s are most commonly found in the elder peoples or from the age of (60 and above). It requires an efficient and automated system which can detect the disease and classify it in the basis of Alzheimer’s stages like Mild Demented(MD), Moderate Demented(MOD), Non Demented(ND), Very Mild Demented(VMD). For the prediction we use Machine learning and deep learning Algorithm’s like convolutional neural networks for imaging data(CNNs), Random forest and Gradient Boosting(XGBoost / LightGBM), Support Vector Machines(SVM) Which is much more efficient from the preexisting models of the Alzheimer Detection. Of relying on methods, like CNNs and SVM for our model design like Random Forest and XGBoost do typically with fixed structures and manual feature selection processes; we take a different approach thats more intricate and advanced by utilizing transfer learning through the InceptionV3 network already trained on ImageNet for its robust feature extraction abilities. To boost our models effectiveness in handling datasets adequately; we integrate various data augmentation methods such as adjusting image angles and proportions along, with mirroring techniques. Address the issue of class distribution by adjusting the weights for classes to focus more on identifying cases of Alzheimers disease accurately. In addition, to this adjustment in class weighting strategy consider implementing techniques like dropout regularization method and early stopping along with model checkpoint mechanism to prevent the model from learning noise and improve generalization. This holistic strategy leads to a model that's proficient in reducing both positives and false negatives which is crucial, in accurate medical diagnosis.

An Analysis of the Application of High-Performance Concrete in Building Structures

Authors: Vishal Ranjan, Dr. Jyoti Yadav

Abstract: High-Performance Concrete (HPC) has become an essential material in modern construction due to its superior mechanical properties, durability, and environmental benefits. This paper explores the use of HPC in building structures within India, with a focus on its performance, advantages, and the impact on the construction industry. By reviewing recent studies, case studies, and performance data, this research demonstrates the role of HPC in enhancing structural integrity, reducing maintenance costs, and contributing to sustainability. The paper also discusses the challenges and potential future directions for the use of HPC in India’s infrastructure development.

DOI: https://doi.org/10.5281/zenodo.18091902

Analysis Design of Structures with High Performance Concrete

Authors: Vishal Ranjan, Dr. Jyoti Yadav

Abstract: High-Performance Concrete (HPC) is an advanced form of cement concrete where ingredients are selected and proportioned to enhance various properties of the concrete in both fresh and hardened states. One key feature of HPC is its higher strength, which offers significant structural advantages. The primary components contributing to the cost of a structural member are concrete, steel reinforcement, and formwork. This paper compares these components when higher-grade concrete, specifically HPC, is used, and highlights how high-strength concrete provides the most economical solution for designing load-bearing members, particularly in carrying vertical loads to the building foundation through columns. The mix design variables critical to concrete strength include the water-cementitious material ratio, total cementitious material, cement-admixture ratio, and superplasticizer dosage, which are analyzed to achieve the desired high-grade concrete mix.

DOI: https://doi.org/10.5281/zenodo.18092124

Active Cell Balancing For Efficient Battery Management System

Authors: Ms. Nirmala R G, Pratap K V, Nithilan I

Abstract: The growing adoption of electric vehicles (EVs), renewable-energy microgrids, and portable power systems has intensified the need for efficient and reliable battery management strategies. Conventional passive balancing circuits in lithium-ion battery packs dissipate excess energy as heat, resulting in low efficiency, poor scalability, and thermal stress. This paper presents an Active Cell Balancing Battery Management System (ACB-BMS) employing a bidirectional buck–boost converter topology integrated with an Extended Kalman Filter (EKF)-based state-of-charge (SOC) estimation algorithm. The system dynamically redistributes charge between cells, achieving faster equalization and significantly reduced energy loss compared with resistor-based methods. The EKF enables accurate real-time tracking of each cell’s SOC, improving safety and charge control under varying load and temperature conditions. A complete MATLAB/Simulink simulation model of the proposed system has been developed and validated, demonstrating superior voltage uniformity, faster balancing response, and enhanced energy efficiency. The proposed approach forms a practical foundation for next- generation intelligent BMS architectures suitable for electric vehicles and hybrid renewable-energy storage. Future hardware implementation is planned to extend the technology toward commercial-grade embedded platforms.

Engineering-Grade Delivery for Salesforce in Integration-Heavy Enterprises: Metadata Graphs, Contract Tests, and Deterministic Operations

Authors: Mallesh Miryala

Abstract: In integration-heavy Salesforce environments, release reliability depends less on the deployment tool and more on engineering controls: correct metadata scoping and ordering, explicit boundary contracts, retry-safe synchronization, and operational feedback. This article presents an end-to-end delivery model that (i) represents metadata, code, and access controls as a dependency graph to build deterministic delta packages and select relevant tests; (ii) treats system boundaries as executable API and event contracts, verified in CI by both providers and consumers to prevent drift; and (iii) implements integrations as idempotent, retry-safe state machines using external identifiers, payload digests, and bounded deduplication windows. We show how layered quality gates—static analysis, targeted suites, contract checks, and observability signals—create a control loop that reduces change-failure rate and shortens recovery time. The result is an implementation-oriented guide, with algorithms, diagrams, and reference patterns for teams operating Salesforce alongside middleware such as MuleSoft and legacy systems including GIS, ERP, and data platforms.

Impact of Ai Chatbots on Human Emotional Well-Being: A Psychological Study

Authors: Esakkiammal N, Deebika S

Abstract: Artificial Intelligence (AI) chatbots have become integral to modern communication, providing services ranging from customer support to mental health assistance. Their rapid adoption raises critical questions about their psychological influence on users. This study investigates the impact of AI chatbots on human emotional well-being, emphasizing psychological mechanisms such as emotional regulation, social support, companionship, and dependency. Using a mixed-methods approach—combining surveys and semi-structured interviews—the study examines the extent to which chatbots contribute to emotional support and their potential to induce dependency or social withdrawal. Results suggest that while AI chatbots can positively influence emotional well-being by providing accessible support, they may also create challenges, such as emotional over-reliance and diminished real-world social engagement. The paper concludes with practical, ethical, and design recommendations for AI chatbot developers, emphasizing the importance of balancing technology with human-centric emotional care.

Adversarial Pedagogy In The Laṅkāvatāra Sūtra: A Comparative Study With Deep Learning And Generative Adversarial Networks_332

Authors: Dr Saumya Bahadur

Abstract: This paper examines the Laṅkāvatāra Sūtra, a foundational text of Yogācāra Buddhism, through the lens of adversarial pedagogy and compares it with contemporary machine learning models, particularly Generative Adversarial Networks (GANs). The Sūtra is notable for its dialogical structure, in which the bodhisattva Mahāmati poses questions, challenges, and objections to the Buddha, who systematically deconstructs these conceptual formulations. This adversarial exchange is not merely rhetorical but functions as a pedagogical process: erroneous views and dualistic constructs are generated, tested, refuted, and refined until the practitioner’s reliance on conceptual elaboration collapses. In this way, the teaching method itself resembles an adversarial learning model, where insight emerges through continuous confrontation with errors. In this paper the author explores the method of learning where adversarial views are used to engage in deep learning and transcendence. GANs provide a modern analogue: they consist of two competing networks—a generator that produces synthetic outputs and a discriminator that evaluates their authenticity. Through iterative feedback and critique, both models improve in tandem, eventually producing outputs indistinguishable from real data. Similarly, the Buddha’s adversarial dialogues expose the “generated illusions” of discriminative thinking, while the “discriminator” function is represented by wisdom (prajñā), which identifies and dismantles conceptual fabrications. The comparison highlights both parallels and divergences. While GANs aim at convergence toward increasingly realistic outputs within representational constraints, the Buddhist adversarial method seeks not fidelity to appearances but the transcendence of representational frameworks altogether, pointing toward non-dual realization and liberation from suffering. This contrast underscores how ancient epistemic practices may resonate with modern computational paradigms while also exceeding them in scope, embedding cognitive, ethical, and soteriological dimensions absent in machine learning. The paper thus proposes that reading the Laṅkāvatāra Sūtra as an adversarial pedagogy provides fertile ground for interdisciplinary inquiry, bridging Buddhist philosophy, cognitive science, and artificial intelligence research.

Federated Learning On Cloud Platforms: Privacy-Preserving AI For Distributed Data

Authors: Mahavani Kb, Bavithra Rs

Abstract: Federated learning has also become a paradigm shift to making machine learning collaborative and not centralized around sensitive data. Federated learning solves the increasing privacy, regulatory compliance, and data sovereignty concerns by preventing the transfer of model training to centralized model training clients, like hospitals, financial institutions, and IoT devices. Cloud platforms are critical to the operationalization of this paradigm as it offers scalable orchestration, secure aggregation, and communication-efficient frameworks. The paper discusses how cloud native federated learning systems decrease the amount of communication, enhance the model convergence, and provide more robust privacy guarantees without violating regulation of systems like GDPR and HIPAA. By applying federated learning to the medical diagnostic and financial fraud detection domains, the study shows that federated learning can be successful in providing a high level of model accuracy and strong privacy protection. The results indicate the significance of supporting federated learning by cloud-native infrastructure that will allow implementing privacy-safe AI solutions that can be widely adopted in regulated industries. From a privacy and regulatory perspective, cloud-based federated learning systems provide strong guarantees that align with data protection regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). By eliminating the need for raw data transfer, federated learning inherently supports privacy-by design principles. When combined with advanced privacy-preserving techniques such as differential privacy, secure multi-party computation, and homomorphic encryption, federated learning further strengthens its compliance with strict legal and ethical requirements. To demonstrate the effectiveness of cloud-native federated learning, this study applies the proposed framework to two critical application domains: medical diagnosis and financial fraud detection. Experimental results show that federated models achieve performance levels comparable to, and in some cases exceeding, those of traditional centralized models, while significantly enhancing data privacy and security. In medical diagnostics, federated learning enables collaborative training across multiple healthcare institutions without exposing sensitive patient records. Similarly, in financial fraud detection, federated learning facilitates cross institutional intelligence sharing without compromising proprietary or customer data.

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AI-Driven Financial Fraud Detection Systems: Enhancing Financial security Through Real-Time Transaction Analysis

Authors: Sakthivel S, Vikash P

Abstract: The rapid expansion of digital financial services has significantly transformed the global financial ecosystem by enabling fast, convenient, and seamless transactions. However, this transformation has also increased the vulnerability of financial systems to fraudulent activities such as credit card fraud, identity theft, phishing attacks, insider fraud, and money laundering. Financial fraud results in substantial economic losses, damages institutional reputation, and undermines customer trust in digital banking systems. Traditional fraud detection mechanisms primarily rely on rule-based systems and manual audits, which are reactive, inflexible, and often incapable of detecting complex and evolving fraud patterns in real time. Advancements in artificial intelligence (AI), machine learning (ML), and data analytics have paved the way for intelligent financial fraud detection systems capable of processing large volumes of transaction data efficiently. By learning patterns from historical transaction data and identifying anomalies, AI-driven systems enable early detection and prevention of fraudulent activities. This paper presents an AI-based financial fraud detection framework that integrates data preprocessing, feature engineering, and machine learning-based classification for real-time fraud analysis. The proposed system aims to improve detection accuracy, reduce false positives, and enhance the overall security of digital financial transactions. Experimental results and analysis demonstrate that intelligent fraud detection systems provide scalable, adaptive, and reliable solutions for modern financial environments.

Digital Storytelling for Mental Health Awareness: Exploring Impact on Knowledge, Attitude, and Engagement

Authors: Priya Palanimurugan, Mr Kalaiselvan S, Dr.Thulasi Bharathi M, M.sakthivel

Abstract: Mental health issues remain a global public health concern, especially among the youth and digitally active population. This study examines the effect of digital storytelling as an intervention tool to increase mental health awareness, reduce stigma and encourage positive behavior changes. A quantitative research design involving 300 graduate students aged 18–25 in diverse educational subjects was employed. Participants were divided into an intervention group, which reflects a control group that receives curate digital stories and traditional information-based materials reflecting real-life mental health experiences.Advanced statistical techniques were used to assess the results in three time points (Post, Post-up). Descriptive data briefly presented demographic data; Alpha of Cronback confirmed the reliability of the scale; Confirmation factor analysis (CFA) valid measurement construction; And the multi -comprehensive analysis of the covalent (mancova) identified important group differences. Repeated measures Anova and Structural Equation Modeling (SEM) further detected time-based reforms in the intention of mental health awareness and behavior, mediate by low stigma. Moderation and latent development analysis highlighted demographic effects and individual trajectory patterns. Conclusions suggest that digital storytelling improves mental health awareness and reduces stigma compared to traditional approaches (P <0.01). The narrative-based method was particularly effective among the pre-risk participants for high digital literacy and mental health materials. The study supports the integration of digital story stories in public health education and mental health advocacy programs. These results contribute to increasing evidence that creative digital equipment can change mental health communication, offering scalable, attractive and human-focused solutions.

Cinematic Healing: The Psychology of Memory, Trauma, and Recovery in Balu Mahendra’s Films

Authors: Priya Palanimurugan, Miss. R.Christy Alice, Dr.Thulasi Bharathi.M, M.sakthivel

Abstract: This essay delves into the complex confluence of emotional realism, cinematic expression, and mental health in the cinema of Balu Mahendra, India's most empathetic director. Through his sensitive explorations of human vulnerability, psychological trauma, and resilience, Mahendra remapped the way the Tamil cinema represented the human mind and its delicate complexities. Exceeding commercial norms, his films like Moondram Pirai (1982), Marupadiyum (1993), Sandhya Raagam (1989), and Veedu (1988) demonstrate a deep psychological realism that humanizes characters normally pushed to the periphery by social or emotional pain. This research uses a psychological model based on trauma theory, studies on empathy, and humanistic psychology to examine how Mahendra's film language turns pain into poetry and silence into emotional conversation. The study places Mahendra's films within the larger framework of Indian cinema's shifting approach to mental health, highlighting how his stories avoid the melodramatic spectacle commonly linked with psychological illness. Rather, his characters are characterized by a quiet dignity that mirrors the internal struggles of memory, loss, identity, and moral dissonance. The paper also investigates the aesthetic aspects of Mahendra's visual realism—his natural lighting, long takes, and close-ups—as methods that conjure emotional truth and ask viewers to enter a reflective psychological zone. Finally, this essay maintains that Balu Mahendra's films work as sympathetic case studies of the human mind, providing social commentary and emotional counseling. His world of film challenges viewers to see mental illness neither as weakness nor as supernatural affliction but as a vital aspect of human experience. In this process, Mahendra's body of work helps in the destigmatization of mental pain and promotes a different cinematic language based on compassion, realism, and psychological complexity.

Smart Community Health Monitoring and Early Warning System for Water-Borne Diseases in Rural Areas.

Authors: Pranav Dhondibhau Gawade, Sarthak Vivek Sagare, Sujan Anna Kambale, Piyush Vinod Chaudhary, Neelam N Kavale

Abstract: The proposed Smart Community Health Monitoring and Early Warning System for rural areas offers a transformative, cost-effective alternative to expensive, sensor-dependent technologies by prioritizing syndromic surveillance and community-led data collection. Recognizing that traditional IoT infrastructure often fails in remote regions due to high maintenance costs and power instability, this model empowers community health workers to act as "human sensors," manually reporting clinical symptoms like fever and diarrhea via an offline-capable mobile interface. By integrating these health reports with periodic, low-cost chemical water testing, the system utilizes a centralized analytical engine to run statistical aberration detection algorithms that compare real-time trends against historical baselines. This proactive framework identifies potential pathogenic outbreaks at their nascent stage, triggering a tiered Early Warning System (EWS) that alerts local authorities through automated SMS and voice calls. Ultimately, this research demonstrates that public health resilience is not solely dependent on high-tech hardware but can be achieved through strategic data management, community participation, and smart analytics, providing a scalable and sustainable blueprint for disease prevention in resource-constrained environments globally.

Vibration Analysis of Aircraft Wing

Authors: Abhishek Rawat, Basant Agarwal

Abstract: In recent years, extensive research has been conducted on vibrations in air-craft. Vibration can cause some serious failure in the structure. The increase in disquisition in vibration has led to taking design considerations in the bod-ies. This paper specifically focuses on the vibration in bodies causing defor-mation in aircraft wing. Modal analysis of two different wing is done using Fi-nite Element Analysis. Three different material namely: Aluminium Alloy, Copper Alloy and Titanium Alloy have been incorporated to describe the ef-fects of free vibration on different wings.

Transformer Health Monitoring System Using Iot

Authors: Dr. Chetana Reddy, Divya K, Madhumitha B, Maithra K, Melisha K Sunny

Abstract: Power transformers must work well and be reliable to keep the flow of electricity stable and uninterrupted. Standard periodic maintenance often does not find problems in their early stages, which can lead to insulation breakdown, oil breakdown, thermal stress, overloading, and unexpected outages. This paper proposes an IoT-based Transformer Health Monitoring System to address these limitations. The system can continuously and in real time monitor important operating parameters. The system uses sensors for temperature, oil level, load current, and input voltage that are connected to a microcontroller. The microcontroller processes the data and sends it to a cloud-based monitoring platform. The data analyzed by the IOT platform ensures early fault detection for maintenance planning. To support predictive maintenance, the suggested framework provides threshold-based alert notifications, historical logging, real-time data visualization, and remote access. The system creates automated alerts to stop overheating, insulation failure, and possible transformer failures when anomalous conditions are identified. This system monitors multiple transformers at different distributed substation. This IoT- enabled strategy prolongs transformer lifespan, lowers maintenance costs, minimizes downtime, and improves operational safety. The solution offers a scalable architecture for intelligent monitoring across substations and distribution networks and is in line with efforts to modernize smart grids.

Artificial Intelligence Rack Cooling: Direct-to-Chip Liquid Cooling Systems

Authors: Girish Kishor Ingavale

Abstract: The exponential growth in computational power and industrial processes has led to an increased demand for efficient cooling solutions in data centers. Traditional air-cooling systems are becoming inadequate due to their limitations in managing high thermal loads and their high energy consumption. In response to these challenges, Direct-to-Chip Liquid Cooling Systems (D2C LCS) have emerged as a promising alternative for thermal management in high-density computing environments. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) technologies to optimize the performance of D2C LCS in rack-mounted data center setups. The primary objective of this research is to develop and implement AI-driven models that can predict temperature and fluid flow within D2C LCS, thereby enabling the optimization of cooling strategies. By leveraging advanced algorithms such as Linear Regression and Support Vector Machine, the study aims to enhance thermal efficiency and reduce the energy consumption of data centers. Experimental data was collected from a simulated data center environment equipped with D2C LCS. The data was used to train and validate ML models, ensuring their accuracy and reliability in real-world applications. The results demonstrate that AI-optimized cooling strategies can achieve a 15% reduction in temperature and a 20% decrease in energy consumption compared to traditional air-cooling systems. The findings of this study highlight the significant benefits of integrating AI and ML technologies with D2C LCS for thermal management in data centers. The predictive models and optimized cooling strategies presented herein provide a robust framework for improving the efficiency and sustainability of data center operations. Future research directions include the development of more advanced AI models and the implementation of real-time monitoring systems to further enhance the performance of D2C LCS.

DOI: http://doi.org/10.5281/zenodo.18125704

Inertial Navigation Systems (INS) And Monitoring Systems

Authors: Maram Mounika, Rakshitha L, Ramya R, Ramya Shree D, Dr Manasa P

Abstract: Inertial Navigation Systems (INS) are mission- critical subsystems in naval platforms, providing continuous nav- igation information independent of Global Navigation Satellite Systems (GNSS). In contested or GNSS-denied environments, the reliability of INS data directly impacts navigation accuracy, weapon control, and overall mission effectiveness. This paper presents the design and implementation of a real-time INS monitoring platform developed for Indian Naval shipboard appli- cations. The proposed system acquires high-speed navigation data from dual Ring Laser Gyro (RLG) based INS units operating at 10 Hz and 100 Hz through RS-422 interfaces. Using industrial- grade USB-to-serial hardware and a LabVIEW-based software framework, the system performs real-time data acquisition, frame validation, parameter extraction, visualization, logging, and replay. Experimental results demonstrate reliable, lossless data capture and synchronized monitoring of heading, roll, and pitch from forward and aft INS units, validating the effectiveness of the platform for onboard monitoring, testing, and post-mission analysis.

Simulink Simulation of Load-Controlled Memcapacitor for Reducing Output Voltage Ripple in Buck-Boost Converters

Authors: Dr. Osman Zenk

Abstract: As is known, memcapacitors, which are memory elements, are electrical circuit elements ex-pected to revolutionize various research and many engineering fields thanks to their variable capacitance and non-volatile memory effects. Although there is not yet enough scientific research on memcapacitors in power electronics systems, they have serious scientific discovery potential, especially in terms of reducing output voltage ripple, increasing voltage stability, and improving energy efficiency. In this study, theoretical and simulation results performed in the Matlab/Simulink environment are presented, showing that adding a memcapacitor to the output of a commonly used buck-boost converter, a type of DC-DC converter, significantly reduces the output voltage ripple. In the study, a memcapacitor emulator that can be implemented using commercial components is first proposed and validated. Then, this emulator was used to exam-ine the steady-state output voltage ripple and transient response of the buck-boost converter. The results show that the use of a load-controlled memcapacitor can reduce the output voltage ripple by up to 96%.

DOI: https://doi.org/10.5281/zenodo.18127037

Implementation Of A Convolutional Neural Network For Binary Image Classification Using Tensor Flow

Authors: K. Nagarathna, Mallikarjun Aralimard

Abstract: This paper presents the design and implementation of a simple Convolutional Neural Network (CNN) using Tensor Flow for binary image classification. The proposed model classifies 5×5 pixel images into two categories: images containing the pattern of an 'X' and images that do not. The study demonstrates dataset generation, model architecture, training, and evaluation, highlighting the effectiveness of CNNs for pattern recognition tasks.

DOI: http://doi.org/10.5281/zenodo.18129745

Deep Learning-Based Audio Stegware Detection Through CNNLSTM With Spectrogram And MFCC Integration Of Features

Authors: Shaun Paul Moses, Vignesh. S

Abstract: Another emerging danger in the world of cybersecurity is the term steganography, which means concealing secret data in digital form, because concealed messages can be easily transferred to a different information exchange format. Other modalities such as audio steganography possess unique features that make it difficult to detect such signals, such as the temporal-frequency properties and audio signals are high dimensional. This project offers a DLDA, Deep Learning Based Detection System Stegware in Audio Files, that will inform whether the audio sample is a real cover or it is a stegware, i.e. it has embedded data in it. The system employs improved methods of feature extraction like Spectrogram Analysis and Mel-Frequency Cepstral Coefficients (MFCCs) to identify requisite frequency, amplitude and temporal indications to identify stegmodifications. The CNNs and LSTMs process subsequently learn a discriminative feature (CNNs) and temporal patterns (LSTM) that occurs between normal and manipulated audio. Training and testing are done using a dataset of clean audiofiles and audiofiles with various modifications done using steganography. The performance is measured by the accuracy, precision, recall and F1-score and the system has been found to be very reliable with accuracy of 97.8 and very few false detections. In the experimental results, it is seen that the model works fairly well when noise and compression is introduced, indicating its strength in the real world. Overall, the framework that is created due to the research effectively applies deep learning to offer a scalable, automated and accurate method of audio steganalysis, which is an outstanding achievement that can provide cybersecurity, digital forensics and secure communications as the number of illegal data transmission via audio channels decreases.

Full Stack Donar Hub System for Real-Time Donation and Volunteer Coordination

Authors: Mrs.S. Dhivya,, Kanika N, Kavipriya A, Madhumitha S, Preethi S

Abstract: This This report presents the project titled “Donar Hub,” a web-based platform developed to connect individuals in need with donors and volunteers through a structured and accessible system. The platform enables the sharing of essential resources such as food, clothes, books, and medical assistance. Users can create Request Help and Offer Help posts, which are categorized and searchable to ensure efficient matching of needs and available support. Donar Hub aims to reduce resource wastage while promoting social responsibility and community collaboration. To ensure authenticity and prevent misuse, the system incorporates Aadhaar-based identity verification for users. Requests related to medical assistance require verification of medical reports or hospital-issued documents before approval. This validation mechanism enhances trust, transparency, and security within the platform. The application is designed with intuitive forms and a user-friendly interface, making it accessible to users with varying levels of digital literacy.

Operationalizing Zero Trust Principles In AI-Native Architectures: A Framework For Securing Autonomous, Model-Driven Systems

Authors: Ashok Kumar Kanagala

Abstract: The proliferation of AI-native architectures has introduced autonomous, model-driven systems with unprecedented capabilities and complex security challenges. These systems, often deployed across multi-agent pipelines and edge environments, expand the attack surface and exhibit dynamic, unpredictable behaviors that traditional security frameworks fail to address. Despite emerging research on AI robustness and alignment, comprehensive strategies for proactively securing agentic AI remain underdeveloped. This paper investigates the operationalization of Zero Trust principles in AI-native architectures, aiming to provide a forward-looking framework for resilient and accountable systems. The proposed approach integrates continuous model verification, alignment assurance with transparency tooling, lifecycle-integrated security validation, and autonomous red-teaming to proactively identify and mitigate vulnerabilities. Key findings indicate that embedding self-assessing mechanisms, standardizing behavioral benchmarks, and applying cross-layer defenses significantly enhance system resilience and reduce dependency on reactive interventions. This research contributes a structured methodology for securing autonomous AI, advancing both practical and theoretical understanding of AI-native security in complex, adaptive environments.

DOI: http://doi.org/10.5281/zenodo.18137101

IoT-Based Real-Time Vehicle Tracking And Fuel Monitoring System With Theft Alert

Authors: K. Nagarathna, Darshan C D, Chetan Shanawad, Channu Anand Honnammanavar, Vijay Musaguri

Abstract: This project presents an IoT-based system for real-time vehicle tracking, fuel monitoring, and theft detection. The system integrates an ESP32 microcontroller, GPSmodule, GSMmodule, andfuel -levelsensorstomonitor vehicle conditions and transmit data to the cloud. A comprehensive alert mechanism notifies the user during unauthorizedvehiclemovement, fueltheft, orcaptampering. The system is designed to be cost-effective, accurate, and reliable, making it suitable for fleet management and personal vehicle security.

DOI: http://doi.org/10.5281/zenodo.18137733

Regulatory Effectiveness, Air Quality, And Health Risks Around Gas-Fired Power Plants In The Niger Delta

Authors: Adefila Adewale James, Onosemuode Christopher

Abstract: Effective environmental regulation is critical for minimizing the air quality impacts of energy infrastructure, particularly in regions with dense industrial activity. In Nigeria’s Niger Delta, gas-fired power plants form the backbone of electricity generation, yet concerns persist regarding their environmental compliance and regulatory oversight. This study evaluates the effectiveness of air quality regulation and environmental compliance mechanisms governing gas-fired power plants in selected Niger Delta states. Using a mixed-methods approach, the study integrates ambient air quality measurements, regulatory document review, institutional analysis, and stakeholder interviews to assess compliance with national air quality standards and the enforcement capacity of regulatory agencies. Findings reveal persistent exceedances of regulatory limits for particulate matter and nitrogen dioxide in host communities, alongside systemic gaps in monitoring, enforcement, and inter-agency coordination. While regulatory frameworks exist on paper, weak implementation, limited technical capacity, and poor data transparency undermine their effectiveness. The study provides policy-relevant insights and proposes actionable reforms to strengthen air quality governance and protect public health in Nigeria’s energy-producing regions.

DOI: http://doi.org/10.5281/zenodo.18139392

Management Of Local Food Tourism In Varanasi Via Investigation Of Culture & Values

Authors: Dr. Himanshu Sharma, Dr. Ankur Goel, Dr. Sapna Deshwal

Abstract: Present-day tourism research has increasingly focused on food tourism. Food is always together with human’s life and will always have a hope to grow in the tourism industry. Food experiences both inside and outside the country is always a part of food tourism. Varanasi is expanding in all directions, and as a result, its tourism industry is expanding as well. This will open up a lot of new opportunities for the locals of this tourist destination to improve their food experiences and share them with others. The fundamental ideas surrounding food tourism are identified as a major research concern in this paper, which focuses on tourism research. In addition, this study reveals that employment generation and the development of local culture are directly linked to the expansion of local food tourism. The researcher also finds food tourism research from a cultural perspective. Most people agree that eating local food is an important part of what tourists do. Food that is both original and one-of-a-kind to the area can be important as a tourist attraction in and of itself as well as in shaping a destination's image. Experiences with local food have the potential to significantly support agricultural diversification, maintain regional recognition, and contribute to sustainable development.

AI Driven Crop Disease Prediction And Management System

Authors: Sukanya, G.Bharath Kumar, Karthik.D, Nikhil Reddy, ChannaKeshwa

Abstract: Crop diseases pose a major global threat to agricultural productivity, farmer income, and overall food security. Widespread disease outbreaks reduce crop quality, decrease yield, and contribute to economic instability—especially in regions dependent on agriculture for livelihood. The complexity of crop diseases arises from diverse environmental conditions, varying plant species, and the presence of multiple visually similar infections. Addressing these challenges requires a systematic, data-driven approach capable of identifying hidden patterns and supporting farmers and agricultural experts with timely, actionable insights. This project presents the design and development of an AI- Driven Crop Disease Prediction and Monitoring Dashboard, an interactive platform built using Streamlit. The dashboard enables visualization, prediction, and analysis of plant disease data using a trained Convolutional Neural Network (CNN). The system architecture is organized into three primary layers: the Presentation Layer, the Logic Layer, and the Data Layer. The Presentation Layer provides a user-friendly web interface developed in Streamlit, integrating dynamic components such as real-time prediction panels, probability bars, and comparative disease charts generated with Plotly Express. It also includes essential UI elements such as an image upload section and model output visualization to ensure smooth user interaction. The Logic Layer performs core analytical and computational tasks. It preprocesses leaf images, applies the CNN model for classification, generates confidence scores, and provides diseasespecific treatment recommendations. Pandas handles metadata processing, while session-state management ensures efficient handling of user inputs and outputs. The Data Layer consists of a structured plant disease dataset derived from sources such as PlantVillage, supplemented with augmented images to improve model robustness across lighting and environmental variations.

To study the fabrication and mechanical properties of magnesium-based nanocomposite for different weight fractions

Authors: Dr. Bangarappa L, Dr. Danappa G.T

Abstract: This present study has provided the fabrication of Mg/MWCNT nano composites, Mg/FA and Mg/MWCNT/FAhybrid nano composites with powder metallurgy processing techniques. The specimens prepared were characterized for mechanical properties like density of the materials, Vickers hardness, elastic modulus, and tensile properties. Nanocomposites are versatile material or multi-functional materials achieved by the unnatural mixture of verities of materials in turn to attain the characteristics in separate components by it that can’t be overcome. The extraordinary attention on carbon nano tubes were due to their unique structure and characteristics, they have a very tiny size of about 0.42nm and less than in diameter & the mechanical properties they exhibit. Carbon nanotubes have been expected to be one of the best reinforced materials to enhance the mechanical characterization as they possess good young’s modulus along with material strength and aspect ratios.

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Need to Empower Learners with Communication Skills: A Survey

Authors: Dr. Pranav Mulaokar

Abstract: When interviewing the first year engineering students, it was observed that there is the urgent need to empower them with communication skills. This research paper focuses on the importance of communication, proficiency in English, concepts of LSRW, common mistakes, soft skills and global relevance. During the survey, the observations and recommendations were noted. English communication comes into light for international collaboration, technical documentation, workplace situations, etc. Real-life application of communication should be taught from the beginning of the learning process. The challenges which students face are discussed and solutions provided. The global scope of being a good communicator is noted in the paper.

A Next-Generation Adaptive Semantic Video Transmission Framework for Wireless Networks

Authors: Drupad Gowda N, GS Rajdeep, Puneeth Kumar GJ, Vignesh Shenoy R

Abstract: Videos are now used everywhere — in education, smart cameras, video calls, hospitals, industries, and home security. But whenever the internet becomes slow or the wireless signal becomes weak, the normal video transmission systems fail and the video quality becomes very poor. The MDVSC (Model Division Video Semantic Communication) technique solves this by transmitting only the meaningful information from video frames instead of sending the entire frame pixel by pixel. It carefully separates the information that stays the same across frames (like background or static objects) and the information that changes (like moving objects). Then, it sends only the important features first. Because of this, MDVSC provides better video clarity, uses less data, and continues working even when network strength is low.

An Intelligent System For Automated Detection And Identification Of Bone Trauma Using Deep Learning

Authors: Anirudh S, Tanuja R

Abstract: Fractures and bone trauma are serious injuries that are increasing in frequency worldwide. In some cases, these injuries are not easily visible through traditional diagnostic methods such as x-rays, leading to misdiagnosis and inadequate treatment. To address this issue, a Computer-Aided Diagnosis and Recommendation System could be developed, which utilizes various deep learning techniques to accurately detect the severity of the fracture and recommend appropriate exercises, diet plans, and surgeries for recovery. This system would incorporate techniques such as deep learning convolutional neural networks, edge detection, ridge regression, and image smoothing to enhance accuracy and provide more precise recommendations. Each technique would contribute unique features to the system, resulting in better outcomes for patients.

DOI: https://doi.org/10.5281/zenodo.18169049

“Virtual Mouse Using Hand & Eye Gesture and Chatbot”

Authors: Prof. Supriya G Purohit, Mr.Tanveer Ahmed, Mr.Syed Adnan, Mr.Mohammed Zaid Noman

Abstract: In an increasingly digital world, the need for accessible and intuitive human-computer interaction (HCI) solutions is more critical than ever—especially for individuals with physical disabilities. This project proposes a Virtual Mouse System that integrates hand gestures, eye tracking, and an AI-powered chatbot to offer a seamless, multimodal interface for touch-free computing. By combining real-time computer vision, deep learning, and natural language processing (NLP), the system replaces traditional input devices like keyboards and mice with a more inclusive and efficient alternative. The hand gesture module utilizes OpenCV, MediaPipe, and Convolutional Neural Networks (CNNs) to detect and interpret finger movements and predefined gestures for cursor movement, clicking, and scrolling. The eye-tracking module employs Haar cascade classifiers, Hough Transform, and Eye Aspect Ratio (EAR) techniques to track gaze and blinks for cursor control and selection, enabling hands-free navigation. To enhance interactivity, a chatbot module powered by NLP models such as BERT or GPT handles voice and text-based queries for performing system-level commands and basic computational tasks. Communication among these modules is managed through a Flask-based backend, ensuring synchronized, responsive interaction. Designed for both general users and those with motor impairments, the system addresses limitations found in standalone gesture or voice- based solutions, such as lighting sensitivity, gesture misrecognition, or voice command errors. By integrating multiple input modalities, the system enhances accuracy, usability, and user autonomy. Applications span from accessibility tools to smart environments, virtual reality, and beyond. Future work includes improving gaze estimation through deep learning and enhancing chatbot capabilities for broader conversational interaction.

DOI: https://doi.org/10.5281/zenodo.18169212

Effective Policy and Enforcement for Resolving Atrocities/Conflicts Enabled by Landed Property Ownership in Nigeria

Authors: M. O. O. Ifesemen, Dr Dulari A Rajput

Abstract: This thesis examines the persistent rise of land-related conflicts and associated criminal activities in Nigeria, tracing their roots to historical, cultural, administrative, and governance-related inadequacies in the management of landed property. Land, traditionally communally owned and essential for livelihood, has evolved into a highly contested asset due to population growth, modernization, and weak implementation of the Land Use Act. The study highlights how ineffective administration, corruption, poor enforcement of regulations, and conflicting customary and statutory land rights have created conditions enabling violence, territorial claims, extortion, communal clashes, and other atrocities across the country. Materials and Methods: The research adopts a qualitative approach grounded in criminological theory, supported by documentary analysis, non-participant observation, and unstructured interviews. Data were sourced through long-term observational studies of land-related activities in communities, motor parks, markets, land registries, and informal settlements across Nigeria. A combination of cross-sectional and longitudinal designs enabled the researcher to observe patterns, behaviours, and criminal tendencies linked to land ownership struggles. Content analysis was used to interpret data within the theoretical framework of causes of crime—including cultural, economic, psychological, and environmental determinants. Results and Discussion: Findings reveal that inadequacies in land administration—such as corrupt allocation practices, weak enforcement of land regulations, multiple sales of land, extortion by traditional actors (e.g., “omo-onile”), unregulated territorial control, and government-enabled demolitions—have significantly fueled criminal activities. These include communal clashes, armed conflicts, thuggery, property destruction, kidnapping, territorial cultism, and conflict between farmers and herdsmen. The study establishes that such crimes persist largely because of institutional weaknesses, inconsistent policies, and failure to implement culturally sensitive, transparent systems of land governance. Conclusion: The study concludes that strengthening policy enforcement, enhancing governance structures, and implementing culturally aligned regulatory frameworks are essential to reducing land-related atrocities. Effective land administration and accountability at all levels will help curb crime, promote peace, and support sustainable national development.

DOI: https://doi.org/10.5281/zenodo.18169532

Blame culture and its effects on organisational productivity– a case study of Mcpee Limited.

Authors: M. O. O. Ifesemen, Dr Dulari Rajput

Abstract: This research critically examines the pervasive effects of blame culture on organisational productivity, using Mcpee Limited—a production-oriented company based in Southern Nigeria—as a case study. The study explores how blame culture is embedded within the operational and social fabric of the company and investigates its impact on employee behaviour, work procedures, and overall organisational performance. This research investigates the pervasive effects of blame culture on organisational productivity, using Mcpee Limited, a production-oriented firm in Southern Nigeria, as a case study. The study aims to explore how blame culture is embedded within the company’s operational and social environment and its influence on employee behaviour, work procedures, and overall productivity. An inductive research approach with a descriptive design was adopted, employing a mixed- methods data collection strategy. Quantitative data were gathered through questionnaires administered to 314 employees across varied departments, while qualitative insights were obtained from 80 department heads and supervisors via in-depth interviews. This triangulation enabled a comprehensive understanding of how blame culture permeates the organization and affects its functioning. The findings reveal that blame culture cultivates a tense and insecure workplace, where employees avoid assuming responsibility for mistakes due to fear of punitive consequences. This environment suppresses risk-taking and innovation, thereby constraining the organization’s ability to adapt and improve continuously. Several factors perpetuate this culture, including rigid procedural frameworks that restrict employee discretion, entrenched favoritism and nepotism, and ineffective recognition and reward systems that fail to engage or motivate staff adequately. Moreover, blame culture fosters demotivation, learned helplessness, micromanagement, and erodes employee empowerment, trust, and cooperation. Managers, concerned about protecting their reputations, frequently shift blame downward instead of promoting accountability, resulting in excessive bureaucracy and decreased employee engagement. To counteract these detrimental effects, the study recommends shifting organizational culture from blame-oriented to accountability-focused. This transformation calls for promoting fairness and meritocracy by eliminating favouritism, encouraging teamwork and collaboration aligned with shared goals, and streamlining work processes to reduce unnecessary rigidity. Empowering employees to exercise discretion, creativity, and problem- solving initiative without fear of unjust repercussions is emphasized as critical for fostering innovation and boosting productivity. The study concludes that blame culture significantly undermines organizational productivity by creating a fearful and rigid work environment. It recommends transforming the culture from blame-oriented to accountability-focused by promoting fairness, teamwork, flexible work practices, and problem-solving approaches. Empowering employees to take initiative without fear of unjust punishment and recognizing their contributions can foster innovation and enhance productivity. These findings offer valuable insights for organizations seeking to cultivate a positive, supportive, and accountable workplace culture conducive to sustained performance improvement.

DOI: https://doi.org/10.5281/zenodo.18171125

An AI-Driven Personalized Learning Recommendation System For Enhancing Student Academic Performance

Authors: Rithica B, Sai Srija R

Abstract: Personalized learning has emerged as a vital component of modern educational systems due to the diversity in students’ learning abilities, preferences, and academic backgrounds. Traditional e-learning platforms often provide uniform learning content to all learners, which fails to address individual needs and learning gaps. This limitation results in reduced student engagement and suboptimal learning outcomes. To overcome these challenges, this paper proposes an AI-driven personalized learning recommendation system that dynamically suggests learning materials, assessments, and learning paths tailored to individual students. The proposed system utilizes machine learning techniques to analyze learner profiles, historical academic performance, learning behavior, and preferences. Based on these parameters, intelligent recommendations are generated to support adaptive and learner-centric education. Experimental evaluation demonstrates that the proposed system significantly improves student engagement, learning efficiency, and academic performance when compared with conventional learning management systems. The findings highlight the effectiveness of artificial intelligence in transforming digital education into a personalized and adaptive learning environment. Personalized learning has emerged as a vital component of modern educational systems due to the diversity in students’ learning abilities, preferences, and academic backgrounds. Traditional e-learning platforms often provide uniform learning content, resulting in reduced engagement and limited academic effectiveness. This paper proposes an AI-driven personalized learning recommendation system that utilizes learning analytics and machine learning techniques to analyze learner profiles, academic performance, and behavioral patterns.

Reducing Workplace Incidents / Poor Performance by Holding Organisations and Leaders Accountable

Authors: M. O. O. Ifesemen, Dr Dulari A Rajput

Abstract: This study investigates the intricate link between workplace operational incidents and administrative errors, emphasizing the critical role of organizational and leadership accountability in mitigating error-enforcing conditions that precipitate incidents and degrade performance. Employing a robust qualitative approach, the research integrates a mixed- methods design encompassing naturalistic observation—both participant and non- participant—and unstructured interviews conducted with over 300 personnel within a Nigerian-based transnational organization. Data were meticulously analyzed using descriptive and deductive reasoning frameworks to elucidate the impact of leadership decisions and organizational practices on the prevalence of workplace errors and related incidents. The findings reveal a compelling pattern: more than 80% of workplace incidents, encompassing both physical injuries and psychological harm, originate from administrative errors linked to leadership styles and organizational culture. Key error-enforcing conditions identified include pervasive blame culture, inadequate fatigue management, favoritism, bullying, flawed performance appraisal systems, and a pronounced lack of employee empowerment. Notably, psychological injuries arising from these administrative errors—such as diminished self-esteem, depression, and chronic stress—were found to be more detrimental than physical injuries, exerting profound negative effects on employee motivation, productivity, and overall organisational performance. The study further underscores the frequent misinterpretation of incident causality and highlights the paramount importance of objective evaluation and leadership accountability as mechanisms to reduce incident recurrence effectively. In conclusion, the research advocates cultivating accountability at all organisational levels, enhancing leadership competencies, and promoting a culture grounded in empathy and objectivity within performance appraisal and incident management processes. Implementation of these measures is projected to foster safer, more productive work environments, thereby driving improved organisational outcomes. The study also calls for integrating accountability principles into corporate governance frameworks. It emphasises the need for transformational learning through causal reasoning to address the root causes of workplace errors and incidents, ultimately contributing to sustainable organisational excellence.

DOI: https://doi.org/10.5281/zenodo.18172551

Design and Implementation of a Contactless Automatic Door Opening and Closing System using Ultrasonic Sensing

Authors: Ms. Achal A. Koyale, Ms. Shravani S. Golegaonkar, Ms. Maithili V. Mangalagiri

Abstract: In modern public and commercial environments, frequent physical contact with door handles increases hygiene risks and creates accessibility challenges for elderly and physically challenged individuals. To overcome these limitations, this paper presents the design and implementation of a contactless automatic door opening and closing system using ultrasonic distance sensing and microcontroller-based control logic. The proposed system employs an HC-SR04 ultrasonic sensor to continuously monitor the presence of approaching objects. When the detected distance falls below a predefined threshold, a servo motor is actuated to control the opening and closing of the door. A delay-based safety control algorithm is implemented to prevent unintended door closure and to ensure smooth and reliable operation. The system is developed using low-cost and easily available hardware components, making it suitable for small-scale and public applications such as hospitals, offices, shopping malls, and public washrooms. Experimental results demonstrate accurate object detection within a range of 2 cm to 80 cm, stable door operation, and minimal response delay. The proposed system provides an efficient, economical, and scalable solution for contactless door automation and can be further enhanced through IoT integration and advanced sensing technologies.

Effects of Ananda Parisar on the Academic and Socio-Emotional Development of Students in Rural Primary Schools of West Bengal

Authors: Md. Parvej

Abstract: The holistic development of children has become a central concern of contemporary primary education. In West Bengal, Ananda Parisar has been introduced as a joyful learning initiative in primary schools. The present study examines its impact on academic engagement and socio-emotional development of students in rural primary schools. Using a descriptive survey method, data were collected from selected rural blocks of Malda district. Findings reveal significant improvement in motivation, participation, social interaction, and emotional well-being. The study concludes that Ananda Parisar is an effective pedagogical intervention for rural primary education.

Deep Fake Detection

Authors: Prof. Keerti M, Mr.Narendra, Mr.Vishal, Mr.Kevin Dutt

Abstract: Deep fake detection technology has advanced rapidly with the progress of deep learning, enabling the generation of highly realistic manipulated images and videos. While such technology has beneficial applications in entertainment and media, its misuse poses serious threats including misinformation, identity fraud, political manipulation, and erosion of public trust. Traditional video authentication techniques are insufficient to detect subtle manipulations introduced by modern deepfake generation algorithms. This paper presents a deep learning–based deepfake detection system that analyzes both spatial and temporal inconsistencies in video frames. The proposed approach employs transfer learning–based convolutional neural networks for facial feature extraction and sequence-based models for capturing temporal variations across frames. Preprocessing techniques such as face detection, frame extraction, normalization, and data augmentation are applied to enhance detection robustness. Experimental evaluation using benchmark datasets demonstrates that the proposed system achieves reliable detection accuracy even for high-quality deepfake videos. The system provides an effective and scalable solution for digital forensics, cybersecurity, and social media content verification.

Rfid Based Petrol Pump Automation System

Authors: Pranav Shelar, Om Shinde, Om Shinde, Omkar Solat, Prof. Italkar Sanika

Abstract: In conventional petrol pump systems, fuel dispensing and billing are carried out manually, which often leads to issues such as fuel theft, human errors, inaccurate billing, and increased waiting time for customers. With the growing demand for automation and secure cashless systems, there is a strong need for an efficient fuel management solution. This paper presents the design and implementation of an RFID-based automated petrol pump system using Arduino UNO, which ensures secure user authentication and accurate fuel dispensing with automatic balance deduction. Each user is provided with an RFID card containing unique identification and prepaid balance INFORMATION. When the card is scanned, the system verifies user credentials, checks available balance, and activates the DC pump accordingly. The pump automatically stops once the predefined fuel amount is dispensed or the balance limit is reached. The proposed system reduces manual intervention, prevents fuel fraud, and improves operational efficiency. Experimental results demonstrate reliable card detection, accurate fuel control, and real-time balance deduction, making the system suitable for modern smart petrol stations.

DOI: https://doi.org/10.5281/zenodo.18182542

Test Paper Submit By Saquib Siddiqui 3232025saquib Latest_990

Authors: Mohd saquib siddiqui

Abstract: Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

Explainable AI for medical or financial predictions

Authors: Pradhebaa S

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) models have become powerful tools for predictive analytics in medical and financial domains, enabling early diagnosis of disease, fraud detection, and risk forecasting with remarkable accuracy. Despite these advancements, most state-of-the-art models operate as complex black-box systems, offering minimal transparency into how predictions are formed. In healthcare, where predictions influence clinical decisions, lack of interpretability reduces clinician trust, raises ethical concerns, and limits real-world deployment. Similarly, in finance, opaque ML systems create challenges in regulatory audits, accountability, and fairness in automated risk scoring. These limitations motivate the need for Explainable AI (XAI) frameworks that provide human-interpretable reasoning without sacrificing predictive performance. This paper proposes a unified, model-agnostic explainable machine learning framework tailored for high-stakes prediction tasks. The system employs predictive models such as Random Forest, XGBoost, and LSTM for structured and longitudinal clinical data, integrated with XAI methods including SHAP, LIME, attention visualization, and counterfactual reasoning to generate both global and instance-level explanations. To enhance explanation reliability, the framework incorporates stability analysis, imbalance-aware training, and a composite trust scoring mechanism validated by domain experts. The approach aims to improve transparency, support clinician and analyst decision-making, and enable safer, auditable deployment of AI in medical prediction pipelines. Experimental results from existing research demonstrate that combining high-accuracy ML with robust explanation layers significantly improves stakeholder trust and practical adoption, positioning the framework as a step toward responsible and interpretable predictive intelligence in real-world applications.

RoadGuardian: A Multi-Modal AI Framework for Enhanced Road Safety through Real-Time Drowsiness, Pothole, and Vehicle Detection

Authors: Sri Raghuvardhan B, Srujan A U, Vinay Shankar H V, Willson Kumar, Dr. T N Anitha

Abstract: Road accidents remain a global concern, with human er- ror, road infrastructure defects, and environmental fac- tors contributing to millions of fatalities annually. This paper presents RoadGuardian, an integrated multi- modal AI framework designed to enhance road safety through real-time detection of three critical risk factors: driver drowsiness, road potholes, and surrounding ve- hicles. The system employs computer vision techniques with specialized architectures for each detection mod- ule. Drowsiness detection utilizes facial landmark anal- ysis with EAR (Eye Aspect Ratio) and MAR (Mouth Aspect Ratio) metrics. Pothole detection implements a custom YOLO architecture trained on augmented road datasets. Vehicle detection leverages YOLOv8 for ro- bust object recognition. These modules are integrated into a unified dashboard that provides real-time alerts, risk assessment scoring, and situational awareness visu- alization. Experimental results demonstrate high accu- racy rates: 96.8% for drowsiness detection, 94.2% for pothole detection, and 97.5% for vehicle detection with an average inference time of 45ms per frame on stan- dard hardware. The framework represents a significantadvancement in proactive road safety systems, offering a comprehensive solution to mitigate multiple accident risk factors simultaneously.

DOI: https://doi.org/10.5281/zenodo.18195863

Online Parking Management System

Authors: Ragini Shivashetti, Nikita Waghamare, Pranita Bhosale, Namrata Shinde, Pranoti Hukkire, Professor Ms. Savita Kadam

Abstract: An online booking system is a web-based platform that automates scheduling and reservations, allowing customers to book services or events (like movies, appointments, or travel) 24/7, while providing administrators tools to manage availability, bookings, and payments efficiently, reducing manual work and improving customer experience through features like user registration, seat selection, payment integration, and real-time confirmations.

Assessing The Capabilities of Ai in Private Real Estate Development Within the Construction Sector

Authors: Ms Ruchi Natekar

Abstract: In Mumbai’s fast-growing private real estate construction sector, persistent challenges—cost overruns, schedule delays, and inconsistent quality—continue to limit project performance despite rising demand and increasing urban pressures. Artificial Intelligence (AI) has emerged globally as a transformative tool capable of reshaping construction planning, execution, and monitoring. Yet, in Mumbai, AI adoption remains at a formative stage, shaped by a complex interplay of technological limitations, cultural resistance, and organisational readiness. This study explores how AI is currently being used, where it creates value, and what barriers must be overcome for meaningful transformation. A mixed-methods research design was employed to capture both the breadth and depth of AI adoption. Quantitative insights were gathered through a structured survey of 99 construction professionals, spanning developers, engineers, consultants, and project managers. To complement this, qualitative interviews and focus group discussions were conducted with industry experts to understand their lived experiences, perceptions, and concerns regarding AI-enabled practices. Data were analysed using descriptive statistics, factor analysis, and thematic coding to produce an integrated, evidence-based understanding of AI’s real-world impact within Mumbai’s construction environment. Findings reveal that while AI adoption is still emerging, its footprint is steadily expanding. The most recognised and frequently applied AI tools include predictive analytics for cost estimation, automated scheduling systems, and computer-vision-based quality inspections. Respondents involved in AI-enabled projects reported heightened confidence in the technology’s potential to enhance efficiency, reduce rework, and improve decision-making. However, this optimism exists alongside significant obstacles. The study identifies notable barriers such as low digital literacy, fragmented data systems, regulatory ambiguity, and organisational cultural resistance. Many firms struggle to integrate AI into legacy workflows, and small and medium-sized enterprises face higher financial and technical hurdles. The discussion highlights that successful AI-enabled transformation requires more than just technological investment—it demands structural, cultural, and behavioural shifts within organisations. AI’s impact is therefore as socio-technical as it is operational, requiring alignment across people, processes, and platforms. This research confirms that AI holds strong promise for reducing chronic inefficiencies in Mumbai’s real estate construction sector. Yet, the gap between theoretical potential and on-ground performance remains wide. To bridge this divide, organisations must adopt a phased, context-appropriate strategy that prioritises digital literacy, data standardisation, regulatory clarity, and targeted workforce upskilling. The study offers a practical implementation roadmap tailored to Mumbai’s unique ecosystem, serving as a valuable resource for developers, project managers, policymakers, and technology providers. Ultimately, AI is positioned not as a replacement for human expertise, but as a powerful enabler of smarter, safer, and more resilient urban development.

DOI: https://doi.org/10.5281/zenodo.18204579

Design And Development Of An AI–ML Framework For Higher Education: An Education 5.0 Perspective

Authors: Mrs, Seema Amol More, Professor Dr. Swati Nitin Sayankar

Abstract: Education 5.0 represents a paradigm shift toward human-centric, ethical, and sustainable learning ecosystems by synergizing advanced digital technologies with societal needs. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as key enablers in transforming higher education through personalized learning, predictive analytics, and intelligent decision support. However, the absence of a unified and scalable framework often leads to fragmented adoption and ethical concerns. This paper proposes a comprehensive AI–ML framework tailored for higher education institutions from an Education 5.0 perspective. The framework integrates data-driven learning analytics, adaptive instructional systems, student performance prediction, and automated academic administration while emphasizing transparency, inclusivity, and data privacy. The proposed architecture consists of layered modules encompassing data acquisition, intelligent processing, decision intelligence, and stakeholder interaction. A conceptual case study demonstrates the applicability of the framework in a university environment. Comparative analysis highlights improvements in academic outcomes, operational efficiency, and learner engagement. The proposed framework provides a structured pathway for institutions seeking sustainable and ethical AI adoption, contributing to the evolving discourse on next-generation higher education systems.

DOI: http://doi.org/10.5281/zenodo.18204804

A Low-Cost Self-Healing Smart Grid Prototype Using Embedded Random Forest Classification And ESP-NOW Wireless Coordination

Authors: Angel Lalu, Dr Prakash R, Shreyas Sunil, Nandhakumar S, Divya Bharti

Abstract: Self-healing distribution systems are one of the foundational requirements for future smart grids that are built to withstand disturbances, accommodate bidirectional power flow, and also maintain reliability despite the threat of in- creasing renewable penetration. Traditional FLISR (Fault Location, Isolation, and Service Restoration) solutions used currently depend mostly on SCADA, PMUs, and other high- cost protection relays. This infrastructure is usually not un- available in low-voltage networks, microgrids, and academic environments for teaching purposes. Our work proposes a novel low-cost, microcontroller-based self-healing grid pro- totype that uses ACS712 current sensors, ESP32/ESP8266 wireless sensing nodes communicating via ESP-NOW, and an STM32 Nucleo 64 (F446RE) microcontroller executing an embedded Random Forest classifier through the Eloquent- TinyML library. This system automatically and autonomously detects, classifies, and isolates faults based on a real-time multi- feature current signature. Our experimental setup and further validation shows an overall classification accuracy of 92.76%, ESP-NOW latency of 12-to 18 ms over 22 metres, and a pro- tection response time under 200 ms. Compared to other con- ventional schemes, our proposed architecture provides an inex- pensive yet robust platform similar to SCADA-like self-healing behaviours.

To Find Material Performance Assessment For Efficient Leachate Filtration Bed

Authors: Tushar Kadam, Dhiraj Gadhave, Nirzara Sarole, Shital Shinde

Abstract: Landfills are a potential threat to human health and the environment, especially from the detrimental and toxic heavy metals. This study focuses on the assessment of heavy metals contamination in leachate and surface soils from different landfills in Pune. The impacted soils showed high heavy metal concentrations especially at non-sanitary unlined landfills, as compared to background values, and natural soil nearby the landfills. Leachate possesses potential risk to surface and groundwater aquifer within the area surrounding the landfill site. The aim of this chapter is to assess the physical parameters and heavy metal levels in leachate. Heavy metals are one of the important pollutants in landfill leachate. Plants and soil near the landfill may be contaminated by leachate. In this study, by evaluating the heavy metals in the leachate of three landfills, the amount of pollution caused by the leachate in the environment around the landfills in Pune was investigated.

Morph Detect

Authors: K. Sai Teja, M.Surya Teja, S.Bharath Simha Rao, Y.Hemanth Kumar

Abstract: Face morphing attacks represent a critical vulnerability in biometric authentication sys- tems, where two or more facial images are digitally blended to create a forged identity. Such morphed images can successfully deceive automated face verification systems, leading to severe risks in applications like passport issuance, border control, and iden- tity management. Traditional detection techniques, relying on handcrafted features or differential meth- ods, often fail to generalize across diverse morphing techniques and image qualities. To overcome these limitations, we propose MorphDetect, a deep learning-based Single- Image Morphing Attack Detection (S-MAD) system powered by the EfficientNet-B7 model. The system first preprocesses face images for normalization and then extracts high- dimensional features using EfficientNet-B7’s advanced convolutional blocks. These features are passed through a classification layer that determines whether an input is genuine or morphed, producing a reliable confidence score for decision-making. MorphDetect eliminates the need for a trusted reference image and provides a scal- able, real-time solution for morph detection. By leveraging a strong pretrained back- bone, it ensures robustness against unseen morphing techniques and diverse imaging conditions. This makes the system well-suited for deployment in high-security appli- cations such as e-passport verification, financial KYC procedures, and secure access systems.

Cloud Computing Adoption in Educational Institutions

Authors: Gayathri S, Varshameena. M

Abstract: Cloud computing has emerged as a revolutionary technology that enables on-demand access to shared computing resources such as storage, applications, and processing power through the internet. In recent years, educational institutions have increasingly adopted cloud computing to modernize teaching, learning, and administrative processes. This shift is driven by the growing demand for flexible learning environments, digital collaboration, remote accessibility, and cost-effective infrastructure management. Traditional educational systems rely heavily on physical hardware and locally installed software, which often leads to high maintenance costs, limited scalability, and restricted access to learning resources. Cloud computing overcomes these limitations by offering scalable, reliable, and affordable solutions tailored to academic needs. This paper explores the adoption of cloud computing in educational institutions, focusing on its architecture, service models, and practical applications. Cloud-based platforms such as Learning Management Systems (LMS), virtual classrooms, digital libraries, and online assessment tools have transformed the educational ecosystem by enabling anytime-anywhere learning. The study highlights key benefits of cloud adoption, including reduced operational costs, improved collaboration among students and faculty, enhanced data storage and backup capabilities, and increased institutional efficiency. Additionally, cloud computing supports innovation in education by integrating emerging technologies such as artificial intelligence, big data analytics, and smart learning environments. Despite its advantages, the adoption of cloud computing in education also presents challenges such as data security, privacy concerns, internet dependency, and vendor lock-in. This paper discusses these challenges and emphasizes the importance of implementing strong security policies, data protection mechanisms, and regulatory compliance to ensure safe and effective cloud usage. The study concludes that cloud computing plays a vital role in the digital transformation of educational institutions and has the potential to significantly improve the quality, accessibility, and sustainability of education. With proper planning and governance, cloud computing can serve as a powerful enabler for the future of education.

Multiple Disease Prediction System: An AI-Driven Smart Healthcare System For Multiple Disease Prediction And Early Diagnosis

Authors: Omkar Walunj, Pranav Hole, Sarthak Thigale, Sohan SandbhorD

Abstract: With the rapid advancement of Artificial Intelligence (AI), healthcare systems are shifting from reactive to proactive models capable of predicting, diagnosing, and preventing diseases. This paper presents Smarthealth, a cloud-based predictive healthcare system that utilizes machine learning algorithms to analyze patient data, anticipate potential health issues, and generate timely alerts. The system integrates AI models for disease prediction and employs Firebase for real-time synchronization and secure data storage. The objective of this work is to develop an efficient, scalable, and secure AI-driven healthcare prediction platform that assists doctors and patients in early diagnosis and informed medical decision-making.

Review of Indoor, outdoor 222Rn exposure assessment and modelling

Authors: Narasimhamurthy K N, Ashok G V, Ashwini S

Abstract: In view of this, Indoor as well as outdoor radon concentration measurement has been carried out in specific residential and schools located in Mandya, Karnataka using well known SSNTD technique. The indoor radon level is predicted in the same selected dwellings using the suitable model which is based on the mass balance equation and the results are compared with the measured values. Annual mean values of 222Rn in selected houses and schools were found to be 19.68 Bq m-3 respectively. Annual mean values in some other survey for 222Rn and 220Rn concentrations was found to be 22.4 and 24.1 Bq m-3 respectively. The total annual effective dose received by the general public due to radon and thoron is found to be 1.1 mSv y-1, which is close to the Indian average value of 1.11 mSv y-1. The doses to different organs and tissues were calculated using the ICRP model of the respiratory tract and inter comparison was discussed.

DOI: https://doi.org/10.5281/zenodo.18228616

Digital Transformation of Public Relations: Automating Workflows in State and Intergovernmental Press Office Using AI-driven technologies

Authors: Ekaterina Gubina

Abstract: Digital transformation is reshaping public sector communication, particularly within state and intergovernmental press offices that operate under conditions of high accountability, limited resources, and constant public scrutiny. This paper explores how artificial intelligence (AI)–driven technologies can be leveraged to automate public relations workflows, enhance message consistency, and improve responsiveness to media and citizens. Focusing on tools such as natural language processing, automated content generation, media monitoring, sentiment analysis, and workflow orchestration systems, the study examines both the operational benefits and governance challenges of AI adoption. Through analysis of existing practices and emerging use cases, the paper proposes a framework for responsible automation that balances efficiency, transparency, ethical communication, and human oversight. The findings suggest that AI, when strategically implemented, can strengthen institutional credibility, support evidence-based communication, and enable press offices to better manage the growing complexity of public information ecosystems.

DOI: http://doi.org/10.5281/zenodo.18384412

Operational Performance and Reliability Improvement Strategies for the Port Harcourt Mains 33kv Distribution Network

Authors: Hachimenum Nyebuchi Amadi, Ogadinma Agha Onya,, Richeal Chinaeche Ijeoma

Abstract: The reliability of 33kV distribution networks is crucial to the stability of Nigeria's electricity supply. Serving as the interface between the transmission grid and 11kV feeders, these networks directly affect service delivery, customer satisfaction, and operational efficiency. This paper examines the operational challenges and reliability issues of the 33kV feeders within the Port Harcourt Electricity Distribution Company (PHEDC) network, with a focus on performance assessment using standard indices such as SAIDI, SAIFI, and CAIDI. Preventive maintenance, feeder automation, and improved operational practices are identified as key measures for enhancing reliability. Results reveal major network challenges such as overloaded feeders, poor voltage profiles, high technical losses, and frequent interruptions. Reliability indices, including SAIFI, SAIDI, CAIDI, and ENS, were significantly above IEEE and NERC thresholds, indicating poor service continuity. To address these deficiencies, the study proposes targeted improvement strategies such as feeder reconfiguration, installation of automated reclosers and sectionalizers, preventive maintenance, and upgrading of aging conductors and transformers. The study concludes that targeted investment in maintenance, automation, and workforce training can significantly reduce outages and improve service continuity.

DOI: https://doi.org/10.5281/zenodo.18427035

Fault Location in Power System Networks with Phasor Measurement Units using Modified Sparsity Genetic Algorithm Optimizer

Authors: Hachimenum N. Amadi, Sopakiriba Maxwell West, Richeal Chinaeche Ijeoma

Abstract: The incessant national grid collapse has become a global embarrassment; from 2015 to May 2024 the Transmission Company of Nigeria (TCN) has recorded 105 cases of grid collapse. Phasor Measurement Units (PMUs) are necessary for the extensive use and efficient running of international power networks, the present Supervisory Control and Data Acquisition (SCADA) system used in Nigeria does not provide a robust and dependable solution that improves the power grid’s real time monitoring and control capabilities. PMU will reduce the frequency of power grid breakdowns and also resolve fault location troubleshooting safely and timely. The optimal placement of Phasor Measurement Units (PMUs) is an important requirement in power systems research, particularly for the localization of transmission line faults. This research has proposed a Modified Sparsity Genetic Algorithm Optimizer (MS-GAO) for optimal placement of Phasor Measurement Units (PMUs) in Power Systems over the standard Genetic Algorithm (GA) approach used in various related studies. To further validate the performance, the time complexity studies were performed to determine the better technique considering enhanced PMU placement. The proposed approach has been applied to two IEEE power system networks – the IEEE 6-Bus and 14-bus power networks. The simulations were performed using the MATLAB software tool and results compared with the standard Genetic Algorithm (sGA) on the basis of the percentage Classification Efficiency (CE) and the number of trial iteration runs (iters) used per simulation. The results showed that the proposed MS-GAO gave comparable CEs when compared to sGA with 100% CE for 100iters. However, it was found that reducing the iterations to about 50iters resulted in a degradation of CE. Thus, a compromise should be made between the number of iterations required and the level of CE needed in the problem solution. In addition, computational run-time complexity results considering the 6-bus power network revealed that the MS-GAO will give better run-times when compared to the sGA with an average run-time reduction of about 0.5s. Thus, it is recommended that the MS-GAO be employed for a higher power bus networks as the computational demands will obviously be higher using a sGA.

DOI: https://doi.org/10.5281/zenodo.18427464