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

Published by: vikaspatanker

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.

Published by: vikaspatanker

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.

Published by: vikaspatanker

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

Published by: vikaspatanker

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

Published by: vikaspatanker

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

Published by: vikaspatanker

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

Published by: vikaspatanker

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.

Published by: vikaspatanker

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

Published by: vikaspatanker

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.

Published by: vikaspatanker