Category Archives: Uncategorized

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

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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.

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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.

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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

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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.

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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.

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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

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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

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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

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