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VOLUME 13, ISSUE 10, OCTOBER 2026
Publication is in progress..
Development of Low-Cost, Sustainable Aquafeed Using Local Ingredients for Small-Scale Fish Farmers in Birnin-Kebbi, Kebbi State, Nigeria
Bello D. Muhammed*, Nkechi Funmilola Adebagbo, Matthew Sunday Oladele, Abdulrahman Dantani, Abdulahi Alhassan, Umar Abubakar
Conversational Image Recognition Chatbot Using Computer Vision and Natural Language Processing
Mr. Premkumar Pawar, Prof. Shubham Lotwala
Thermal Analysis and Experimental Studies of a Plate-Fin Heat Sink Integrated with U-Shaped Liquid-Cooling Channel
Mohan Kumar Simma, Srinivasa Prasad Katrenipadu, Pradesh, India
A MATHEMATICAL AND COMPUTER- BASED ALGORITHMIC METHOD FOR CONSTRUCTING ODD-ORDER EQUAL-SUM MATRIX NUMBER PUZZLES FROM ARITHMETIC SEQUENCES.
R.M.N.B. Rathnayake
Farmers Disease Diagnostic / Reporting Portal: An AI – Based Mobile Portal
Miss. Gayatri R. Patil, Asst. Prof. Chetana M. Kawale
SOFTWARE FOR SPEECH LANGUAGE THERAPY CLINICAL SERVICES
Miss. Damini Anil Varpe, Prof. Miss. Pratiksha R. Lad
Enhancing Navigation for Railway Station Facilities and Locations
Mr. Sahil Husain Tadavi, Miss. Pratiksha R. Lad
AlgoQuest: A Web-Based and Mobile-Based Educational Game for Teaching Algorithmic Thinking
Ms. Divya Patil, Ms. Chetana Kawale
AI-Powered Student Assistant Chatbot for the Department of Technical Education
Ms. Shivani Satish Naik, Asst. Prof. Chetana M. Kawale
Learning Path Dashboard for Enhancing Skills
Miss. Shraddha. A. Patil, Dr. Sarkarsinha H. Rajput, Dr. Shital A. Patil, Mr.Shubham Lotwala
A Software Application – Ground Water Level Predictor
Mr. Lalit Sharad Patil, Asst. Prof. Shubham M. Lotwala
DESIGN AND DEVELOPMENT OF AN ONLINE SYSTEM FOR ISSUANCE OF CASTE AND OTHER CERTIFICATES BY REVENUE DEPARTMENT
Mr. Pankaj Sambhaji Patil, Asst. Prof. Shubham Lotwala
Human Resource Development Practices and Employability Skills among Deaf and Hard-of- Hearing Students in Chennai: An Analytical Study
N.Sundar, Dr.P.Manjula
Farmers Disease Diagnostic and Reporting Portal:An AI-Based Mobile Platform for Crop Disease Screening
Miss. Rutika Sudhir Pradhan, Prof. Shubham Lotwala
Forecasting Nigerian Foreign Direct Investment One Quarter Ahead: A Leakage-Free Comparison of Six Regression Models Ofierohor Ufuoma Earnest
Ofierohor Ufuoma Earnest
A Hybrid AES-256 and Permissioned Blockchain Framework with Smart-Contract-Style Access Control for Tamper-Evident Educational Record Management: Design and Performance Evaluation
Suraj Kumar Sahu, Aradhana Kumari Singh
Impact of generative AI on Teaching and Learning Processes
Ms. Manjusha Gundale, Dr. Priya Porwal, Ms. Usha V
A Machine-Learning-Based Tool for Redaction, Masking and Anonymization of Sensitive Data
Mr. Mayur Saraswat, Pratiksha Lad
Online Chatbot-Based Ticketing System
Miss.Vaishali Narayan Patil, Prof. Shubham Lotwala
Smart AI-Based Solution for Traffic Management on Roads
Miss. Pooja C. Patil, Prof. Shubham M. Lotwala
Intelligent Fuzzy Logic Models for Predicting Post-Operative Cardiac Complications
Varsha, Prof. Diwari Lal
Impact Of Recyclable Packaging on Consumer Buying Decisions for Ready-To-Cook Products
Asst. Prof. Sheitaal Desai, Prof. Dr. Mona Mehta
Use Of Digital Knowledge Sharing Platform Like Wikis On Sharing Of Water-Efficient Techniques And Methods For Minimizing Water Scarcity
Miss. Priyanka Sanjay Patil, Prof. Mr. Shubham Lotwala
Online Exam Proctoring using AI
Mr. Sudhir Ramesh Nikam, Prof. Miss. Chetana Kawale
Exploring the Queer Inner World, Emotional Terrain, and Psychological Challenges in Sachin Kundalkar’s Cobalt Blue
Dr. S. Parvin Banu, M.A., M.Phil., PhD, R.Tamil Elakkia, M.A., M.Phil, M.Ed.
AI-Recruitment And Talent Analytics System
Mrs. Hemalatha M, Ms. Sakshi Mahesh Mirji, Ms. Sanjana, Mr. Braham Abhijat Singh, Mr. Priyanshu Kumar
Live Location Tracking System: Architecture, Algorithms, and Real-Time Visualization
Miss. Bhagyashri Premsing Patil, Asst. Prof. Chetana Kawale
Mechanical Analysis of Diving-Evasion and Seated-Block Defensive Skills in Kho-Kho
Dr. Pathan Yusufkhan Hidayatkhan
Thermo-Mechanical Performance Evaluation Of Mechanical Seal Materials in Fire Tender Pumps Using Ansys Finite Element Simulation
Aloyce N. Waryoba and Eng. Dr. Salum K. Chang’waro
AI Laboratories and the Future Academic Library: A Framework for Assessing User Perceptions and Acceptance of AI-Driven Services in the Indian Institutes of Management
Abhishek Kumar Gupta (Dashora), Dr Rakesh Kumar Khare
Abstract
Development of Low-Cost, Sustainable Aquafeed Using Local Ingredients for Small-Scale Fish Farmers in Birnin-Kebbi, Kebbi State, Nigeria
Bello D. Muhammed*, Nkechi Funmilola Adebagbo, Matthew Sunday Oladele, Abdulrahman Dantani, Abdulahi Alhassan, Umar Abubakar
DOI: 10.17148/IARJSET.2026.131001
Abstract: In northern Nigeria, feed constitutes the bulk of the cost of catfish production, and this is mainly due to imported fish meal. The effect of the 50:50 ratio of toasted sesame seed meal and sun-dried moringa leaf meal produced from farm-raised moringa leaves in replacing 0, 25, 50 and 75% of fishmeal in isonitrogenous (35% crude protein) diets on juveniles of Clarias gariepinus for 12 weeks was evaluated in the study in Birnin-Kebbi, Kebbi State. Replacement did not significantly affect final weight (33.21–34.51 g), specific growth rate (1.70–1.73% per day), survival (93.3–96.7%) or carcass composition (p > 0.05). Mortality-corrected feed conversion ratio was lowest at 50% (1.771) and 0% (1.775) replacement (p < 0.001). The cost of feed per kg gain decreased from ₦4,967 at control to ₦4,580 (7.8%) at 75% replacement. Replacement of up to 75% of fishmeal can be achieved, and 50% replacement is optimal in terms of feed efficiency and cost.
Keywords: Clarias gariepinus, fishmeal replacement, Moringa oleifera, Sesamum indicum, feed cost, small-scale aquaculture
Keywords: Clarias gariepinus, fishmeal replacement, Moringa oleifera, Sesamum indicum, feed cost, small-scale aquaculture
Abstract
Conversational Image Recognition Chatbot Using Computer Vision and Natural Language Processing
Mr. Premkumar Pawar, Prof. Shubham Lotwala
DOI: 10.17148/IARJSET.2026.131002
Abstract: Conversational image recognition combines image understanding with natural language interaction so that a user can upload an image and ask questions about its content. This research proposes a practical framework for a conversational image recognition chatbot using Computer Vision and Natural Language Processing (NLP). The proposed approach accepts an image and a natural-language query, processes visual and textual information, combines the resulting representations, and generates a conversational response. Visual recognition can use Convolutional Neural Networks or Vision Transformers for extracting image features, while language processing handles the user question and conversation context. Vision-language models such as CLIP, BLIP, BLIP-2, and LLaVA provide useful directions for connecting visual and textual information. The chatbot can support image description, object-related questions, simple visual question answering, and follow-up questions. Evaluation will consider recognition quality, answer relevance, response consistency, error cases, and processing time. The study is designed as a practical research framework in which the final performance values will be obtained after implementation and testing.
Keywords: Conversational AI, Image Recognition, Computer Vision, Natural Language Processing (NLP), Visual Question Answering, Image Captioning, Vision-Language Models, Multimodal Chatbot.
Keywords: Conversational AI, Image Recognition, Computer Vision, Natural Language Processing (NLP), Visual Question Answering, Image Captioning, Vision-Language Models, Multimodal Chatbot.
Abstract
Thermal Analysis and Experimental Studies of a Plate-Fin Heat Sink Integrated with U-Shaped Liquid-Cooling Channel
Mohan Kumar Simma, Srinivasa Prasad Katrenipadu, Pradesh, India
DOI: 10.17148/IARJSET.2026.131003
Abstract: The continuous miniaturization and rising power densities of modern electronic modules demand advanced thermal management solutions capable of dissipating high heat fluxes safely. Conventional air-cooled natural convection systems often fail under elevated loads due to limited fluid heat capacity and restricted thermal conductivity. In this study, both experimental and numerical investigations were carried out to evaluate the transition from standard natural convection to a hybrid cooling configuration that integrates a plate-fin aluminium heat sink with a U-shaped liquid-cooling channel. Controlled trials were performed at heat inputs of 10 W, 20 W, and 30 W, with transient and steady-state temperatures recorded at the base, top fins, and coolant outlet. Under natural convection, base temperatures rose sharply to 56.11 °C (10 W), 78.06 °C (20 W), and a critical 93.02 °C (30 W), highlighting the severe limitations of pure air cooling. In contrast, the hybrid water-cooled channel significantly suppressed base temperatures to 43 °C (10 W), 59 °C (20 W), and 74 °C (30 W). Energy balance analysis showed that the liquid channel absorbed 3.40 W, 7.30 W, and 11.10 W across the respective loads, while fin conduction contributed 2.89 W, 5.46 W, and 8.57 W. Complementary CFD simulations performed in ANSYS Fluent exhibited strong agreement with experimental data, with deviations confined to 5–8%. These findings validate the hybrid cooling system’s capability to overcome thermal bottlenecks, making it a reliable solution for high-density engineering and advanced electronic packaging applications.
Keywords: Plate-Fin Heat Sink, Natural Convection; Liquid Cooling Channel; Thermal Management; CFD Validation; Heat Dissipation; Compact Thermal Systems.
Keywords: Plate-Fin Heat Sink, Natural Convection; Liquid Cooling Channel; Thermal Management; CFD Validation; Heat Dissipation; Compact Thermal Systems.
Abstract
A MATHEMATICAL AND COMPUTER- BASED ALGORITHMIC METHOD FOR CONSTRUCTING ODD-ORDER EQUAL-SUM MATRIX NUMBER PUZZLES FROM ARITHMETIC SEQUENCES.
R.M.N.B. Rathnayake
DOI: 10.17148/IARJSET.2026.131004
Abstract: It is important to say that number puzzles and equal sum matrices have been highly researched because of their strong connections to mathematics, pattern recognition, and problem solving by means of algorithms. However, a lot of methods of design of such matrices involve mathematical formulas and approaches that can be challenging for those who lack necessary mathematical knowledge. The goal of this research is to introduce the technique of design of Matrix Number Puzzles of odd orders based on arithmetic progressions where the sum of all rows, columns, and two diagonals must be the equal. Design Science Research Methodology (DSRM), an algorithm development, and mathematical validation methodology are selected for this research. Construction involves Straight-Line Left-Angle Procedure (SLLAP) and Straight-Line Right-Angle Procedure (SLRAP) processes in generating the embossed matrix pattern. The other values which are not part of the embossed matrix are then organized by using of the Interchanging Values and Placing Them in the Corresponding Locations (IVPTCL) process. The process is demonstrated by the use of odd-order square matrices of (3×3), (5×5), (7×7), (9×9), and (11×11). In the finished matrices, it can be observed that the construction process being discussed is able to generate equal sums of the selected arithmetic sequences in all rows, columns, and main diagonals. Furthermore, the process performances as the foundation for implementing the process in a computer program in which the input parameters are the order of the matrix, first value, and common difference using the programming language of Python.
Keywords: Square matrix of odd order, arithmetic series, Matrix Number Puzzle, SLLAP, SLRAP, IVPTCL, equal-sum matrix, algorithm design, mathematics.
Keywords: Square matrix of odd order, arithmetic series, Matrix Number Puzzle, SLLAP, SLRAP, IVPTCL, equal-sum matrix, algorithm design, mathematics.
Abstract
Farmers Disease Diagnostic / Reporting Portal: An AI – Based Mobile Portal
Miss. Gayatri R. Patil, Asst. Prof. Chetana M. Kawale
DOI: 10.17148/IARJSET.2026.131005
Abstract: Farmers Disease Diagnostic/Reporting Portal is an AI-based mobile portal designed to help farmers identify and report crop diseases in an easy, fast, and accessible manner. Crop disetthjaases can significantly affect agricultural production and may cause financial losses when they are not identified and managed at an early stage. Farmers, especially in rural areas, may face difficulties in obtaining timely information about crop diseases and appropriate management practices.
The proposed system allows farmers to upload or capture an image of an affected crop or plant through a mobile portal. The uploaded image is processed using image processing and Artificial Intelligence techniques to identify possible crop diseases. Based on the analysis, the system provides the predicted disease along with relevant information and recommended management measures. The portal also provides a disease reporting facility through which farmers can submit details about affected crops, disease symptoms, location, and other relevant information.
The collected reports can be stored in a centralized database and accessed by authorized administrators or agricultural experts for monitoring and analysis. The system aims to create a digital connection between farmers, disease information, and agricultural support services. By integrating AI- based disease detection with mobile-based reporting, the proposed portal can support early identification of crop diseases, improve access to agricultural information, and assist in better crop disease management.
Keywords: Artificial Intelligence, Crop Disease Detection, Farmers, Mobile Portal, Disease Diagnosis, Image Processing, Machine Learning, Crop Health, Disease Reporting, Agricultural Technology, Digital Agriculture, Plant Disease Management.
The proposed system allows farmers to upload or capture an image of an affected crop or plant through a mobile portal. The uploaded image is processed using image processing and Artificial Intelligence techniques to identify possible crop diseases. Based on the analysis, the system provides the predicted disease along with relevant information and recommended management measures. The portal also provides a disease reporting facility through which farmers can submit details about affected crops, disease symptoms, location, and other relevant information.
The collected reports can be stored in a centralized database and accessed by authorized administrators or agricultural experts for monitoring and analysis. The system aims to create a digital connection between farmers, disease information, and agricultural support services. By integrating AI- based disease detection with mobile-based reporting, the proposed portal can support early identification of crop diseases, improve access to agricultural information, and assist in better crop disease management.
Keywords: Artificial Intelligence, Crop Disease Detection, Farmers, Mobile Portal, Disease Diagnosis, Image Processing, Machine Learning, Crop Health, Disease Reporting, Agricultural Technology, Digital Agriculture, Plant Disease Management.
Abstract
SOFTWARE FOR SPEECH LANGUAGE THERAPY CLINICAL SERVICES
Miss. Damini Anil Varpe, Prof. Miss. Pratiksha R. Lad
DOI: 10.17148/IARJSET.2026.131006
Abstract: Speech and language disorders are common among individuals and often require appropriate therapeutic interventions, regular evaluation, and continuous monitoring to assess improvement. Conventional approaches to speech therapy management frequently depend on manual documentation and record maintenance, which may result in difficulties with appointment scheduling and inadequate tracking of patient progress. To overcome these limitations, a software-based system is proposed to simplify and organize the management of speech therapy services. The proposed system provides a user-friendly and interactive interface that allows therapists to manage multiple patients efficiently. It facilitates the systematic maintenance of comprehensive information related to therapy sessions, treatment activities, and patient outcomes. Furthermore, integrated analytical features help therapists monitor individual progress and modify therapy strategies according to patient needs. Overall, the system improves communication, facilitates efficient data handling, strengthens progress monitoring, and enables the delivery of more organized, effective, and personalized speech therapy services.
Abstract
Enhancing Navigation for Railway Station Facilities and Locations
Mr. Sahil Husain Tadavi, Miss. Pratiksha R. Lad
DOI: 10.17148/IARJSET.2026.131007
Abstract: Railway stations are intricate, multi-level public transportation environments encompassing platforms, foot over bridges (FOBs), concourses, ticket counters, waiting halls, restrooms, food plazas, parcel offices, and emergency services. While stations serve millions of passengers daily, navigating these dense transit hubs remains challenging due to visual clutter in conventional signage, multi-floor architectural barriers, complex circulation flows, and the complete attenuation of Global Positioning System (GPS) signals within indoor and covered shed structures. This research presents an advanced software application architecture for indoor railway station navigation that integrates a topological digital station map, facility search with synonym expansion, multi-source indoor positioning fusion (QR codes, Bluetooth Low Energy (BLE) beacons, Wi-Fi fingerprinting, and smartphone inertial sensors), graph-theoretic route planning (Dijkstra’s and A* algorithms), turn-by-turn voice and visual guidance, universal accessibility routing, emergency evacuation assistance, and administrative facility management. The architecture adopts a three-stage incremental lifecycle model, maintaining operational continuity while progressively deploying spatial digitization, sensor fusion, and dynamic crowdsourced updates. An empirical baseline analysis of transit wayfinding reveals that unassisted passengers spend between 6 to 14 minutes searching for platforms and essential amenities in major junction stations, with over 32% of passenger delays and missed connections stemming from navigational confusion at multi-level junctions.
Abstract
AlgoQuest: A Web-Based and Mobile-Based Educational Game for Teaching Algorithmic Thinking
Ms. Divya Patil, Ms. Chetana Kawale
DOI: 10.17148/IARJSET.2026.131008
Abstract: Game-based learning can close the gap between what students hear in lectures and what they can actually do when they write algorithms. In practice, though, educational software runs into three recurring problems: keeping progress in sync across devices, getting acceptable performance on phones as well as desktops, and keeping learners with very different backgrounds motivated. This paper describes the design, implementation and evaluation of AlgoQuest, an educational game that runs in web browsers (HTML5/WebGL) and on Android and iOS from one shared codebase. A lightweight serverless backend stores progress. A Dynamic Difficulty Adjustment (DDA) engine reads each learner's completion time, then adjusts puzzle difficulty and the amount of scaffolding. We tested the game in a four- week controlled study with 80 MCA students, using a pre-test/post-test design. The experimental group improved more than the control group (Hake's normalized gain g = 0.581 vs. 0.297; p < 0.0001; Cohen's d = 1.81). The experimental group also reported much lower frustration on the NASA-TLX and rated usability at 81.42 on the System Usability Scale. On a mid-range Android phone the game held about 58 FPS, with cloud synchronization under 180 ms. These results suggest that a single architecture can support both browser and mobile delivery of an algorithm-learning game. Because the study covers one department and one term, the results should be read as promising rather than final.
Keywords: digital game-based learning, cross-platform architecture, WebGL, dynamic difficulty adjustment, flow theory, System Usability Scale, instructional technology.
Keywords: digital game-based learning, cross-platform architecture, WebGL, dynamic difficulty adjustment, flow theory, System Usability Scale, instructional technology.
Abstract
AI-Powered Student Assistant Chatbot for the Department of Technical Education
Ms. Shivani Satish Naik, Asst. Prof. Chetana M. Kawale
DOI: 10.17148/IARJSET.2026.131009
Abstract: Students and parents seeking information about technical education frequently depend on websites, telephone calls, emails, and personal visits to obtain admission-related and academic information. These channels can become difficult to manage during admission periods because information is distributed across multiple sources and administrative staff must repeatedly answer similar questions. This paper proposes an AI-powered student assistant chatbot for the Department of Technical Education, Maharashtra, designed as a centralized conversational interface for common student and stakeholder enquiries. The proposed system focuses on admission procedures, eligibility criteria, available institutes and programmes, fee information, scholarships, curriculum, hostel facilities, previous-year allotment information, and placement-related information. A retrieval-oriented architecture is proposed so that responses can be grounded in an approved departmental knowledge base rather than relying only on unrestricted generative output. The methodology combines requirements analysis, knowledge-base preparation, natural-language processing, intent/query understanding, document retrieval, response generation, and human-oriented evaluation. Literature on educational chatbots and admission consultation shows that conversational systems can improve accessibility and response speed, while accuracy, outdated information, hallucination, privacy, multilingual interaction, and evaluation remain important challenges. The proposed framework therefore treats the chatbot as a decision-support and information-access system, with escalation to official human channels for uncertain or sensitive queries. The study provides a practical foundation for developing and evaluating a centralized technical-education information assistant.
Keywords: Artificial Intelligence, Educational Chatbot, Student Assistant, Natural Language Processing, Technical Education, Admission Support, Retrieval-Augmented Generation, Maharashtra
Keywords: Artificial Intelligence, Educational Chatbot, Student Assistant, Natural Language Processing, Technical Education, Admission Support, Retrieval-Augmented Generation, Maharashtra
Abstract
Learning Path Dashboard for Enhancing Skills
Miss. Shraddha. A. Patil, Dr. Sarkarsinha H. Rajput, Dr. Shital A. Patil, Mr.Shubham Lotwala
DOI: 10.17148/IARJSET.2026.131010
Abstract: In today’s fast-changing educational and professional environment, having the right skills is very important for students and job seekers. However, many learners find it difficult to understand which skills they already have, which skills they need to improve, and what resources they should use for learning. This research presents a Learning Path Dashboard for Enhancing Skills, which is designed to help learners understand their current skills and plan their learning journey in a simple and organized way.The proposed dashboard provides a personalized learning path based on a learner’s existing skills, interests, career goals, and skill gaps. It uses data visualization to display information such as skill levels, completed courses, learning progress, recommended skills, and career-related learning paths. Tools such as Python, SQL, and Power BI can be used to analyze the data and create interactive visualizations.The dashboard allows learners to track their progress, identify areas where improvement is needed, and decide what they should learn next. By providing learning information in a clear and visual format, the proposed system can make the learning process more organized and easier to follow. Overall, the Learning Path Dashboard can support students and job seekers in improving their skills and working toward their academic and career goals.
Abstract
A Software Application – Ground Water Level Predictor
Mr. Lalit Sharad Patil, Asst. Prof. Shubham M. Lotwala
DOI: 10.17148/IARJSET.2026.131011
Abstract: Groundwater is a major source of water for drinking, farming and industry, so knowing how its level will change is very important. Many studies have shown that machine learning and deep learning can predict groundwater levels well, but most of them focus on building and comparing models. Few of them offer a complete and easy-to-use software application. This paper presents a Ground Water Level Predictor, a software application that brings data input, data cleaning, prediction and result display together in one system. The application checks the uploaded data, fills missing values, creates useful features such as previous groundwater levels, rainfall and month, and trains three models: Linear Regression, Random Forest and Gradient Boosting. It then selects the model with the lowest error and uses it to predict the groundwater level. The results are shown as charts in a web-based interface, so no programming knowledge is needed. The system was demonstrated on a dummy monthly dataset of 120 records, where Linear Regression performed best with an RMSE of 0.205 m and an R² of 0.980. The application shows that groundwater prediction can be made simple and practical for users, and it can be tested next with real well data.
Keywords: Groundwater level prediction; software application; machine learning; Linear Regression; Random Forest; Gradient Boosting; data preprocessing; user interface.
Keywords: Groundwater level prediction; software application; machine learning; Linear Regression; Random Forest; Gradient Boosting; data preprocessing; user interface.
Abstract
DESIGN AND DEVELOPMENT OF AN ONLINE SYSTEM FOR ISSUANCE OF CASTE AND OTHER CERTIFICATES BY REVENUE DEPARTMENT
Mr. Pankaj Sambhaji Patil, Asst. Prof. Shubham Lotwala
DOI: 10.17148/IARJSET.2026.131012
Abstract: Certificates such as caste, income and domicile certificates issued by the Revenue Department are required by citizens to access reservation, scholarships, employment reservation and other welfare schemes. In many parts of India, however, the process of obtaining these certificates is still largely manual, requiring repeated visits to government offices and giving the applicant little information about the status of an application. This paper presents the design and development of an online system for the issuance of caste and other certificates by the Revenue Department. The proposed system supports role-based access for four types of users, namely the applicant, the clerk, the revenue officer and the administrator, and it automates the complete life cycle of a certificate application, from submission and document upload through multi-level verification and approval to the generation of a verifiable PDF certificate. The system records every change of status together with the reason and the responsible official, sends an email notification to the applicant at each stage, and allows any person to verify an issued certificate using its certificate number. The system was developed using Java, Spring Boot and MySQL and was evaluated through functional testing and a small user study conducted with sample data. The results indicate that the proposed workflow reduces the number of steps required from the applicant and makes the status of an application visible at every stage, addressing the delay, lack of transparency and repeated office visits reported in the existing literature on caste certification in India.
Keywords: E-Governance, Caste Certificate, Online Certificate Issuance, Revenue Department, Role-based Workflow, Application Tracking.
Keywords: E-Governance, Caste Certificate, Online Certificate Issuance, Revenue Department, Role-based Workflow, Application Tracking.
Abstract
Human Resource Development Practices and Employability Skills among Deaf and Hard-of- Hearing Students in Chennai: An Analytical Study
N.Sundar, Dr.P.Manjula
DOI: 10.17148/IARJSET.2026.131013
Abstract: This study examines human resource development practices and employability skills among deaf and hard-of- hearing students in Chennai, with particular emphasis on skill development, career preparedness, workplace readiness, and institutional support. The study adopts a quantitative research approach to explore the educational and employment- related experiences of students with hearing disabilities and to understand the factors that influence their preparedness for future careers. Data were collected through a structured research instrument covering educational access,
preparedness, and institutional support. Descriptive statistics and appropriate non-parametric statistical techniques were used to analyse the data and examine the relationships among the major dimensions of the study. The study highlights that academic qualifications alone may not be sufficient to support successful transition into employment. Communication abilities, digital literacy, interpersonal skills, problem-solving skills, vocational competencies, and workplace readiness are important components of employability. The findings also underscore the role of accessible learning resources, assistive technologies, career counselling, practical training, internships, reasonable workplace accommodations, and supportive institutional environments in strengthening employment preparedness. The study contributes to the growing understanding of inclusive human resource development and offers practical insights for educational institutions, employers, policymakers, and support organizations seeking to improve employability and create more accessible pathways to meaningful and sustainable employment for persons with hearing disabilities.
Keywords: Deaf and Hard-of-Hearing Students, Human Resource Development, Employability Skills, Skill Development, Workplace Readiness, Career Development, Institutional Support, Inclusive Education, Hearing Disability, Chennai
preparedness, and institutional support. Descriptive statistics and appropriate non-parametric statistical techniques were used to analyse the data and examine the relationships among the major dimensions of the study. The study highlights that academic qualifications alone may not be sufficient to support successful transition into employment. Communication abilities, digital literacy, interpersonal skills, problem-solving skills, vocational competencies, and workplace readiness are important components of employability. The findings also underscore the role of accessible learning resources, assistive technologies, career counselling, practical training, internships, reasonable workplace accommodations, and supportive institutional environments in strengthening employment preparedness. The study contributes to the growing understanding of inclusive human resource development and offers practical insights for educational institutions, employers, policymakers, and support organizations seeking to improve employability and create more accessible pathways to meaningful and sustainable employment for persons with hearing disabilities.
Keywords: Deaf and Hard-of-Hearing Students, Human Resource Development, Employability Skills, Skill Development, Workplace Readiness, Career Development, Institutional Support, Inclusive Education, Hearing Disability, Chennai
Abstract
Farmers Disease Diagnostic and Reporting Portal:An AI-Based Mobile Platform for Crop Disease Screening
Miss. Rutika Sudhir Pradhan, Prof. Shubham Lotwala
DOI: 10.17148/IARJSET.2026.131014
Abstract: A farmer who notices a damaged leaf needs both an initial assessment and a way to obtain further advice. This paper describes a mobile portal that connects these tasks through AI-assisted crop screening and structured case reporting. Farmers would record photographs, crop details, and the date of observation. A compact model would suggest possible disease categories for supported crops, while unclear images and uncertain cases would be referred for review. Each case would retain the original evidence, the model version, and any later assessment by a crop specialist. Offline storage would allow observations to be captured without a connection and submitted once connectivity returns. The study presents a system design rather than an implemented service. It defines requirements, processing steps, diagrams, and a plan for evaluating classification quality, synchronization, expert review, and farmer usability. Its contribution is a traceable route from the first observation to a reviewed response. Field trials are still needed to determine whether the design improves reporting or access to useful advice. A photograph-based suggestion would remain preliminary and would not replace field examination or laboratory confirmation when these are required.
Abstract
Forecasting Nigerian Foreign Direct Investment One Quarter Ahead: A Leakage-Free Comparison of Six Regression Models Ofierohor Ufuoma Earnest
Ofierohor Ufuoma Earnest
DOI: 10.17148/IARJSET.2026.131015
Abstract: This study compares six supervised regression models for forecasting Nigerian foreign direct investment one quarter ahead, using 67 quarterly observations from 2008Q2 to 2024Q4. Two feature sets are tested: a compact core set of six contemporaneous predictors, and an enhanced set adding lags, four-quarter rolling means and percentage changes, all shifted so that no future information enters. Both sets are scored on an identical evaluation window, which matters because an earlier version of this work compared them on different windows and reached the opposite conclusion. Models are tuned with four-fold expanding-window cross-validation and evaluated by expanding-window walk-forward prediction. Gradient boosting on the enhanced feature set performs best, with MAE 385.59, RMSE 523.37 and out-of- sample R-squared of 0.0257. It is the only one of twelve model-specification pairs to beat a holdout-mean benchmark. Naive persistence returns RMSE 691.51 under both feature sets, confirming that the two are being scored on the same observations. Enhanced features improve on core features in three of five model classes. The main finding is negative and, we think, useful: at this sample size and horizon, quarterly Nigerian FDI is close to unforecastable, and eleven of twelve approaches do worse than predicting the mean.
Keywords: Foreign Direct Investment; Time-Series Forecasting; Feature Engineering; Walk-Forward Validation; Nigeria.
Keywords: Foreign Direct Investment; Time-Series Forecasting; Feature Engineering; Walk-Forward Validation; Nigeria.
Abstract
A Hybrid AES-256 and Permissioned Blockchain Framework with Smart-Contract-Style Access Control for Tamper-Evident Educational Record Management: Design and Performance Evaluation
Suraj Kumar Sahu, Aradhana Kumari Singh
DOI: 10.17148/IARJSET.2026.131016
Abstract: Educational institutions hold academic records whose confidentiality and integrity are essential for credential verification. This paper presents a four-layer framework combining AES-256-CBC encryption, SHA-256 hash anchoring on a permissioned ledger, off-chain ciphertext storage, and smart-contract-style role-based access control with an append-only audit log. A Python prototype was evaluated functionally on three anonymised records and quantitatively on 200 synthetic records using a university marks-sheet schema, with metrics averaged over seven runs. Mean encryption and decryption times were 0.0354 ms and 0.0227 ms per record, while mean block-append latency was 0.0068 ms. Encrypted storage increased by 37.85%, whereas the on-chain digest footprint remained 64 bytes per record. All 20 deliberately corrupted records were detected. The single-node in-memory ledger makes throughput figures computational upper bounds rather than production measurements.
Keywords: student data security; AES-256; permissioned blockchain; SHA-256 hash anchoring; role-based access control; smart contracts; tamper detection; educational records
Keywords: student data security; AES-256; permissioned blockchain; SHA-256 hash anchoring; role-based access control; smart contracts; tamper detection; educational records
Abstract
Impact of generative AI on Teaching and Learning Processes
Ms. Manjusha Gundale, Dr. Priya Porwal, Ms. Usha V
DOI: 10.17148/IARJSET.2026.131017
Abstract: Generative Artificial Intelligence (Generative AI) has become a highly transformative technology with the capacity to reshape teaching and learning in schools and other educational institutions. Since it can facilitate intelligent content generation, adaptive learning, automated assessment, and personalized instructional support, Generative AI provides numerous opportunities for improving the quality of education, increasing learner engagement, and boosting instructional efficiency. This paper gives a critical look at the effect of Generative AI on teaching and learning by looking at its applications, advantages, challenges, and future potential in the education sector. The study looks at major opportunities such as personalized learning, intelligent tutoring, content creation, and collaborative learning environments, while also tackling important issues like algorithmic opacity, explainability, data privacy and security, bias and fairness, academic integrity, and the reliability of content produced by AI. The paper also examines ways of responsibly introducing Generative AI by creating transparent and ethical AI frameworks, establishing strong data governance mechanisms, setting up appropriate regulatory policies, and using high-quality training datasets. It stresses the need to give teachers the necessary AI skills through ongoing professional development so that they can effectively incorporate AI-based teaching methods. Lastly, the study highlights some of the new trends in AI-driven education, such as intelligent tutoring systems, immersive virtual learning environments, adaptive assessment, and collaborative human– AI learning ecosystems. The results show that although Generative AI offers great potential for enhancing educational accessibility, personalization, and learning outcomes, its successful implementation relies on ethical governance, responsible use, and continued cooperation between educators, researchers, policymakers, and technology developers.
Keywords: Generative AI, Teaching learning processes, AI in education, Obstacles, Ethics, Privacy
Keywords: Generative AI, Teaching learning processes, AI in education, Obstacles, Ethics, Privacy
Abstract
A Machine-Learning-Based Tool for Redaction, Masking and Anonymization of Sensitive Data
Mr. Mayur Saraswat, Pratiksha Lad
DOI: 10.17148/IARJSET.2026.131018
Abstract: Sensitive information appears in many forms of digital data, including documents, text records, images, logs and other files. Traditional redaction approaches often depend on manually defined rules, which can be difficult to maintain across diverse input formats and may not provide a controllable level of anonymization. RE-DACT is proposed as a natural-language-processing and machine-learning-based redaction tool designed to redact, mask or anonymize sensitive information while preserving the structural and logical usefulness of the original data.
The proposed system accepts user-defined levels of redaction and aims to provide customized output. A learning component can be introduced to improve its ability to generate realistic synthetic replacements while maintaining the required data characteristics. The research design emphasizes security, minimal data retention, support for multiple input sources, computational efficiency, usability and operation in both web-based and offline environments. Evaluation is proposed using precision, recall and F1-score on an open-source testing dataset, together with measurements of speed, computing usage, preservation of structure and effectiveness of the selected redaction level.
Keywords: Data Redaction, Anonymization, Masking, NLP, Machine Learning, Synthetic Data, Privacy, Cybersecurity, Data Security
The proposed system accepts user-defined levels of redaction and aims to provide customized output. A learning component can be introduced to improve its ability to generate realistic synthetic replacements while maintaining the required data characteristics. The research design emphasizes security, minimal data retention, support for multiple input sources, computational efficiency, usability and operation in both web-based and offline environments. Evaluation is proposed using precision, recall and F1-score on an open-source testing dataset, together with measurements of speed, computing usage, preservation of structure and effectiveness of the selected redaction level.
Keywords: Data Redaction, Anonymization, Masking, NLP, Machine Learning, Synthetic Data, Privacy, Cybersecurity, Data Security
Abstract
Online Chatbot-Based Ticketing System
Miss.Vaishali Narayan Patil, Prof. Shubham Lotwala
DOI: 10.17148/IARJSET.2026.131019
Abstract: Efficient incident response and customer query resolution are critical challenges in modern IT service management and enterprise operations. Although conventional ticketing platforms exist, necessary diagnostic context and initial troubleshooting steps are often scattered across static FAQs, user manuals, and delayed manual response queues. This leads to extended resolution times, high ticket backlogs, and suboptimal user satisfaction. The purpose of this research is to study and propose an online chatbot-based ticketing system that automatically processes preliminary inquiries, creates structured support tickets, and intelligently routes complex issues to appropriate human support personnel.
The research examines existing literature, frameworks, and methodologies related to Natural Language Processing (NLP), Natural Language Understanding (NLU), and automated helpdesk workflows. The proposed platform combines real-time conversational AI assistance with automated ticket categorization, priority tagging, status tracking, and basic automated self-service resolution workflows.
The expected outcome is a user-friendly, highly scalable, and accessible customer support framework that significantly reduces average resolution time and operational overhead. Automating routine service requests frees human engineers to focus on critical technical tasks, optimizing overall organizational workflow and operational productivity.
Keywords: Chatbot, Conversational AI, Natural Language Processing (NLP), Information Technology Infrastructure Library (ITIL), Incident Management, Ticket Categorization.
The research examines existing literature, frameworks, and methodologies related to Natural Language Processing (NLP), Natural Language Understanding (NLU), and automated helpdesk workflows. The proposed platform combines real-time conversational AI assistance with automated ticket categorization, priority tagging, status tracking, and basic automated self-service resolution workflows.
The expected outcome is a user-friendly, highly scalable, and accessible customer support framework that significantly reduces average resolution time and operational overhead. Automating routine service requests frees human engineers to focus on critical technical tasks, optimizing overall organizational workflow and operational productivity.
Keywords: Chatbot, Conversational AI, Natural Language Processing (NLP), Information Technology Infrastructure Library (ITIL), Incident Management, Ticket Categorization.
Abstract
Smart AI-Based Solution for Traffic Management on Roads
Miss. Pooja C. Patil, Prof. Shubham M. Lotwala
DOI: 10.17148/IARJSET.2026.131020
Abstract: Traffic congestion is a major challenge in rapidly growing cities. Increasing vehicle numbers, limited road capacity, unpredictable traffic patterns, accidents, road construction, and inefficient signal timing can result in long travel times, fuel wastage, air pollution, and increased stress for road users. Traditional traffic management systems generally depend on fixed signal timings and manual monitoring, which may not respond effectively to changing traffic conditions. This research proposes a smart AI-based solution for traffic management on roads. The proposed system can use cameras, sensors, historical traffic data, and real-time vehicle information to understand traffic conditions. Computer vision can be used for vehicle detection and counting, while machine learning can be applied to predict congestion and identify unusual traffic conditions. Based on the estimated traffic density, an intelligent controller can recommend or adjust signal timings to improve traffic flow. The research also considers emergency-vehicle priority and incident detection as important extensions. The methodology includes problem identification, literature survey, system design, data collection, preprocessing, AI model selection, evaluation, and analysis. The expected outcome is a scalable and practical framework that can support faster traffic decisions, reduce unnecessary waiting, improve road utilization, and provide useful information to traffic authorities. The study can serve as a foundation for future smart-city traffic management systems.
Abstract
Intelligent Fuzzy Logic Models for Predicting Post-Operative Cardiac Complications
Varsha, Prof. Diwari Lal
DOI: 10.17148/IARJSET.2026.131021
Abstract: Cardiac complications are one of the biggest concerns after a patient has undergone cardiac surgery. It is therefore important to have predictive tools or models that can give both a reliable estimate of the patient's overall risk as well as a measure of how reliably this model predicts their individual level of risk. The purpose of this research was to develop an evidence-conserving selective fuzzy inference system (EC-SFIS) in order to predict the occurrence of serious cardiac complications postoperatively and postoperative atrial fibrillation from clinical input values including age, left ventricular ejection fraction (LVEF), serum creatinine, and duration of cardiopulmonary bypass (CPB). Unlike conventional fuzzy inference, the proposed framework decomposes rule-based information into supporting, opposing, and unresolved evidence and introduces a conflict-conservation operator that transfers contradictory evidence into an unresolved component while preserving the directional evidence balance Evidence-ratio defuzzification, probability calibration, and a selective prediction mechanism are subsequently employed to accept predictions with adequate evidence resolution and low conflict while flagging uncertain cases for additional assessment. A numerical case study demonstrates that the framework can distinguish between predictions having similar evidence resolution but different conflict levels, resulting in outcome-specific acceptance or flagging decisions. The comparative evaluation presented in the study indicates lower prediction error across risk–coverage levels and favorable probability calibration relative to the considered fuzzy and statistical comparators. The proposed EC-SFIS therefore provides an interpretable mathematical framework in which postoperative risk prediction is accompanied by explicit quantification of evidence consistency and prediction reliability.
Keywords: Evidence-Conserving Fuzzy Inference System, fuzzy logic; postoperative cardiac complications, atrial fibrillation; evidence conflict, selective prediction; uncertainty quantification, risk prediction, clinical decision support.
Keywords: Evidence-Conserving Fuzzy Inference System, fuzzy logic; postoperative cardiac complications, atrial fibrillation; evidence conflict, selective prediction; uncertainty quantification, risk prediction, clinical decision support.
Abstract
Impact Of Recyclable Packaging on Consumer Buying Decisions for Ready-To-Cook Products
Asst. Prof. Sheitaal Desai, Prof. Dr. Mona Mehta
DOI: 10.17148/IARJSET.2026.131022
Abstract: With increasing awareness of environmental issues that include stringent waste packaging regulations, recyclable packaging has become an increasingly salient signal during the process of purchasing food, but it remains a poorly explored issue in academic literature, particularly concerning the rapidly expanding category of ready-to-cook (RTC) products. In this study, the effect of the attributes of recyclable packaging such as labelling, recycled content, biodegradable nature, and low plastic usage on consumer food RTC purchasing decisions is studied. The methodology of the study included the application of quantitative cross-sectional survey research design, whereby primary data were collected among 145 urban consumers using a structured questionnaire through convenience and snowball sampling. The data collected were analysed using descriptive statistics, Pearson correlation analysis, multiple linear regression and independent samples t-test in SPSS version 28. The results indicated that environmental concern, quality perception and the willingness to pay a premium for environmentally friendly packaging were significant positive determinants of purchase intention, with environmental concern being the strongest determinant (β = 0.41, p < .001). The regression model explained roughly 38% of the variation in purchase intention (R² = .383, F(3, 141) = 29.18, p < .001). Age group analysis revealed that young consumers (18 to 34 years old) and females were more sensitive towards recyclable packaging compared to their older and male counterparts. This study contributes to the theory of planned behaviour, which is supported by the empirical findings of the research concerning environmental concern and quality perception being precursors to green purchase intention among the RTC consumers.
Keywords: recyclable packaging, sustainable packaging, consumer buying behaviour, ready-to-cook food products, purchase intention, green consumerism, environmental concern.
Keywords: recyclable packaging, sustainable packaging, consumer buying behaviour, ready-to-cook food products, purchase intention, green consumerism, environmental concern.
Abstract
Use Of Digital Knowledge Sharing Platform Like Wikis On Sharing Of Water-Efficient Techniques And Methods For Minimizing Water Scarcity
Miss. Priyanka Sanjay Patil, Prof. Mr. Shubham Lotwala
DOI: 10.17148/IARJSET.2026.131023
Abstract: Water scarcity is one of the major environmental challenges affecting communities, agriculture, industries, and sustainable development. The effective sharing of knowledge about water-efficient techniques and conservation methods can play an important role in reducing water wastage and promoting sustainable water management. This research focuses on the use of digital knowledge-sharing platforms, particularly Wiki-based platforms, for collecting, organizing, sharing, and accessing information related to water-efficient techniques and methods for minimizing water scarcity. The study aims to examine how a collaborative digital platform can enable users, researchers, farmers, water- management organizations, and communities to contribute and access practical knowledge about water conservation, efficient irrigation, rainwater harvesting, wastewater reuse, and other water-saving practices. The proposed approach involves designing a structured knowledge-sharing platform where information can be collaboratively created, updated, categorized, and accessed by users. The platform can improve the availability and accessibility of reliable information while encouraging knowledge exchange and community participation. The study highlights that digital knowledge- sharing platforms can support awareness, collaboration, and the adoption of water-efficient practices. The proposed Wiki-based approach can therefore serve as a useful digital resource for sharing knowledge and supporting sustainable water management and efforts to minimize water scarcity.
Keywords: Water Scarcity, Water Conservation, Digital Knowledge Sharing, Wikis, Water-Efficient Techniques, Knowledge Management
Keywords: Water Scarcity, Water Conservation, Digital Knowledge Sharing, Wikis, Water-Efficient Techniques, Knowledge Management
Abstract
Online Exam Proctoring using AI
Mr. Sudhir Ramesh Nikam, Prof. Miss. Chetana Kawale
DOI: 10.17148/IARJSET.2026.131024
Abstract: The main aim of this study is to understand how Artificial Intelligence can be used to create a secure and effective system for monitoring online examinations. The proposed system uses a webcam to observe students during the exam and check their activities. Different AI methods, such as face detection, face recognition, object detection, head movement tracking, eye-gaze monitoring, and behavior analysis, can help identify unusual or suspicious activities. For example, the system can check whether the correct student is taking the exam, whether another person appears in front of the camera, or whether any restricted objects are being used. If the system notices any suspicious activity, it can generate an alert or save the details of the event for later review. This can help make online examinations more secure, fair, and trustworthy.
Abstract
Exploring the Queer Inner World, Emotional Terrain, and Psychological Challenges in Sachin Kundalkar’s Cobalt Blue
Dr. S. Parvin Banu, M.A., M.Phil., PhD, R.Tamil Elakkia, M.A., M.Phil, M.Ed.
DOI: 10.17148/IARJSET.2026.131025
Abstract: This paper analyzes the internal experiences of queer individuals depicted in the novel, highlighting the psychological, emotional, and existential dimensions of same-gender attraction and self-conception. By exploring the intertwined stories of brother and sister Tanay and Anuja,kundalkar create a reflective atmosphere in which unspoken feelings of love and sadness are conveyed beyond the limits typical heterosexual standards. The novel’s clarity and poetic nature act as a psychological compass for longing, reflecting the repressed ambitions and divided identities found in queer experiences. This study employs a queer theoretical lens in conjunction with psychoanalytic and sociocultural approaches to reveal how Kundalkar shapes the private realms of queer experience. The implementation of a fragmented storyline and the absence of audio and symbolic features, especially the Cobalt Blue, act as an artistic representation of profundity, sadness, and concealed identity. The story demonstrates the changing nature of desire and challenges the distinctions between gender and sexuality, depicting queer identity as fundamentally human and profoundly authentic in its feelings and theoretically uncovers how the characters in Kundalkar’s Cobalt Blue articulate the conflict between individual genuineness and cultural standards. It highlights how the novel serves as a platform for affirming and resisting queer identity in Indian literature. This research reveals that Kundalkar’s Cobalt Blue goes beyond the tale of taboo romance, acting as a deep investigation of the queer psyche in the spaces where identity, love, and grief converge, and a fight for personal freedom and selfhood kundalkar
Keywords: Psychological, Emotional, Socio-cultural, same gender attraction
Keywords: Psychological, Emotional, Socio-cultural, same gender attraction
Abstract
AI-Recruitment And Talent Analytics System
Mrs. Hemalatha M, Ms. Sakshi Mahesh Mirji, Ms. Sanjana, Mr. Braham Abhijat Singh, Mr. Priyanshu Kumar
DOI: 10.17148/IARJSET.2026.131026
Abstract: Recruitment is an important function of Human Resource Management, but traditional recruitment processes become difficult to manage when organizations receive a large number of job applications. Manual resume screening, subjective interview evaluation and limited use of recruitment data can increase the time required for hiring and may introduce inconsistency and bias into candidate selection. This paper presents an AI Recruitment and Talent Analytics System that combines Artificial Intelligence (AI),
Machine learning(ML),Natural Language Processing(NLP), and data analytics to support an intelligent recruitment workflow. The proposed system automates major stages of recruitment, beginning with candidate registration and resume submission. NLP-based resume processing is used to extract skills, education, certifications, and work experience. Candidate profiles are compared with job requirements to support candidate-job matching and ranking. The system also proposes an AI-based interview module for evaluating technical knowledge, communication abilities, behavioral responses, and overall performance. A recruitment analytics dashboard provides candidate rankings, hiring insights, reports, and decision-support information. The current prototype focuses on the landing page, authentication interface, candidate dashboard, HR dashboard, and AI interview interface, providing a foundation for subsequent backend, database, AI/ML, and predictive analytics integration.
Keywords: Artificial Intelligence, Recruitment ,Talent Analytics, Machine Learning, Natural Language Processing, Resume Screening, Candidate Matching, HR Analytics, AI Interview, Hiring Automation.
Machine learning(ML),Natural Language Processing(NLP), and data analytics to support an intelligent recruitment workflow. The proposed system automates major stages of recruitment, beginning with candidate registration and resume submission. NLP-based resume processing is used to extract skills, education, certifications, and work experience. Candidate profiles are compared with job requirements to support candidate-job matching and ranking. The system also proposes an AI-based interview module for evaluating technical knowledge, communication abilities, behavioral responses, and overall performance. A recruitment analytics dashboard provides candidate rankings, hiring insights, reports, and decision-support information. The current prototype focuses on the landing page, authentication interface, candidate dashboard, HR dashboard, and AI interview interface, providing a foundation for subsequent backend, database, AI/ML, and predictive analytics integration.
Keywords: Artificial Intelligence, Recruitment ,Talent Analytics, Machine Learning, Natural Language Processing, Resume Screening, Candidate Matching, HR Analytics, AI Interview, Hiring Automation.
Abstract
Live Location Tracking System: Architecture, Algorithms, and Real-Time Visualization
Miss. Bhagyashri Premsing Patil, Asst. Prof. Chetana Kawale
DOI: 10.17148/IARJSET.2026.131027
Abstract: Live location tracking systems have revolutionized modern logistics, emergency response, and personal safety applications. These systems facilitate the continuous acquisition, transmission, processing, and visualization of geographical position data in near real-time. This comprehensive research paper presents a robust and scalable architecture for a live location tracking system. The architecture integrates a mobile client for GNSS/GPS coordinate acquisition, an optimized communication layer using lightweight protocols like MQTT and WebSockets, a robust backend for spatial data processing, and a dynamic webbased dashboard for visualization. Furthermore, this study delves deeply into the underlying algorithms, including Kalman Filtering for coordinate smoothing and the Haversine formula for distance calculation. Security and privacy, paramount in location-based services, are addressed through strict role-based access control and TLS 1.3 encryption. Extensive experimental evaluation demonstrates that the proposed architecture minimizes end-to-end latency while maintaining high accuracy and low battery consumption. The findings provide a foundational framework for deploying enterprise-grade location tracking solutions.
Keywords: Live Location Tracking, GNSS, Real-Time Systems, IoT, WebSockets, Privacy, Spatial Data, MQTT.
Keywords: Live Location Tracking, GNSS, Real-Time Systems, IoT, WebSockets, Privacy, Spatial Data, MQTT.
Abstract
AI POWRED EDUCAD USING SLM AND NLP
P. Aparna Devi, K. Shiva Kumar
DOI: 10.17148/IARJSET.2026.131028
Abstract: Natural Language Processing (NLP) systems enable machines to understand, interpret, and generate human- like language, bridging the gap between human communication and computer understanding. Natural Language Interface to Databases (NLIDB) and Natural Language Interface to Visualization (NLIV) systems are designed to enable non- technical users to retrieve and visualize data through natural language queries. However, these systems often face challenges in handling complex correlation and analytical questions, limiting their effectiveness for comprehensive data analysis. Additionally, current Business Intelligence (BI) tools also struggle with understanding the context and semantics of complex questions, further hindering their usability for strategic decision-making. Also, when building these models for generating the queries from natural language, the system handles only the semantic parsing issues as each column header is being changed manually to their normal names by all existing models which is time-consuming, tedious, and subjective. Recent studies reflect the need for attention to context, semantics, and especially ambiguities in dealing with natural language questions. To address this problem, the proposed architecture focuses on understanding the context, correlation-based semantic analysis, and removal of ambiguities using a novel approach. An Enhanced Longest Common Subsequence (ELCS) is suggested where existing LCS is modified with a memorization component for mapping the natural language question tokens with ambiguous table column headers. This can speed up the overall process as human intervention is not required to manually change the column headers. The same is evidenced by carrying out thorough experimentation and comparative study in terms of precision, recall, and F1 score. By synthesizing the latest advancements and addressing challenges, this paper has proved how NLP can significantly enhance the accuracy and efficiency of information retrieval and visualization, broadening the inclusivity and usability of NLIDB, NLIV, and BI systems.
Keywords: Correlation analysis,Enhanced Longest Common Subsequence (ELCS),Feature extraction,Semantic analysis.
Keywords: Correlation analysis,Enhanced Longest Common Subsequence (ELCS),Feature extraction,Semantic analysis.
Abstract
Mechanical Analysis of Diving-Evasion and Seated-Block Defensive Skills in Kho-Kho
Dr. Pathan Yusufkhan Hidayatkhan
DOI: 10.17148/IARJSET.2026.131029
Abstract: Two further skills complete the mechanical picture of kho-kho: the runner's diving evasion, a low-trajectory dive used to gain distance from a chaser or escape a tap attempt, and the seated blockers' defensive posture, in which players seated along the central lane must react and extend to obstruct or intercept play. Despite their frequent occurrence, neither skill has been examined using projectile-motion or postural-stability frameworks. This paper analyses diving evasion through the principles of projectile motion and impulse-momentum landing mechanics, and analyses the seated- block posture through principles of static and reactive stability, including base of support and centre-of-pressure control. Sixteen state-level kho-kho players (runners and seated defenders) were filmed using high-speed video and a landing- zone force platform while executing match-simulated diving evasions and reactive blocking actions. Dive take-off angle, horizontal displacement, flight time, and landing ground reaction force (GRF) were extracted for the diving skill; base of support width, centre-of-pressure displacement, and reactive extension time were extracted for the seated-block skill. Results showed that successful diving evasions -- defined as gaining sufficient horizontal distance to avoid an immediate tap -- were associated with a low take-off angle (mean 16.8° from horizontal) that maximised horizontal range relative to flight time, and with a progressive, sequential flexion of the wrist, elbow, and shoulder on landing that reduced peak vertical GRF compared with a stiff-armed landing strategy. Among seated blockers, players who maintained a wider base of support and pre-flexed knee and hip angles demonstrated significantly faster reactive extension toward an approaching ball or runner than those in a more upright, narrow-based posture. These findings characterise diving evasion as a low- angle projectile skill constrained by a landing-safety trade-off, and characterise seated blocking as a stability-for-speed postural skill, with direct implications for technique coaching and injury prevention in both.
Abstract
Thermo-Mechanical Performance Evaluation Of Mechanical Seal Materials in Fire Tender Pumps Using Ansys Finite Element Simulation
Aloyce N. Waryoba and Eng. Dr. Salum K. Chang’waro
DOI: 10.17148/IARJSET.2026.131030
Abstract: Mechanical seals are critical components of centrifugal fire tender pumps because excessive leakage or loss of sealing performance can reduce pump effectiveness during emergency firefighting operations. This study evaluated the predicted thermo-mechanical response of Carbon Graphite, Silicon Carbide (SiC), and Tungsten Carbide (WC) mechanical seal materials for a fire tender pump at Julius Nyerere International Airport (JNIA), Tanzania, using finite element analysis (FEA) in ANSYS Workbench. A three-dimensional seal assembly was modelled using field-measured geometry and operational information, supplemented by literature and manufacturer/reference data where direct measurement was not available. A sequential one-way thermo-mechanical procedure was used in which the steady-state thermal field was transferred to a structural analysis. The principal loading conditions were hydraulic pressure of 1.5 MPa, rotational speed of 4000 rpm, seal-face temperature of 180 °C, ambient temperature of 35 °C, friction coefficient of 0.12, contact pressure of 0.8 MPa and convection coefficient of 25 W/m²·K. The predicted results were 0.0330 mm deformation, 82.4 MPa equivalent stress and 0.74 MPa average contact pressure for Carbon Graphite; 0.0105 mm, 54.7 MPa and 0.82 MPa for SiC; and 0.0085 mm, 48.2 MPa and 0.84 MPa for WC, respectively. WC exhibited the lowest predicted deformation and equivalent stress among the investigated materials under the applied loading conditions, whereas sic showed a relatively balanced thermo-mechanical response. The results are numerical predictions and should not be interpreted as complete evidence of service reliability. Experimental testing, wear assessment and leakage measurements are required for complete material qualification.
Keywords: Mechanical seals; fire tender pumps; thermo-mechanical analysis; ANSYS; finite element analysis; Carbon Graphite; Silicon Carbide; Tungsten Carbide; deformation; stress; contact pressure.
Keywords: Mechanical seals; fire tender pumps; thermo-mechanical analysis; ANSYS; finite element analysis; Carbon Graphite; Silicon Carbide; Tungsten Carbide; deformation; stress; contact pressure.
Abstract
AI Laboratories and the Future Academic Library: A Framework for Assessing User Perceptions and Acceptance of AI-Driven Services in the Indian Institutes of Management
Abhishek Kumar Gupta (Dashora), Dr Rakesh Kumar Khare
DOI: 10.17148/IARJSET.2026.131031
Abstract: Artificial intelligence (AI) is reshaping teaching, research and scholarly communication, and several Indian Institutes of Management (IIMs) are now building AI schools, centres and laboratories. This paper examines the importance and future role of AI laboratories in IIMs and asks what this institutional shift means for users' perceptions and acceptance of AI-driven library services. Drawing on official institutional sources and recent literature on AI in academic libraries and technology acceptance, the paper (i) maps AI laboratories and AI-focused units across the 21 IIMs, identifying four IIMs (Ahmedabad, Calcutta, Lucknow and Udaipur) with an identified AI laboratory or unit; (ii) proposes an AI Lab–Library research-support framework; and (iii) sets out a research design for assessing user perceptions at IIM Udaipur, covering awareness, perceived usefulness, ease of use, trust, privacy and ethical concerns, satisfaction and behavioral intention. The paper is conceptual and design-oriented and reports no primary survey results. It argues that AI laboratories should be treated as research-support infrastructure that creates new scholarly information needs, that libraries are well placed to provide a human-centred and ethical layer between AI infrastructure and users, and that acceptance cannot be assumed from the availability of technology alone. Practical recommendations for IIM libraries and testable propositions for future empirical work are provided.
Keywords: artificial intelligence; academic libraries; AI-driven library services; user perception; technology acceptance; AI laboratory; IIM Udaipur; research support; responsible AI.
Keywords: artificial intelligence; academic libraries; AI-driven library services; user perception; technology acceptance; AI laboratory; IIM Udaipur; research support; responsible AI.