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VOLUME 15, ISSUE 10, OCTOBER 2026
Publication in progress..
A Review of Deep Learning for Real-Time Sign Language Recognition
Minal V. Patil, Pravin I. Patil, Dr. D. M. Patel
A Novel Hybrid Dynamic Pheromone Optimization Algorithm with Entropy-Guided Learning and Historical Route Intelligence for the Travelling Salesman Problem
Mrs. M. Hemalatha, Dr. N. Kamaraj
Neocube Realty: A Web-based approach for comprehensive real estate Business System
Pranjal Rane, Muskan Madhwani, Vaishnavi Sawarkar, Kajal Gawande, Chanchal Karwa, Achal Pande, Prof. S.A. Solanke
Fake News Detection Using Machine Learning and Natural Language Processing
Dhanashri Satish Deshmukh, Ms Rucha Mohite
Bridging the Language Gap: NLP Based Translation of Government Schemes for Marathi- Speaking Farmers
Pooja Suryabhan Gaygaye, Ms.Yogita Kadbane
SIX-YEAR ENROLLMENT TREND ANALYTICS AT WESLEYAN UNIVERSITY-PHILIPPINES: A DATA-DRIVEN BASIS FOR REGISTRAR'S OFFICE DIGITALIZATION AND AUTOMATION
Yolanda M. Javier, Shirley DC. Ramos, Abigail C. Bautista, Carmeliza S. Valdez, Rozen Jake DR. Punay, Gemmuel M. Borja, John Andrei J. Ignacio
Automated Multi-Student Attendance Using Video-Based Facial Recognition and Embedding Matching
Likhitha Alajangi, Dr. Kuda Nageswara Rao
AI-Based Semantic Understanding of Code Mixed Tamil-English Text in Informal Digital Communication
Aditya Raman, Sushmita Kundu, Aditya Balaji, G.Paavai Anand
Comparative Analysis of Convolutional Neural Network Architectures for Automated Waste Classification
Sakshi Anil Parkhe
MACHINE LEARNING FRAMEWORK FOR DETECTION OF MENTAL STRESS
Archana Borse, MS. Yogita Kadbane
HemaSight AI: A Four-Stage Deep Learning Pipeline for Automated Leukemia Detection and Post-Transplant Monitoring
Varun Doddagoudar, Vishwa, Shashank, Madhushree M
Smart Irrigation System for Precision Farming
Mr. Bhagyesh N Chaudhari, Dr. Dinesh D Puri
Multi-Crop Disease Detection and Management Recommendation System Using Deep Learning
K. Hemalatha, Prof. K. Venkata Rao, P. Swathi
CUSTOMER COMPLAINT TRACKING SYSTEM
Dr. L. Baby Victoria, Abinaya D
Crop Recommendation System for Farmers Using Convolutional Neural Network
D. Savitha, Gayathri U, Priya S
Equilibrium Dynamics and Threshold Conditions in Tumor–Immune Models with Chemotherapy
Shivangi Chauhan*, Prof. Diwari Lal
Quantum Assisted Predictive Maintenance and Scheduling System for Industry 4.0
Muppuri Satish Kumar
Chronic Kidney Disease Prediction System Using Machine Learning
Savitha. D, Haritha. V P, Mathisha. N
Women Enterprise Hub: A Web-Based Platform for Women Entrepreneurs
D. Savitha, S. Nitharsana, S. Subarna
DESIGN AND DEVELOPMENT OF AN
ESP-BASED SMART MOBILE, MANIPULATOR FOR REMOTE OBJECT, HANDLING, Kuncham Preetham, Dr. K. Raja Kumar
An Intelligent Risk-Adaptive Framework for Secure Examination Paper Distribution, Traceability, and Early Leak Detection
Mr. Swapnil S. Chaudhari, Asst. Prof. Sapana A. Fegade
Smart Classroom Seat Allocation System Using IoT, Artificial Intelligence, Machine Learning and Optimization Techniques: A Review
Dr. P. Anitha, V Karishni
PERSONALISED HEALTHCARE RECOMMENDATION SYSTEM USING XGBOOST AND SHAP
Savitha. D, Sowmiya. V, Akshaya. C
Real-Time Sign Language Translator Using Video Prediction
Geetha. N, Mohanashankare.V.K, Prathanyaa.K
Agent-less Windows System Vulnerability and Network Scanner for Security Assessment and Reporting
Satvik Dilip Barhate, Dr. Dinesh Puri
Healthcare Insurance Fraud Detection Using Machine Learning and Deep Learning
Siddanathi Prasanth Srinivas Sai
Grouping-Aware Numerical Verification for Offline Retrieval-Augmented Question Answering over Institutional Documents
Sudheer Katuri, G. Sharmila Sujatha
Genuine Co-Movement, Spurious Trend, and a Ratio That Does Not Help: Variable Construction for Nigerian Macroeconomic Models
Ofierohor Ufuoma Earnest
Abhimanyu Chakravyuha: An Evidence-Aware AI Framework for Fraud Intelligence Through Semantic Analysis, Mutation Detection, and Graph-Based Reasoning
MD Auranzeb Khan, Kunal, Srijan Mani Tripathi, Vyom Pandey, Dr.G Paavai Anand
AI-DRIVEN CLINICAL DECISION SUPPORT SYSTEM FOR INTELLIGENT MEDICAL REPORT ANALYSIS AND DISEASE PREDICTION
Sravanthi Duvvuri, Dr. G. Sharmila Sujatha
AI-Enabled Structural Vibration Prediction for Enhanced Stealth Performance
Harish Kumar Kuppili, Kuda Nageswara Rao
Contract Sentinel: A Literature Survey on Transformer-Based Clause Extraction and Risk Detection in Legal Contracts
Vaishnavi Karpe, Tanishka Makode, Maria Shaikh, P. J. Jambhulkar
SR-Screen-AI: An Explainable Rule-Based AI-Assisted Decision Support System for Human-in-the-Loop Systematic Literature Review Screening
Sruthi R, Rajeev P R
How Many Normal Images Are Needed? Training-Free Few-Shot Anomaly Detection on Scanning Electron Microscope Images of Integrated Circuits
K. Susmitha Reddy, G. Lavanya Devi
A Nightshade-SWIN Transformer-Based Framework for Enhanced Multi-Crop Plant Disease Classification
Dr. Barkha Joshi
A Secure and Privacy-Aware Hybrid Framework for Digital Credential Verification
Jayashri Digambar Sable, Swaranjali Dinesh Shelar, More N.N
An AI-Based Intelligent System for Early Urban Health Risk Assessment
Mali Rajashri Savata, Salunkhe Samruddhi Mahadev, Lokare Sujata Sharad, Ghalame Mayuri Kishor, Linge Sandeep Ganpat
Self-Evolving AI Agent Framework for Predicting and Preventing Zero-Day Cyberattacks in a Multi-Cloud Environment Using Federated Learning and Explainable AI
Samruddhi Santosh Gawade, Gayatri Gunwant Gayaki, Ms. Ghodake G.K
School Management System for Dutuwewa Maha Vidyalaya
Keshika Rathakrishnan, Parahera Nekathige Kaumini Viduthala
A Voice-Powered Intelligent Healthcare Platform for Elderly Patients with Dementia and Alzheimer's Care
Shinde Akshata Kumar, Pathan Ekhara Mushtak, Parchande Sanika Harishchandra, Khilare Sakshi Kalidas, Sawant Namdev Manik
MetaSociety: A Simulation-Driven AI Policy Advisor for Data-Grounded Smart Governance
Priyanka Jadhav, Payal Dongre, Priti Chaugule, Mayuri Kulkarni
Smart AI-Powered E-Commerce Platform with Personalized Recommendation and Sales Analytics
Wadekar Gargi Prashant, Bole Ishwari Santosh, Patil Harshvardhan Sunil, Mr. S. S. Ingole
IOT-Based Machine Health Monitoring System
Chavan Rutuja Umesh, Babar Dnyaneshwari Namdev, Gajare Pragati Dnyaneshwar, Mrs. P. S. Gavali
AI-Based Road Accident Detection and Alert System
Mr. S. A. Zambare, Saniya Mainuddin Mulla, Kiran Sanjay Jadhav, Shivprasad Suresh Kadam, Reshma Mujofar Pathan
Smart Village Automation Using IoT
Ms. J. K. Hipparkar, Pooja Karande, Shreya Joshi, Arjiya Mulani
VocaLyze – AI Based Emotion Recognition from Voice
Sadigale Shravani Santosh, Mali Vaishnavi Sukhdev, Gotavle Sushma Sanjay, Linge Sandeep Ganpat
An Adaptive and Risk-Aware Access Control Framework for Secure Cloud File Sharing Using Hybrid Cryptography and Context-Aware Key Release
Ghodekar Snehal Ramesh, Ghodekar Vaishnavi Suresh, Miss. Ghodake G. K.
Information Security Copilot for Cyber Threat Monitoring and Risk Management in Small and Medium-Sized Businesses
Yashowardhan Thakare, Rutuja Ghongade, Payal Pawar, Radha Mankar, Anisha Kaware, Dr V B Gadicha
Smart Public Infrastructure Damage Reporting and Prediction System: A Web-Based Platform with Category-Based Rule Priority Classification
Khushi, Harsha Bhat T.M, Varun P, Shreecharan I G, Prof. Raksha K
AI in Healthcare Applications: A Study of Patient Awareness and Acceptance
Priyanka Ambdekar, Aditi Hingane, Prof. Pramod Gopale
CNN-Based Framework for Maize leaf Disease Detection and Crop Monitoring
Dr.K.L.S.Soujanya, V. Sumedha, M. R. V. Sravanthi, A. Supriya
A ROBUST FRAMEWORK FOR COPYWRIGHT PROTECTION AND OWNERSHIP VERIFICATION OF DIGITAL IMAGES
Pavithra N, Anushree P M, Kavana K, Megha Manjappa Mogali, V Tejaswini
Abstract
Agentic AI-Driven Intrusion Detection and Autonomous Incident Response
Lipsa Dash
Abstract: Limitations associated with the traditional IDS systems which are based on pre-defined rules and human intervention have been highlighted due to the quick rise of the advanced cyber-attacks. Goal-driven software agents which can constantly watch the events in the network, recognize malicious activities, plan for the defence plans and take intelligent reaction actions independent of human intervention are called as agentic artificial intelligence (AI). This novel Agentic AI-Driven Intrusion Detection and Autonomous Incident Response Framework proposed in this paper is comprised of the explainable AI, machine learning, reinforcement learning, threat intelligence and autonomous decision making. In real-time, the proposed framework can detect anomalies, learn adaptive policies, automate threat containment and recover incidents having minimal false positives and reaction time. According to the experimental results, the proposed approach outperforms traditional IDS systems in terms of detection accuracy, precision, response time and resilience. A proactive and intelligent cybersecurity architecture is provided by the framework for the enterprise networks, cloud environments, Internet of Things ecosystem and vital infrastructure that needs continuous protection against new cyber threats.
Keywords: Agentic AI, Intrusion Detection System, Autonomous Incident Response, Cybersecurity, Threat Intelligence, Reinforcement Learning, Explainable AI, Network Security.
Keywords: Agentic AI, Intrusion Detection System, Autonomous Incident Response, Cybersecurity, Threat Intelligence, Reinforcement Learning, Explainable AI, Network Security.
Abstract
A Review of Deep Learning for Real-Time Sign Language Recognition
Minal V. Patil, Pravin I. Patil, Dr. D. M. Patel
Abstract: Communication is the fundamental exchange of thoughts and messages through speech, signals, visuals, and behavior. For deaf and hard-of-hearing individuals, sign language serves as the primary medium for interaction. However, because most non-disabled individuals do not understand sign language, a significant communication barrier exists. This paper presents a user-friendly Human-Computer Interface (HCI) software system designed to translate American Sign Language (ASL) fingerspelling hand gestures into text and speech in real time. The proposed system captures gesture images via a webcam, extracts hand landmarks using the MediaPipe library, and projects them onto a clean white canvas to overcome real-world lighting and background variations. A Convolutional Neural Network (CNN) model is trained on these skeletal representations to recognize individual alphabets (A–Z) and form complete words. The recognized text is then converted into spoken audio using the pyttsx3 library. The proposed approach achieves 97% recognition accuracy under standard conditions and over 99% accuracy in controlled environments.
Keywords: American Sign Language, Convolutional Neural Network, MediaPipe, Human-Computer Interface, Gesture Recognition, Text-to-Speech Conversion.
Keywords: American Sign Language, Convolutional Neural Network, MediaPipe, Human-Computer Interface, Gesture Recognition, Text-to-Speech Conversion.
Abstract
A Novel Hybrid Dynamic Pheromone Optimization Algorithm with Entropy-Guided Learning and Historical Route Intelligence for the Travelling Salesman Problem
Mrs. M. Hemalatha, Dr. N. Kamaraj
Abstract: The Travelling Salesman Problem (TSP) remains one of the most extensively studied NP-hard combinatorial optimization problems due to its wide range of applications in logistics, transportation, manufacturing, and network design. Although Ant Colony Optimization (ACO) has demonstrated promising performance for solving TSP, traditional pheromone updating mechanisms often suffer from premature convergence, loss of population diversity, and stagnation in local optima. This paper proposes Hybrid Dynamic Pheromone Optimization (HDPO), a novel optimization algorithm that integrates entropy-guided learning, adaptive pheromone regulation, historical route intelligence, and dynamic local search mechanisms into a unified framework. HDPO dynamically adjusts pheromone evaporation and reinforcement based on search diversity, route entropy, and solution improvement rates. Furthermore, an elite historical memory structure preserves high-quality route knowledge across generations, while a stagnation recovery mechanism reintroduces exploration when convergence becomes excessive. Experimental evaluations on benchmark instances from TSPLIB can demonstrate that HDPO achieves superior solution quality, faster convergence, and enhanced robustness compared with classical ACO variants. The proposed method offers an effective balance between exploration and exploitation, making it suitable for large-scale TSP instances and other combinatorial optimization problems.
Keywords: Community Network, Travelling Salesman Problem (TSP), Ant Colony Optimization (ACO), HDPO, TSPLIB.
Keywords: Community Network, Travelling Salesman Problem (TSP), Ant Colony Optimization (ACO), HDPO, TSPLIB.
Abstract
Neocube Realty: A Web-based approach for comprehensive real estate Business System
Pranjal Rane, Muskan Madhwani, Vaishnavi Sawarkar, Kajal Gawande, Chanchal Karwa, Achal Pande, Prof. S.A. Solanke
Abstract: The real estate sector faces challenges in fragmented property management, manual inquiry processing, and inefficient lead allocation. This paper presents Neocube Realty, a centralized web-based platform developed using React, Spring Boot, Spring Security, and MySQL. The system implements RESTful APIs, role-based access control (RBAC), ORM, and workflow automation for integrated property and transaction management. An automated inquiry- to-lead conversion mechanism and round-robin lead assignment algorithm distribute leads efficiently among active brokers. The platform further incorporates advanced property filtering, geospatial mapping, Point-of-Interest (POI) analysis, and video-based property visualization, including customer-provided property videos collected during the listing process, to enhance property discovery and decision making. The system supports the complete lead lifecycle, including broker assignment, status tracking, site visits, bookings, and deal management, through role-specific interfaces and analytical dashboards. The proposed system demonstrates the integration of workflow automation, geospatial intelligence, and immersive visualization into a unified and scalable real estate management platform.
Keywords: Real Estate Management, React, Spring Boot, REST API, RBAC, MySQL, Round-Robin Algorithm, Geospatial Intelligence, POI Analysis, Blueprint Visualization.
Keywords: Real Estate Management, React, Spring Boot, REST API, RBAC, MySQL, Round-Robin Algorithm, Geospatial Intelligence, POI Analysis, Blueprint Visualization.
Abstract
Fake News Detection Using Machine Learning and Natural Language Processing
Dhanashri Satish Deshmukh, Ms Rucha Mohite
Abstract: The use of websites social media and online news platforms has made it possible to receive information quickly at the same time the amount of misleading and false news available online has increased checking every news article manually can be time-consuming this study therefore explores a machine learning-based method for automatically classifying news articles the proposed system uses the t fake news separate collections of fake and real news articles the news text is cleaned and converted into using term frequency-inverse document frequency tf-idf these features are given to a logistic regression classifier for binary assigns each article fake or real category on the test data the developed model obtained an accuracy of 9882 the results indicate that the selected combination of text performs well on in study keywords fake news detection machine learning natural language processing tf-idf logistic regression
Abstract
FARMER E-MARKET FOR BETTER PRODUCT DEMAND PREDICTION
Revati Vishwas Shitole, Dr. Shveti Chandan
Abstract: Farmers often have to decide what to grow and how much to bring to the market without having a clear idea of future demand. When too much of a crop reaches the market, some of it may remain unsold and prices may fall. When the available quantity is too low, buyers may struggle to find the product they need. This paper proposes a Farmer E-Market system that brings farmers and market information together and uses past demand records to estimate future demand for selected crops. The proposed system allows a farmer to enter the crop name, quantity and expected harvest date. It then compares the farmer’s planned supply with current supply, expected future supply and predicted demand for the selected region and period. The first version uses a Random Forest regression model trained on historical crop-demand records containing year, month, region, crop and market demand. The project uses a time-based train-test split so that earlier records are used for training and later records are used for testing. The system is intended to provide a simple, understandable view of demand and supply rather than guarantee a sale or a particular price. Its usefulness will depend on the quality, coverage and freshness of the data available.
Keywords: Farmer E-Market, Crop Demand Prediction, Random Forest, Supply-Demand Comparison, Digital Agriculture.
Keywords: Farmer E-Market, Crop Demand Prediction, Random Forest, Supply-Demand Comparison, Digital Agriculture.
Abstract
AI-BASED RANSOMWARE DETECTION FRAMEWORK USING MACHINE LEARNING
Kajal Shildar Padvi, Dr.Rucha Mohite
Abstract: Ransomware remains one of the major cybersecurity threats because modern ransomware variants continuously evolve and may differ considerably from previously identified samples. Traditional signature-based detection approaches are effective against known threats but may have limited capability against previously unseen variants. Machine learning provides an alternative approach by learning discriminative patterns from malicious and benign executable samples. However, high classification performance under a conventional random train-test split does not necessarily demonstrate generalization to ransomware families that were not represented during training.
This research proposes an AI-Based Ransomware Detection Framework Using Machine Learning based on static characteristics of Windows Portable Executable (PE) files. The framework extracts a compact set of eight static features consisting of API count, DLL count, unique API count, unique DLL count, file size, packed status, entropy, and PE timestamp year. Five machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, XGBoost, and LightGBM—are trained and compared using accuracy, precision, recall, F1score, ROC-AUC, false positive rate, and false negative rate.
The experimental framework additionally incorporates validation-based probability threshold analysis, ransomware- family-disjoint evaluation, family-level generalization analysis, Isolation Forest-based novelty detection, and SHAP- based explainability. Random Forest achieved the best overall standard-test performance with 95.42% accuracy, 96.22% precision, 94.28% recall, 95.24% F1-score, and 98.98% ROC-AUC. When evaluated on ransomware families excluded from training, the Random Forest model achieved 90.20% accuracy, 98.88% precision, 87.02% recall, 92.57% F1-score, and 97.72% ROC-AUC. The family-disjoint experiment demonstrates that detection becomes more challenging when the model encounters ransomware families not represented during training. Isolation Forest additionally identified approximately 8.04% of unseen ransomware samples as anomalous relative to the known ransomware distribution.
The results demonstrate that a compact static PE representation combined with ensemble machine learning can provide strong ransomware classification performance while enabling additional analysis of generalization, novelty, and interpretability.
Keywords: Ransomware Detection, Machine Learning, Static Analysis, Portable Executable, Random Forest, Explainable AI, SHAP, Unseen Ransomware Families, Anomaly Detection, Malware Detection.
This research proposes an AI-Based Ransomware Detection Framework Using Machine Learning based on static characteristics of Windows Portable Executable (PE) files. The framework extracts a compact set of eight static features consisting of API count, DLL count, unique API count, unique DLL count, file size, packed status, entropy, and PE timestamp year. Five machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, XGBoost, and LightGBM—are trained and compared using accuracy, precision, recall, F1score, ROC-AUC, false positive rate, and false negative rate.
The experimental framework additionally incorporates validation-based probability threshold analysis, ransomware- family-disjoint evaluation, family-level generalization analysis, Isolation Forest-based novelty detection, and SHAP- based explainability. Random Forest achieved the best overall standard-test performance with 95.42% accuracy, 96.22% precision, 94.28% recall, 95.24% F1-score, and 98.98% ROC-AUC. When evaluated on ransomware families excluded from training, the Random Forest model achieved 90.20% accuracy, 98.88% precision, 87.02% recall, 92.57% F1-score, and 97.72% ROC-AUC. The family-disjoint experiment demonstrates that detection becomes more challenging when the model encounters ransomware families not represented during training. Isolation Forest additionally identified approximately 8.04% of unseen ransomware samples as anomalous relative to the known ransomware distribution.
The results demonstrate that a compact static PE representation combined with ensemble machine learning can provide strong ransomware classification performance while enabling additional analysis of generalization, novelty, and interpretability.
Keywords: Ransomware Detection, Machine Learning, Static Analysis, Portable Executable, Random Forest, Explainable AI, SHAP, Unseen Ransomware Families, Anomaly Detection, Malware Detection.
Abstract
Bridging the Language Gap: NLP Based Translation of Government Schemes for Marathi- Speaking Farmers
Pooja Suryabhan Gaygaye, Ms.Yogita Kadbane
Abstract: Government provide many schemes for farmer.the scheme is on official portal,but most of the scheme are in English .In Maharashtra many Farmers know only Marathi language,they did not aware about English language,due to this they are not able to take the benefits of the governments schemes.
My Research objectives is ,all farmers can get benefited from governments schemes,they can get advantage of the government policies also. I am used Experimental Methodology and I am used A Transformer-based Neural Machine Translation .the performance of model was evaluated using BLEU and CHRF metrics.the system helps to farmers for easily understand government scheme information,specially for Marathi speaking farmers.In future I am focusing on expanding the Data set of farmer related schemes,supporting additional Indian languages and voice based interaction.
Keyword: Natural Language Processing(NLP),English-Marathi Translation ,Transformer,Neural Machine Translation,Government Schemes,Agricultural Information,Marathi Language
My Research objectives is ,all farmers can get benefited from governments schemes,they can get advantage of the government policies also. I am used Experimental Methodology and I am used A Transformer-based Neural Machine Translation .the performance of model was evaluated using BLEU and CHRF metrics.the system helps to farmers for easily understand government scheme information,specially for Marathi speaking farmers.In future I am focusing on expanding the Data set of farmer related schemes,supporting additional Indian languages and voice based interaction.
Keyword: Natural Language Processing(NLP),English-Marathi Translation ,Transformer,Neural Machine Translation,Government Schemes,Agricultural Information,Marathi Language
Abstract
SIX-YEAR ENROLLMENT TREND ANALYTICS AT WESLEYAN UNIVERSITY-PHILIPPINES: A DATA-DRIVEN BASIS FOR REGISTRAR'S OFFICE DIGITALIZATION AND AUTOMATION
Yolanda M. Javier, Shirley DC. Ramos, Abigail C. Bautista, Carmeliza S. Valdez, Rozen Jake DR. Punay, Gemmuel M. Borja, John Andrei J. Ignacio
DOI: 10.17148/IJARCCE.2026.151001
Abstract: This study analyzed institutional enrollment trends across six academic years, from Academic Year (AY) 2018–2019 to AY 2023–2024, with emphasis on changes by college and sex. Specifically, it compared the combined enrollment counts during the first three academic years (2018–2019 to 2020–2021) with those during the last three academic years (2021–2022 to 2023–2024). A descriptive-comparative approach was used to analyze institutional enrollment records. Frequency counts, percentages, numerical differences, and percentage changes were employed to identify patterns of enrollment growth and decline. Findings revealed that annual enrollment initially decreased by 5.34% from AY 2018–2019 to AY 2019–2020 but subsequently recovered, with the largest annual increase of 26.97% occurring in AY 2021–2022. Enrollment reached a six-year high of 15,043 in AY 2023–2024. Comparing the two three-year periods, overall enrollment increased from 29,901 to 42,231, representing an increase of 12,330 or 41.24%. Seven of the eight colleges recorded enrollment growth, while the College of Education (COED) decreased by 3.73%. The College of Nursing (CON) demonstrated the largest percentage increase at 157.23%, followed by the College of Arts and Sciences (CAS) at 44.15% and the College of Allied Medical Sciences (CAMS) at 41.05%. Female enrollment remained higher than male enrollment, accounting for 61.30% of enrollment during the last three-year period. Female enrollment increased by 48.81%, compared with 30.70% among males. However, sex composition differed across colleges, with males remaining the majority in CCJE and CECT. The findings demonstrate substantial institutional enrollment expansion, although the magnitude and sex distribution of growth varied across academic units. These enrollment analytics provide an empirical and data-driven basis for institutional planning and for advancing the digitalization and automation of registrar operations, particularly in enrollment monitoring, automated reporting, data visualization, and decision support. Integrating historical enrollment data into a digital registrar information environment may facilitate more efficient monitoring of enrollment patterns, resource allocation, staffing, student services, and program development. The figures represent enrollment counts across the first semester, second semester, and summer terms and should not be interpreted as counts of unique individual students.
Keywords: academic programs, enrollment trends, higher education, institutional planning, sex distribution
Keywords: academic programs, enrollment trends, higher education, institutional planning, sex distribution
Abstract
Enhanced Sentiment-Based Stock Market Recommendation System Using Machine Learning
Sanapala Ramesh
DOI: 10.17148/IJARCCE.2026.151002
Abstract: Stock prices are influenced not only by historical price movement but also by public sentiment expressed through financial news. Retail investors usually consult charting platforms and news platforms separately, with no automated bridge between them. This project presents an implemented, Flask-based web application that combines quantitative technical analysis of stock prices with machine-learning based sentiment analysis of financial news to produce a transparent BUY, HOLD or SELL recommendation.
Historical price data is sourced from Yahoo Finance and used to calculate the 20-day moving average, the 14-period Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD). Financial news is gathered from NewsAPI, cleaned with NLTK, and converted into TF-IDF and hand-crafted sentiment features. Three classifiers, namely Random Forest, Linear Support Vector Machine and Multinomial Naive Bayes, are trained, and the best is selected by test-set accuracy. Article-level predictions are aggregated into a daily Sentiment Index, which is combined with a price-trend score using the weighted formula 0.6 x Sentiment + 0.4 x Trend. An RSI-based safety filter prevents extreme-market signals from being followed blindly. All data and results are stored in SQLite and displayed on an interactive Chart.js dashboard.
The system was run on 411 stored news articles covering seven US and Indian stocks. On an 80/20 split, the Linear SVM achieved the highest test accuracy (100.00%), followed by Random Forest (98.80%) and Naive Bayes (79.52%), and was selected automatically. Because the sentiment labels are generated by a financial keyword lexicon rather than by human annotators, these metrics measure agreement with that labelling heuristic and not validated real-world sentiment accuracy. This limitation is noted explicitly throughout the report.
Keywords: stock market, sentiment analysis, technical indicators, RSI, MACD, TF-IDF, Random Forest, SVM, Naive Bayes, Flask.
Historical price data is sourced from Yahoo Finance and used to calculate the 20-day moving average, the 14-period Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD). Financial news is gathered from NewsAPI, cleaned with NLTK, and converted into TF-IDF and hand-crafted sentiment features. Three classifiers, namely Random Forest, Linear Support Vector Machine and Multinomial Naive Bayes, are trained, and the best is selected by test-set accuracy. Article-level predictions are aggregated into a daily Sentiment Index, which is combined with a price-trend score using the weighted formula 0.6 x Sentiment + 0.4 x Trend. An RSI-based safety filter prevents extreme-market signals from being followed blindly. All data and results are stored in SQLite and displayed on an interactive Chart.js dashboard.
The system was run on 411 stored news articles covering seven US and Indian stocks. On an 80/20 split, the Linear SVM achieved the highest test accuracy (100.00%), followed by Random Forest (98.80%) and Naive Bayes (79.52%), and was selected automatically. Because the sentiment labels are generated by a financial keyword lexicon rather than by human annotators, these metrics measure agreement with that labelling heuristic and not validated real-world sentiment accuracy. This limitation is noted explicitly throughout the report.
Keywords: stock market, sentiment analysis, technical indicators, RSI, MACD, TF-IDF, Random Forest, SVM, Naive Bayes, Flask.
Abstract
Automated Multi-Student Attendance Using Video-Based Facial Recognition and Embedding Matching
Likhitha Alajangi, Dr. Kuda Nageswara Rao
DOI: 10.17148/IJARCCE.2026.151003
Abstract: A major aspect of determining student attendance is having accurate records; unfortunately, traditional attendance systems are inefficient, not accurate, and can be manipulated for ”proxy” attendance. A way around these obstacles is to create an intelligent, automated attendance method using deep learning facial recognition technology based on a pretrained model. In addition to traditional still-image approaches, our system begins with recording a short video of each participating student, leading to frame-by-frame extraction for constructing a robust and diverse dataset containing facial image samples of each student. This new pipeline from video to image will improve the representation of students’ faces by using the three ways students’ faces are captured (pose, expression, and lighting) to provide a more realistic photo of each student. In this approach, we will utilise a pretrained deep learning face recognition algorithm to extract the distinctive features of student faces from our generated dataset. A single classroom image containing multiple students will be used for attendance. The system will identify faces in the group image and extract their unique features by using three face detection methods, namely Haar Cascade, MTCNN, and InsightFace, which are evaluated and compared. For recognition, FaceNet and ArcFace embedding models are investigated using cosine similarity matching to perform both extraction and recognition, which helps us generate face embeddings for each person, compare those features to the database using cosine similarity, and mark attendance for recognized students with no manual intervention. This approach should successfully connect individual-level training data with group-level testing conditions, allowing for accurate facial recognition in the classroom.
Keywords: Automated Attendance System, Facial Recognition, Pretrained Deep Learning Model, Video Frame Extraction.
Keywords: Automated Attendance System, Facial Recognition, Pretrained Deep Learning Model, Video Frame Extraction.
Abstract
AI-Based Semantic Understanding of Code Mixed Tamil-English Text in Informal Digital Communication
Aditya Raman, Sushmita Kundu, Aditya Balaji, G.Paavai Anand
DOI: 10.17148/IJARCCE.2026.151005
Abstract: Tamil-English code-mixing, popularly known as “Tanglish,” is a pervasive mode of informal digital communication among Tamil speakers, frequently rendered in Romanized script across social media and messaging platforms. Existing natural language processing (NLP) systems, largely trained on monolingual or formally structured corpora, are not optimized to interpret the semantic, sentiment, and intent-level nuances of such hybrid, non-standardized text. This paper proposes a comparative research framework to evaluate the capability of different computational approaches—traditional NLP techniques, BERT-based transformer models, and large language models (LLMs)—in understanding Tamil-English code-mixed text, alongside a proposed preprocessing pipeline designed to normalize Romanized Tamil variants prior to model inference. We outline a methodology for constructing an annotated code-mixed dataset, detail the architecture of each comparative model, and define evaluation metrics spanning accuracy, F1-score, and semantic similarity. The study aims to establish whether targeted preprocessing can meaningfully improve semantic fidelity for low-resource, code-mixed language understanding, contributing toward AI systems that better preserve and process minority and regional language expression in digital spaces.
Keywords: code-mixing, Tanglish, Tamil-English NLP, Romanized Tamil, semantic preservation, low-resource language processing, transformer models
Keywords: code-mixing, Tanglish, Tamil-English NLP, Romanized Tamil, semantic preservation, low-resource language processing, transformer models
Abstract
Comparative Analysis of Convolutional Neural Network Architectures for Automated Waste Classification
Sakshi Anil Parkhe
DOI: 10.17148/IJARCCE.2026.151006
Abstract: Effective waste classification is a critical component of modern smart waste management systems, enabling automated sorting and recycling at scale. This paper presents a comprehensive comparative study of six convolutional neural network (CNN) architectures (VGG16, ResNet50, MobileNetV3, EfficientNet-B0, AlexNet, and a custom- designed CNN) for the task of automated waste classification into six categories: cardboard, glass, metal, paper, plastic, and trash. Each model is evaluated on a standardized dataset of 2,527 images under identical experimental conditions, including data augmentation, learning rate scheduling, and transfer learning where applicable. Performance is assessed using accuracy, precision, recall, F1-score, receiver operating characteristic (ROC) curves, and precision-recall (PR) curves. Experimental results demonstrate that the custom CNN achieves the highest classification accuracy of 95.05% with a macro-average ROC-AUC of 0.9974, outperforming established architectures. ResNet50 and MobileNetV3 follow closely at 94.07% and 93.87%, respectively, offering favorable trade-offs between accuracy and computational complexity. The analysis further includes per-class performance evaluation, confusion matrix analysis, and computational efficiency comparison, providing actionable insights for deploying waste classification systems in resource-constrained environments.
Keywords: waste classification, convolutional neural networks, transfer learning, deep learning, image classification, recycling, smart waste management
Keywords: waste classification, convolutional neural networks, transfer learning, deep learning, image classification, recycling, smart waste management
Abstract
MACHINE LEARNING FRAMEWORK FOR DETECTION OF MENTAL STRESS
Archana Borse, MS. Yogita Kadbane
DOI: 10.17148/IJARCCE.2026.151007
Abstract: Mental stress is becoming a common issue among students and individuals due to factors such as workload, lack of sleep, screen time, deadline pressure, financial concerns, personal responsibilities, and limited relaxation time. Identifying stress levels can be difficult because different factors can affect each individual differently. This research presents a machine learning framework for the detection of mental stress using questionnaire-based data. Data was collected from 51 respondents through a Google Form and included factors such as sleep hours, screen time, workload, physical activity, socialization, sleep quality, deadline pressure, financial concerns, personal or family responsibilities, and social support. The collected data was processed and used to calculate a stress score, based on which respondents were classified into Low, Moderate, and High stress levels. The dataset contained 17 columns and the stress-level distribution included 10 Low, 37 Moderate, and 4 High stress responses. A machine learning classification model was developed using Python to identify stress-level patterns. The model achieved an accuracy of approximately 81.82%. The study also used data visualization to understand the distribution of stress and the relationship between different factors and stress levels. The proposed framework shows how data analysis and machine learning can be used as a preliminary approach for detecting and understanding mental stress patterns.
Keywords: Mental Stress, Machine Learning, Stress Detection, Stress Level, Data Analysis, Python, Classification
Keywords: Mental Stress, Machine Learning, Stress Detection, Stress Level, Data Analysis, Python, Classification
Abstract
HemaSight AI: A Four-Stage Deep Learning Pipeline for Automated Leukemia Detection and Post-Transplant Monitoring
Varun Doddagoudar, Vishwa, Shashank, Madhushree M
DOI: 10.17148/IJARCCE.2026.151008
Abstract: Leukemia is a life-threatening hematological malignancy where early and accurate detection significantly improves patient survival. Conventional diagnosis relies on manual microscopic examination of peripheral blood smears, a process that is time-consuming, observer-dependent, and prone to error. This paper presents HemaSight AI, a four- stage artificial intelligence pipeline for automated leukemia screening and post-transplant monitoring. Stage 1 applies XGBoost to classify complete blood count (CBC) parameters, including 17+ extended markers such as SGOT, SGPT, creatinine, potassium, chimerism, and GVHD status. Stage 2 employs a conditional generative adversarial network (cGAN) to synthesize microscopic blood smear images alongside a ResNet18-based similarity engine that computes a weighted four-metric match (semantic, color, structural, pixel). Stage 3 uses linear regression on longitudinal blood test data to detect early signs of relapse before clinical symptoms appear. Stage 4 integrates Google Gemini via a multilingual chatbot supporting seven languages. The platform is implemented using Next.js, FastAPI, Supabase, and PyTorch, deployed entirely on free-tier infrastructure. Evaluation across 68 test cases achieved a 98.5% pass rate with sub-3-second API response times. Results demonstrate that a low-cost, scalable, and clinically accessible AI tool can be built without proprietary infrastructure.
Keywords: Leukemia detection, XGBoost, conditional GAN, ResNet18, time-series analysis, large language model, medical AI, multilingual chatbot.
Keywords: Leukemia detection, XGBoost, conditional GAN, ResNet18, time-series analysis, large language model, medical AI, multilingual chatbot.
Abstract
Smart Irrigation System for Precision Farming
Mr. Bhagyesh N Chaudhari, Dr. Dinesh D Puri
DOI: 10.17148/IJARCCE.2026.151010
Abstract: Smart Irrigation System for Precision Farming is an IoT-based and sensor-driven approach designed to improve water management, crop productivity, and agricultural sustainability. Conventional irrigation practices often depend on fixed schedules or manual decisions, which can result in over-irrigation, under-irrigation, water wastage, and increased labour requirements. The proposed system integrates soil-moisture, temperature, humidity, and rainfall sensors with a microcontroller such as Arduino or ESP32 to continuously monitor field conditions in real time. Based on the collected data, the system automatically determines the irrigation requirement and controls water pumps or solenoid valves accordingly. Weather information can also be incorporated to avoid unnecessary irrigation during expected rainfall and to adapt watering according to environmental conditions. IoT connectivity enables farmers to remotely monitor soil and weather parameters through a web or mobile dashboard. The system provides a low-cost, scalable, and farmer-friendly solution that supports precision agriculture by delivering the right amount of water at the right time. Such intelligent irrigation can conserve water and energy, reduce manual effort, improve crop health, and contribute to sustainable agricultural development.
Abstract
Multi-Crop Disease Detection and Management Recommendation System Using Deep Learning
K. Hemalatha, Prof. K. Venkata Rao, P. Swathi
DOI: 10.17148/IJARCCE.2026.151011
Abstract: Crop diseases are a major challenge in agriculture, as late or incorrect identification can cause crop damage and reduce productivity. Existing crop disease detection systems mainly focus on identifying diseases from plant leaf images, but provide limited support for disease management. This creates a gap between disease identification and practical management. The proposed Multi-Crop Disease Detection and Management Recommendation System Using Deep Learning addresses this gap by combining disease detection with management recommendations. The system uses deep learning and transfer learning models to classify crop diseases from leaf images. It provides chemical and organic management approaches based on the identified crop and disease. Thus, the system connects disease identification with suitable management practices, making it more useful for practical crop disease management.
Keywords: Crop Disease Detection, Deep Learning, Management Recommendation System, Multi-Crop Classification, Transfer Learning.
Keywords: Crop Disease Detection, Deep Learning, Management Recommendation System, Multi-Crop Classification, Transfer Learning.
Abstract
Digital Document Verification System
Dr. S.S. Kokila, Sriniva.S
DOI: 10.17148/IJARCCE.2026.151012
Abstract: Digital documents are widely used in education, banking, healthcare, government, employment, and business. Ensuring the authenticity and integrity of these documents has become increasingly important due to the growing number of forged, altered, and fraudulent documents. Traditional verification methods rely heavily on manual inspection and comparison with original records, which can be time-consuming, costly, and prone to human errors. Recent developments in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Optical Character Recognition (OCR) have enabled automated document verification with improved accuracy and efficiency. Machine learning algorithms such as Random Forest, Support Vector Machine, Decision Trees, Logistic Regression, and K-Nearest Neighbors can classify genuine and fraudulent documents using extracted features. Deep learning models such as Convolutional Neural Networks (CNNs) can analyze document images and detect manipulation and forgery. This review examines recent advancements in digital document verification, including OCR, image processing, digital signatures, blockchain, biometric verification, cloud computing, and multimodal document analysis. The study also discusses commonly used datasets, verification methods, evaluation metrics, challenges, research gaps, and future research directions. The findings indicate that integrating AI-based verification, secure digital signatures, blockchain, and multimodal analysis can support the development of reliable, secure, scalable, and efficient digital document verification systems
Keywords: Digital Document Verification, Document Authentication, Machine Learning, Deep Learning, Optical Character Recognition, Artificial Intelligence, Blockchain, Digital Signature, Forgery Detection.
Keywords: Digital Document Verification, Document Authentication, Machine Learning, Deep Learning, Optical Character Recognition, Artificial Intelligence, Blockchain, Digital Signature, Forgery Detection.
Abstract
CUSTOMER COMPLAINT TRACKING SYSTEM
Dr. L. Baby Victoria, Abinaya D
DOI: 10.17148/IJARCCE.2026.151013
Abstract: Customer complaints provide valuable information about problems in products, services, delivery, support, and customer experience. However, organizations often receive a large number of complaints through websites, email, social media, call centers, and mobile applications, making manual processing slow and difficult to manage. A customer complaint tracking system can improve this process by automatically collecting, classifying, prioritizing, routing, monitoring, and resolving complaints. This paper presents a review of recent approaches based on Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL), sentiment analysis, topic modelling, and transformer-based models for intelligent complaint management. NLP techniques can convert unstructured complaint text into useful information, while machine learning algorithms can classify complaints according to category, urgency, department, or severity. Sentiment analysis can identify customer emotions and support priority assignment, whereas automated routing can forward complaints to the appropriate department. Recent research has also explored BERT and AI-enabled complaint-handling systems. The review identifies important research gaps related to multilingual complaints, limited labelled datasets, explainability, privacy, real-time tracking, integration with existing business systems, and human-AI collaboration. Future systems should combine NLP, explainable AI, large language models, sentiment analysis, automated escalation, and real-time dashboards to provide faster and more transparent complaint resolution.
Keywords: Customer Complaint Tracking, Complaint Management, Natural Language Processing, Machine Learning, Sentiment Analysis, Text Classification, BERT, Topic Modelling, Automated Routing, Customer Service.
Keywords: Customer Complaint Tracking, Complaint Management, Natural Language Processing, Machine Learning, Sentiment Analysis, Text Classification, BERT, Topic Modelling, Automated Routing, Customer Service.
Abstract
Crop Recommendation System for Farmers Using Convolutional Neural Network
D. Savitha, Gayathri U, Priya S
DOI: 10.17148/IJARCCE.2026.151014
Abstract: Agriculture plays an important role in food security and the economic development of many countries. Selecting the most suitable crop for a particular field is a major challenge for farmers because crop growth depends on soil properties, nutrients, temperature, humidity, rainfall, pH, and climatic conditions. Traditional crop selection mainly depends on farmers experience and local knowledge, which may not always provide accurate recommendations under changing environmental conditions. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) provide effective solutions for data-driven agricultural decision making. This paper presents a review of crop recommendation systems with particular emphasis on Convolutional Neural Networks (CNNs). CNNs can automatically learn useful patterns from soil images, remote-sensing images, and other agricultural data, while CNN- based hybrid models can also be combined with environmental and soil parameters. Recent studies have explored Random Forest, XGBoost, Support Vector Machine, LSTM, CNN, attention-based networks, Transformer models, and hybrid deep-learning architectures for crop recommendation. This review examines research published from 2020 to 2026, summarizes their datasets, preprocessing methods, algorithms, and major findings, and identifies research gaps related to dataset availability, regional generalization, explainability, multimodal data integration, and real-time deployment. A CNN-based crop recommendation system can potentially provide farmers with localized and intelligent recommendations while reducing inappropriate crop selection, improving productivity, and supporting sustainable farming.
Keywords: Crop Recommendation, Convolutional Neural Network, CNN, Deep Learning, Precision Agriculture, Smart Farming, Soil Classification, Machine Learning, Farmers, Agriculture.
Keywords: Crop Recommendation, Convolutional Neural Network, CNN, Deep Learning, Precision Agriculture, Smart Farming, Soil Classification, Machine Learning, Farmers, Agriculture.
Abstract
Equilibrium Dynamics and Threshold Conditions in Tumor–Immune Models with Chemotherapy
Shivangi Chauhan*, Prof. Diwari Lal
DOI: 10.17148/IJARCCE.2026.151015
Abstract: This research presents a non-linear tumor-immune-chemotherapy model that distinguishes drug sensitive from drug resistant tumors with respect to chemotherapy dynamics and immune response. The model includes Gompertzian tumor growth, immune mediated killing, differential drug sensitivity, treatment induced drug resistance, immune suppression, and drug clearance. Biologically feasible equilibrium states were established by analytical investigation along with biological conditions of threshold for invasion by sensitive or resistant tumor cell types. Chemotherapy is demonstrated to have a double-edged sword effect in this model; increasing treatment intensity will be effective at suppressing sensitive tumor cells, however it also promotes the relative proportion of resistant tumor cells. Numerically simulated results demonstrate that local stability remains around positive equilibria and resistant tumor cells can remain present within the model despite an increase in chemotherapy. Further sensitivity analysis was conducted to identify factors that influence the models behavior including tumor growth rate, immune killing rates, chemotherapy efficacy, selective pressure on resistance development, degree of immune suppression and drug clearance. Ultimately the data supports that reduction in sensitive tumor population through chemotherapy does not eliminate all cancer (resistant cells), nor prevent recurrence due to continued presence of resistant cells.
Keywords: Tumor–immune dynamics, chemotherapy, drug resistance, mathematical modeling, equilibrium analysis, threshold conditions, Gompertz growth, resistance invasion
Keywords: Tumor–immune dynamics, chemotherapy, drug resistance, mathematical modeling, equilibrium analysis, threshold conditions, Gompertz growth, resistance invasion
Abstract
Quantum Assisted Predictive Maintenance and Scheduling System for Industry 4.0
Muppuri Satish Kumar
DOI: 10.17148/IJARCCE.2026.151016
Abstract: Predictive Maintenance is one of the primary applications of Industry 4.0 for detecting potential equipment failures in advance of unplanned outages. With the increasing number of Industrial Internet of Thing(IoT) sensors available today, there is now the ability to access multi-modal data from machines that can be used to monitor conditions and predict failures. Traditional methods have been limited in their ability to capture the complex relationship between the different sensor measurements and make decisions regarding maintenance based upon limited resources. A quantum- assisted framework for integrating Machine Failure Prediction and Maintenance Scheduling. A Hybrid Quantum Neural Network (HQNN) will be utilized to analyze the different types of industrial sensor data and determine the likelihood of failure. The quantum component of our model will use a parameterized quantum circuit to represent the analyzed sensor data as quantum states; then measure these quantum states to produce a classical representation of the probability of failure. Once we have determined this probability of failure, it will be combined with the priority of the machine and its maintenance needs to create a maintenance scheduling problem. A QAOA-based method will be developed to develop a multi-shift maintenance schedule while considering all available resources.
Keywords: Industry 4.0, Predictive Maintenance, Hybrid Quantum Neural Network (HQNN), Parameterized Quantum Circuit (PQC), CNOT Ring Entanglement, QAOA, QUBO, Shift Scheduling
Keywords: Industry 4.0, Predictive Maintenance, Hybrid Quantum Neural Network (HQNN), Parameterized Quantum Circuit (PQC), CNOT Ring Entanglement, QAOA, QUBO, Shift Scheduling
Abstract
Chronic Kidney Disease Prediction System Using Machine Learning
Savitha. D, Haritha. V P, Mathisha. N
DOI: 10.17148/IJARCCE.2026.151017
Abstract: Chronic kidney disease (CKD) is a progressive condition that often remains unnoticed until a large part of kidney function has been lost, which makes early screening one of the most effective ways of delaying dialysis and transplantation. Machine learning (ML) can learn the relationship between routine clinical and laboratory measurements and the presence of CKD, and can turn it into a fast, low-cost decision-support tool. This paper presents a structured
and ensemble classifiers, feature selection, class-imbalance handling, explainable artificial intelligence and progression prediction. The studies are compared in a summary table and the common gaps are identified: reliance on small public datasets, near-perfect but weakly validated accuracy, limited external validation, explanations that never reach the user, and few complete systems that connect prediction to patient records and reports. Based on these gaps, the motivation, application areas and future directions for a practical web-based CKD prediction system built on a Random Forest classifier are discussed.
Keywords: Chronic kidney disease, Machine learning, Randomforest, Early prediction, Explainable AI, Feature selection, Clinical decision support.
and ensemble classifiers, feature selection, class-imbalance handling, explainable artificial intelligence and progression prediction. The studies are compared in a summary table and the common gaps are identified: reliance on small public datasets, near-perfect but weakly validated accuracy, limited external validation, explanations that never reach the user, and few complete systems that connect prediction to patient records and reports. Based on these gaps, the motivation, application areas and future directions for a practical web-based CKD prediction system built on a Random Forest classifier are discussed.
Keywords: Chronic kidney disease, Machine learning, Randomforest, Early prediction, Explainable AI, Feature selection, Clinical decision support.
Abstract
Women Enterprise Hub: A Web-Based Platform for Women Entrepreneurs
D. Savitha, S. Nitharsana, S. Subarna
DOI: 10.17148/IJARCCE.2026.151018
Abstract: Women Enterprise Hub is a web-based platform designed to support women entrepreneurs, particularly those involved in home-based and small-scale sectors such as handicrafts, clothing, jewellery and food products. Many women entrepreneurs depend on informal networks, local markets and scattered social media channels to promote their businesses, while information about government schemes, funding opportunities, mentoring and training is distributed across different sources. The proposed system brings these activities together through a centralized portal. The platform provides user registration and authentication, business profile management, product listing, marketplace functions, order processing, delivery estimation, government scheme information, mentor discovery and session booking, training information, reviews and suggestions. A database-driven architecture is used to manage users, businesses, products, orders and supporting services. The system aims to provide a structured digital environment that improves visibility, access to support information and interaction between women entrepreneurs, mentors and customers.
Keywords: Women Entrepreneurship, Web Portal, Marketplace, Mentoring, Government Schemes, PHP, MySQL
Keywords: Women Entrepreneurship, Web Portal, Marketplace, Mentoring, Government Schemes, PHP, MySQL
Abstract
DESIGN AND DEVELOPMENT OF AN
ESP-BASED SMART MOBILE, MANIPULATOR FOR REMOTE OBJECT, HANDLING, Kuncham Preetham, Dr. K. Raja Kumar
DOI: 10.17148/IJARCCE.2026.151020
Abstract: The integration of robotic mobility and object manipulation provides a practical solution for remote robotic operations. This work presents a manually controlled mobile robotic platform integrated with a servo-based robotic arm for basic object handling. An ESP32 microcontroller coordinates the DC motors, servo motors, and ultrasonic sensing according to user commands and sensed distance. The vehicle supports forward, backward, left, right, and stop operations, while the robotic arm provides base rotation, shoulder and elbow movement, and gripper open/close functions. A wireless control interface provides remote operation, and an ultrasonic sensor is used for obstacle detection. Experimental demonstration shows successful vehicle movement, obstacle stopping, arm actuation, and pick- and-place operation. The resulting platform is compact, portable, economical, and suitable for educational, remote- handling, and basic mobile robotic applications.
Keywords: ESP32, mobile manipulator, robotic arm, ultrasonic sensor, wireless control, servo motor, obstacle detection, pick-and-place.
Keywords: ESP32, mobile manipulator, robotic arm, ultrasonic sensor, wireless control, servo motor, obstacle detection, pick-and-place.
Abstract
An Intelligent Risk-Adaptive Framework for Secure Examination Paper Distribution, Traceability, and Early Leak Detection
Mr. Swapnil S. Chaudhari, Asst. Prof. Sapana A. Fegade
DOI: 10.17148/IJARCCE.2026.151021
Abstract: Examination-paper leakage threatens the fairness, credibility, and operational continuity of academic and competitive examinations. Conventional protection mechanisms rely heavily on controlled physical handling, restricted access, and manual supervision; digital distribution reduces some transportation risks but introduces new requirements for encryption, authentication, key management, traceability, and monitoring. This research presents a risk-adaptive examination-security framework that integrates four complementary capabilities: secure digital distribution, tamper- evident chain-of-custody tracking, semantic leak detection, and risk-based security control selection. The study follows a design-science and applied-experimental approach. The proposed architecture separates a Java/Spring Boot security core from a Python-based natural-language-processing service. Question papers are protected through encryption, controlled release, copy-level fingerprinting, and auditable access events. Suspected public-source leaks are represented as a semantic-search problem in which original questions are compared with noisy, partial, paraphrased, OCR-derived, and multilingual variants using sentence embeddings. A risk engine maps examination characteristics to an interpretable risk score and recommends stronger controls for higher-risk examinations. The empirical evidence retained from the study is a pilot analysis of 66 documented Indian examination-paper-leak incidents from 2014-2024. Logistic Regression and Random Forest were evaluated using five-fold stratified cross-validation against a majority-class baseline. The baseline achieved an F1-score of 0.842, compared with 0.674 for Logistic Regression and 0.739 for Random Forest, indicating that the small incident dataset and limited features are insufficient for reliable prediction of incident- confirmation status. This classification task is treated as a pilot study rather than as evidence that future examination risk can already be predicted reliably. The manuscript therefore defines a reproducible experimental protocol for evaluating semantic leak detection and the complete risk-adaptive system without fabricating unperformed results. A working prototype implementing all four modules - the Java/Spring Boot security core with role-based access control, the SHA- 256 hash-linked chain of custody, a Python microservice offering both a TF-IDF baseline and Sentence-BERT similarity, and the risk-scoring engine - was built and verified through fourteen automated checks on the core security logic and an end-to-end run confirming encryption, tamper-evident logging, and tamper detection. The principal contribution is an integrated and testable research architecture, evidenced by a working prototype, that explicitly connects prevention, traceability, detection, and adaptive response, together with an identification of the data and validation requirements needed before such a system can be deployed in a real examination environment.
Keywords: Examination Security; Question-Paper Leakage; Risk-Adaptive Security; Semantic Similarity; Sentence- BERT; Blockchain; Chain of Custody; Digital Watermarking; NLP; Secure Distribution.
Keywords: Examination Security; Question-Paper Leakage; Risk-Adaptive Security; Semantic Similarity; Sentence- BERT; Blockchain; Chain of Custody; Digital Watermarking; NLP; Secure Distribution.
Abstract
Smart Classroom Seat Allocation System Using IoT, Artificial Intelligence, Machine Learning and Optimization Techniques: A Review
Dr. P. Anitha, V Karishni
DOI: 10.17148/IJARCCE.2026.151022
Abstract: The transformation of conventional classrooms into intelligent learning environments has created new opportunities for automating classroom management and improving student learning experiences. Among these challenges, student seat allocation is an important task because seating arrangements can influence accessibility, classroom interaction, student engagement, learning conditions and effective utilization of available space. Traditional seat allocation is predominantly performed manually and becomes increasingly complex when the number of students, classrooms and allocation constraints increases. A Smart Classroom Seat Allocation System can address these limitations by integrating the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML) and optimization techniques. This paper presents a comprehensive review of research published between 2020 and 2026 concerning classroom seating, intelligent seat allocation, smart classroom technologies, IoT-based monitoring and optimization-based allocation approaches. Existing studies have explored genetic algorithms, mathematical programming, graph-based heuristics, agent-based modelling, sociometric sensing, RFID, IoT devices and automated classroom management systems. Research findings indicate that seating position and proximity can affect student engagement, academic performance, peer interaction and social relationships. IoT technologies can further provide real-time information regarding seat occupancy and classroom conditions, enabling dynamic allocation and monitoring. However, existing approaches frequently address individual aspects of the problem, such as examination seating, social distancing, classroom interaction or occupancy monitoring, rather than providing an integrated allocation framework. Therefore, there is a significant opportunity to develop an adaptive system that combines student characteristics, classroom constraints, seat availability, accessibility requirements, learning preferences and real-time occupancy information within a unified optimization framework. This review examines existing approaches, identifies their limitations and research gaps and discusses potential applications and future directions for developing intelligent, flexible and scalable smart classroom seat allocation systems.
Keywords: Smart Classroom, Seat Allocation, Artificial Intelligence, Machine Learning, Internet of Things, Optimization, RFID, Student Management, Smart Education.
Keywords: Smart Classroom, Seat Allocation, Artificial Intelligence, Machine Learning, Internet of Things, Optimization, RFID, Student Management, Smart Education.
Abstract
PERSONALISED HEALTHCARE RECOMMENDATION SYSTEM USING XGBOOST AND SHAP
Savitha. D, Sowmiya. V, Akshaya. C
DOI: 10.17148/IJARCCE.2026.151023
Abstract: The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has brought significant improvements to healthcare by supporting disease prediction, individual health assessment, and clinical decision- making. However, conventional healthcare systems often provide generalized recommendations without adequately considering individual differences in demographic information, lifestyle, physiological conditions, medical history, and health risks. This review proposes an AI-Based Personalized Healthcare Recommendation System that integrates Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP). The approach combines the strong predictive performance of XGBoost with Explainable Artificial Intelligence (XAI) to generate personalized and understandable healthcare insights. The system analyzes various healthcare attributes, including demographic details, body measurements, vital signs, lifestyle factors, medical history, family history, laboratory results, and existing health conditions to assess potential disease risks. XGBoost is effective for structured healthcare data as it can capture complex nonlinear relationships between different health-related features. SHAP provides explanations by identifying the contribution of individual features to each prediction, enabling users and healthcare professionals to understand important factors such as blood glucose, body mass index, cholesterol levels, physical activity, sleep duration, and family history.
Keywords: Personalized Healthcare, Machine Learning, XGBoost, SHAP
Keywords: Personalized Healthcare, Machine Learning, XGBoost, SHAP
Abstract
Real-Time Sign Language Translator Using Video Prediction
Geetha. N, Mohanashankare.V.K, Prathanyaa.K
DOI: 10.17148/IJARCCE.2026.151025
Abstract: Sign language is a visual language in which meaning is conveyed through hand shape, movement, body posture, facial expression, and temporal relationships between gestures. Automatic translation from video to text can reduce communication barriers between sign-language users and people who do not know the language. This paper presents a real-time sign language translator using video prediction, in which a webcam or uploaded video is processed as a sequence rather than as independent images. The proposed framework combines video acquisition, frame preprocessing, hand and pose landmark extraction, spatial feature learning, temporal sequence modelling, sign prediction, text generation, and optional speech synthesis. OpenCV can be used for frame handling, while MediaPipe provides efficient landmark extraction. CNN(Convolutional Neural Network), GRU(Gated Recurrent Unit), LSTM( Long Short-Term Memory), or Transformer models can then learn spatial and temporal patterns in the video. Recent research from 2020 to 2026 shows a transition from static and isolated-sign recognition toward continuous sign- language recognition and end-to-end sign-to-text translation. However, practical deployment remains challenging because of signer variation, occlusion, variable signing speed, limited continuous datasets, non-manual cues, temporal alignment, and computational latency. The proposed research direction therefore emphasizes efficient video prediction, robust temporal modelling, and real-time deployment, with particular relevance to Indian Sign Language.
Keywords: Sign Language Recognition, Video Prediction, Computer Vision, Deep Learning, MediaPipe, Temporal Modeling, Transformer, Indian Sign Language, Real-Time Translation, Speech Synthesis.
Keywords: Sign Language Recognition, Video Prediction, Computer Vision, Deep Learning, MediaPipe, Temporal Modeling, Transformer, Indian Sign Language, Real-Time Translation, Speech Synthesis.
Abstract
Agent-less Windows System Vulnerability and Network Scanner for Security Assessment and Reporting
Satvik Dilip Barhate, Dr. Dinesh Puri
DOI: 10.17148/IJARCCE.2026.151026
Abstract: Modern Windows endpoints and small enterprise networks expose multiple security-relevant services, software versions, configurations, and network paths. Conventional vulnerability assessment approaches may require endpoint agents, extensive deployment, or separate tools for network discovery and host-level inspection. This research presents CyberWolf, an agent-less Windows system vulnerability and network scanner designed to combine network discovery, service enumeration, structured scan-result processing, and rule-based security assessment in a lightweight workflow. The proposed approach uses Nmap for network discovery and XML output as a machine-readable data source, followed by parsing, normalization, security checks, finding prioritization, and report generation. The design is intended for controlled and authorized assessment of Windows hosts without installing a permanent software agent on each target. The research defines a repeatable assessment lifecycle, a normalized data model, example security rules, evaluation dimensions, limitations, and future extensions. The work is positioned as a defensive assessment framework for identifying network-exposed conditions and supporting remediation decisions.
Keywords: Windows Security, Vulnerability Assessment, Agent-less Scanning, Network Discovery, Nmap, CyberWolf, Security Automation, Vulnerability Management
Keywords: Windows Security, Vulnerability Assessment, Agent-less Scanning, Network Discovery, Nmap, CyberWolf, Security Automation, Vulnerability Management
Abstract
Healthcare Insurance Fraud Detection Using Machine Learning and Deep Learning
Siddanathi Prasanth Srinivas Sai
DOI: 10.17148/IJARCCE.2026.151027
Abstract: Healthcare insurance fraud causes significant financial losses and is difficult to detect due to the large volume and complexity of healthcare claims. This paper presents an integrated fraud detection framework using beneficiary, inpatient, outpatient, and provider-level fraud-label data. The datasets are cleaned, integrated, and transformed into provider-level features, with 558,211 inpatient and outpatient claim records forming the unified claims dataset. XGBoost, Random Forest, and TabNet are employed to identify providers associated with potential fraudulent activity, while class imbalance is addressed during model development. A local Llama 3.2-based explanation layer generates human-readable explanations, and a SHA-256 hash-chained audit ledger provides tamper-evident records of detection activities. A dashboard is also used to present prediction results and model performance. The proposed framework integrates fraud detection, explainability, and secure auditing for effective healthcare insurance fraud analysis.
Keywords: Healthcare Insurance Fraud Detection, TabNet, XGBoost, Random Forest, Ensemble Learning, Explainable Artificial Intelligence (XAI), Llama 3.2, SHA-256 Hash-Chained Audit Ledger.
Keywords: Healthcare Insurance Fraud Detection, TabNet, XGBoost, Random Forest, Ensemble Learning, Explainable Artificial Intelligence (XAI), Llama 3.2, SHA-256 Hash-Chained Audit Ledger.
Abstract
Grouping-Aware Numerical Verification for Offline Retrieval-Augmented Question Answering over Institutional Documents
Sudheer Katuri, G. Sharmila Sujatha
DOI: 10.17148/IJARCCE.2026.151028
Abstract: Institutions hold large volumes of policy documents, reports and spreadsheets that are still searched manually. Retrieval-augmented generation (RAG) offers a natural interface to such collections, but mainstream deployments route confidential material through hosted language-model APIs and provide no guarantee that figures quoted in an answer actually occur in the source text. This paper presents an offline RAG system for institutional document question answering in which ingestion, embedding, retrieval and generation all execute on commodity hardware with no external network dependency, and in which every numeral in a generated answer is verified against the retrieved context before the answer is shown. The central observation is that naive numeric extraction — the digit-matching regular expression used in comparable pipelines — fragments digit-grouped quantities such as 45,23,000 into unrelated components, causing a verifier built on it to reject faithful answers. We describe a grouping-aware extraction and verification procedure that handles Indian and Western grouping conventions, decimal and percentage forms, and enumeration markers. On a 15case extraction benchmark the proposed extractor is exact on 15/15 cases against 5/15 for the baseline, and substituting the baseline extractor into the verifier introduces false rejections of faithful answers. Measured retrieval latency over an exhaustive 384dimensional index remains below one millisecond to roughly 5,000 chunks and grows linearly to about 10.5 ms at 50,000 chunks, which bounds the corpus size at which exact search remains appropriate.
Keywords: retrieval-augmented generation, hallucination detection, offline inference, vector search, document question answering, privacy-preserving systems.
Keywords: retrieval-augmented generation, hallucination detection, offline inference, vector search, document question answering, privacy-preserving systems.
Abstract
Genuine Co-Movement, Spurious Trend, and a Ratio That Does Not Help: Variable Construction for Nigerian Macroeconomic Models
Ofierohor Ufuoma Earnest
DOI: 10.17148/IJARCCE.2026.151029
Abstract: Macroeconomic models routinely include exchange-rate and trade variables side by side as separate regressors. This paper examines whether they are separate, using 67 quarterly Nigerian observations from 2008Q2 to 2024Q4, and reports two results that point in different directions. First, the co-movement among the exchange rate, exports, imports and total trade is genuine rather than a shared trend. Levels correlations run from 0.95 to 0.99, and they survive first differencing at 0.68 to 0.93, so this is not a spurious-regression artefact. Second, the co-movement between these series and GDP is spurious: the level correlation between the exchange rate and GDP is 0.607 and falls to -0.132 in differences, and trade against GDP falls from 0.599 to -0.106. This matters for a common variable-construction choice. Normalising trade by GDP is often justified as removing shared scale, but here the numerator and denominator have no genuine co- movement, and the available series compounds the problem by dividing nominal trade by real GDP. The resulting ratio reaches 189% of GDP and tests as I(2), which invalidates the ARDL bounds procedure. We recommend testing correlations in differences before treating a ratio as a solution to collinearity, and checking that numerator and denominator share a price basis.
Keywords: Multicollinearity; Spurious Correlation; Trade Openness; Variable Construction; Nigeria.
Keywords: Multicollinearity; Spurious Correlation; Trade Openness; Variable Construction; Nigeria.
Abstract
Abhimanyu Chakravyuha: An Evidence-Aware AI Framework for Fraud Intelligence Through Semantic Analysis, Mutation Detection, and Graph-Based Reasoning
MD Auranzeb Khan, Kunal, Srijan Mani Tripathi, Vyom Pandey, Dr.G Paavai Anand
DOI: 10.17148/IJARCCE.2026.151030
Abstract: Fraudulent activities increasingly involve evolving narratives, altered textual claims, interconnected entities, and coordinated patterns that cannot always be identified by examining individual records in isolation. This paper presents Abhimanyu Chakravyuha, a proposed AI-powered fraud intelligence framework integrating natural language processing, semantic similarity analysis, mutation detection, graph-based relationship analysis, and evidence-aware verification.
The framework processes textual claims and related records, retrieves semantically relevant evidence, identifies potential modifications to previously observed patterns, and represents relationships using a graph structure. An evidence- verification component evaluates whether available information supports, contradicts, or fails to establish a claim. An abstention mechanism allows the system to return an inconclusive result when evidence is insufficient or conflicting. The design emphasizes evidence traceability, uncertainty management, and human oversight. It separates semantic similarity from factual support, suspicious patterns from confirmed fraud, and automated analysis from human-reviewed decisions. This paper specifies the architecture, methodology, and experimental design; quantitative performance is not claimed without experimental validation.
Keywords: Fraud Intelligence, Natural Language Processing, Semantic Similarity, Claim Verification, Mutation Detection, Knowledge Graphs, Explainable AI, Evidence Retrieval, Abstention, Uncertainty Calibration
The framework processes textual claims and related records, retrieves semantically relevant evidence, identifies potential modifications to previously observed patterns, and represents relationships using a graph structure. An evidence- verification component evaluates whether available information supports, contradicts, or fails to establish a claim. An abstention mechanism allows the system to return an inconclusive result when evidence is insufficient or conflicting. The design emphasizes evidence traceability, uncertainty management, and human oversight. It separates semantic similarity from factual support, suspicious patterns from confirmed fraud, and automated analysis from human-reviewed decisions. This paper specifies the architecture, methodology, and experimental design; quantitative performance is not claimed without experimental validation.
Keywords: Fraud Intelligence, Natural Language Processing, Semantic Similarity, Claim Verification, Mutation Detection, Knowledge Graphs, Explainable AI, Evidence Retrieval, Abstention, Uncertainty Calibration
Abstract
AI-DRIVEN CLINICAL DECISION SUPPORT SYSTEM FOR INTELLIGENT MEDICAL REPORT ANALYSIS AND DISEASE PREDICTION
Sravanthi Duvvuri, Dr. G. Sharmila Sujatha
DOI: 10.17148/IJARCCE.2026.151031
Abstract: The AI-Driven Clinical Decision Support System for Intelligent Medical Report Analysis and Disease Prediction enhances healthcare decision-making by integrating Artificial Intelligence into clinical data processing. The system uses Natural Language Processing (NLP) to extract symptoms from patient complaints, suggests relevant medical tests, and applies machine learning algorithms to analyze laboratory reports and detect abnormal values. It predicts possible heart, liver, and kidney diseases with probability scores, identifies the key factors behind each prediction, and generates structured clinical summaries to assist doctors in faster evaluation. The heart disease module is built on the merged UCI Heart Disease dataset containing 920 records and 14 attributes. The prototype, developed in Python with spaCy, scikit-learn, SQLite, and Streamlit, demonstrates the complete workflow from patient registration to an explained prediction. Designed as a decision-support tool, the system reduces diagnostic workload, improves patient prioritization, and supports timely intervention while ensuring that final medical decisions remain under professional supervision.
Keywords: Clinical decision support system, electronic health records, natural language processing, machine learning, disease prediction, explainable AI, SHAP, clinical summary.
Keywords: Clinical decision support system, electronic health records, natural language processing, machine learning, disease prediction, explainable AI, SHAP, clinical summary.
Abstract
AI-Enabled Structural Vibration Prediction for Enhanced Stealth Performance
Harish Kumar Kuppili, Kuda Nageswara Rao
DOI: 10.17148/IJARCCE.2026.151033
Abstract: Increasing engineering complexity and the need for dependable structural health monitoring have created a demand for accurate and computationally efficient vibration-prediction methods. Conventional approaches—including physics-based models, extensive experimental testing across operating conditions, and computationally intensive simulations—can be difficult to apply when structures and service environments vary widely and decisions must be made under uncertainty. This study presents an AI-based framework for predicting structural vibration responses using regression algorithms. Five approaches—artificial neural network (ANN), k-nearest neighbours (KNN), decision tree (DT), gradient boosting regression (GBR), and random forest regression (RFR)—were evaluated using historical and simulated data representing material characteristics, stiffened geometries, and applied loading conditions. The comparison indicates that ensemble methods, particularly RFR and GBR, provide stronger predictive accuracy and generalisation than the other evaluated models. GBR delivered the best test performance, while RFR achieved a closely comparable result. ANN could represent nonlinear relationships but required additional computational effort and tuning, whereas KNN and DT were more sensitive to variation in the data. The findings indicate that AI-based regression can support faster preliminary assessment, reduce design-cycle time, and contribute to structural monitoring. This work demonstrates the potential of ensemble learning as a scalable approach to vibration prediction and as a foundation for data-driven infrastructure monitoring, proactive maintenance, anomaly detection, and improved structural safety.
Keywords: Artificial intelligence, vibration prediction, structural health monitoring, predictive models, data-driven methods, machine learning.
Keywords: Artificial intelligence, vibration prediction, structural health monitoring, predictive models, data-driven methods, machine learning.
Abstract
Contract Sentinel: A Literature Survey on Transformer-Based Clause Extraction and Risk Detection in Legal Contracts
Vaishnavi Karpe, Tanishka Makode, Maria Shaikh, P. J. Jambhulkar
DOI: 10.17148/IJARCCE.2026.151034
Abstract: Legal contracts such as non-disclosure agreements, lease agreements, and employment contracts often contain complex clauses that can create substantial financial, legal, and operational risk. Manually reviewing these documents takes time and requires domain expertise that individuals, startups, or small businesses do not always have, leaving them vulnerable to missing unfavourable or unusual terms. In this project, we present Contract Sentinel, an AI system that automatically reviews legal contracts and flags risky or unusual provisions for manual review. The system is based on natural language processing and machine learning algorithms trained on the CUAD (Contract Understanding Atticus Dataset), consisting of 510 real-world contracts with more than 13,000 expert labels across 41 clause types. The system takes a contract document as input, extracts clauses from it, and assigns each clause to a known risk category such as indemnification, liability caps, termination rights, non-compete terms, and auto-renewal conditions. Unusual or unfair clauses are marked with a description of the danger in plain English, allowing users to rapidly grasp and negotiate complex agreements without the need for legal expertise. Contract Sentinel uses transformer-based language models and a curated legal clause taxonomy to speed up and improve contract review, making it more accessible and reliable by reducing the need for expensive legal consultation for routine document checks.
Keywords: Legal NLP, Contract Review, Clause Classification, Risk Detection, CUAD, Legal-BERT, Transformers, Explainable AI
Keywords: Legal NLP, Contract Review, Clause Classification, Risk Detection, CUAD, Legal-BERT, Transformers, Explainable AI
Abstract
SR-Screen-AI: An Explainable Rule-Based AI-Assisted Decision Support System for Human-in-the-Loop Systematic Literature Review Screening
Sruthi R, Rajeev P R
DOI: 10.17148/IJARCCE.2026.151035
Abstract: Systematic literature review screening requires assessment of bibliographic records against predefined criteria. This paper presents SR-Screen-AI, an explainable rule-based AI-assisted decision-support system integrating import, duplicate detection, weighted screening, explanations, reviewer validation, and reporting. Eight software tests passed, and a 50-record baseline trial achieved 81.82% precision, 90.00% recall, and 85.71% F1-score, supporting transparent reviewer-controlled screening.
Keywords: Artificial intelligence, Decision support systems, Explainable artificial intelligence, Human-in-the-loop, Literature screening, Rule-based systems, Systematic literature review
Keywords: Artificial intelligence, Decision support systems, Explainable artificial intelligence, Human-in-the-loop, Literature screening, Rule-based systems, Systematic literature review
Abstract
How Many Normal Images Are Needed? Training-Free Few-Shot Anomaly Detection on Scanning Electron Microscope Images of Integrated Circuits
K. Susmitha Reddy, G. Lavanya Devi
DOI: 10.17148/IJARCCE.2026.151036
Abstract: Anomaly detection on scanning electron microscope (SEM) images of integrated circuits is an essential element in the semiconductor manufacturing quality control process to ensure that no defective chip is delivered. Because SEM data is specific to each product and imaging tool, inspection models are usually built or tuned for each production line. Developing these models involves a large quantity of images and takes considerable effort to collect the data and refine the model until it works reliably. Also, most of the time, collecting data results in a disproportionate amount of normal images compared to anomalous images. Vision foundation models, pretrained on very large image collections, can describe images in great detail and have become strong tools for finding anomalies in photographs. Even so, the best anomaly detection models on the public MIIC dataset have been trained on 23,888 normal images, which is still a large number. In this research, we studied training-free few-shot anomaly detection on MIIC using frozen vision foundation backbones to assess whether their features transfer to SEM images. We extracted features from k normal reference images to form a memory bank, and assigned an anomaly score to each test feature based on its distance to the nearest stored reference feature in the memory bank. We tested six backbones with four values of k and three random seeds. DINOv2- B with 64 references yielded 94.53% image AUROC, which was better than full-data PatchCore (93.33%), while DINOv3 and CLIP were less transferable. A layer analysis revealed that averaging the last four transformer layers played a critical role in localization, raising the AUPRO of DINOv3-S to 95.2% compared with 76.2% for the last layer alone. When the 16 most similar normal references were retrieved for every test image, AUPRO reached 99.23%, nearly equivalent to full-data CFA (99.04%), and the nearest-neighbour search was 129 times quicker than searching through a 2,000-image pool. However, image-level AUROC (94.78%) lagged behind full-data CFA (99.01%). A decision layer calibrated solely on normal images rejected only 0.39% of normal images and detected every anomaly covering at least 0.25% of the image.
Keywords: Few-shot anomaly detection, foundation models, defect inspection, semiconductor manufacturing, electron microscopy.
Keywords: Few-shot anomaly detection, foundation models, defect inspection, semiconductor manufacturing, electron microscopy.
Abstract
A Nightshade-SWIN Transformer-Based Framework for Enhanced Multi-Crop Plant Disease Classification
Dr. Barkha Joshi
DOI: 10.17148/IJARCCE.2026.151037
Abstract: Nightshade crops (Solanaceae) face significant yield threats from phenotypically similar foliar diseases that can be difficult to distinguish during the early stages of infection. Accurate and timely disease identification is therefore essential for reducing crop losses, improving disease management, and supporting sustainable agricultural practices. While Convolutional Neural Networks (CNNs) have demonstrated strong performance in image-based plant disease classification due to their ability to extract spatial and local features, they may have limitations in capturing long-range dependencies and global contextual relationships within leaf images. These limitations become particularly important when disease symptoms exhibit subtle differences in texture, color, shape, and lesion distribution or when images are captured against complex field backgrounds. To address these challenges, we propose Nightshade-SWIN, an optimized deep learning framework based on the Swin Transformer architecture. The proposed framework utilizes a hierarchical shifted-window self-attention mechanism to effectively capture both minute lesion textures and global leaf morphology. By progressively learning multi-scale representations, Nightshade-SWIN can identify discriminative pathological characteristics while maintaining computational efficiency. The model was evaluated on a curated dataset of 5,000 multi- crop images collected under variable field conditions, including differences in illumination, background complexity, leaf orientation, and disease severity. Experimental results demonstrate that Nightshade-SWIN achieves a peak classification accuracy of 94.6%, outperforming ResNet50 and EfficientNet-B0 by 4.9% and 3.4%, respectively. These results indicate the effectiveness of transformer-based feature learning for distinguishing visually similar foliar diseases. Furthermore, interpretability analysis using LIME and Grad-CAM confirms that the model primarily focuses on biologically relevant pathological regions, such as lesions, discoloration, spots, and infected leaf areas. This enhances the transparency and reliability of the proposed framework and supports its potential application in precision agriculture, automated crop monitoring, and early disease diagnosis.
Keywords: Nightshade Crops, Leaf Disease Detection, SWIN-Transformer, Grad-CAM and LIME, Deep Learning
Keywords: Nightshade Crops, Leaf Disease Detection, SWIN-Transformer, Grad-CAM and LIME, Deep Learning
Abstract
A Secure and Privacy-Aware Hybrid Framework for Digital Credential Verification
Jayashri Digambar Sable, Swaranjali Dinesh Shelar, More N.N
DOI: 10.17148/IJARCCE.2026.151040
Abstract: In most organizations, identity is still checked against a central database. This is convenient to run. The catch is that one party ends up owning the records, and people must keep uploading the same documents every time they sign up for something new. In this paper we propose a hybrid framework that uses a blockchain for one job only: keeping a shared record of trust. Each issuer and holder gets a decentralized identifier (DID), and the claims go inside verifiable credentials (VCs). For every credential, a smart contract stores just its hash and its status, so nothing personal is written on the chain. We describe the architecture, follow a credential from issue to revocation, give an eight-step verification algorithm, and discuss the threats and a test plan. We have not built the prototype yet, so no performance numbers appear here; we will report them after we have measured them.
Keywords: blockchain, decentralized identifiers, digital identity, privacy, self-sovereign identity, verifiable credentials
Keywords: blockchain, decentralized identifiers, digital identity, privacy, self-sovereign identity, verifiable credentials
Abstract
An AI-Based Intelligent System for Early Urban Health Risk Assessment
Mali Rajashri Savata, Salunkhe Samruddhi Mahadev, Lokare Sujata Sharad, Ghalame Mayuri Kishor, Linge Sandeep Ganpat
DOI: 10.17148/IJARCCE.2026.151041
Abstract: Rapid urbanization and changing environmental conditions can affect public health in many ways. Factors such as temperature, humidity, rainfall, and air quality may increase the risk of different diseases. This research proposes an AI-Based Community Health Risk Assessment System to predict possible health risks in a community. The proposed system collects real-time and historical environmental data and uses Machine Learning to analyze the data and predict health risks. Based on the prediction, the system can provide early warnings and help authorities take preventive actions. The system also includes health risk hotspot detection feature to identify high risk areas in city based on environmental conditions and predicted health risk. In addition, the system collects combined symptoms reports from citizens and collects them with environmental data and health risk prediction. Using this information, the system generates AI based community health advisories to provide useful health warnings and guidance Overall, the system combines health risk prediction, early warning, hotspot detection, and health advisories in a single platform. It can help health authorities identify high-risk areas, take preventive measures, and make better data-based decisions to improve community health and safety.
Keywords: Artificial Intelligence, Machine Learning, Community Health, Health Risk Prediction, Environmental Data, Hotspot Detection, Early Warning System, Citizen Symptom Reports, Smart City, Public Health.
Keywords: Artificial Intelligence, Machine Learning, Community Health, Health Risk Prediction, Environmental Data, Hotspot Detection, Early Warning System, Citizen Symptom Reports, Smart City, Public Health.
Abstract
Self-Evolving AI Agent Framework for Predicting and Preventing Zero-Day Cyberattacks in a Multi-Cloud Environment Using Federated Learning and Explainable AI
Samruddhi Santosh Gawade, Gayatri Gunwant Gayaki, Ms. Ghodake G.K
DOI: 10.17148/IJARCCE.2026.151042
Abstract: The proposed framework uses intelligent AI agents to continuously monitor cloud activities, detect unusual patterns, and identify potential security threats at an early stage. Federated learning enables different cloud environments to work together in training AI models without sharing their raw security data, thereby helping to maintain data privacy. Explainable AI makes the model’s decisions easier to understand by providing clear reasons behind its predictions, allowing security professionals to investigate suspicious activities and take appropriate action. Additionally, the self- evolving component is designed to adapt to emerging threats by learning from new information and changing attack patterns.
Overall, the framework aims to strengthen threat detection, protect sensitive information, and improve transparency in multi-cloud environments. However, its actual performance needs to be verified through practical implementation and testing with realistic datasets and simulated cyberattacks. Future research should focus on evaluating important factors such as detection accuracy, false-positive rate, response time, and communication overhead to determine the framework’s effectiveness and suitability for real-world cloud security applications.
Keywords: Zero-day cyberattacks, artificial intelligence, self-evolving AI agents, multi-cloud security, federated learning.
Overall, the framework aims to strengthen threat detection, protect sensitive information, and improve transparency in multi-cloud environments. However, its actual performance needs to be verified through practical implementation and testing with realistic datasets and simulated cyberattacks. Future research should focus on evaluating important factors such as detection accuracy, false-positive rate, response time, and communication overhead to determine the framework’s effectiveness and suitability for real-world cloud security applications.
Keywords: Zero-day cyberattacks, artificial intelligence, self-evolving AI agents, multi-cloud security, federated learning.
Abstract
School Management System for Dutuwewa Maha Vidyalaya
Keshika Rathakrishnan, Parahera Nekathige Kaumini Viduthala
DOI: 10.17148/IJARCCE.2026.151043
Abstract: This report presents the design and implementation of a comprehensive School Management System for Dutuwewa Maha Vidyalaya, a streamlined, user-friendly platform aimed at improving efficiency and communication within the school. Existing manual systems often focus on singular functionalities like attendance or grade taking, but there is a growing need for an integrated approach that addresses multiple administrative, academic, and communicative needs. The School Management System provides a centralized platform for school administrators, teachers, students, and parents to seamlessly manage and interact with key aspects of school life, including student enrolment, attendance, event management, resource allocation, and communication. With tools to support notifications, grade selection, and attendance taking, the system promotes increased transparency and engagement between the school and its community. By offering an intuitive interface and robust features, the system enhances operational efficiency, improves communication, and contributes to the school's overall success in meeting academic and administrative goals
Keywords: School Management System, Administrative automation, educational communication, Digital Transformation.
Keywords: School Management System, Administrative automation, educational communication, Digital Transformation.
Abstract
A Voice-Powered Intelligent Healthcare Platform for Elderly Patients with Dementia and Alzheimer's Care
Shinde Akshata Kumar, Pathan Ekhara Mushtak, Parchande Sanika Harishchandra, Khilare Sakshi Kalidas, Sawant Namdev Manik
DOI: 10.17148/IJARCCE.2026.151044
Abstract: Cognitive impairments, including Alzheimer's disease and dementia, represent rapidly expanding global health challenges that steadily erode patient memory, reasoning, and autonomy [1][3]. Standard caregiving frameworks rely on constant human oversight, an approach that is financially demanding and susceptible to delayed emergency interventions. This study introduces a Voice-Driven Smart Healthcare System that merges automated speech processing, voice processing, AI-assisted behavioral analysis, and continuous monitoring to assist elderly users. The architecture allows patients to utilize conversational commands to obtain medication prompts, daily scheduling support, and emergency trigger mechanisms. Simultaneously, an interface supplies caregivers with instantaneous activity records and health metrics. The system's methodology converts clinical necessities into three distinct operational elements: a Voice Processing Engine, an AI-Driven Health Assistance Module, and a Caregiver Administration Dashboard, all supported by a secure cloud-based architecture designed with privacy, consent, and role-based access considerations. Evaluations of feasibility indicate that this solution is practically deployable in domestic and assisted-care environments, offering a more economical alternative to round-the-clock manual supervision. The proposed platform is anticipated to boost medication compliance, accelerate emergency reactions, and alleviate the strain on caregivers, all while safeguarding the privacy of users with minimal digital literacy [9][14]. Future enhancements will target wearable device integration, multi- language conversational capabilities, and predictive risk modeling [15]. Ultimately, by integrating natural voice interfaces with AI analytics and real-time oversight, this initiative seeks to transition dementia management from reactive supervision to a model that promotes dignity and proactive independence.
Keywords: Voice-Driven Healthcare, Dementia Management, Alzheimer's Disease, Voice Interaction , Live Health Tracking
Keywords: Voice-Driven Healthcare, Dementia Management, Alzheimer's Disease, Voice Interaction , Live Health Tracking
Abstract
MetaSociety: A Simulation-Driven AI Policy Advisor for Data-Grounded Smart Governance
Priyanka Jadhav, Payal Dongre, Priti Chaugule, Mayuri Kulkarni
DOI: 10.17148/IJARCCE.2026.151045
Abstract: Policy decisions in local governance are frequently taken with limited empirical grounding, relying on precedent, intuition, or small-scale pilots that are slow and costly to run. This paper presents Meta Society, a smart- governance platform that lets a policymaker state a goal in natural language, receive structured policy recommendations, and evaluate candidate policies against a virtual model of a town before committing to a real-world deployment. The platform combines three components. First, an AI Policy Advisor executes a multi-stage large-language-model (LLM) reasoning pipeline-orchestrated with LangGraph and served through a FastAPI backend-that turns a natural-language goal and multi-domain town statistics into structured candidate policies. Second, a Simulation Lab reports its outcomes from a deterministic engine rather than from an LLM: one agent per citizen is generated for a real town (Pandharpur, Solapur district, Maharashtra) so that published Census 2011 totals are reproduced exactly; a 35-node Bayesian network- thirty core nodes plus five exogenous shock roots-is rolled forward one period at a time with conditional-probability tables counted from the population wherever the data can speak; a historical-grounding stage transfers characteristics from real predecessor programmes to the proposed policy and derives a grounded effect scale that the causal network consumes; an external-shock layer stress-tests the policy under hypothetical events; and a Differential Evolution search with NSGA-II non-dominated selection, reported alongside an equal-budget random-search control, explores the policy- parameter space. Third, a data and dashboard layer renders the same population together with a field-by-field provenance ledger that separates verified Census fields from modelled ones. Every headline metric is a stated summation over the agent population, and each run re-checks the accounting identities, the network’s documented response directions, and reproducibility, reporting the outcome as an evidence pack. We describe the architecture, the modules, the workflow, and the verification the engine performs, report a seed-ensemble uncertainty protocol and a draw-count sensitivity analysis, and state the system’s boundaries plainly: response directions are validated but magnitudes are not calibrated against an evaluated real programme, income and sector are modelled rather than measured, and the platform is a counterfactual comparison tool rather than a forecasting service.
Keywords: smart governance, policy simulation, large language models, LangGraph, Bayesian networks, differential evolution, NSGA-II, agent-based modeling, microsimulation, synthetic population, external shocks, historical grounding.
Keywords: smart governance, policy simulation, large language models, LangGraph, Bayesian networks, differential evolution, NSGA-II, agent-based modeling, microsimulation, synthetic population, external shocks, historical grounding.
Abstract
Smart AI-Powered E-Commerce Platform with Personalized Recommendation and Sales Analytics
Wadekar Gargi Prashant, Bole Ishwari Santosh, Patil Harshvardhan Sunil, Mr. S. S. Ingole
DOI: 10.17148/IJARCCE.2026.151046
Abstract: Online retail has become a primary shopping channel, and customers increasingly expect experiences that are tailored to them [1]. Many small and mid-sized merchants, however, still operate storefronts that show every visitor the same catalog and give owners little insight into what is selling, what is running out, and why. This paper presents a Smart AI-Powered E-Commerce Platform that combines personalized product recommendation, a database-grounded multilingual shopping assistant, and an analytics-driven administration console in a single full-stack web application. The recommendation module ranks products with an explainable score built from category affinity, popularity, customer rating, and price suitability, and derives “frequently bought together” suggestions from real co-purchase history. The shopping assistant interprets free-text queries in English, Hindi, and Marathi, retrieves products only from the store database, and phrases its replies through a language-model layer with an automatic rule-based fallback, so it never invents items or prices. For the merchant, the platform provides sales-trend, category, and regional analytics together with an inventory module that estimates sales velocity and proposes restock quantities. The system is implemented with React, Node.js/Express, and MongoDB, and includes JWT-based sessions, e-mail one-time-password two-factor login, server- side price computation, Razorpay online payment, and cash on delivery. A working prototype comprising 89 REST endpoints and 12 data collections demonstrates the design across customer and administrator interfaces. The platform is expected to improve product discovery, reduce stock-outs, and support data-driven decisions for local retailers; future work covers collaborative filtering, payment-gateway webhooks, and large-scale usability and load evaluation.
Keywords: E-Commerce, Personalized Recommendation, Sales Analytics, AI Shopping Assistant, Inventory Intelligence, MERN Stack
Keywords: E-Commerce, Personalized Recommendation, Sales Analytics, AI Shopping Assistant, Inventory Intelligence, MERN Stack
Abstract
IOT-Based Machine Health Monitoring System
Chavan Rutuja Umesh, Babar Dnyaneshwari Namdev, Gajare Pragati Dnyaneshwar, Mrs. P. S. Gavali
DOI: 10.17148/IJARCCE.2026.151047
Abstract: Motors, pumps, compressors, generators and conveyors underpin modern manufacturing, but each can break down without warning, leading to unplanned stoppages, expensive repairs and growing maintenance costs. This paper describes an Internet of Things (IOT) system that continuously supervises such equipment. An ESP32 microcontroller reads temperature and vibration sensors and transmits the readings over Wi-Fi to a FastAPI backend, which stores them in a MySQL database and serves them to a ReactJS dashboard. Each reading is evaluated against limits configured for the individual machine, and the machine is classified as Healthy, Warning or Critical. A Critical state triggers a local buzzer and a remote notification through Blynk Cloud. In addition, a machine learning module examines the stored sensor history to anticipate probable faults and so supports predictive maintenance. The result is a low-cost, intelligent approach to industrial condition monitoring.
Keywords: IOT, Predictive Maintenance, Machine Health Monitoring, Machine Learning, ESP32
Keywords: IOT, Predictive Maintenance, Machine Health Monitoring, Machine Learning, ESP32
Abstract
AI-Based Road Accident Detection and Alert System
Mr. S. A. Zambare, Saniya Mainuddin Mulla, Kiran Sanjay Jadhav, Shivprasad Suresh Kadam, Reshma Mujofar Pathan
DOI: 10.17148/IJARCCE.2026.151048
Abstract: Road accidents cause heavy loss of life, and the outcome for victims depends strongly on how quickly emergency services are informed. Manual CCTV monitoring is slow and error-prone. This paper presents the design, architecture and implementation of an AI-based Road Accident Detection and Alert System that analyses CCTV or user- uploaded video frame by frame using Open CV and a YOLO deep-learning detector, and forwards confirmed accidents to emergency services. The system is built with Python, Flask, HTML, CSS and JavaScript and offers a public accident- reporting page with automatic GPS location capture, an automatic video-processing pipeline, an accident review dashboard with statistics, and a separate police/ambulance operations console with live alerts and filters. In the test session the dashboard logged 42 detected events, of which 16 were confirmed on review (38.1 %). The work shows that automated detection with human confirmation and location-tagged alerts can shorten the path from an accident to emergency response, and it identifies precision improvement as the main area for further work.
Keywords: Road Accident Detection, Computer Vision, Deep Learning, YOLO, Open CV, CCTV Surveillance, Emergency Alert System, Django, Intelligent Transportation System.
Keywords: Road Accident Detection, Computer Vision, Deep Learning, YOLO, Open CV, CCTV Surveillance, Emergency Alert System, Django, Intelligent Transportation System.
Abstract
Smart Village Automation Using IoT
Ms. J. K. Hipparkar, Pooja Karande, Shreya Joshi, Arjiya Mulani
DOI: 10.17148/IJARCCE.2026.151049
Abstract: Water is one of the most crucial resources of a village, but its availability and demand is majorly calculated manually, thereby leading to delayed decision-making and poor planning. Smart Village Automation Using IoT is a low- cost solution which leverages the power of the Internet Of Things and Machine Learning to manage the water distribution of rural areas and village services [1] [2]. An ultrasonic sensor is used to calculate the water level of the storage tank, which is then transmitted via Wi-Fi (MQTT) to an application server which validates the data and stores it in a MongoDB database. The Python FastAPI server is used to host the machine learning models; a demand prediction model that predicts the demand based on water level, temperature, rainfall, population, date-time and water used, and a Random Forest Regressor model which classifies between normal consumption and leakage/theft. The citizen and moderator portals built using React allow users to view water-related information, government schemes, complaints, and Razorpay payment integration. The complaint management system escalates unaddressed complaints to the relevant authority after 25 days of creation. In the leakage/theft model, the pressure has a feature importance of 48.3%, followed by the actual consumption at 26.8%, flow rate at 18.1%, and expected consumption at 6.8%. The low-cost implementation has only one hardware sensor, the ultrasonic sensor, but factors like high implementation cost, lack of technical know-how, connectivity issues, training data, and maintenance can affect the adoption rate. Future improvements include the addition of more sensors and tanks, better training data, deploying the application on mobile and cloud platforms and exploring other avenues of Smart Village such as agricultural support and waste management.
Keywords: IoT, Smart Village, Machine Learning, Water Distribution, Water Demand Prediction, Leakage Detection, ESP32, Ultrasonic Sensor
Keywords: IoT, Smart Village, Machine Learning, Water Distribution, Water Demand Prediction, Leakage Detection, ESP32, Ultrasonic Sensor
Abstract
VocaLyze – AI Based Emotion Recognition from Voice
Sadigale Shravani Santosh, Mali Vaishnavi Sukhdev, Gotavle Sushma Sanjay, Linge Sandeep Ganpat
DOI: 10.17148/IJARCCE.2026.151050
Abstract: When a Person emotion change Changes can be seen in their voice tone, pitch speaking speed and intensity. The name of our project is VocaLyze. It is an AI-based web application that identifies a person emotion from their voice. In this system the user can provide their voice in two ways first by recording their voice using a microphone and second by uploading an existing audio file. After receiving the voice, the system first removes background noise and unwanted silent parts from the audio. Then the system extracts voice features such as MFCC, pitch, energy and spectral features. These features are then given to machine Learning and Deep Learning models such as Random Forest, SVM, CNN and LSTM. These models help the system to identify and classify the emotion present in the user voice. The system also provides a confidence score for the detected emotion. Our project does not only identify the emotion. It also provides recommendations according to the detected emotion. These recommendations can include quotes, calming music, breathing, meditation and wellness activities.
Keywords: Artificial Intelligence, Machine Learning, Voice Emotion Recognition, MFCC, CNN, LSTM, Emotion Classification, Personalized Recommendation.
Keywords: Artificial Intelligence, Machine Learning, Voice Emotion Recognition, MFCC, CNN, LSTM, Emotion Classification, Personalized Recommendation.
Abstract
An Adaptive and Risk-Aware Access Control Framework for Secure Cloud File Sharing Using Hybrid Cryptography and Context-Aware Key Release
Ghodekar Snehal Ramesh, Ghodekar Vaishnavi Suresh, Miss. Ghodake G. K.
DOI: 10.17148/IJARCCE.2026.151051
Abstract: Cloud platforms have become a common place to keep business records because they are easy to use and accessible from anywhere. Still, sensitive documents stored this way face two problems: the service provider should not be able to read them, and a password alone should not decide whether a request is genuine. This study proposes ARAC- HC, a framework where the key needed to open a document is released only after the system judges the situation of each request. At upload time, the document is protected on the user’s own device with a 256-bit AES-GCM key. This key is then wrapped separately for every approved reader through a short-lived X25519 exchange followed by HKDF. Before handing over the wrapped key, the framework checks seven signals: device state, location, access time, failed login attempts, recent user activity, stored trust level and the sensitivity of the requested document. Based on the calculated risk, the system either allows normal access, asks for an extra check, permits restricted access or refuses the request. In tests, protecting a 100 MB document took close to 75 ms. The X25519-based method finished key protection roughly 2.6 times faster than RSA-2048. Across 20,000 simulated requests, the framework caught 95.3% of malicious attempts, and 97% of legitimate users got access without any extra verification step.
Keywords: Context-aware key release, risk-based authorisation, cloud document sharing, envelope encryption, AES- GCM, X25519, insider misuse, zero trust.
Keywords: Context-aware key release, risk-based authorisation, cloud document sharing, envelope encryption, AES- GCM, X25519, insider misuse, zero trust.
Abstract
Information Security Copilot for Cyber Threat Monitoring and Risk Management in Small and Medium-Sized Businesses
Yashowardhan Thakare, Rutuja Ghongade, Payal Pawar, Radha Mankar, Anisha Kaware, Dr V B Gadicha
DOI: 10.17148/IJARCCE.2026.151052
Abstract: Small and Medium Businesses (SMBs) increasingly depend on cloud platforms, email, CRM systems, and other digital services, while limited budgets and cybersecurity expertise increase their exposure to threats. This paper proposes an AI Security Copilot for continuous security monitoring in SMB environments. The system collects security data through authorized APIs, analyzes activities and anomalies using AI/ML techniques, prioritizes risks, and provides actionable security recommendations through a centralized dashboard. The proposed approach aims to make cybersecurity monitoring more accessible, manageable, and effective for resource-constrained
Keywords: AI Security Copilot, Cybersecurity, SMB Security, Threat Detection, Anomaly Detection, Risk Assessment, Security Monitoring.
Keywords: AI Security Copilot, Cybersecurity, SMB Security, Threat Detection, Anomaly Detection, Risk Assessment, Security Monitoring.
Abstract
Smart Public Infrastructure Damage Reporting and Prediction System: A Web-Based Platform with Category-Based Rule Priority Classification
Khushi, Harsha Bhat T.M, Varun P, Shreecharan I G, Prof. Raksha K
DOI: 10.17148/IJARCCE.2026.151053
Abstract: Potholes, damaged roads, faulty streetlights and water leakage often remain unresolved because citizens lack a simple reporting channel and authorities lack consolidated data. This paper presents a web-based Smart Public Infrastructure Damage Reporting and Prediction System built with HTML, CSS, JavaScript, PHP and MySQL. Citizens register with OTP verification, submit geotagged complaints with photographs, and track status; administrators manage status, priority and department assignment, and view complaints on an interactive map and analytics dashboard. Source- code tracing shows priority is assigned by a transparent category rule (High for Water Leakage, Pothole and Traffic Signal Failure, otherwise Medium), while several interface features labeled “AI” are fixed values or dormant code. Functional testing of ten cases passed. The paper documents the system as built and outlines steps to close this gap.
Keywords: infrastructure complaint reporting, civic technology, rule-based prioritization, map visualization, web application, PHP, MySQL.
Keywords: infrastructure complaint reporting, civic technology, rule-based prioritization, map visualization, web application, PHP, MySQL.
Abstract
AI in Healthcare Applications: A Study of Patient Awareness and Acceptance
Priyanka Ambdekar, Aditi Hingane, Prof. Pramod Gopale
DOI: 10.17148/IJARCCE.2026.151055
Abstract: Artificial Intelligence (AI) is becoming an important part of the healthcare sector by providing innovative solutions to improve patient care and healthcare services. However, the increasing demand for medical services and the shortage of healthcare professionals have created a need for advanced technologies in healthcare. AI-based applications, such as healthcare chatbots, care robots, mental health support systems, and machine learning-based diagnostic tools, are being developed to assist patients and healthcare professionals. These technologies can help improve healthcare accessibility, support early disease detection, and provide better patient assistance. Despite these benefits, the successful implementation of AI in healthcare depends on patients' awareness, trust, and acceptance of these technologies. Factors such as privacy concerns, reliability, safety, and a lack of knowledge may influence patients' willingness to use AI-based healthcare applications. Therefore, this study focuses on understanding patient awareness and acceptance of AI-based healthcare applications and identifying the factors that influence their adoption. The findings may help in understanding patients' perceptions of AI in healthcare and encourage the effective use of AI technologies in medical services.
Keywords: Artificial Intelligence (AI), AI in Healthcare, Healthcare Applications, Patient Awareness, Patient Acceptance, Patient Perception, Patient Trust, Machine Learning, Healthcare Technology, Data Privacy, Technology Adoption.
Keywords: Artificial Intelligence (AI), AI in Healthcare, Healthcare Applications, Patient Awareness, Patient Acceptance, Patient Perception, Patient Trust, Machine Learning, Healthcare Technology, Data Privacy, Technology Adoption.
Abstract
CNN-Based Framework for Maize leaf Disease Detection and Crop Monitoring
Dr.K.L.S.Soujanya, V. Sumedha, M. R. V. Sravanthi, A. Supriya
DOI: 10.17148/IJARCCE.2026.151056
Abstract: Maize is an agricultural crop but its productivity is significantly affected by diseases that are often hard to notice at an early stage. Traditional ways of identifying diseases depend on people looking at the plants, which can take a lot of time and needs someone who knows a lot about plants. This paper shows a Smart Maize Crop Monitoring and Disease Detection System that uses machine learning and image processing to help with identifying and managing diseases on maize leaves. The system uses a Convolutional Neural Network to sort images of maize leaves into four groups: Healthy, Common Rust, Blight and Gray Leaf Spot. The images are changed in size. Made different in ways to help the model work better when the pictures are in different positions or under different conditions. The model that was trained is part of a web app built with Flask so people can send images of maize leaves. Get predictions about the disease through an easy way to use. Also the system gives information about how to treat the disease advice on what stage the crop's in and tips on watering the plants. Testing showed that the system could correctly say what the disease was 92% of the time which shows that this way of doing things can be a tool, for growing maize. The system is meant to help find diseases stop people from using the wrong treatments and make growing crops better.
Keywords: Maize Disease Detection, Convolutional Neural Network (CNN), Image Processing, Machine Learning, Smart Agriculture, Flask Web Application.
Keywords: Maize Disease Detection, Convolutional Neural Network (CNN), Image Processing, Machine Learning, Smart Agriculture, Flask Web Application.
Abstract
A ROBUST FRAMEWORK FOR COPYWRIGHT PROTECTION AND OWNERSHIP VERIFICATION OF DIGITAL IMAGES
Pavithra N, Anushree P M, Kavana K, Megha Manjappa Mogali, V Tejaswini
DOI: 10.17148/IJARCCE.2026.151057
Abstract: The widespread sharing of digital images through websites and social media has made unauthorized copying, redistribution, and manipulation easier. These issues create difficulties for photographers, designers, content creators, and organizations seeking to establish ownership of their work. This paper presents a robust framework for copyright protection and ownership verification of digital images using digital watermarking and cryptographic hashing. The proposed system is designed to register an image with creator metadata, embed copyright information as a watermark, generate a SHA-256 hash, and store the related ownership record for later verification. During verification, the submitted image is checked against stored records; a hash mismatch indicates that the file differs from the registered version, while watermark extraction can provide an additional ownership check when supported by the implemented watermarking method. The framework is planned around Python and image- processing libraries, with a database for ownership metadata. The design aims to combine image-level copyright marking with cryptographic integrity checking. Quantitative performance and robustness must be established through implementation and testing; this paper therefore does not claim unmeasured accuracy or performance results.
Keywords: Digital image copyright, digital watermarking, SHA-256, cryptographic hashing, ownership verification, image integrity, Python.
Keywords: Digital image copyright, digital watermarking, SHA-256, cryptographic hashing, ownership verification, image integrity, Python.