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VOLUME 15, ISSUE 5, MAY 2026
Publication in progress..
Data-Driven Analysis of Mortality and Survival Outcomes in Patients with Liver Cirrhosis: A Global Healthcare Analytics Study
Mehdi Mostofi, Prasanna Poreddy, Jayasri Katragadda, Nadeem Basha Syed, Sunandamala Bodaballa
An Effective Heart Disease Detection and Severity Level Classification Model Using Machine Learning and Hyperparameter Optimization Methods
Dr. Ganesh.G. Taware, Miss.P.D. Nale, Kaveri Kalbhor, Gaurav Karande, Swaraj Navale, Yogesh Gaikwad
Smart Hybrid Bus Tracking System with Mobile Sensing and Real-Time Updates
Anil Kumar T R, Harshavardhan Pattar, Indusri J, Joshi Chetan, Dr. Yeresime Suresh, Dr. Anita Patil
Smart Health Tracker with Doctor Consultation
Satyam Sahu, Jitendra Gupta, Suraj Verma, Mr. Kumar Bibhuti
Smart Food Donation System: A Web-Based Approach for Efficient Food Redistribution–A Review
Akshay, Prashanth K, K.Shiva darshan, Kishan kumar B.V, Pavan kumar P, Anitha Patil P
AI-Based Video surveillance System
Afsa Saboo, B Sai Dikshitha, C Mohammad Athiq, K Sudeep Gouda, Nagateja P, Anita Patil
Smart AI-Integrated Exam Security Gate
A Sunitha, B Lavanya, B Pallavi, Manisha Patel, Anita Patil
SMART RAILWAY TRACK HEALTH MONOTORING SYSTEM by IOT
Dr. C.N. Deshmukh, Mayur S. Salode, Aryan R. Kale, Minal G. Lonkar, Kshitij M. Thotange
Farm2Door: A Smart Digital Platform for Farmer-to-Customer Agricultural Marketplace-A Review
K Naveen, K Shyam, Kothapalli Vamshi, Kothapalli Vardhan Babu, Dr. Anita Patil, Asst.Prof. Shashikantha Raddi
CLEARBON: A Blockchain-Based Dual Market System for Real-Time Carbon Credit Verification and Trading
Sohail, Sooraj B, Suma Shree S, Swetha Rani H S, Anita Patil
EVOLUTION OF AI TECHNOLOGIES IN THE EDUCATIONAL SECTOR
Yashvardhan Chahal, Yash Goswami, Tushar Mahaur, Yash Jain, Dr. K.S. Mishra
Talent Track Workforce Employment Solutions Platform
Sathiyaa Balajee R, Mrs. Saranya R
SMART EXAM SEAT ALLOCATION SYSTEM USING ANDROID APPLICATION
Riya Bhoir, Bhumika Beldar, Kanishka Chindarkar, Prof. Smita Chunamari
“Smart AI-Based Student Attendance System with Monthly Analytics”
Harsh Sharma, Miss. Taniya Jain, Dr. Uruj Jaleel, Dr. Satish Kumar Soni
AI-Driven Personalized Education Platform: Design, Architecture, and Implementation
Sajid Raza, Vishal Kumar Sah, Rishabh Tiwari, Saif Siddiqui, Dr. Sandeep Kumar Dubey
Artificial Intelligence in Cybersecurity: A Comprehensive Survey on AI-Driven Insider Threat Detection
Deepak Kumar G., Deevaraj M., H Pramodh, Lakshmi Narayana, Dr. Muhibur Rahman T R
A Comprehensive Study on Data Storage Security Issues and Services in Cloud Computing
Sujal Satish Godse, Bhushan Sunil Matsagar, Kirti Dinkar More
SMART ROAD ACCIDENT DETECTION AND RESCUE SYSTEM
JEEVITHA R, B SAHANA REDDY, G TEJASHWINI, SHREYA B R
MultiCart- Ai Based Multi Vendor Cart Platform
Kamlesh Kumar Pal, Abhishek Gupta, Harshit Mall, Mr. Deepak Kumar
Public Transport Tracking Systems: A Comprehensive Study of Real-Time Solutions for Small Cities
C Renuka, Divya Rajeev M, J Kavyashree, Bindu AV, Muhibur Rahman T R
CLICKET – Be There or Make It Happen With AI
Sharad Singh, Ritik Chaurasiya, Shivam, Prince Yadav, Kumar Bibhuti Bhushan Singh
AWAZE-E-JANATA- VOICE OF THE PEOPLE
Rajveer Pratap Singh, Aman Tiwari, Raj Yadav, Mr. Dileep Kumar Gupta
Smart Battlefield Helmet with Soldier Monitoring System
Dr. Pramod Sharma, Aryansh Gupta, Divya Gupta, Jayanth Bharadwaj, Sagar Mishra
AUTOFACE - Attendance Simplified Through Vision
Shrestha Gupta, Shreoshi Roy
DejaView: An AI-Powered Academic Project Integrity and Evaluation Portal Using Vector Embeddings and Large Language Models
Mrs. Trupthi Rao, Mr. Sumit Kumar Yadav, Aditya Raj Swain, Mahadev Jagtap, Pratik, Gajanan
EMPLOYMENT HIRING PLATFORM USING AI
Sanjay Kumar, Prince Pandey, Md Shahjad, Deepak Kumar
A Review of Voice-Driven Accessible Vocational Training Platforms for Persons with Visual Impairment
Bharath Reddy, Chinta Vishnu Vardhan, H M Sharanabasava, K Ravi Kumar, Dr. Muhibur Rahman
NeuroSecure: A Comprehensive Survey on Deep Learning Approaches for Cyber Defense and Intrusion Detection
P. Anirudh, B. Adarsh Reddy, G. Raghavendra, K. Naveen Kumar, Dr. Muhibur Rahman T R
Case Study on Experimental Setup Design in Robotics Research: A Comprehensive Review of Future Directions
M A Kaif, Md Akif Bari, Md Muntaseeb, Punith Raj, Dr. Muhibur Rahaman T.R
A Review on Machine Learning Techniques for Crop Yield Prediction
A Renukamma, Arathi C G, Aruna B, K Ananya, Dr. Muhibur Rahman T.R
AI-Based Real-Time Traffic Congestion Prediction and Signal Optimization System
B Deepika, Basavarajeshwari, D R Pallavi, D Suhasini, Dr. Muhibur Rahman T.R
Crop Disease Detection Using AI: A Comprehensive Survey
G Sai Supriya, G Sanjana, Harshita S S, V Indu, Muhibur Rahman T.R
Development Of Eye Disease Detection Using Deep Learning
Afrin Amin Makandar, Pranav Sanjay Kumbhar
AUTONOMOUS/TELE-OPERATED LANDMINE DETECTION ROBOT
Deepak Yadav, Kundan Gautam, Sarfaraz Ansari, Shailesh Srivastava, Roshni dubey, Prof. Prabhakar dubey
AGRITRADEHUB: A MERN Stack Based Digital Platform for Advancing India's Farmer-Merchant Direct Trade Ecosystem
Mukesh Gupta, Dheeraj Singh, Nidhi Shukla, Roshni Soni, Ms. Shreoshi Roy
CONTENT FILTER BASED MOVIE RECOMMENDATION SYSTEM USING AI AND ML
Asharf Khan, Alok Kumar Singh, Vishal Singh, Akrar Khan, Pankaj Kumar Gupta
Alzheimer’s Disease Detection Using AI
Divyanshi Kashyap, Divya pandey, Mansi Pandey, Divyanshi Yadav, Dr. Nikhat Akhtar
Alzheimer’s Disease Detection Using AI
Divyanshi Kashyap, Divya pandey, Mansi Pandey, Divyanshi Yadav, Dr. Nikhat Akhtar
Student Health and Stress Prediction System using Machine Learning
A Revanth, G J Sachidananda, G Mahesh Gouda, Dr. Muhibur Rahman T.R
UNIEVENTS : A COLLEGE EVENT HANDLER USING AI
Laxmi Kanaujiya, Tanya Prajapati, Samriddhi Malhotra, Ranjana Kumari Chirania, Ms Prachi Yadav
AI-Based Smart Home Intrusion Detection & Alert System Using Behavior Analysis and Face Recognition
Mallappa H, Yashwanth T M, Naveendra Reddy, Ameer S, Asst. Prof. Rajashekar Reddy P, Dr. Anita Patil
Generative AI: A Comprehensive Survey on Transformer-Based Models
J Hemanth, Harsha B, Ashish Kumar, Anurag N, Dr. Muhibur Rahman T.R
AI BNB: AI-BASED TRAVEL LISTING AND BOOKING PLATFORM
Gaurav Yadav, Tanveer Alam, Balbir Singh, Rishabh Shukla, Ms. Deepika Pandey
Smart Career Platform: An AI-Driven Personalised Career Guidance System for Multi-Domain Professionals Using MERN Stack and Groq Large Language Models
Purshottam Mishra, Awadhesh Kumar, Mr. Dileep Kumar Gupta
A STUDY OF FINANCIAL STATEMENT ANALYSIS OF HDFC BANK
Rushikesh Prakash Kapate, Prasad Shivajirao Kadam, Dr. Pradyuman Shastri
ZIGBEE BASED SMART HELMET FOR MINERS
Payal Survase, Pratik Ghortale, Ashwini Adasre
Blockchain-Based Secure Voting System
Palak S Sachar, Sania Shaikh, Muktha Reddy, Dr. Muhibur Rehman T.R
Smart Hospital Management System
Rakesh Yadav, Arvind Yadav, Bhanu Chandra
AFFECTIVE COMPUTING TECHNIQUES FOR HUMAN–MACHINE INTERACTION
Rabiya Fathima, C S Swetha
SafeHer: A Proactive AI-Driven Safety System for Women Using Contextual Risk Assessment and Real-Time Monitoring
Rohit Kumar Yadav, Aditya Upadhyay, Kajal Kasaudhan, Dr. Nikhat Akhtar
COVID SAFETY SYSTEM – DOOR HANDLE SANITIZER & TEMPERATURE DETECTION
Bhalghare Kajal Sanjay
AI-Powered Document Question Answering System Using Agentic RAG and Local Language Models
Varshitha D, Vidya S
Blockchain Driven Multi Tier Academic Credential Validation System
G. Priyadharshini M.E., Prakash B, Logesh P
IndianAura: A Secure and Scalable E-Commerce Platform for Certified Made-in-India Products
Aishwarya V S, Anagha I V, Bindu B, Deepika G S, Veeramreddy Rajasekhar
A Secure Task Management System for Employee Workflow Optimization
G. Priyadharshini M.E., Priyadharshene R, Vimandhani G
AI-Enabled Eye Screening Tool for Early Detection of Common Eye Diseases
G. Priyadharshini M.E., Balananthakumar B, Jancy M
Intelligent Taste Prediction Engine Using Scroll, Hover & View-Time Patterns in E-Commerce
G. Priyadharshini M.E., Roshani C, Pradeesh T
Seasonal Crop Price Prediction using AI/ML
Aniket Sawant, Harsh Dudhal, Tejas Jadhav, Sarthak Kakade, Mrs. Dhage T.S.
HELMET PROTECTION DETECTION SYSTEM USING YOLOV8 FOR REAL-TIME TRAFFIC SAFETY MONITORING
Ramya Patani, Dr. Darapu Uma
Phishing URL and Text Detection
R. Janaki M.E., (phd), Akalya A, Dharshini J
An Integrated IoT and Web-Based Framework for Energy and Machine Monitoring in SMEs
S Abhinaya, M.E., Akash M, Manimaran B
Detection of thyroid stages classification by convolutional neural network techniques
R. Janaki M.E.(phd), Ramya Shree V, Sweatha N
PHISHING WEBSITE DETECTION SYSTEM USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Mr. D.S. Jaybhay, Ms. Chaitrali S. Shinde, Ms. Bhakti D. Nannaware, Ms. Sakshi A. Harnawal Ms. Priyanka S. Gadhe
AI Driven IDS System in Network Security
Kartik P Shetty, Prof. Theerthashree G S
Brain Tumor detection using Artificial Intelligence
Rushikesh Todekar, Shejal Kawale, Sakshi Khankar, Mayuri Sudake, Dr. Sachin Bere, Prof. Mrs. Jagtap P.S
TimelimeX – Exploring the History using Gen AI
Dr. C N Shariff, Anjali, B Aishwarya, Bhavana Joshi, G Sahethi
Secure Digital Identity Verification Using Blockchain
K Senbagam M.E., Kishore S, Divya K
VisionGuard: Deep Learning–Based Weapon Detection Framework
K. Rajavadhani, Aswini. R. C & Vijayalakshmi. J
Review on Potential Applications of Geopolymer Concrete in Construction
Vasanth S, Likhith Kumar T, Vamshi A, Mohammed Afiz Roshan
CallMind: An AI-Powered Real-Time Voice Agent Platform Using a Pluggable Speech-to-Text, Large Language Model, and Text-to-Speech Pipeline
Sahil Makandar, Rohini Magamdum, Aditya Mali, Sudesh Kumbhar, Radheshyam Sah, Rajendra Hiremath
Smart Pothole Detection and Rapid Emergency Response System for Four-Wheeler Vehicles
Abhishek N, Dr. Kavitha A S, Nandish S, Sanjana Sindol, Bhavana
Coder Buddy – An Agentic AI-Powered Python Development Assistant for Code Review, Code Generation, and Automated Mini Project Building
Sonika D, Harshitha L
MCP-Based Context-Aware System Monitoring and Threat Detection Agent
G. Priyadharshini, M.E., Balaji A, Vishnu S, Mohamed Noufal M
AI-BASED VERIFICATION OF LLM RESPONSE
Mrs. Asha Sattigeri, Lavanya R, Pavan Gowda S
Controlling Ethical Hacking: Operating System Security
Dipali Girhe, R.V. Daund
AI-Based Health Monitoring System
Ummi Habiba, Vijetha S P, C B Shekhara, Prajwal V, Anita Patil
Iot Based Patient Health Monitoring System
Basavanagowda B S, Basavaraju Bailaannavar, Gagangowda, Vijaykumar Hovale, Prof. Padmavathi N*
Emotion-Aware Adaptive Learning Systems: A Comprehensive Survey on Artificial Intelligence-Based Personalized Education
A Rashmi, A Keerthi, Harika K, Likitha K, Dr. Muhibur Rahman T.R
Automated Threat Hunting Using AI: AI-Driven Defence Strategy
Vikram G.D, K. Sharath
Medicinal Plant Identification and Classification
Anushree N N, Deepika V M
Cisix: Technology for Net-Zero Carbon Ecosystem
Darshan D Kolmi, Goutham E, Hrishikesh Bodapatti, Javeed Hussain P, Dr. Anita Patil, Vijay Kumar
Recommendation system for cloud service on Trust management services
Deepak BN, Anusha J, Krishnaveni A, Karthik R
Intelligent Crop Disease Detection Systems: A Review of Deep Learning Approaches
Siddessh K S, Anita Patil
Intelligent Fingerprint Storage and Management System for Authentication Applications on Cloud Storage
Vaasthava Sree Sai Reddy N, Subhamay Parya, Spoorti Patil, Vaishnavi Reddy, Dr.SoniaMariaD'souza
Abstract
Data-Driven Analysis of Mortality and Survival Outcomes in Patients with Liver Cirrhosis: A Global Healthcare Analytics Study
Mehdi Mostofi, Prasanna Poreddy, Jayasri Katragadda, Nadeem Basha Syed, Sunandamala Bodaballa
DOI: 10.17148/IJARCCE.2026.15501
Abstract: Liver cirrhosis is a progressive chronic disease associated with high morbidity and mortality, requiring accurate risk stratification to support clinical decision-making. This study presents a data-driven analytical framework integrating survival analysis, machine learning, and uncertainty quantification to improve mortality prediction in cirrhosis patients. A retrospective dataset of 418 patients was analyzed using Kaplan–Meier estimation and Cox proportional hazards modeling to evaluate survival patterns and identify significant predictors. For predictive modeling, multiple supervised learning algorithms—including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, and Extreme Gradient Boosting (XGBoost)—were implemented to classify patients into high- and low-risk groups. Model performance was assessed using accuracy, recall, F1-score, and ROC-AUC, with cross-validation employed to ensure robustness. To enhance reliability, conformal prediction was applied to quantify predictive uncertainty at a predefined confidence level. Results indicate that disease stage, age, bilirubin, and albumin are significant predictors of mortality. Ensemble models demonstrated superior predictive performance, with XGBoost achieving the highest recall and strong discrimination. Conformal prediction provided well-calibrated uncertainty estimates, improving the interpretability and trustworthiness of model outputs. The findings demonstrate that integrating statistical and machine learning approaches enhances mortality risk stratification and supports the development of reliable clinical decision-support systems.
Keywords: Liver Cirrhosis; Survival Analysis; Machine Learning; Mortality Prediction; Cox Proportional Hazards; XGBoost; Risk Stratification; Conformal Prediction
Keywords: Liver Cirrhosis; Survival Analysis; Machine Learning; Mortality Prediction; Cox Proportional Hazards; XGBoost; Risk Stratification; Conformal Prediction
Abstract
An Effective Heart Disease Detection and Severity Level Classification Model Using Machine Learning and Hyperparameter Optimization Methods
Dr. Ganesh.G. Taware, Miss.P.D. Nale, Kaveri Kalbhor, Gaurav Karande, Swaraj Navale, Yogesh Gaikwad
DOI: 10.17148/IJARCCE.2026.15502
Abstract: The rapid advancement of Machine Learning (ML) techniques has enabled the development of intelligent systems for early disease prediction. This research presents a web-based Heart Disease Prediction System that leverages ML algorithms to estimate the risk of cardiovascular disease using patient health parameters. The system is developed using the Django framework and integrates a trained Random Forest Classifier to analyze clinical inputs uch as age, blood pressure, cholesterol level, and other relevant medical attributes.
The proposed model processes user-provided data and generates a probability score, which is further categorized into Low, Borderline, and High-risk levels. The system also incorporates rule-based adjustments to handle ex- treme cases, thereby improving the reliability of predictions. Additionally, all predictions are stored in a data- base, enabling users to track their health assessment his- tory over time.
The experimental evaluation demonstrates that the model achieves satisfactory accuracy and provides quick and ac- cessible results through a user-friendly interface. Although the system is not intended to replace professional medical diagnosis, it serves as an effective preliminary screening tool for raising awareness and encouraging early medical consultation. This work highlights the potential of integrating machine learning with web technologies to build scalable and accessible healthcare support systems.
The proposed model processes user-provided data and generates a probability score, which is further categorized into Low, Borderline, and High-risk levels. The system also incorporates rule-based adjustments to handle ex- treme cases, thereby improving the reliability of predictions. Additionally, all predictions are stored in a data- base, enabling users to track their health assessment his- tory over time.
The experimental evaluation demonstrates that the model achieves satisfactory accuracy and provides quick and ac- cessible results through a user-friendly interface. Although the system is not intended to replace professional medical diagnosis, it serves as an effective preliminary screening tool for raising awareness and encouraging early medical consultation. This work highlights the potential of integrating machine learning with web technologies to build scalable and accessible healthcare support systems.
Abstract
Smart Hybrid Bus Tracking System with Mobile Sensing and Real-Time Updates
Anil Kumar T R, Harshavardhan Pattar, Indusri J, Joshi Chetan, Dr. Yeresime Suresh, Dr. Anita Patil
DOI: 10.17148/IJARCCE.2026.15503
Abstract: Growing urban populations have created an increasing need for intelligent public transportation systems, with real-time bus tracking becoming a critical requirement. Traditional approaches rely extensively on specialized GPS equipment, creating significant costs and operational challenges. This research presents a Smart Hybrid Bus Tracking System that utilizes smartphones that drivers and passengers already carry, removing the requirement for dedicated hardware. The system uses the driver's mobile device as the main location provider, with passenger smartphone data enhancing position precision. During network outages, the platform saves information locally and updates it when connectivity returns. The system also integrates sophisticated algorithms such as Kalman Filter, XGBoost, and LSTM to improve prediction accuracy. This approach delivers a cost-effective, flexible, and practical tracking solution that works effectively across various real-world applications.
Keywords: Bus Tracking, GPS, Mobile Sensing, Real-Time System, Kalman Filter, XGBoost, LSTM, Smart Transportation
Keywords: Bus Tracking, GPS, Mobile Sensing, Real-Time System, Kalman Filter, XGBoost, LSTM, Smart Transportation
Abstract
Smart Health Tracker with Doctor Consultation
Satyam Sahu, Jitendra Gupta, Suraj Verma, Mr. Kumar Bibhuti
DOI: 10.17148/IJARCCE.2026.15504
Abstract: Healthcare is undergoing a quiet but consequential shift. For generations, patients have relied on physical visits to hospitals and clinics for diagnosis, consultation, and record management—a model that works well for some individuals and poorly for many others. The rise of digital technologies and intelligent systems offers a practical way out of this limitation. This paper presents a Smart Health Tracker with Doctor Consultation platform that collects patient health data, analyses it efficiently, and enables continuous monitoring, record management, and remote consultation. Rather than treating health tracking and doctor consultation as separate services layered on top of conventional systems, we integrate them into a unified architecture from the ground up. The platform comprises multiple tightly coupled modules: a data collection layer, a health record management system, a report analysis component, a doctor consultation module, and an administrative dashboard. A functional evaluation with users demonstrated improved accessibility to healthcare services, better management of medical records, and more efficient interaction between patients and doctors compared with traditional healthcare approaches. The system is modular and cloud-deployable, making it easy to extend or integrate with existing healthcare infrastructure.
Keywords: Smart Health Tracker; Telemedicine; Artificial Intelligence; Health Monitoring; Doctor Consultation; Medical Report Analysis; Digital Healthcare; Web Application
Keywords: Smart Health Tracker; Telemedicine; Artificial Intelligence; Health Monitoring; Doctor Consultation; Medical Report Analysis; Digital Healthcare; Web Application
Abstract
Smart Food Donation System: A Web-Based Approach for Efficient Food Redistribution–A Review
Akshay, Prashanth K, K.Shiva darshan, Kishan kumar B.V, Pavan kumar P, Anitha Patil P
DOI: 10.17148/IJARCCE.2026.15505
Abstract: Food waste continues to be a serious global issue even as many communities face hunger and food insecurity. This review presents a Smart Food Donation Management System (SFDMS) - a web-based platform that connects food donors with recipients efficiently. Donors like restaurants, households, and institutions can post details of available surplus food, while receivers such as NGOs and shelters can request and collect it easily. The system supports location- based matching, tracks expiry dates, and provides built-in communication features for better coordination. An administrative dashboard enables monitoring, report generation, and data management. Testing results demonstrate the system to be user-friendly, reliable, and effective in reducing food waste while improving food distribution.
Keywords: Food Donation, Web Application, Waste Reduction, NGO Support.
Keywords: Food Donation, Web Application, Waste Reduction, NGO Support.
Abstract
AI-Based Video surveillance System
Afsa Saboo, B Sai Dikshitha, C Mohammad Athiq, K Sudeep Gouda, Nagateja P, Anita Patil
DOI: 10.17148/IJARCCE.2026.15506
Abstract: Rapid urbanisation and escalating public safety demands have created an urgent need for intelligent, automated surveillance solutions that can operate without continuous human oversight. Conventional CCTV infrastructure places excessive cognitive load on operators monitoring multiple feeds simultaneously, increasing the risk of missed incidents due to fatigue and delayed response. This paper proposes a dual-model AI surveillance framework that concurrently detects three real-world emergency categories — road accidents, fire incidents, and suspicious human activity — by combining YOLOv8 spatial object detection with ResNet-50 temporal activity classification in a unified processing pipeline. On emergency detection, the system autonomously assembles an alert payload containing a timestamped snapshot, GPS-tagged camera location, and event confidence score, dispatching notifications in parallel via SMS, email, and mobile push notification to relevant authorities. Experimental evaluation on publicly available benchmark datasets yields detection accuracies of 89.2%, 91.5%, and 86.0% for accidents, fire, and suspicious activity respectively, with per- frame inference latency of 0.8–1.2 seconds and end-to-end alert delivery within three seconds. The proposed framework significantly reduces reliance on manual monitoring and offers a scalable, deployable foundation for smart city infrastructure, transportation hubs, and public safety control rooms.
Keywords: Artificial intelligence; video surveillance; YOLO; CNN; emergency detection; computer vision; real-time alerts; smart cities; deep learning.
Keywords: Artificial intelligence; video surveillance; YOLO; CNN; emergency detection; computer vision; real-time alerts; smart cities; deep learning.
Abstract
Smart AI-Integrated Exam Security Gate
A Sunitha, B Lavanya, B Pallavi, Manisha Patel, Anita Patil
DOI: 10.17148/IJARCCE.2026.15507
Abstract: Examination systems face major challenges such as malpractice, unauthorized entry, and the use of prohibited electronic devices. Traditional manual checking methods are often time-consuming, require more manpower, and may fail to detect hidden devices or impersonation attempts effectively. With the advancement of technologies such as Artificial Intelligence (AI), Machine Learning (ML), and sensor-based systems, smarter security solutions can be developed for examination environments. This project presents a Smart AI-Integrated Exam Security Gate that combines object detection and meta-detection sensors to provide automated verification and security screening at exam hall entry points. The system focuses on key features, such as student identity verification, prohibited item detection, alert generation, and real-time monitoring. It aims to improve the efficiency, accuracy, and reliability of the examination process while reducing human effort and security risks. This study also highlights the limitations of existing manual security systems and emphasizes the need for a more intelligent, automated, and secure examination management solution.
Keyword: Artificial Intelligence (AI), Exam Security, Object Detection, Metal Detection Sensor, Student Verification, Real-Time Monitoring, Smart Security Gate, Alert System.
Keyword: Artificial Intelligence (AI), Exam Security, Object Detection, Metal Detection Sensor, Student Verification, Real-Time Monitoring, Smart Security Gate, Alert System.
Abstract
SMART RAILWAY TRACK HEALTH MONOTORING SYSTEM by IOT
Dr. C.N. Deshmukh, Mayur S. Salode, Aryan R. Kale, Minal G. Lonkar, Kshitij M. Thotange
DOI: 10.17148/IJARCCE.2026.15508
Abstract: Undetected railway track anomalies, such as surface cracks and tilting, pose severe safety risks to modern transportation networks. To address this vulnerability, this paper presents a Smart Railway Track Health Monitoring System utilizing Industrial Internet of Things (IIoT) principles for continuous, real-time structural assessment. Powered by an ESP32 microcontroller and the ESP RainMaker cloud platform, the framework autonomously scans for physical faults, alignment shifts, and leveling irregularities. Sensor telemetry is processed locally and routed directly to the cloud, pushing instant, percentage-based severity alerts to remote control room personnel. By replacing reactive manual inspections with an automated predictive pipeline, this low-cost system significantly accelerates maintenance response times, minimizes operational downtime, and ensures a safer railway infrastructure.
Keywords: IoT, ESP32, Railway Crack Detection, Tilt Sensor, VibrationSensor, Arduino Uno, Track Monitoring, Railway Safety
Keywords: IoT, ESP32, Railway Crack Detection, Tilt Sensor, VibrationSensor, Arduino Uno, Track Monitoring, Railway Safety
Abstract
Farm2Door: A Smart Digital Platform for Farmer-to-Customer Agricultural Marketplace-A Review
K Naveen, K Shyam, Kothapalli Vamshi, Kothapalli Vardhan Babu, Dr. Anita Patil, Asst.Prof. Shashikantha Raddi
DOI: 10.17148/IJARCCE.2026.15509
Abstract: The demand for fresh agricultural products has increased significantly in recent years, but the existing supply chain still faces multiple inefficiencies. In traditional systems, farmers depend on intermediaries to sell their produce, which often leads to reduced profits and higher prices for customers. To address this issue, this paper presents Farm2Door, a digital platform that directly connects farmers with consumers.
The platform allows farmers to upload their products and manage availability, while customers can browse, compare, and place orders easily. Features such as order tracking, simple payment options, and customer feedback are included to improve usability and trust. By reducing the dependency on middlemen, the system helps in maintaining fair pricing and better transparency.
Although the model is simple, it has the potential to improve local agricultural trade if implemented effectively. Future improvements can include smarter delivery systems and better demand prediction.
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The platform allows farmers to upload their products and manage availability, while customers can browse, compare, and place orders easily. Features such as order tracking, simple payment options, and customer feedback are included to improve usability and trust. By reducing the dependency on middlemen, the system helps in maintaining fair pricing and better transparency.
Although the model is simple, it has the potential to improve local agricultural trade if implemented effectively. Future improvements can include smarter delivery systems and better demand prediction.
I
Abstract
CLEARBON: A Blockchain-Based Dual Market System for Real-Time Carbon Credit Verification and Trading
Sohail, Sooraj B, Suma Shree S, Swetha Rani H S, Anita Patil
DOI: 10.17148/IJARCCE.2026.15510
Abstract: Industrial carbon markets face delays in credit issuance and lack real-time verification and predictive trading mechanisms. This paper proposes CLEARBON, a blockchain-based system integrating IoT sensors, AI models, and satellite data for real-time emission monitoring and instant carbon credit issuance. The system introduces a dual-market approach with spot and futures trading, enabling efficient, transparent, and secure carbon transactions while supporting predictive emission reduction strategies.
Keywords: Blockchain, Carbon Credits, IoT, Machine Learning, Smart Contracts, Emission Monitoring
Keywords: Blockchain, Carbon Credits, IoT, Machine Learning, Smart Contracts, Emission Monitoring
Abstract
EVOLUTION OF AI TECHNOLOGIES IN THE EDUCATIONAL SECTOR
Yashvardhan Chahal, Yash Goswami, Tushar Mahaur, Yash Jain, Dr. K.S. Mishra
DOI: 10.17148/IJARCCE.2026.15511
Abstract: Artificial Intelligence (AI) has rapidly evolved from early rule-based systems to today’s advanced machine learning models, significantly transforming the global educational landscape. Over the years, AI technologies such as adaptive learning platforms, intelligent tutoring systems, virtual assistants, predictive analytics, and automated assessments have revolutionized how students learn and how teachers manage academic tasks. This research paper explores the historical evolution, technological progression, applications, challenges, and future potential of AI-driven educational solutions. Despite issues such as data security, cost, and ethical concerns, AI continues to reshape education, making learning more personalized, inclusive, and efficient. The study concludes that AI technologies will play a central role in building next-generation smart learning environments.
Keywords: Artificial Intelligence, Educational Technology, Intelligent Tutoring Systems, Personalized Learning, Machine Learning.
Keywords: Artificial Intelligence, Educational Technology, Intelligent Tutoring Systems, Personalized Learning, Machine Learning.
Abstract
Talent Track Workforce Employment Solutions Platform
Sathiyaa Balajee R, Mrs. Saranya R
DOI: 10.17148/IJARCCE.2026.15512
Abstract: The Talent Track Workforce Employment Solutions Platform is a comprehensive digital system designed to streamline and modernize the recruitment and workforce management process. It serves as an integrated platform that connects job seekers, employers, and administrators, enabling efficient communication, skill matching, and hiring decisions. The system aims to reduce the complexity of traditional recruitment methods by providing a centralized environment where users can manage job postings, applications, and candidate profiles with ease. It also supports data organization and reporting, enabling better decision-making and workforce planning. The platform is designed with scalability and flexibility in mind, making it suitable for organizations of varying sizes and industries.
Keywords: Employer Dashboard, Resume Management, Skill Matching, Online Recruitment, Hiring Process Automation, Web-Based Application, Job Seekers, Employers, Talent Acquisition, Data Management, Performance Tracking, Scalable System, User-Friendly Interface.
Keywords: Employer Dashboard, Resume Management, Skill Matching, Online Recruitment, Hiring Process Automation, Web-Based Application, Job Seekers, Employers, Talent Acquisition, Data Management, Performance Tracking, Scalable System, User-Friendly Interface.
Abstract
SMART EXAM SEAT ALLOCATION SYSTEM USING ANDROID APPLICATION
Riya Bhoir, Bhumika Beldar, Kanishka Chindarkar, Prof. Smita Chunamari
DOI: 10.17148/IJARCCE.2026.15513
Abstract: Smart Exam Seat Allocation System is an Android-based application designed to automate the process of assigning seats to students during examinations. The system reduces manual effort, ensures fairness, and optimizes classroom utilization. It integrates modules such as login authentication, classroom creation, student data management, seat generation, and student view. This paper discusses the motivation, objectives, system architecture, methodology, technology stack, features, advantages, limitations, and future scope of the system.
Keywords: Seat Allocation, Android Application, Java, Firebase, Automation, Classroom Management, Exam System
Keywords: Seat Allocation, Android Application, Java, Firebase, Automation, Classroom Management, Exam System
Abstract
“Smart AI-Based Student Attendance System with Monthly Analytics”
Harsh Sharma, Miss. Taniya Jain, Dr. Uruj Jaleel, Dr. Satish Kumar Soni
DOI: 10.17148/IJARCCE.2026.15514
Abstract: Attendance management is a critical administrative function in educational institutions, playing a vital role in monitoring student participation, discipline, and academic performance. Accurate attendance records are essential for evaluating student engagement and ensuring compliance with institutional policies. However, traditional attendance systems, including manual registers and biometric-based systems, suffer from several limitations such as time inefficiency, susceptibility to human errors, proxy attendance, and lack of real-time monitoring and analytics. With the rapid advancement of emerging technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision, there is a growing demand for intelligent systems capable of automating repetitive tasks while providing meaningful insights. In this context, facial recognition technology has emerged as a powerful tool for identity verification and automation.
This research proposes a Next-Generation Smart AI-Based Student Attendance System integrated with Monthly Analytics and Predictive Insights. The system utilizes real-time facial recognition techniques to automatically identify students and mark attendance without requiring manual input or physical interaction. The integration of deep learning algorithms ensures high accuracy and robustness under varying environmental conditions. In addition to attendance automation, the proposed system incorporates a comprehensive analytics module that processes attendance data to generate monthly reports, identify trends, and predict future attendance behavior. These predictive insights enable educators and administrators to identify students at risk due to low attendance and take proactive measures. The system is implemented using Python, OpenCV, and advanced machine learning techniques, ensuring scalability and efficiency. Experimental results demonstrate that the proposed system significantly reduces time consumption, eliminates proxy attendance, and enhances data reliability. This research highlights the potential of combining AI- driven automation with data analytics to transform traditional attendance systems into intelligent decision-support systems, contributing to the development of smart education environments.
Keywords: Artificial Intelligence, Face Recognition, Computer Vision, Attendance Analytics, Machine Learning, Smart Education.
This research proposes a Next-Generation Smart AI-Based Student Attendance System integrated with Monthly Analytics and Predictive Insights. The system utilizes real-time facial recognition techniques to automatically identify students and mark attendance without requiring manual input or physical interaction. The integration of deep learning algorithms ensures high accuracy and robustness under varying environmental conditions. In addition to attendance automation, the proposed system incorporates a comprehensive analytics module that processes attendance data to generate monthly reports, identify trends, and predict future attendance behavior. These predictive insights enable educators and administrators to identify students at risk due to low attendance and take proactive measures. The system is implemented using Python, OpenCV, and advanced machine learning techniques, ensuring scalability and efficiency. Experimental results demonstrate that the proposed system significantly reduces time consumption, eliminates proxy attendance, and enhances data reliability. This research highlights the potential of combining AI- driven automation with data analytics to transform traditional attendance systems into intelligent decision-support systems, contributing to the development of smart education environments.
Keywords: Artificial Intelligence, Face Recognition, Computer Vision, Attendance Analytics, Machine Learning, Smart Education.
Abstract
AI-Driven Personalized Education Platform: Design, Architecture, and Implementation
Sajid Raza, Vishal Kumar Sah, Rishabh Tiwari, Saif Siddiqui, Dr. Sandeep Kumar Dubey
DOI: 10.17148/IJARCCE.2026.15515
Abstract: Education is undergoing a quiet but consequential shift. For generations, classrooms have delivered the same lesson to every student at the same pace—a model that works well for some learners and poorly for many others. The rise of machine learning offers a practical way out of this impasse. This paper presents an AI-Driven Personalized Education Platform that collects fine-grained learner data, analyses it in real time, and continuously adapts the content, assessments, and feedback each student receives. Rather than treating personalisation as a premium feature layered on top of a conventional system, we built it into the architecture from the ground up. The platform comprises five tightly coupled modules: a data collection layer, an AI analysis engine, a recommendation module, an adaptive assessment component, and a teacher-facing dashboard. A semester-long pilot with undergraduate Computer Science students showed improved quiz performance, faster identification of weak topics, and more targeted teacher interventions compared with a static content pathway. The codebase is modular and cloud-deployable, making it straightforward to extend or integrate with existing institutional infrastructure.
Keywords: Personalized Learning; Artificial Intelligence; Machine Learning; Adaptive Assessment; Recommendation System; Educational Data Mining; E-Learning.
Keywords: Personalized Learning; Artificial Intelligence; Machine Learning; Adaptive Assessment; Recommendation System; Educational Data Mining; E-Learning.
Abstract
Artificial Intelligence in Cybersecurity: A Comprehensive Survey on AI-Driven Insider Threat Detection
Deepak Kumar G., Deevaraj M., H Pramodh, Lakshmi Narayana, Dr. Muhibur Rahman T R
DOI: 10.17148/IJARCCE.2026.15516
Abstract: Insider threats are one of the most difficult cybersecurity problems organizations face today. Unlike attacks that come from outside, insider threats involve people who already have authorized access — employees, contractors, or trusted partners — who either deliberately misuse that access or unknowingly create security risks. Because these individuals operate within normal system boundaries, traditional security tools like firewalls tend to miss them entirely. This paper looks at how artificial intelligence is being used to tackle this problem, drawing on published research from IEEE Xplore, ACM Digital Library, Springer, and ScienceDirect. We looked at a range of approaches — deep learning, graph-based analysis, User Behavior Analytics (UBA), Support Vector Machines, rule-based methods, and even psychosocial behavioral modeling. To make sense of this variety, we put together a four-tier framework that organizes these systems from the simplest rule-based tools all the way up to fully adaptive AI platforms. We also measured how these systems perform in terms of detection accuracy, false alarm rates, scalability, and speed. One finding kept coming up: no existing system brings together real-time monitoring, automated risk scoring, explainable outputs, and adaptive learning in a single working platform. We explore why this gap exists and what it would take to close it.
Keywords: Artificial Intelligence; Cybersecurity; Insider Threat Detection; Machine Learning; Deep Learning; User Behavior Analytics; Anomaly Detection; Graph-Based Detection; Support Vector Machine; LSTM; Risk Scoring; Behavioral Analysis; Explainable AI; Real-Time Monitoring; Intrusion Detection Systems.
Keywords: Artificial Intelligence; Cybersecurity; Insider Threat Detection; Machine Learning; Deep Learning; User Behavior Analytics; Anomaly Detection; Graph-Based Detection; Support Vector Machine; LSTM; Risk Scoring; Behavioral Analysis; Explainable AI; Real-Time Monitoring; Intrusion Detection Systems.
Abstract
A Comprehensive Study on Data Storage Security Issues and Services in Cloud Computing
Sujal Satish Godse, Bhushan Sunil Matsagar, Kirti Dinkar More
DOI: 10.17148/IJARCCE.2026.15517
Abstract: Cloud computing has become an important platform for storing and managing data due to its scalability, flexibility, and cost effectiveness. However, outsourcing data to third-party cloud service providers introduces several security concerns, particularly related to confidentiality, integrity, and availability. This paper presents a study of data storage security issues in cloud computing and discusses cloud service models and deployment models from a storage perspective. It reviews existing security techniques such as encryption, identity-based authentication, and third-party auditing for ensuring data protection. The paper also examines major challenges including data privacy, data recoverability, media sanitization, insecure APIs, vendor lock-in, and network dependency. The study highlights the need for effective security mechanisms to improve trust and reliability in cloud storage environments.
Keywords: Cloud computing, Cloud storage, Data security, Zero Trust, Blockchain, Confidential computing
Keywords: Cloud computing, Cloud storage, Data security, Zero Trust, Blockchain, Confidential computing
Abstract
SMART ROAD ACCIDENT DETECTION AND RESCUE SYSTEM
JEEVITHA R, B SAHANA REDDY, G TEJASHWINI, SHREYA B R
DOI: 10.17148/IJARCCE.2026.15518
Abstract: Road accidents are a major global issue, leading to significant loss of life due to delayed emergency response. This paper proposes a Smart Accident Detection and Rescue System that uses IoT sensors, GPS, and communication technologies to automatically detect accidents and alert emergency services. The system integrates accelerometers, gyroscopes, GPS modules, and GSM communication to identify collisions and transmit real-time location data. Advanced approaches using machine learning further improve detection accuracy and reduce false alarms. The proposed system ensures faster rescue operations, minimizes response time, and enhances road safety.
Abstract
MultiCart- Ai Based Multi Vendor Cart Platform
Kamlesh Kumar Pal, Abhishek Gupta, Harshit Mall, Mr. Deepak Kumar
DOI: 10.17148/IJARCCE.2026.15519
Abstract: The rapid growth of e-commerce has created a need for scalable and efficient platforms that can support multiple vendors within a single ecosystem. Traditional online shopping systems are often limited to single vendors, restricting product variety and reducing operational flexibility. To overcome these limitations, this project presents “MultiCart- Ai Based Multi Vendor Cart Platform,” an intelligent web-based application designed to integrate multiple sellers and provide a seamless shopping experience for users.
The proposed system introduces a unified cart mechanism that allows customers to add and purchase products from different vendors in a single transaction. It incorporates Artificial Intelligence (AI) techniques to enhance user experience through personalized product recommendations, smart filtering, and behavior-based suggestions. The platform also provides dedicated dashboards for vendors to manage produc t s, t r ack orde rs, and analyze performance, while administrators can monitor system activities, approve vendors, and maintain platform integrity.
The system is developed using modern web technologies such as React.js for the frontend, Node.js and Express for the backend, and MongoDB for database management. Secure authentication and efficient data handling ensure reliability and scalability of the platform.
The implementation of Multicart demonstrates i m prove d usa bi l i t y, e f fi c i ent vendor m a na ge me nt, and enhance d custome r satisfaction compared to traditional systems. This project highlights the potential of combining multi-vendor architecture with AI-driven features to build a next-generation e-commerce platform.
The architecture of Multicart follows a modular and layered design, promoting flexibility, maintainability, and efficient data handling. The implementation results demonstrate that the system effectively manages multi-vendor operations, reduces redundancy, and provides a smooth and intelligent shopping experience.
Furthermore, the platform addresses key issues in existing systems, such as lack of personalization, inefficient cart management, and limited scalability.
The proposed AI-powered multi-vendor cart platform offers a robust and future-ready solution for modern e-commerce applications. It not only improves operational efficiency for vendors and administrators but also enhances the overall user experience through intelligent automation and seamless integration of services. Future enhancements may include advanced machine learning models, mobile application support, and secure payment gateway integration.
The proposed system introduces a unified cart mechanism that allows customers to add and purchase products from different vendors in a single transaction. It incorporates Artificial Intelligence (AI) techniques to enhance user experience through personalized product recommendations, smart filtering, and behavior-based suggestions. The platform also provides dedicated dashboards for vendors to manage produc t s, t r ack orde rs, and analyze performance, while administrators can monitor system activities, approve vendors, and maintain platform integrity.
The system is developed using modern web technologies such as React.js for the frontend, Node.js and Express for the backend, and MongoDB for database management. Secure authentication and efficient data handling ensure reliability and scalability of the platform.
The implementation of Multicart demonstrates i m prove d usa bi l i t y, e f fi c i ent vendor m a na ge me nt, and enhance d custome r satisfaction compared to traditional systems. This project highlights the potential of combining multi-vendor architecture with AI-driven features to build a next-generation e-commerce platform.
The architecture of Multicart follows a modular and layered design, promoting flexibility, maintainability, and efficient data handling. The implementation results demonstrate that the system effectively manages multi-vendor operations, reduces redundancy, and provides a smooth and intelligent shopping experience.
Furthermore, the platform addresses key issues in existing systems, such as lack of personalization, inefficient cart management, and limited scalability.
The proposed AI-powered multi-vendor cart platform offers a robust and future-ready solution for modern e-commerce applications. It not only improves operational efficiency for vendors and administrators but also enhances the overall user experience through intelligent automation and seamless integration of services. Future enhancements may include advanced machine learning models, mobile application support, and secure payment gateway integration.
Abstract
Public Transport Tracking Systems: A Comprehensive Study of Real-Time Solutions for Small Cities
C Renuka, Divya Rajeev M, J Kavyashree, Bindu AV, Muhibur Rahman T R
DOI: 10.17148/IJARCCE.2026.15520
Abstract: Public Transport Tracking Systems leverage advanced location-based technologies to enable accurate real- time vehicle monitoring and efficient transit management, departing from traditional systems that rely on fixed schedules and limited passenger information. As urban mobility evolves—particularly in small cities with growing population and infrastructure constraints—the need for scalable, cost-effective, and accessible real-time transport solutions has intensified. This paper presents a structured review of multiple studies from IEEE, Springer, ScienceDirect, and related sources, covering core technologies such as GPS-based tracking, IoT-enabled transport systems, mobile application integration, and cloud-based data processing. A novel four-tier taxonomy is proposed, classifying systems based on functional capabilities: real-time vehicle tracking, passenger information systems, fleet management and optimization, and smart transport assistant platforms. Performance aspects including tracking accuracy, latency, system reliability, scalability, and cost-efficiency are analyzed. Comparative evaluation reveals that no existing solution fully integrates real-time tracking, predictive arrival estimation, route optimization, and user-interactive platforms into a unified system suitable for small cities. Several research gaps are identified, and a strategic roadmap toward intelligent, affordable, and integrated public transportation systems is outlined.
Abstract
CLICKET – Be There or Make It Happen With AI
Sharad Singh, Ritik Chaurasiya, Shivam, Prince Yadav, Kumar Bibhuti Bhushan Singh
DOI: 10.17148/IJARCCE.2026.15521
Abstract: The rapid advancement of digital technologies has significantly transformed the way events are organized, managed, and experienced across various domains such as education, business, entertainment, and social engagement. Most existing systems focus on isolated functionalities such as ticket booking or event listing, without providing a comprehensive solution that integrates all stages of the event lifecycle. As a result, users often face challenges related to event discovery, complex booking processes, and lack of centralized management, while organizers struggle with operational inefficiencies and the need to rely on multiple tools.
A key innovation of the proposed system lies in its integration of artificial intelligence to enhance user experience and system efficiency. The AI-powered category suggestion feature analyzes event descriptions and automatically recommends appropriate categories, improving event discoverability and reducing the effort required by organizers. Additionally, the system incorporates intelligent workflows that enable better organization and classification of events, thereby improving search relevance and user engagement.
Keywords: Event Management System, Artificial Intelligence, QR Code Verification, Next.js, MongoDB, RBAC, Web Application
A key innovation of the proposed system lies in its integration of artificial intelligence to enhance user experience and system efficiency. The AI-powered category suggestion feature analyzes event descriptions and automatically recommends appropriate categories, improving event discoverability and reducing the effort required by organizers. Additionally, the system incorporates intelligent workflows that enable better organization and classification of events, thereby improving search relevance and user engagement.
Keywords: Event Management System, Artificial Intelligence, QR Code Verification, Next.js, MongoDB, RBAC, Web Application
Abstract
AWAZE-E-JANATA- VOICE OF THE PEOPLE
Rajveer Pratap Singh, Aman Tiwari, Raj Yadav, Mr. Dileep Kumar Gupta
DOI: 10.17148/IJARCCE.2026.15522
Abstract: Awaze-e-Janata – Voice of the People is a digital platform designed to bridge the communication gap between citizens and government authorities. The primary objective of this system is to provide a simple, accessible, and transparent medium through which people can raise their complaints, issues, and suggestions related to public services. This platform enables users to submit complaints manually or with the assistance of Artificial Intelligence, making the process faster and more efficient. The system categorizes and forwards the complaints to the relevant authorities, ensuring that the issues reach the correct department without delay. Additionally, it allows users to track the status of their complaints, promoting transparency and accountability. The project focuses on solving real-world societal problems by empowering citizens and enhancing public participation in governance. By integrating modern technologies such as web development and AI-based assistance, the system aims to improve communication, reduce response time, and build trust between the public and government institutions.
Overall, Awaze-e-Janata serves as an effective tool for digital governance, ensuring that every voice is heard and every issue is addressed efficiently.
Overall, Awaze-e-Janata serves as an effective tool for digital governance, ensuring that every voice is heard and every issue is addressed efficiently.
Abstract
Smart Battlefield Helmet with Soldier Monitoring System
Dr. Pramod Sharma, Aryansh Gupta, Divya Gupta, Jayanth Bharadwaj, Sagar Mishra
DOI: 10.17148/IJARCCE.2026.15523
Abstract: This paper presents a Smart Battlefield Helmet with Soldier Monitoring System. The aim of the project is to build a low-cost, embedded system that can continuously monitor a soldier’s health and surroundings and transmit the data wirelessly to a base station. The helmet uses an Arduino Nano as the main microcontroller, paired with an ESP32 for communication. Sensors include the MAX30102 for heart rate and SpO₂ monitoring, DS18B20 for body temperature, an MQ gas sensor for detecting toxic or flammable gases, and an MPU6050 IMU for fall detection. A NEO-6M GPS module provides real-time location tracking, and a LoRa SX1278 radio module transmits all data to a base station dashboard several kilometres away. The circuit runs on a 5 V regulated supply using an LM7805 regulator powered from a 9 V DC adapter.
Keywords: Helmet, Soldier, Monitoring, Temperature, Tracking, Battlefield
Keywords: Helmet, Soldier, Monitoring, Temperature, Tracking, Battlefield
Abstract
AUTOFACE - Attendance Simplified Through Vision
Shrestha Gupta, Shreoshi Roy
DOI: 10.17148/IJARCCE.2026.15524
Abatract: AutoFace: Attendance Simplified
through Vision is an automated attendance management system designed to overcome the
limitations of traditional manual methods, such as inefficiency, human error,
and proxy attendance. The proposed system leverages deep learning–based facial
recognition using the SSD MobileNet v1 architecture for real-time face
detection under varying conditions. Detected faces are encoded into
128-dimensional embeddings and matched against a secure database using the
Euclidean Distance metric for accurate identity verification.
Developed using the MERN stack, the system ensures
scalability, real-time data synchronization, and platform independence. It also
provides features such as live session monitoring and automated report
generation. Experimental results demonstrate an accuracy of 97.8% with an
average latency of less than 1.5 seconds per individual. The system offers a
secure, contactless, and efficient solution, significantly improving
reliability and reducing administrative overhead in attendance management.
Keywords: AutoFace,
Face Recognition, Attendance Management System, Deep Learning, SSD MobileNet,
Facial Embeddings, Euclidean Distance, MERN Stack, Cloud-Based System,
Real-Time Monitoring
Abstract
DejaView: An AI-Powered Academic Project Integrity and Evaluation Portal Using Vector Embeddings and Large Language Models
Mrs. Trupthi Rao, Mr. Sumit Kumar Yadav, Aditya Raj Swain, Mahadev Jagtap, Pratik, Gajanan
DOI: 10.17148/IJARCCE.2026.15525
Abstract: The proliferation of student project submissions in higher education institutions has introduced acute challenges in maintaining academic integrity and evaluation consistency. Manual review processes are labour-intensive, subjective, and fail to scale with the growing volume of submissions. This paper presents DejaView, a full-stack, AI-powered academic project management portal developed for the Department of Computer Science and Engineering (AI & ML) at Dayananda Sagar University. DejaView automates two critical pain-points: (1) detection of plagiarised or over-similar submissions using 384- dimensional dense vector cosine similarity powered by the FastEmbed BAAI/bge-small-en-v1.5 model, and (2) structured generation of executive summaries, technology stack labels, and personalised viva voce questions using the Groq LLM (llama- 3.3-70b-versatile). The system is built on a modular three-layer Python architecture comprising a Streamlit frontend, an AI sentinel engine, and a thread-safe SQLite persistence layer. Institutional access is enforced through strict email domain gating and bcrypt password hashing, ensuring that only verified university stakeholders interact with the platform. Experimental results demonstrate that the system reliably flags submissions with greater than 75% cosine similarity, generates domain-relevant AI insights with high contextual precision, and exports structured grade sheets for archival in a single click.
Keywords: Academic Integrity, Plagiarism Detection, Vector Embeddings, Large Language Models, Federated Evaluation, Streamlit, Cosine Similarity, Viva Question Generation, FastEmbed, Groq API.
Keywords: Academic Integrity, Plagiarism Detection, Vector Embeddings, Large Language Models, Federated Evaluation, Streamlit, Cosine Similarity, Viva Question Generation, FastEmbed, Groq API.
Abstract
EMPLOYMENT HIRING PLATFORM USING AI
Sanjay Kumar, Prince Pandey, Md Shahjad, Deepak Kumar
DOI: 10.17148/IJARCCE.2026.15526
Abstract: This paper presents JobVista, a full stack employment hiring platform designed to support students, job seekers, and recruiters through a role-aware digital recruitment workflow. The platform provides secure authentication, candidate profile management, recruiter company management, job posting, job browsing, application tracking, applicant review, and assistant-driven guidance through JobMate. The implemented system uses React, Vite, Redux Toolkit, Express.js, Node.js, MongoDB, Mongoose, JWT-based cookie authentication, and bcrypt password hashing. JobVista also improves early platform usefulness by showing selected external opportunities and by providing template and Gemini-backed assistant responses for resumes, interviews, cover letters, job posts, and screening questions. The paper discusses the problem background, proposed methodology, architecture, database design, module implementation, testing, results, limitations, and future enhancements. The final system demonstrates that a college- level job portal can be technically meaningful, visually usable, and extensible without becoming unnecessarily complex.
Keywords: JobVista, job portal, employment hiring platform, React, Node.js, Express.js, MongoDB, role-based access control, application tracking, JobMate, AI career assistant
Keywords: JobVista, job portal, employment hiring platform, React, Node.js, Express.js, MongoDB, role-based access control, application tracking, JobMate, AI career assistant
Abstract
A Review of Voice-Driven Accessible Vocational Training Platforms for Persons with Visual Impairment
Bharath Reddy, Chinta Vishnu Vardhan, H M Sharanabasava, K Ravi Kumar, Dr. Muhibur Rahman
DOI: 10.17148/IJARCCE.2026.15527
Abstract: The increasing demand for inclusive education and skill development has brought significant attention to assistive technologies designed for persons with visual impairments. Voice-driven vocational training platforms represent a transformative approach to delivering accessible learning experiences without reliance on visual interfaces. This review examines existing literature, system architectures, and technological frameworks used in voice-based e-learning and vocational training tools tailored for visually impaired users. The paper presents a four-tier taxonomy of platform sophistication, analyses key methods including Text-to-Speech (TTS), Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and screen reader integration, and identifies critical research gaps. Comparative analysis of reviewed systems highlights strengths and limitations. The review concludes by recommending a unified, adaptive voice- driven platform with offline capabilities and multi-language support to bridge the vocational accessibility gap.
Keywords: Visual Impairment, Voice-Driven Learning, Vocational Training, Assistive Technology, Text-to-Speech, Speech Recognition, Accessible Education, NLP
Keywords: Visual Impairment, Voice-Driven Learning, Vocational Training, Assistive Technology, Text-to-Speech, Speech Recognition, Accessible Education, NLP
Abstract
NeuroSecure: A Comprehensive Survey on Deep Learning Approaches for Cyber Defense and Intrusion Detection
P. Anirudh, B. Adarsh Reddy, G. Raghavendra, K. Naveen Kumar, Dr. Muhibur Rahman T R
DOI: 10.17148/IJARCCE.2026.15528
Abstract: Cyber threats have grown dramatically in both scale and sophistication, outpacing the detection capabilities of classical signature-based and rule-driven security tools. This paper surveys the evolution of Intrusion Detection Systems (IDS) from their early reliance on static pattern matching through to modern deep learning–driven architectures, drawing on peer-reviewed publications and benchmark evaluation studies. We review work spanning core algorithmic approaches—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer architectures, and ensemble methods—alongside their application to network traffic analysis using the NSL-KDD and CICIDS2017 benchmark datasets. To bring structure to this body of literature, we introduce a four-tier classification framework organized around increasing system sophistication: from basic signature matching through anomaly detection and hybrid approaches, to fully integrated, context-aware deep learning platforms. We then present a hybrid ensemble IDS framework that combines CNN, RNN, and Transformer models through a majority-voting fusion layer, achieving detection accuracy exceeding 96% with precision, recall, and F1-score consistently above 94% across all attack categories. Performance dimensions examined include classification accuracy, precision-recall balance, generalization to unseen threats, and scalability for enterprise and cloud environments. A recurring observation across reviewed studies is the absence of any single system that simultaneously handles diverse attack types, class imbalance, feature redundancy, and real-time traffic volumes within one coherent architecture. We discuss the practical implications of this gap and outline directions for future research.
Keywords: Intrusion Detection Systems; Deep Learning; Convolutional Neural Networks; Recurrent Neural Networks; Transformer Models; Network Security; NSL-KDD; CICIDS2017; Ensemble Learning; Cybersecurity; Anomaly Detection; Zero-Day Attacks.
Keywords: Intrusion Detection Systems; Deep Learning; Convolutional Neural Networks; Recurrent Neural Networks; Transformer Models; Network Security; NSL-KDD; CICIDS2017; Ensemble Learning; Cybersecurity; Anomaly Detection; Zero-Day Attacks.
Abstract
Case Study on Experimental Setup Design in Robotics Research: A Comprehensive Review of Future Directions
M A Kaif, Md Akif Bari, Md Muntaseeb, Punith Raj, Dr. Muhibur Rahaman T.R
DOI: 10.17148/IJARCCE.2026.15529
Abstract: Robotics research has become a rapidly evolving domain with applications spanning industrial automation, healthcare, autonomous systems, and smart environments. A critical component of robotics research is the design of experimental setups, which ensures reliable testing, reproducibility, and performance evaluation of robotic systems. This paper presents a comprehensive case study on experimental setup design in robotics research, covering methodologies such as simulation-based testing, hardware prototyping, real-time validation, and hybrid experimental approaches. The study explores key application areas including autonomous navigation, robotic manipulation, human-robot interaction, and swarm robotics. It also highlights major challenges such as hardware constraints, environmental variability, sensor inaccuracies, cost limitations, and reproducibility issues. Furthermore, the paper identifies research gaps and proposes future directions involving AI-integrated robotics, digital twins, edge computing, and adaptive experimental frameworks. The findings emphasize that a well-designed experimental setup significantly enhances the reliability, scalability, and real-world applicability of robotics systems.
Keywords: Robotics Research, Experimental Design, Autonomous Systems, Simulation, Hardware Prototyping, Sensors, Control Systems, Human-Robot Interaction, AI Robotics, Edge Computing, Swarm Robotics
Keywords: Robotics Research, Experimental Design, Autonomous Systems, Simulation, Hardware Prototyping, Sensors, Control Systems, Human-Robot Interaction, AI Robotics, Edge Computing, Swarm Robotics
Abstract
A Review on Machine Learning Techniques for Crop Yield Prediction
A Renukamma, Arathi C G, Aruna B, K Ananya, Dr. Muhibur Rahman T.R
DOI: 10.17148/IJARCCE.2026.15530
Abstract: Crop yield prediction plays a crucial role in ensuring food security, efficient resource management, and sustainable agricultural planning. With the rapid advancement of artificial intelligence, machine learning (ML) techniques have emerged as powerful tools for predicting crop productivity using diverse datasets such as weather conditions, soil characteristics, and remote sensing data. This paper presents a comprehensive review of machine learning techniques applied to crop yield prediction. It analyzes commonly used algorithms, including linear regression, decision trees, random forests, support vector machines, and deep learning models such as artificial neural networks and convolutional neural networks. Studies show that environmental factors like temperature, rainfall, and soil type are the most significant features influencing prediction accuracy.
Keywords: Crop Yield Prediction, Machine Learning, Precision Agriculture, Remote Sensing, Predictive Modeling
Keywords: Crop Yield Prediction, Machine Learning, Precision Agriculture, Remote Sensing, Predictive Modeling
Abstract
AI-Based Real-Time Traffic Congestion Prediction and Signal Optimization System
B Deepika, Basavarajeshwari, D R Pallavi, D Suhasini, Dr. Muhibur Rahman T.R
DOI: 10.17148/IJARCCE.2026.15531
Abstract: Traffic congestion has become a major challenge in urban areas due to rapid population growth and increasing vehicle density. Conventional traffic signal systems operate on fixed timing schedules and fail to adapt to real-time traffic conditions, leading to inefficient traffic flow and increased delays. This paper proposes an AI-based real-time traffic congestion prediction and adaptive signal optimization system to address these limitations. The system utilizes techniques from Machine Learning to analyze traffic parameters such as vehicle density, time of day, and historical traffic patterns. A predictive model is developed to classify congestion levels, and based on the predicted output, signal timings are dynamically adjusted to optimize traffic flow. The proposed approach improves traffic efficiency by reducing vehicle waiting time and minimizing congestion at intersections. Experimental results demonstrate that the AI-based system outperforms traditional fixed-time signal control methods in terms of accuracy and overall traffic management performance. This work contributes to the development of intelligent and scalable solutions aligned with modern Smart City initiatives.
Keywords: Artificial Intelligence, Traffic Congestion Prediction, Adaptive Signal Control, Real-Time Traffic Management, Machine Learning, Smart Cities, Intelligent Transportation Systems, Data Analytics
Keywords: Artificial Intelligence, Traffic Congestion Prediction, Adaptive Signal Control, Real-Time Traffic Management, Machine Learning, Smart Cities, Intelligent Transportation Systems, Data Analytics
Abstract
Crop Disease Detection Using AI: A Comprehensive Survey
G Sai Supriya, G Sanjana, Harshita S S, V Indu, Muhibur Rahman T.R
DOI: 10.17148/IJARCCE.2026.15532
Abstract: Crop disease detection is critical for ensuring food security and improving agricultural productivity, especially in India where a large population depends on farming. Traditional disease identification relies on manual inspection by farmers, which is often inaccurate, time-consuming, and leads to delayed treatment and excessive pesticide use. In recent years, Artificial Intelligence (AI) and deep learning, particularly Convolutional Neural Networks (CNN), have emerged as effective solutions for automated plant disease diagnosis.
This project presents an AI-based crop disease detection system that uses CNN for identifying diseases in tomato and potato leaves from uploaded images. The system provides instant classification of Healthy or Diseased leaves along with treatment recommendations and preventive measures. A web-based interface is developed using Flask for easy access by farmers with basic digital skills. The system also maintains a history of past detections to help users track disease patterns over time.
The study evaluates the system’s performance in terms of accuracy, usability, cost-effectiveness, and scalability. Results show that the system delivers fast and reliable predictions, reduces dependency on agricultural experts, and minimizes crop loss. The project highlights the potential of AI in making agriculture more efficient and sustainable, while also identifying scope for future enhancements like multi-crop support and mobile integration.
Keywords: Crop Disease Detection; Artificial Intelligence; Convolutional Neural Network; CNN; VGG-16; Flask; TensorFlow; PlantVillage Dataset; Smart Agriculture; Image Processing.
This project presents an AI-based crop disease detection system that uses CNN for identifying diseases in tomato and potato leaves from uploaded images. The system provides instant classification of Healthy or Diseased leaves along with treatment recommendations and preventive measures. A web-based interface is developed using Flask for easy access by farmers with basic digital skills. The system also maintains a history of past detections to help users track disease patterns over time.
The study evaluates the system’s performance in terms of accuracy, usability, cost-effectiveness, and scalability. Results show that the system delivers fast and reliable predictions, reduces dependency on agricultural experts, and minimizes crop loss. The project highlights the potential of AI in making agriculture more efficient and sustainable, while also identifying scope for future enhancements like multi-crop support and mobile integration.
Keywords: Crop Disease Detection; Artificial Intelligence; Convolutional Neural Network; CNN; VGG-16; Flask; TensorFlow; PlantVillage Dataset; Smart Agriculture; Image Processing.
Abstract
Development Of Eye Disease Detection Using Deep Learning
Afrin Amin Makandar, Pranav Sanjay Kumbhar
DOI: 10.17148/IJARCCE.2026.15533
Abstract: Catching eye diseases early stops patients from going blind. Right now, doctors check eye photos by hand. This manual process takes too much time and causes frequent mistakes. This paper proposes a robust computer- aided diagnosis (CAD) system designed to automatically classify retinal fundus images into eight distinct categories: Normal, Glaucoma, Diabetes, Cataracts, Age-related Macular Degeneration (AMD), Hypertension, Pathological Myopia, and other abnormalities. Utilizing a Multi-class Fundus Image Dataset, the research implements a deep learning framework centered on Convolutional Neural Networks (CNNs). The methodology integrates advanced image preprocessing—including grayscale conversion, noise filtration, and contrast-limited adaptive histogram equalization (CLAHE)—to enhance diagnostic features. The system is deployed via a high-performance web interface, ensuring low computational overhead and seamless user interaction. Experimental results indicate high precision and recall, demonstrating the system’s efficacy in facilitating rapid, large-scale ocular screenings.
Keywords: Deep Learning, Convolutional Neural Networks (CNN), Retinal Fundus Imaging, Computer-Aided Diagnosis (CAD), Medical Image Processing, Ocular Pathology
Keywords: Deep Learning, Convolutional Neural Networks (CNN), Retinal Fundus Imaging, Computer-Aided Diagnosis (CAD), Medical Image Processing, Ocular Pathology
Abstract
AUTONOMOUS/TELE-OPERATED LANDMINE DETECTION ROBOT
Deepak Yadav, Kundan Gautam, Sarfaraz Ansari, Shailesh Srivastava, Roshni dubey, Prof. Prabhakar dubey
DOI: 10.17148/IJARCCE.2026.15534
Abstract. An autonomous/teleoperated land mine detection robot vehicle is an advanced robotic system designed to enhance safety and efficiency in detecting buried landmines in hazardous environments. The primary objective of this system is to minimize human involvement in minefield exploration, thereby reducing the risk of injury or loss of life. The robot is equipped with a combination of sensors such as a metal detector for identifying metallic components of landmines and an ultrasonic sensor for obstacle detection and navigation. It operates in two modes: autonomous mode, where the robot follows a pre-programmed path and makes decisions independently, and teleoperated mode, where it is controlled remotely by an operator using wireless communication technologies like Bluetooth or RF modules. The system is typically built around a microcontroller such as Arduino, which processes sensor data and controls actuators like DC motors through a motor driver. Additional modules such as GPS and GSM can be integrated to provide real-time location tracking and alert notifications when a potential mine is detected. Upon detection, the robot stops, activates visual and audio alerts, and transmits the location information to the operator. This dual-mode operation ensures flexibility in different terrains and operational conditions. Overall, the land mine detection robot offers a cost-effective, reliable, and safer alternative to manual detection methods, with significant applications in military operations, border security, and post-conflict area clearance, while also providing a foundation for future advancements using artificial intelligence and improved sensing technologies.
Abstract
AGRITRADEHUB: A MERN Stack Based Digital Platform for Advancing India's Farmer-Merchant Direct Trade Ecosystem
Mukesh Gupta, Dheeraj Singh, Nidhi Shukla, Roshni Soni, Ms. Shreoshi Roy
DOI: 10.17148/IJARCCE.2026.15535
Abstract: Digital platforms are rapidly reshaping industries globally, and agriculture is no exception. In India, where agriculture plays a pivotal role in the economy and livelihoods of millions, the adoption of web technologies for direct farmer-merchant trading holds immense potential to revolutionize the agri-trade ecosystem. This paper proposes Agritradehub, a MERN stack based platform that eliminates intermediaries and facilitates direct market linkages between farmers and merchants. The study explores how MongoDB, Express.js, React.js, and Node.js can be used to build a scalable, secure, and user-friendly marketplace. From real-time product listing and bidding systems to integrated payment gateways and data analytics for price discovery, the paper analyzes the transformative impact of this platform on market access, supply chain efficiency, and overall transparency. It also highlights how the system differs from government initiatives like eNAM by providing complete price control to farmers. Furthermore, the paper addresses the challenges associated with digital adoption in the agricultural sector, including the digital divide, smartphone penetration, and the need for multilingual interfaces. This paper aims to provide valuable insights for students, researchers, and practitioners involved in promoting digital transformation in Indian agriculture.
Keywords: Agriculture, MERN Stack, Digital Marketplace, Direct Trading, Farmer-Merchant Platform, E-Agriculture, MongoDB, React.js.
Keywords: Agriculture, MERN Stack, Digital Marketplace, Direct Trading, Farmer-Merchant Platform, E-Agriculture, MongoDB, React.js.
Abstract
CONTENT FILTER BASED MOVIE RECOMMENDATION SYSTEM USING AI AND ML
Asharf Khan, Alok Kumar Singh, Vishal Singh, Akrar Khan, Pankaj Kumar Gupta
DOI: 10.17148/IJARCCE.2026.15536
Abstract: Recommendation system refers to the system which, dependent on the particular data set, provides users with recommendations for certain resources, such as books, movies, songs and so forth. Generally speaking, movie recommendation system usually suggests films which the User would love to watch due to particular attributes of the User, as well as previously accumulated data. Recommendation systems are very beneficial for companies which collect their data from a large number of customers and need to provide them with the most appropriate recommendations possible. Several aspects can be considered when building a movie recommendation system, such as the movie genre, the actors, its plot and even the director. Recommendation systems can offer movies based on a single aspect or several combined ones. The recommended system used in our paper is constructed based on the tags generated from the combinations of the film genre, actors and description which may be interesting for the User to watch. We decided to apply Content-based Filtering approach in our work.
Keywords: Movie Recommendation Systems, Content-Based Filtering, Movie recommendation, ai and machine learning project.
Keywords: Movie Recommendation Systems, Content-Based Filtering, Movie recommendation, ai and machine learning project.
Abstract
Alzheimer’s Disease Detection Using AI
Divyanshi Kashyap, Divya pandey, Mansi Pandey, Divyanshi Yadav, Dr. Nikhat Akhtar
DOI: 10.17148/IJARCCE.2026.15537
Abstract: Alzheimer’s disease is a progressive neurological condition that impacts memory, thinking ability, and speech functions. Early detection of Alzheimer’s is very crucial because timely intervention can help slow the progression of symptoms and enhance the overall quality of life for patients. Conventional diagnostic methods mainly depend on neuroimaging, cognitive assessments, and clinical evaluations, which are often costly, time-consuming, and not easily accessible to all individuals.
Recent studies indicate that speech patterns and language usage can provide early indications of cognitive decline. Individuals affected by Alzheimer’s disease frequently show variations in speech fluency, vocabulary diversity, pause pat-terns, and sentence formation. These variations can be exam-ined using computational methods along with machine learning techniques.
In this work, we present a machine learning-based approach for identifying Alzheimer’s disease through speech recordings and linguistic analysis. Acoustic features such as Mel Frequency Cepstral Coefficients (MFCC), pitch, energy, speech rate, and pause duration are derived from audio signals. Linguistic features are obtained from transcribed speech using TF-IDF representation.
Multiple models, including text-based, audio-based, and combined feature models, are tested using the XGBoost classifier. The experimental findings indicate that integrating both acoustic and linguistic features leads to a noticeable improvement in prediction performance. The proposed hybrid model attains an accuracy of 76.44
Recent studies indicate that speech patterns and language usage can provide early indications of cognitive decline. Individuals affected by Alzheimer’s disease frequently show variations in speech fluency, vocabulary diversity, pause pat-terns, and sentence formation. These variations can be exam-ined using computational methods along with machine learning techniques.
In this work, we present a machine learning-based approach for identifying Alzheimer’s disease through speech recordings and linguistic analysis. Acoustic features such as Mel Frequency Cepstral Coefficients (MFCC), pitch, energy, speech rate, and pause duration are derived from audio signals. Linguistic features are obtained from transcribed speech using TF-IDF representation.
Multiple models, including text-based, audio-based, and combined feature models, are tested using the XGBoost classifier. The experimental findings indicate that integrating both acoustic and linguistic features leads to a noticeable improvement in prediction performance. The proposed hybrid model attains an accuracy of 76.44
Abstract
Alzheimer’s Disease Detection Using AI
Divyanshi Kashyap, Divya pandey, Mansi Pandey, Divyanshi Yadav, Dr. Nikhat Akhtar
DOI: 10.17148/IJARCCE.2026.15537
Abstract: Alzheimer’s disease is a progressive neurological condition that impacts memory, thinking ability, and speech functions. Early detection of Alzheimer’s is very crucial because timely intervention can help slow the progression of symptoms and enhance the overall quality of life for patients. Conventional diagnostic methods mainly depend on neuroimaging, cognitive assessments, and clinical evaluations, which are often costly, time-consuming, and not easily accessible to all individuals.
Recent studies indicate that speech patterns and language usage can provide early indications of cognitive decline. Individuals affected by Alzheimer’s disease frequently show variations in speech fluency, vocabulary diversity, pause pat-terns, and sentence formation. These variations can be exam-ined using computational methods along with machine learning techniques.
In this work, we present a machine learning-based approach for identifying Alzheimer’s disease through speech recordings and linguistic analysis. Acoustic features such as Mel Frequency Cepstral Coefficients (MFCC), pitch, energy, speech rate, and pause duration are derived from audio signals. Linguistic features are obtained from transcribed speech using TF-IDF representation.
Multiple models, including text-based, audio-based, and combined feature models, are tested using the XGBoost classifier. The experimental findings indicate that integrating both acoustic and linguistic features leads to a noticeable improvement in prediction performance. The proposed hybrid model attains an accuracy of 76.44
1
Recent studies indicate that speech patterns and language usage can provide early indications of cognitive decline. Individuals affected by Alzheimer’s disease frequently show variations in speech fluency, vocabulary diversity, pause pat-terns, and sentence formation. These variations can be exam-ined using computational methods along with machine learning techniques.
In this work, we present a machine learning-based approach for identifying Alzheimer’s disease through speech recordings and linguistic analysis. Acoustic features such as Mel Frequency Cepstral Coefficients (MFCC), pitch, energy, speech rate, and pause duration are derived from audio signals. Linguistic features are obtained from transcribed speech using TF-IDF representation.
Multiple models, including text-based, audio-based, and combined feature models, are tested using the XGBoost classifier. The experimental findings indicate that integrating both acoustic and linguistic features leads to a noticeable improvement in prediction performance. The proposed hybrid model attains an accuracy of 76.44
1
Abstract
Student Health and Stress Prediction System using Machine Learning
A Revanth, G J Sachidananda, G Mahesh Gouda, Dr. Muhibur Rahman T.R
DOI: 10.17148/IJARCCE.2026.15538
Abstract: This study introduces a Student Health and Stress Prediction System that uses machine learning to better understand students’ well-being. It looks at everyday habits like sleep, study time, screen usage, and physical activity to identify patterns related to stress. The data is collected through surveys and carefully processed to ensure it is accurate and useful. By applying machine learning techniques, the system can categorize students into different stress levels such as low, moderate, or high. Statistical methods are used to check how reliable the predictions are. The system also helps in detecting early signs of stress and suggests simple ways to improve daily routines. Overall, this approach supports students in maintaining better mental health, leading to improved well-being and academic performance.
Keywords: Student stress detection, machine learning techniques, monitoring of mental health, analysis of daily lifestyle habits, data-driven insights.
Keywords: Student stress detection, machine learning techniques, monitoring of mental health, analysis of daily lifestyle habits, data-driven insights.
Abstract
UNIEVENTS : A COLLEGE EVENT HANDLER USING AI
Laxmi Kanaujiya, Tanya Prajapati, Samriddhi Malhotra, Ranjana Kumari Chirania, Ms Prachi Yadav
DOI: 10.17148/IJARCCE.2026.15539
Abstract: This research presents UniEvents, a comprehensive and intelligent web-based platform designed to simplify and enhance the management of university events. In modern educational institutions, events such as technical workshops, seminars, cultural festivals, and competitions play a vital role in student development. However, the management of these events often relies on fragmented communication channels such as notice boards, social media platforms, and messaging applications, which leads to inefficiencies, lack of coordination, and reduced student participation. These challenges highlight the need for a centralized and automated solution.
The proposed system, UniEvents, addresses these issues by providing a unified platform where students and administrators can interact efficiently. The system enables students to browse available events, view detailed information, register online, and receive real-time notifications regarding updates and reminders. For administrators, the platform offers tools to create, modify, and manage events, monitor registrations, and maintain organized records of participants. This reduces manual effort, minimizes errors, and improves overall event coordination.
A key feature of the system is the integration of an AI-based recommendation mechanism, which analyzes user preferences, browsing behavior, and past participation to suggest relevant events. This personalization enhances user engagement and encourages increased participation. Additionally, the platform incorporates a chatbot system that provides instant responses to user queries, assists with navigation, and offers guidance regarding event-related information, thereby improving user experience and accessibility.
The system is developed using the MERN stack, which includes MongoDB for database management, Express.js and Node.js for backend processing, and React.js for the frontend interface. This technology stack ensures scalability, flexibility, and efficient handling of large volumes of data. Security measures such as user authentication and data protection are implemented to ensure reliability and privacy.
The results of the implementation demonstrate that UniEvents significantly improves the efficiency of event management processes within educational institutions. It enhances communication, increases student participation, and provides a structured approach to handling events. Overall, the proposed system offers a modern, intelligent, and scalable solution that bridges the gap between traditional event management methods and current technological advancements.
The proposed system, UniEvents, addresses these issues by providing a unified platform where students and administrators can interact efficiently. The system enables students to browse available events, view detailed information, register online, and receive real-time notifications regarding updates and reminders. For administrators, the platform offers tools to create, modify, and manage events, monitor registrations, and maintain organized records of participants. This reduces manual effort, minimizes errors, and improves overall event coordination.
A key feature of the system is the integration of an AI-based recommendation mechanism, which analyzes user preferences, browsing behavior, and past participation to suggest relevant events. This personalization enhances user engagement and encourages increased participation. Additionally, the platform incorporates a chatbot system that provides instant responses to user queries, assists with navigation, and offers guidance regarding event-related information, thereby improving user experience and accessibility.
The system is developed using the MERN stack, which includes MongoDB for database management, Express.js and Node.js for backend processing, and React.js for the frontend interface. This technology stack ensures scalability, flexibility, and efficient handling of large volumes of data. Security measures such as user authentication and data protection are implemented to ensure reliability and privacy.
The results of the implementation demonstrate that UniEvents significantly improves the efficiency of event management processes within educational institutions. It enhances communication, increases student participation, and provides a structured approach to handling events. Overall, the proposed system offers a modern, intelligent, and scalable solution that bridges the gap between traditional event management methods and current technological advancements.
Abstract
AI-Based Smart Home Intrusion Detection & Alert System Using Behavior Analysis and Face Recognition
Mallappa H, Yashwanth T M, Naveendra Reddy, Ameer S, Asst. Prof. Rajashekar Reddy P, Dr. Anita Patil
DOI: 10.17148/IJARCCE.2026.15540
Abstract: Home security has become a critical concern due to the increasing number of intrusion and theft incidents in residential areas. Traditional surveillance systems rely on manual monitoring and lack intelligent threat detection capabilities. This paper proposes an AI-based smart home intrusion detection and alert system that integrates face recognition and behavior analysis for continuous monitoring. The system captures real-time video data and processes it using deep learning algorithms to identify authorized and unauthorized individuals. It further analyzes human activity patterns to detect abnormal or suspicious behavior. Upon detecting an intrusion, instant alerts are sent to the homeowner through a mobile application. The proposed system enhances security by reducing response time and minimizing false alarms. It supports multi-modal inputs and ensures scalable deployment using IoT devices. The integration of artificial intelligence improves accuracy and automation in home surveillance. This system provides a reliable and efficient solution for modern smart home security.
Keywords: Smart Home Security, Intrusion Detection System, Artificial Intelligence, Face Recognition, Behavior Analysis, Computer Vision, Deep Learning, Continuous Monitoring, IoT-Based Surveillance, Real-Time Alert System
Keywords: Smart Home Security, Intrusion Detection System, Artificial Intelligence, Face Recognition, Behavior Analysis, Computer Vision, Deep Learning, Continuous Monitoring, IoT-Based Surveillance, Real-Time Alert System
Abstract
Generative AI: A Comprehensive Survey on Transformer-Based Models
J Hemanth, Harsha B, Ashish Kumar, Anurag N, Dr. Muhibur Rahman T.R
DOI: 10.17148/IJARCCE.2026.15541
Abstract: The landscape of artificial intelligence has undergone a profound transformation with the emergence of generative models capable of producing coherent text, realistic images, synthesized audio, and functional code. Central to this shift is the Transformer architecture, introduced by Vaswani et al. in their landmark 2017 contribution “Attention Is All You Need,” which fundamentally redefined how sequential data is modeled by replacing recurrence with parallelizable, attention-based processing [1]. This survey provides a structured and thorough examination of Transformer-based generative AI systems, tracing their development from early recurrent sequence architectures through to the most advanced multimodal and agentic AI frameworks. A four-tier taxonomic model is introduced to systematically classify these systems: basic recurrent sequence models, Transformer-based NLP architectures, large-scale language models, and multimodal autonomous AI agents. Comparative analysis is conducted across these tiers, evaluating performance, scalability, and contextual modeling capability. Through review of more than a dozen pivotal studies— spanning bidirectional pretraining via BERT [2], autoregressive generation through the GPT series [3][4][5], and visual representation learning via Vision Transformers [6]—this paper maps out the major inflection points in the field’s evolution. Critical open challenges are also examined, including the substantial computational overhead of large-scale training, the persistent problem of model hallucination, opacity in decision-making, systemic data biases, and the inability to adapt to real-time information. The paper concludes with a forward-looking perspective on next-generation generative AI, emphasizing efficiency, trustworthiness, and ethical design.
Keywords: Generative AI, Transformer Architecture, Self-Attention, BERT, GPT, Large Language Models, Vision Transformer, Multimodal AI, Natural Language Processing, Deep Learning.
Keywords: Generative AI, Transformer Architecture, Self-Attention, BERT, GPT, Large Language Models, Vision Transformer, Multimodal AI, Natural Language Processing, Deep Learning.
Abstract
AI BNB: AI-BASED TRAVEL LISTING AND BOOKING PLATFORM
Gaurav Yadav, Tanveer Alam, Balbir Singh, Rishabh Shukla, Ms. Deepika Pandey
DOI: 10.17148/IJARCCE.2026.15542
Abstract: This paper presents AI BnB, an AI-powered travel and accommodation booking platform built using the MERN stack (MongoDB, Express.js, React.js, Node.js). The platform addresses the growing need for intelligent, personalized property discovery by integrating a machine learning-based recommendation system that analyses user preferences, search history, and behavioural patterns. The system enables users to search, filter, and book properties seamlessly, while hosts manage listings and administrators oversee the platform. Key features include role-based JWT authentication, AI-driven property recommendations, smart search filters, secure booking management, and an AI Concierge powered by the Gemini API for natural language hotel queries. The platform is designed for scalability, responsiveness, and data security, demonstrating how AI combined with modern web development can significantly improve user experience in the hospitality domain.
Keywords: AI Recommendation System, Travel Booking, MERN Stack, Machine Learning, Property Listing, JWT Authentication, Gemini API, MongoDB
Keywords: AI Recommendation System, Travel Booking, MERN Stack, Machine Learning, Property Listing, JWT Authentication, Gemini API, MongoDB
Abstract
Smart Career Platform: An AI-Driven Personalised Career Guidance System for Multi-Domain Professionals Using MERN Stack and Groq Large Language Models
Purshottam Mishra, Awadhesh Kumar, Mr. Dileep Kumar Gupta
DOI: 10.17148/IJARCCE.2026.15543
Abstract: The proliferation of digital career platforms has disproportionately focused on technology professionals, leaving practitioners in law, medicine, finance, design, marketing, and education without adequate AI-driven career guidance. This paper presents the Smart Career Platform — a comprehensive full-stack career development system built on the MERN stack (MongoDB, Express.js, React.js, Node.js) and integrated with Groq's Llama 3.1 Large Language Model (LLM). The platform introduces a novel field-first personalisation architecture that propagates rich user context across all career guidance features: adaptive skill assessments, AI-generated career roadmaps, ATS-optimised resume building, AI mock interview practice, and a multi-session advisory chatbot. A four-step onboarding mechanism captures five context dimensions (field, user type, career goal, target role, and experience level) and injects this context into every AI prompt via system prompt engineering. The assessment engine employs a hybrid strategy: a curated static question bank for 12 established domains and dynamic AI-generated questions for unlimited novel domains. Deployed in production on Vercel and Render with MongoDB Atlas, the platform supports 10+ professional fields and 22+ career roles, achieving sub-4-second AI response times. Empirical comparative analysis demonstrates significant advantages over five leading career platforms across seven key capability dimensions. The system validates that prompt-engineered context injection is a cost-effective alternative to domain fine-tuning for specialised AI advisory applications.
Keywords: Artificial Intelligence, Career Guidance, MERN Stack, Groq AI, Llama 3.1, Large Language Model, Personalised Learning, ATS Resume Builder, Skill Assessment, React.js, MongoDB, Field-First Personalisation, Full- Stack Development.
Keywords: Artificial Intelligence, Career Guidance, MERN Stack, Groq AI, Llama 3.1, Large Language Model, Personalised Learning, ATS Resume Builder, Skill Assessment, React.js, MongoDB, Field-First Personalisation, Full- Stack Development.
Abstract
A STUDY OF FINANCIAL STATEMENT ANALYSIS OF HDFC BANK
Rushikesh Prakash Kapate, Prasad Shivajirao Kadam, Dr. Pradyuman Shastri
DOI: 10.17148/IJARCCE.2026.15544
Abstract: This research paper evaluates the financial performance of HDFC Bank through a comprehensive analysis of its financial statements. The study employs ratio analysis, trend analysis, and comparative analysis to assess key financial parameters, including profitability, liquidity, solvency, and efficiency. Secondary data has been collected from annual reports and financial disclosures of the bank.
The analysis indicates that HDFC Bank has achieved steady growth in revenue and profitability, supported by strong asset quality and efficient management practices. The bank maintains adequate liquidity and capital strength, reflecting financial stability and resilience. Despite facing industry competition and evolving regulatory requirements, the bank continues to perform consistently.
The study concludes that HDFC Bank holds a strong financial position in the Indian banking industry and offers reliable performance indicators for stakeholders. The findings contribute to a better understanding of financial statement analysis in the banking sector.
Keywords: Financial Statement Analysis, Profitability, Liquidity, Solvency, Banking Sector, Ratio Analysis, HDFC Bank
The analysis indicates that HDFC Bank has achieved steady growth in revenue and profitability, supported by strong asset quality and efficient management practices. The bank maintains adequate liquidity and capital strength, reflecting financial stability and resilience. Despite facing industry competition and evolving regulatory requirements, the bank continues to perform consistently.
The study concludes that HDFC Bank holds a strong financial position in the Indian banking industry and offers reliable performance indicators for stakeholders. The findings contribute to a better understanding of financial statement analysis in the banking sector.
Keywords: Financial Statement Analysis, Profitability, Liquidity, Solvency, Banking Sector, Ratio Analysis, HDFC Bank
Abstract
ZIGBEE BASED SMART HELMET FOR MINERS
Payal Survase, Pratik Ghortale, Ashwini Adasre
DOI: 10.17148/IJARCCE.2026.15545
Abstract: Mining is one of the most hazardous industries, exposing workers to dangerous gases, extreme temperatures, and unsafe working conditions. Traditional safety systems lack real-time monitoring and communication, leading to delayed responses in emergencies. This paper presents a ZigBee-based smart helmet designed for miners, integrating environmental and health monitoring sensors such as gas sensors (MQ-7, MQ-2), temperature sensor (DHT11), and pulse sensor (MAX30100). The system continuously monitors the miner’s condition and transmits real-time data using ZigBee wireless communication to a control station. In case of abnormal conditions, alerts are generated through buzzers and LEDs. This system enhances miner safety, reduces accident risks, and improves emergency response efficiency.
Keywords: Smart Helmet, ZigBee, Mining Safety, Gas Detection, IoT, Wearable Technology.
Keywords: Smart Helmet, ZigBee, Mining Safety, Gas Detection, IoT, Wearable Technology.
Abstract
Blockchain-Based Secure Voting System
Palak S Sachar, Sania Shaikh, Muktha Reddy, Dr. Muhibur Rehman T.R
DOI: 10.17148/IJARCCE.2026.15546
Abstract: The process of voting plays a crucial role in maintaining a fair and democratic system, yet existing methods often face issues related to trust, transparency, and security. Traditional paper-based voting can be slow and prone to manual errors, while electronic voting systems, though faster, still depend on centralized control, making them vulnerable to tampering and cyber threats. These challenges highlight the need for a more reliable and secure approach to conducting elections.
To address these concerns, this study proposes a blockchain-based voting system that ensures data integrity and transparency. By using a decentralized ledger, each vote is securely recorded and cannot be altered once it is added to the system. The use of encryption techniques helps maintain voter privacy, while the transparent nature of blockchain allows verification without exposing sensitive information. This approach minimizes the risk of fraud and increases confidence in the voting process.
The proposed system aims to create a balance between security, transparency, and usability. It provides a structured framework where voters can cast their votes securely and verify them if needed, without compromising anonymity. Overall, the system demonstrates how blockchain technology can be effectively applied to modernize voting systems and improve trust in digital elections.
Keywords: Blockchain, E-Voting, Security, Transparency, Smart Contracts, Decentralization
To address these concerns, this study proposes a blockchain-based voting system that ensures data integrity and transparency. By using a decentralized ledger, each vote is securely recorded and cannot be altered once it is added to the system. The use of encryption techniques helps maintain voter privacy, while the transparent nature of blockchain allows verification without exposing sensitive information. This approach minimizes the risk of fraud and increases confidence in the voting process.
The proposed system aims to create a balance between security, transparency, and usability. It provides a structured framework where voters can cast their votes securely and verify them if needed, without compromising anonymity. Overall, the system demonstrates how blockchain technology can be effectively applied to modernize voting systems and improve trust in digital elections.
Keywords: Blockchain, E-Voting, Security, Transparency, Smart Contracts, Decentralization
Abstract
Smart Hospital Management System
Rakesh Yadav, Arvind Yadav, Bhanu Chandra
DOI: 10.17148/IJARCCE.2026.15547
Abstract: This paper details the design, development, and features of a web-based Hospital Management System (HMS) aimed at streamlining patient registration, appointment scheduling, and administrative oversight. The system utilizes a three-module architecture (Patient, Doctor, Admin) providing distinct interfaces and functionalities for each user type. Built using HTML, CSS, JavaScript, and Bootstrap for the front-end, and PHP with a MySQL RDBMS for the back-end logic and data storage, the system offers features including user registration/login, appointment booking and cancellation, appointment history viewing, doctor management (addition/removal by admin), patient/doctor searching, and feedback viewing. The system offers an easy interface and demonstrates a real-world use of standard web technologies for managing basic healthcare needs. We have implemented this project in project-based learning.
Keywords: Hospital Management System, HMS, Web Application, PHP, MySQL, Bootstrap, Appointment Scheduling, Patient Management, Healthcare IT, Modular Design, Database Management
Keywords: Hospital Management System, HMS, Web Application, PHP, MySQL, Bootstrap, Appointment Scheduling, Patient Management, Healthcare IT, Modular Design, Database Management
Abstract
AFFECTIVE COMPUTING TECHNIQUES FOR HUMAN–MACHINE INTERACTION
Rabiya Fathima, C S Swetha
DOI: 10.17148/IJARCCE.2026.15548
Abstract: Affective computing is an emerging domain of artificial intelligence that focuses on enabling machines to recognize, interpret, and respond to human emotions. With the rapid advancement of intelligent systems such as chatbots, virtual assistants, and service robots, the ability to understand human emotions has become essential for improving interaction quality and user satisfaction. This paper presents a comprehensive survey and comparative analysis of various emotion recognition techniques, including facial expression recognition, speech emotion recognition, and text-based sentiment analysis. Deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks are widely used for emotion detection tasks. Furthermore, multimodal approaches that combine multiple data sources are analyzed for their effectiveness in reducing ambiguity and improving accuracy. The study also discusses key challenges such as real-time processing, data bias, and privacy concerns. Finally, the paper highlights future research directions for developing efficient and human-centric emotion- aware systems.
Keywords: Affective Computing, Emotion Recognition, Deep Learning, CNN, LSTM, Multimodal Systems, Human– Machine Interaction
Keywords: Affective Computing, Emotion Recognition, Deep Learning, CNN, LSTM, Multimodal Systems, Human– Machine Interaction
Abstract
SafeHer: A Proactive AI-Driven Safety System for Women Using Contextual Risk Assessment and Real-Time Monitoring
Rohit Kumar Yadav, Aditya Upadhyay, Kajal Kasaudhan, Dr. Nikhat Akhtar
DOI: 10.17148/IJARCCE.2026.15549
Abstract: Women’s safety remains a critical global challenge, particularly in urban and semi-urban environments where conventional manual panic-button applications fail precisely when they are needed most. This paper presents SafeHer, a proactive, AI-driven mobile safety platform that shifts personal protection from reactive alerting to predictive, autonomous risk assessment. The system continuously monitors a user’s contextual environment—GPS location, motion patterns, time-of-day, network signal strength, and environmental isolation—and processes these signals through a two- stage Hybrid Danger Score Engine. The first stage applies deterministic rule-based thresholds on-device (Phase 1), producing a Base Risk Score (0–50) within milliseconds and without network dependency. The second stage transmits a structured context vector to Google Gemini AI via a secure Firebase Cloud Function proxy, obtaining an AI Risk Score (0–50) with contextual reasoning and one-sentence justification. The combined Final Danger Score (0–100) drives a threshold-triggered autonomous alerting pipeline that dispatches Firebase Cloud Messaging push notifications and Twilio SMS to pre-registered trusted contacts, shares a live GPS tracking link, and logs incident context—all without requiring any user interaction. Beyond automated alerting, SafeHer includes a Fake Call module for discreet social exit, an Evidence Vault with a system-update decoy screen for covert audio recording, a community Fear Map with differential privacy (Laplace noise, ε = 0.1) for anonymized urban safety analytics, and a Trust Score system for crowd-validating community hazard reports. Built on a Flutter frontend with a scalable Firebase backend, the platform achieves average alert latency of 1.8–2.7 seconds, background battery consumption of 9.3% over eight hours, a hybrid AUC of 0.94, and a System Usability Scale score of 82.4 across 30 simulated threat scenarios and a 15-participant pilot study.
Keywords: Women Safety, Predictive Safety Systems, AI-Based Risk Detection, Mobile Context Sensing, Firebase Architecture, Google Gemini AI, Proactive Alerting, Real-Time Location Tracking, Fake Call Feature, Evidence Vault, Fear Map, Trust Score, Differential Privacy.
Keywords: Women Safety, Predictive Safety Systems, AI-Based Risk Detection, Mobile Context Sensing, Firebase Architecture, Google Gemini AI, Proactive Alerting, Real-Time Location Tracking, Fake Call Feature, Evidence Vault, Fear Map, Trust Score, Differential Privacy.
Abstract
COVID SAFETY SYSTEM – DOOR HANDLE SANITIZER & TEMPERATURE DETECTION
Bhalghare Kajal Sanjay
DOI: 10.17148/IJARCCE.2026.15550
Abstract: Since December 2019 the world is under tremendous tension, the numbers are increasing day by day, and till date no vaccine has been full proved against the pandemic agent. The COVID-19 virus was unknown to us before it cast its outbreak in Wuhan, China. Being from a large family, a continuous mutation is occurring, forbidding the researchers, microbiologist, pharmaceuticals to search for the cure for the vaccine. Affecting the countries in a chain; China, Italy, Spain, USA, India , Russia, the virus has proved it’s strength and subservient a technologically enhanced race. The policies taken worldwide has lessen its affect to some extent but could not eradicate it. Lockdown has economically weaken many nations, and testing of different medicines has also not proven to be satisfactory. The design shows the preventive measure that can be taken during the COVID-19 pandemic in the whole world. Sanitizers have become the most significant commodities right now. By the new rules and regulations given by WHO vigorous sanitization is needed to survive. The design gave the solution for the problem stated. The design introduces an automatic hand sanitizer and temperature sensing system, to keep the hand sanitized whenever a person wants to do it, without a contact with the sanitizing machine. The temperature sensor on touching gives the body temperature of the person.
Keywords: Automatic hand sanitizer, Arduino, ultrasonic sensor, PIR sensors, TMP36, covid-19
Keywords: Automatic hand sanitizer, Arduino, ultrasonic sensor, PIR sensors, TMP36, covid-19
Abstract
LABOUR LAW COMPLIANCE AND ITS IMPACT ON EMPLOYEE RELATIONS IN ORGANIZATIONS
Shubhada Kumbhar
DOI: 10.17148/IJARCCE.2026.15551
Abstract: Labour law compliance plays a crucial role in maintaining fair and ethical workplace practices. This study examines the impact of labour law compliance on employee relations within organizations. Labour laws are designed to protect employee rights, ensure fair wages, safe working conditions, and promote harmonious industrial relations.
Non-compliance with labour laws can lead to disputes, dissatisfaction, legal penalties, and poor employee morale. On the other hand, effective compliance fosters trust, transparency, and positive relationships between employers and employees.
The study highlights how adherence to labour regulations improves employee satisfaction, reduces conflicts, and enhances organizational productivity. It also emphasizes the importance of HR policies, compliance mechanisms, and ethical leadership in strengthening employee relations.
Keywords: Labour Law Compliance, Employee Relations, Industrial Relations, Workplace Ethics, Job Satisfaction, HR
Non-compliance with labour laws can lead to disputes, dissatisfaction, legal penalties, and poor employee morale. On the other hand, effective compliance fosters trust, transparency, and positive relationships between employers and employees.
The study highlights how adherence to labour regulations improves employee satisfaction, reduces conflicts, and enhances organizational productivity. It also emphasizes the importance of HR policies, compliance mechanisms, and ethical leadership in strengthening employee relations.
Keywords: Labour Law Compliance, Employee Relations, Industrial Relations, Workplace Ethics, Job Satisfaction, HR
Abstract
AI-Powered Document Question Answering System Using Agentic RAG and Local Language Models
Varshitha D, Vidya S
DOI: 10.17148/IJARCCE.2026.15552
Abstract: The rapid growth of digital information and unstructured data has transformed the way users search, analyze, and interact with documents. However, traditional search systems often lack contextual understanding, intelligent reasoning, and the ability to generate precise answers from multiple sources. This paper presents DocAgent, an AI- powered Agentic Retrieval-Augmented Generation (RAG) system designed to provide accurate and context-aware responses from user-provided documents and web content. The proposed system integrates document ingestion, semantic search, and local large language model processing to enable efficient knowledge retrieval. Documents and URLs are processed into vector embeddings and stored in a vector database, allowing the system to perform similarity-based retrieval. An agent-based workflow dynamically decides whether to retrieve relevant context, refine the query, or directly generate responses, improving accuracy and reducing irrelevant outputs. The system utilizes a locally hosted language model through Ollama, ensuring privacy, cost efficiency, and reduced dependency on external APIs. The application is evaluated using real-time query scenarios, demonstrating improved response relevance, reduced hallucinations, and enhanced user interaction compared to traditional document search systems.
Keywords: Artificial Intelligence, Retrieval-Augmented Generation, Agentic AI, Document Question Answering, Ollama, Vector Database, Semantic Search, Web-Based System
Keywords: Artificial Intelligence, Retrieval-Augmented Generation, Agentic AI, Document Question Answering, Ollama, Vector Database, Semantic Search, Web-Based System
Abstract
Blockchain Driven Multi Tier Academic Credential Validation System
G. Priyadharshini M.E., Prakash B, Logesh P
DOI: 10.17148/IJARCCE.2026.15553
Abstract: The rise of fraudulent academic certificates has led to serious real-world issues, such as the use of fake degree certificates for jobs and visas. It also causes noteworthy disruptions in the hiring process. Although trust in institutions has been reduced, some research has been conducted to validate the authenticity of certificates. Blockchain- based verification systems focus on storing and validating certificates but often fail to provide integrated issuer authentication, tamper-proof privacy protection, and low-cost solutions. In this proposed work, research is done on an integrated, comprehensive, decentralised framework for certificate verification that together validates both the certificate and the issuer's authority. In addition, it protects the certificate from being tampered with. In addition, hash function mapping was employed for faster searching. The proposed solution is experimentally validated by creating a structure that incorporates zero-knowledge proofs (ZKPs) to safeguard data, self-destructing smart contracts to exclude replay attacks, and post-quantum lattice-based signatures to ensure security versus failures. In addition, a hash-based indexing method is used to accelerate certificate searches while reducing storage demands. In experimental analysis, measure the performance of proposed work in private. The Ethereum blockchain indicates that the proposed approach attains low transaction and gas costs, fast retrieval times, and high verification compared to previous research techniques. This overall analysis achieves 94% of a scalable and privacy-preserving solution for authenticating academic credentials in real-world scenarios.
Keywords: Block chain, Zero-knowledge proofs (ZKPs), Ethereum, Tamper-Proof, post-quantum lattice, hash-based indexing, smart contract.
Keywords: Block chain, Zero-knowledge proofs (ZKPs), Ethereum, Tamper-Proof, post-quantum lattice, hash-based indexing, smart contract.
Abstract
IndianAura: A Secure and Scalable E-Commerce Platform for Certified Made-in-India Products
Aishwarya V S, Anagha I V, Bindu B, Deepika G S, Veeramreddy Rajasekhar
DOI: 10.17148/IJARCCE.2026.15554
Abstract: The proliferation of counterfeit and non-certified goods on mainstream e-commerce platforms poses a significant threat to consumer trust and the growth of indigenous manufacturing ecosystems. Existing platforms such as Amazon and Flipkart lack structured mechanisms to verify product authen- ticity at the point of listing. This paper presents IndianAura, a secure, role-based, and scalable web e-commerce platform for certified Made-in-India products. The system introduces a multi-tier Product Certification Workflow with mandatory administrative approval. It enforces strict Role-Based Access Control (RBAC) across Administrator, Producer, and Customer roles. A Smart Scan feature leverages AI-based image analysis for product identification, while an Audit Logging subsystem ensures transparency and accountability. Built on a MERN-aligned architecture, IndianAura achieves a mean API response time of 187 ms under 500 concurrent users and a verification accuracy of 94.3%. Results demonstrate improved product trustworthiness, transparency, and certification compliance.
Keywords: E-commerce, Role-Based Access Control, Prod- uct Certification, Audit Logging, Image Recognition, Make in India, Secure Web Architecture.
Keywords: E-commerce, Role-Based Access Control, Prod- uct Certification, Audit Logging, Image Recognition, Make in India, Secure Web Architecture.
Abstract
A Secure Task Management System for Employee Workflow Optimization
G. Priyadharshini M.E., Priyadharshene R, Vimandhani G
DOI: 10.17148/IJARCCE.2026.15555
Abstract: A task management system for employees to work better is a website that helps people do their jobs. It is made to help people in the company work together in a way. This system can put all the tasks in one place so everyone can see what is going on. The tasks include finding security risks fixing problems checking what people are doing making sure only the right people can do things and dealing with incidents that happen during work. The task management system is, like a place where everything is organized and easy to find which helps the company security team do their job better. It is also hard to figure out who is responsible, for what task. The task management system provides role-based access. This means tasks are assigned to users based on their responsibility. Tasks are secure because authorized users can see and manage the tasks that are related to the tasks. The back end of the system uses PHP and MySQL. The system gives us a place to manage tasks create user accounts and store data for audits. It uses HTML5, CSS3 and JavaScript to make dashboards and interfaces that're easy to use. These interfaces help the user track progress check when things are due and get updates. The system lets the team manage tasks based on how important they're work. This means the team can work on the important tasks and fix problems first. The audit log is, like a record that shows what actions the user has taken. The user can check this record to see what they have done. This project is about making task management. It brings together ideas from task management, security operations and compliance tracking. The Secure task management project can help the team do tasks in a secure way. It also helps the team follow standards like the ISO/IEC 27001.The system is good at handling cybersecurity operations. The Secure task management project is useful for making the workflow platform better. It can help the team achieve 90% of the Secure task management projects goals. The Secure task management project is really good, for getting work done in a way. It may support the real-time task execution and monitoring of the audit tracking.
Keywords: Secure Task Management, Role-Based Access Control (RBAC), Cybersecurity Workflow, Audit Logging and Compliance, Web-Based Management System.
Keywords: Secure Task Management, Role-Based Access Control (RBAC), Cybersecurity Workflow, Audit Logging and Compliance, Web-Based Management System.
Abstract
AI-Enabled Eye Screening Tool for Early Detection of Common Eye Diseases
G. Priyadharshini M.E., Balananthakumar B, Jancy M
DOI: 10.17148/IJARCCE.2026.15556
Abstract: Eye diseases are common but they are often not find until later, especially in rural areas. It takes a long time and costs a lot of money to get a traditional diagnosis because you have to go to the hospital, use equipment, and see experts. Current AI-based systems are hard to use in real time because they are so complicated.
This project makes a basic Eye Disease Detection System using a CNN model. The user can either upload an eye image or take one through a web app. The system looks at the picture and finds diseases like cataracts, conjunctivitis, styes, or a normal condition.
Camera support, a better user interface, and better performance make the system better. It gives you quick and correct results along with a confidence score. The system is easy to use, works well, and is good for finding eye diseases early.
Keywords: Convolutional Neural Network (CNN), Machine Learning model Deep Learning, Early eye diseases detection, Accuracy, Artificial intelligence.
This project makes a basic Eye Disease Detection System using a CNN model. The user can either upload an eye image or take one through a web app. The system looks at the picture and finds diseases like cataracts, conjunctivitis, styes, or a normal condition.
Camera support, a better user interface, and better performance make the system better. It gives you quick and correct results along with a confidence score. The system is easy to use, works well, and is good for finding eye diseases early.
Keywords: Convolutional Neural Network (CNN), Machine Learning model Deep Learning, Early eye diseases detection, Accuracy, Artificial intelligence.
Abstract
Intelligent Taste Prediction Engine Using Scroll, Hover & View-Time Patterns in E-Commerce
G. Priyadharshini M.E., Roshani C, Pradeesh T
DOI: 10.17148/IJARCCE.2026.15557
Abstract: E-commerce platforms usually recommend products by looking at a user’s past activities, such as the items they clicked on, rated, or purchased earlier. While this works well for regular users, it does not clearly understand what a user wants during live browsing. This problem is more serious for first-time visitors and users who are not logged in, often resulting in general recommendations, low user engagement, and higher cart abandonment. Although advanced models like deep learning, graph-based methods, and transformers improve accuracy, they need large amounts of historical data, heavy computation, and offline training. Because of this, they are not practical for medium-scale e-commerce platforms. To solve these issues, this paper introduces a lightweight, intent-aware recommendation system that works in real time. The system observes small but meaningful user actions such as how far a user scrolls, how long they hover over products, how much time they spend viewing items, and how often they switch between products. Using these signals, the system understands the user’s intent during the same browsing session and updates recommendations instantly. It does not depend on personal or stored user data, which helps protect user privacy. Experimental results show that the proposed system achieves around 88–90% accuracy, reaching nearly 90% of the performance of complex existing models, while using much less computing power. This makes the system efficient, privacy-friendly, and suitable for real-time use in small and medium-scale e-commerce applications.
Keywords: E-commerce recommendation systems, session-based recommendation, real-time personalization, micro-interaction analysis, user intent prediction, lightweight machine learning, cold-start problem, privacy-preserving recommendation, small and medium enterprises (SMEs).
Keywords: E-commerce recommendation systems, session-based recommendation, real-time personalization, micro-interaction analysis, user intent prediction, lightweight machine learning, cold-start problem, privacy-preserving recommendation, small and medium enterprises (SMEs).
Abstract
Seasonal Crop Price Prediction using AI/ML
Aniket Sawant, Harsh Dudhal, Tejas Jadhav, Sarthak Kakade, Mrs. Dhage T.S.
DOI: 10.17148/IJARCCE.2026.15558
Abstract: Seasonal crop price prediction plays an important role in modern agriculture by helping farmers, traders, and government agencies make better economic decisions. Traditional forecasting methods often fail to capture complex seasonal patterns, weather variations, and market fluctuations present in agricultural data. This project proposes a crop price prediction system using Long Short-Term Memory (LSTM), a deep learning technique designed for time-series forecasting. The proposed model utilizes historical crop prices along with seasonal, climatic, and market-related factors such as rainfall, temperature, humidity, and previous market trends to predict future crop prices accurately. LSTM networks are highly effective in learning long-term dependencies and sequential patterns in time-series datasets, making them suitable for agricultural price forecasting
Keywords: Seasonal Crop Price Prediction, LSTM, Machine Learning, Deep Learning, Time-Series Forecasting, Smart Agriculture, Agricultural Data Analysis.
Keywords: Seasonal Crop Price Prediction, LSTM, Machine Learning, Deep Learning, Time-Series Forecasting, Smart Agriculture, Agricultural Data Analysis.
Abstract
HELMET PROTECTION DETECTION SYSTEM USING YOLOV8 FOR REAL-TIME TRAFFIC SAFETY MONITORING
Ramya Patani, Dr. Darapu Uma
DOI: 10.17148/IJARCCE.2026.15559
Abstract: Road traffic accidents involving two-wheelers result in significant fatalities, with head injuries being the primary cause. Traditional manual helmet compliance monitoring suffers from limited coverage, human error, and scalability issues. This paper presents an automated Helmet Protection Detection System using YOLOv8, a state-of-the-art real-time object detection model. The system processes multiple input sources including static images, video files, live webcam feeds, and uniquely, YouTube livestreams. The YOLOv8 model is trained to classify motorcyclists into two categories: "Helmet" (compliant) and "No- Helmet" (violator), with color-coded bounding boxes and confidence scores. Experimental results demonstrate high detection accuracy with an overall 92%, precision of 90%, and recall of 87%. The system achieves real-time performance of 45 FPS on a standard GPU, making it suitable for live traffic monitoring. This cost-effective solution leverages existing camera infrastructure, reduces dependency on manual supervision, and contributes to enhanced traffic safety enforcement.
Keywords: YOLOv8, Helmet Detection, Real-Time Monitoring, Deep Learning, Traffic Safety, Computer Vision.
Keywords: YOLOv8, Helmet Detection, Real-Time Monitoring, Deep Learning, Traffic Safety, Computer Vision.
Abstract
Phishing URL and Text Detection
R. Janaki M.E., (phd), Akalya A, Dharshini J
DOI: 10.17148/IJARCCE.2026.15560
Abstract: Phishing attacks are going up fast and they are a big problem when we are online. People who do these attacks use links to websites and fake messages to get important information from us, like our passwords and bank details and the special codes we get to confirm who we are. A lot of the systems we have can only find fake website links or fake messages not both so they do not work as well as they should. Phishing attacks use ways to trick us like fake website links and phishing messages to get our sensitive information. Previous studies used machine learning and deep learning techniques like Random Forest, Logistic Regression, NaĂŻve Bayes, CNN, LSTM and BERT. These machine learning techniques were mainly used for detecting things in URLs. Text-based methods were also used, such as TF-IDF, Bag of Words and keyword analysis. The thing is, there is no simple system that can detect both URLs and messages at the same time, which is what machine learning and deep learning techniques, like Random Forest, Logistic Regression, NaĂŻve Bayes, CNN, LSTM and BERT are supposed to do for URLs and messages. It uses machine learning and natural language processing to do this. The system looks at things like how long a website address how many dots it has and if it has special characters. It also checks if the website address uses HTTPS and if it is an IP address. The system uses tools like Logistic Regression and Random Forest to analyze all these things. It uses natural language processing to get the messages ready. Then it uses special tools like TF-IDF and NaĂŻve Bayes with Logistic Regression. This helps the system figure out if the messages are trying to trick people into doing something or if they are regular messages. The phishing detection system is made to catch messages that try to scare people into doing something. The system is, about catching phishing and it uses machine learning and natural language processing to make sure it works well. The combined system improves detection accuracy, achieving 94% accuracy for phishing URLs and 92% accuracy for phishing text. The system can be extended for real-time use in browsers, emails, and SMS to protect users from online fraud.
Keywords: Phishing Detection, Phishing URL Detection, Phishing Text Detection, Machine Learning, Natural Language Processing (NLP), Cyber Security, Online Fraud Detection, Feature Extraction, Classification Models.
Keywords: Phishing Detection, Phishing URL Detection, Phishing Text Detection, Machine Learning, Natural Language Processing (NLP), Cyber Security, Online Fraud Detection, Feature Extraction, Classification Models.
Abstract
An Integrated IoT and Web-Based Framework for Energy and Machine Monitoring in SMEs
S Abhinaya, M.E., Akash M, Manimaran B
DOI: 10.17148/IJARCCE.2026.15561
Abstract: This project is about creating a system that helps industries use energy wisely and keep their machines in shape. The system is called a Smart IoT-Based Energy and Machine Condition Monitoring System. It is meant to make industries use energy efficiently make sure machines are reliable and find problems in time. The system uses a computer called an ESP32 microcontroller to control everything. This computer is connected to sensors. There is a PZEM-004T energy sensor that checks how much voltage, current, power and energy are being used. There is also an SW-420 vibration sensor that checks if machines are vibrating much. There is a DHT11 temperature sensor that checks how hot or cold it is around the machines. The Smart IoT-Based Energy and Machine Condition Monitoring System is very useful, for industries. It helps them use energy wisely and keep their machines in shape. The Smart IoT-Based Energy and Machine Condition Monitoring System is a way to make industries run better. The sensor data that we collect is looked at carefully. We use a method to decide what is normal and what is not. This method is like a warning system that checks the data all the time to make sure it is within limits. If something is wrong like the energy consumption is too high or the machine is vibrating much or getting too hot the system sends us a message right away. It also sends the data to the internet through Wi-Fi so we can look at it later. We use websites like Firebase to look at the data, in real time keep a record of it and see what happened in the past. The system we are talking about is set up from the edge to the cloud. This means it can do things quickly and does not get too busy with a lot of data. It is also cheap to set up. When we tried it out the system was very good at monitoring energy it got it right 95% of the time. It was also very good at finding faults using vibrations it got it right 90% of the time. And it was very good at monitoring temperature it got it right 92% of the time. The system is a help because it can reduce the amount of energy that is wasted by about 20 to 25%. It can also help find faults which is about 30% better than old ways of monitoring. It can reduce the amount of time machines are broken and cannot be used by about 40%. This is a lot better than the ways of monitoring machines.
Keywords: Industrial IoT, Energy Monitoring, Machine Monitoring, Web applications, SME Industry, Fault Detection, Industrial Sensors.
Keywords: Industrial IoT, Energy Monitoring, Machine Monitoring, Web applications, SME Industry, Fault Detection, Industrial Sensors.
Abstract
Detection of thyroid stages classification by convolutional neural network techniques
R. Janaki M.E.(phd), Ramya Shree V, Sweatha N
DOI: 10.17148/IJARCCE.2026.15562
Abstract: Thyroid disease diagnosis and stage classification are critical tasks in medical imaging due to their direct impact on patient treatment and management. Conventional diagnostic approaches based on manual interpretation of thyroid ultrasound images are time-consuming and prone to human error. To address these limitations, this paper presents an automated thyroid stage classification framework using Convolutional Neural Network (CNN) techniques.The proposed work is developed by taking an existing deep learning-based thyroid detection model as the base paper. Approximately 70% of the methodology is derived from the base paper, including image preprocessing concepts and deep feature extraction principles. The remaining 30% represents the proposed project contribution, where the system architecture is simplified and optimized using a CNN-focused approach for effective thyroid stage classification. The methodology involves preprocessing of thyroid ultrasound images to enhance image quality, followed by CNN-based automatic feature extraction and classification into different thyroid stages such as normal and abnormal. Deep learning technology is employed to eliminate manual feature engineering and improve classification performance. Experimental evaluation demonstrates that the proposed CNN-based model provides reliable accuracy and efficient classification compared to traditional diagnostic methods.The results indicate that CNN techniques are effective for thyroid stage classification and can be utilized as a supportive decision-making tool in clinical environments.
Keywords: Thyroid disease detection, thyroid stage classification, convolutional neural network (CNN), medical image classification, ultrasound image processing, deep learning.
Keywords: Thyroid disease detection, thyroid stage classification, convolutional neural network (CNN), medical image classification, ultrasound image processing, deep learning.
Abstract
PHISHING WEBSITE DETECTION SYSTEM USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Mr. D.S. Jaybhay, Ms. Chaitrali S. Shinde, Ms. Bhakti D. Nannaware, Ms. Sakshi A. Harnawal Ms. Priyanka S. Gadhe
DOI: 10.17148/IJARCCE.2026.15563
Abstract: Phishing websites are one of the most common cybersecurity threats used by attackers to steal sensitive information such as usernames, passwords, banking details, and personal data. These fake websites are designed to look similar to legitimate websites, making it difficult for users to identify them manually. Traditional phishing detection methods such as blacklist-based and rule-based systems are not effective for newly generated or unknown phishing websites.
This research presents a Phishing Website Detection System using Artificial Intelligence and Machine Learning. The proposed system analyzes different URL and website-based features such as URL length, number of special characters, suspicious keywords, domain-related attributes, login form presence, iframe usage, redirection behavior, and external links. Machine learning models such as Random Forest, XGBoost, and Multi-Layer Perceptron are used for classification. A hybrid ensemble voting model is applied to improve prediction accuracy and reliability.
The system is implemented as a Flask-based web application where users can enter a website URL and receive an instant prediction result. The output includes the classification result, phishing probability, risk level, important feature values, scan history, and downloadable PDF report. Experimental results show that the ensemble model performs better than individual classifiers and provides an effective solution for phishing website detection.
Keywords: Phishing Detection, Artificial Intelligence, Machine Learning, Cybersecurity, URL Analysis, Flask, Ensemble Learning, Website Security.
This research presents a Phishing Website Detection System using Artificial Intelligence and Machine Learning. The proposed system analyzes different URL and website-based features such as URL length, number of special characters, suspicious keywords, domain-related attributes, login form presence, iframe usage, redirection behavior, and external links. Machine learning models such as Random Forest, XGBoost, and Multi-Layer Perceptron are used for classification. A hybrid ensemble voting model is applied to improve prediction accuracy and reliability.
The system is implemented as a Flask-based web application where users can enter a website URL and receive an instant prediction result. The output includes the classification result, phishing probability, risk level, important feature values, scan history, and downloadable PDF report. Experimental results show that the ensemble model performs better than individual classifiers and provides an effective solution for phishing website detection.
Keywords: Phishing Detection, Artificial Intelligence, Machine Learning, Cybersecurity, URL Analysis, Flask, Ensemble Learning, Website Security.
Abstract
AI Driven IDS System in Network Security
Kartik P Shetty, Prof. Theerthashree G S
DOI: 10.17148/IJARCCE.2026.15564
Abstract: The increasing use of internet technologies, cloud computing, and smart devices has significantly increased cyber threats in modern networks. Traditional Intrusion Detection Systems (IDS) are unable to effectively detect advanced and unknown attacks because they rely mainly on predefined signatures and static security rules. Artificial Intelligence (AI) based IDS provides an intelligent and adaptive approach for improving network security. This paper presents a seminar-based study on AI Driven IDS Systems in Network Security using Machine Learning and Deep Learning techniques. The proposed system analyzes network traffic patterns, identifies abnormal behavior, and detects cyberattacks with improved accuracy and reduced false alarm rates. Various AI algorithms such as Random Forest, Support Vector Machine, Convolutional Neural Network, and Long Short-Term Memory are discussed in this paper. The study highlights the importance of AI-driven security systems in detecting both known and unknown threats efficiently. The proposed approach enhances overall network protection and provides better adaptability against evolving cyberattacks.
Keywords: Artificial Intelligence, Intrusion Detection System, Machine Learning, Deep Learning, Network Security, Cybersecurity
Keywords: Artificial Intelligence, Intrusion Detection System, Machine Learning, Deep Learning, Network Security, Cybersecurity
Abstract
Brain Tumor detection using Artificial Intelligence
Rushikesh Todekar, Shejal Kawale, Sakshi Khankar, Mayuri Sudake, Dr. Sachin Bere, Prof. Mrs. Jagtap P.S
DOI: 10.17148/IJARCCE.2026.15565
Abstract: Brain tumors are one of the most urgent forms of brain disease because they require early detection for effective treatment. The traditional method of diagnosing them is through MRI scans by expert Radiologists and is a time- consuming process. This contribution provides an Artificial Intelligence (AI) supported framework for the automatic detection and classification of brain tumors using Deep Learning. The NeuroScout platform is based on a ResNet based Convolutional Neural Network (CNN) algorithm trained on brain MRI images to classify brain tumors into four categories: Glioma, Meningioma, Pituitary Tumor and No Tumor. The NeuroScout platform contains FastAPI for the backend, Next.js for the frontend, and MongoDB for data storage, which implements a full stack medical web application. Google Gemini AI generates a medical explanation, treatment recommendation and prevention guidelines for brain tumors which have been detected by the NeuroScout Platform. A major advantage of this approach is that the NeuroScout system demonstrates a high degree of classification accuracy and provides a simple, intuitive user interface for patients, doctors and administrators alike. By providing an AI and Deep Learning based medical support system to healthcare professionals for early diagnosis, this approach will help provide greater access to AI based medical decision support systems.
Keywords: Brain Tumor Detection, Deep Learning, MRI Analysis, ResNet, Artificial Intelligence, Medical Image Processing, Machine Learning, Healthcare AI.
Keywords: Brain Tumor Detection, Deep Learning, MRI Analysis, ResNet, Artificial Intelligence, Medical Image Processing, Machine Learning, Healthcare AI.
Abstract
TimelimeX – Exploring the History using Gen AI
Dr. C N Shariff, Anjali, B Aishwarya, Bhavana Joshi, G Sahethi
DOI: 10.17148/IJARCCE.2026.15566
Abstract: TimelineX is an innovative web-based application designed to revolutionize the way history is learned, explored, and experienced. By integrating advanced Generative AI technologies, the system transforms traditional, static historical facts into dynamic, immersive storytelling experiences. Users can explore historical events through an interactive dashboard or by using a keyword-based search system, enabling quick and intuitive access to a vast range of historical moments. Once an event is selected, the AI generates rich and contextual narrations, converting textual descriptions into natural-sounding voice output to enhance engagement and accessibility. A key feature of TimelineX is its ability to generate AI-powered visual content, including images and short video clips that depict the selected historical event. This multimodal approach makes learning more interactive, memorable, and suitable for diverse learning preferences. In addition to narrative and visual enhancements, the platform introduces history- based games designed to reinforce learning through play. These gamified elements help users test their knowledge, encourage active participation, and make the overall experience more enjoyable—particularly for students. By bridging education, artificial intelligence, and interactive media, TimelineX transforms traditional learning into a modern digital experience. The platform provides a personalized and engaging environment for students, educators, self- learners, and history enthusiasts. Through its combination of AI-generated content, intuitive navigation, and educational games, TimelineX makes historical exploration more accessible, captivating, and meaningful. Overall, it demonstrates the potential of AI to reshape digital education by turning history into an engaging journey rather than a static archive of information.
Keywords: Generative AI, Interactive Learning, Natural Language Processing, Text-to-Speech, AI Image Generation, Gamified Education, Historical Visualization, Educational Technology.
Keywords: Generative AI, Interactive Learning, Natural Language Processing, Text-to-Speech, AI Image Generation, Gamified Education, Historical Visualization, Educational Technology.
Abstract
Secure Digital Identity Verification Using Blockchain
K Senbagam M.E., Kishore S, Divya K
DOI: 10.17148/IJARCCE.2026.15567
Abstract: In today’s digital era, safe identity authentication has become a significant concern because of increased Cyber Attacks, identity theft, and breaches.
The conventional digital identity solution is primarily reliant on a centralized database, which retains individuals’ information in one place. This is more prone to hacking, misusing, and unauthorized use. The conventional digital identity solution has limitations, which are addressed to a great extent by blockchain technology in digital identity authentication. the blockchain offers a decentralized and immutable platform on which the identity information can be recorded and verified in a secure manner without the need for any centralized authority. Every identity transaction is encrypted and recorded on multiple nodes in a manner that is extremely difficult to modify or counterfeit. The utilization of the blockchain technology enables individuals to control their identity information in a self-sovereign manner to share the necessary information only while maintaining privacy. The utilization of keys for encryption and the utilization of smart contracts also adds to the trust and authenticity pertaining to the verification process Secure digital identity verification using blockchain reduces fraud, improves privacy protection, and speeds up authentication processes across various sectors such as banking, healthcare, education, and government services. Overall, blockchain-based identity systems create a more secure, reliable, and user-centric approach to digital identity management, helping build trust in modern digital ecosystems.
Keywords: Secure Digital Identity, Blockchain Technology, Identity Authentication, Decentralized Identity, Self- Sovereign Identity (SSI), Privacy Preservation, Cryptographic Security, Smart Contracts, Identity Verification, Cybersecurity.
The conventional digital identity solution is primarily reliant on a centralized database, which retains individuals’ information in one place. This is more prone to hacking, misusing, and unauthorized use. The conventional digital identity solution has limitations, which are addressed to a great extent by blockchain technology in digital identity authentication. the blockchain offers a decentralized and immutable platform on which the identity information can be recorded and verified in a secure manner without the need for any centralized authority. Every identity transaction is encrypted and recorded on multiple nodes in a manner that is extremely difficult to modify or counterfeit. The utilization of the blockchain technology enables individuals to control their identity information in a self-sovereign manner to share the necessary information only while maintaining privacy. The utilization of keys for encryption and the utilization of smart contracts also adds to the trust and authenticity pertaining to the verification process Secure digital identity verification using blockchain reduces fraud, improves privacy protection, and speeds up authentication processes across various sectors such as banking, healthcare, education, and government services. Overall, blockchain-based identity systems create a more secure, reliable, and user-centric approach to digital identity management, helping build trust in modern digital ecosystems.
Keywords: Secure Digital Identity, Blockchain Technology, Identity Authentication, Decentralized Identity, Self- Sovereign Identity (SSI), Privacy Preservation, Cryptographic Security, Smart Contracts, Identity Verification, Cybersecurity.
Abstract
VisionGuard: Deep Learning–Based Weapon Detection Framework
K. Rajavadhani, Aswini. R. C & Vijayalakshmi. J
DOI: 10.17148/IJARCCE.2026.15568
Abstract: We have a lot of surveillance systems in places and private areas these days. This means we really need to have systems that can find threats right away. Usually people watch the cameras. We use simple rules to figure out what is going on. This does not work very well when there are a lot of people around and things get complicated. This paper is about a surveillance system that uses artificial intelligence and looks at pictures to find threats, in real time and understand what is happening. The system uses deep learning techniques to do this. The system they are talking about uses a Swin Transformer to find objects like weapons or unattended bags. It can also detect when someone is not supposed to be in an area. They also have a model that looks at how people move to see if they are doing something weird. This model uses something called Graph Convolutional Networks. It can tell if someone is just hanging around being violent or moving in a way. The Swin Transformer and the movement model work together to make sure the system is accurate and does not send out a lot of warnings. The system is really good at finding objects and strange movements, like the Swin Transformer finding weapons and the movement model finding unusual movement patterns. When we find something that could be a problem the system sends out alerts to help the security team act fast. The tests we did show that this way of doing things works well even in tough situations, which means it is a good choice for new smart surveillance systems that are being developed.
Keywords: Surveillance Systems, Threat Detection, Computer Vision, Deep Learning, Swin Transformer, Graph Convolutional Networks, Situational Awareness
Keywords: Surveillance Systems, Threat Detection, Computer Vision, Deep Learning, Swin Transformer, Graph Convolutional Networks, Situational Awareness
Abstract
Review on Potential Applications of Geopolymer Concrete in Construction
Vasanth S, Likhith Kumar T, Vamshi A, Mohammed Afiz Roshan
DOI: 10.17148/IJARCCE.2026.15569
Abstract: This paper review discusses the concept and applications of geopolymer concrete (GPC) as an alternative to conventional cement concrete. The reviewed study explains how industrial by-products such as fly ash and slag can be used to produce eco-friendly concrete. This review summarizes the main points of the paper, including materials used, mechanical properties, and practical uses. It also evaluates the strengths and weaknesses of the study. The findings show that geopolymer concrete is a promising material for sustainable construction, although more research is required for large-scale implementation.
Keywords: Geopolymer concrete, eco-friendly materials, fly ash, sustainable construction, alkali activation
Keywords: Geopolymer concrete, eco-friendly materials, fly ash, sustainable construction, alkali activation
Abstract
CallMind: An AI-Powered Real-Time Voice Agent Platform Using a Pluggable Speech-to-Text, Large Language Model, and Text-to-Speech Pipeline
Sahil Makandar, Rohini Magamdum, Aditya Mali, Sudesh Kumbhar, Radheshyam Sah, Rajendra Hiremath
DOI: 10.17148/IJARCCE.2026.15570
Abstract: Intelligent voice agents offer a scalable alternative to traditional Interactive Voice Response (IVR) systems and human call centres, yet their deployment remains technically complex. This paper presents CallMind, an AI-powered voice agent platform that enables businesses and individuals to deploy conversational telephone agents within minutes. The platform implements a pluggable, channel-agnostic pipeline that normalises all input modalities—phone calls, browser audio, and direct text—into a unified QueryPayload abstraction before processing. The core intelligence pipeline routes each query through Azure Cognitive Speech Services for real-time streaming Speech-to-Text (STT), Groq API (LLaMA 3.3 70B) for large language model inference, and Azure Neural Text-to-Speech (TTS) for audio synthesis. A sentence-by-sentence streaming architecture achieves end-to-end response latency of approximately 780 ms, comparable to natural human conversational response time. The system is built on a dual-backend architecture: a managed Supabase layer for multi-tenant agent configuration, knowledge base management, and conversation persistence; and a containerised Python FastAPI server on Microsoft Azure for the real-time AI pipeline. Live validation through Twilio Programmable Voice demonstrates natural conversation quality with accurate transcription, contextually relevant responses, and seamless audio delivery. The architecture provides a clear migration path from full-context LLM prompting to Retrieval-Augmented Generation (RAG) and from Docker Compose to Kubernetes without modifying the application layer.
Keywords: Voice Agent, Speech-to-Text, Text-to-Speech, Large Language Model, Real-Time Pipeline, WebSocket, Twilio, FastAPI, Docker, Pluggable Architecture, Conversational AI, Redis, Supabase, Retrieval- Augmented Generation
Keywords: Voice Agent, Speech-to-Text, Text-to-Speech, Large Language Model, Real-Time Pipeline, WebSocket, Twilio, FastAPI, Docker, Pluggable Architecture, Conversational AI, Redis, Supabase, Retrieval- Augmented Generation
Abstract
Smart Pothole Detection and Rapid Emergency Response System for Four-Wheeler Vehicles
Abhishek N, Dr. Kavitha A S, Nandish S, Sanjana Sindol, Bhavana
DOI: 10.17148/IJARCCE.2026.15571
Abstract: Potholes are one of the most dangerous road hazards in India, causing tyre bursts, loss of vehicle control, and fatal accidents every monsoon season. Yet no vehicle-mounted system currently warns the driver in real time and automatically notifies family members when a dangerous incident follows. This paper presents a Smart Pothole Detection and Rapid Emergency Response System for four-wheeler vehicles. A YOLOv8n model on a Raspberry Pi 4 detects potholes at 18 FPS from a dashboard camera, immediately alerting the driver via buzzer, LED, and mobile app. An MPU- 6050 inertial sensor then monitors vehicle dynamics for 10 seconds; if acceleration exceeds 4G or yaw rate surpasses 45°/s, a danger event is confirmed. A photo snapshot and live GPS coordinates are dispatched simultaneously via Firebase Cloud Messaging and GSM SMS to family members, whose app opens automatically showing the photo and location. Hardware costs under Rs. 9,000 with no vehicle modification required. Tests across Bengaluru roads confirmed 87.3% mAP detection and sub-5-second family alert delivery.
Keywords: Pothole Detection, YOLOv8, Emergency Response, IoT, Raspberry Pi, GPS Tracking, MPU-6050, Flutter Mobile App, Firebase, Road Safety, Four-wheeler Vehicles, Driver Alert System.
Keywords: Pothole Detection, YOLOv8, Emergency Response, IoT, Raspberry Pi, GPS Tracking, MPU-6050, Flutter Mobile App, Firebase, Road Safety, Four-wheeler Vehicles, Driver Alert System.
Abstract
Coder Buddy – An Agentic AI-Powered Python Development Assistant for Code Review, Code Generation, and Automated Mini Project Building
Sonika D, Harshitha L
DOI: 10.17148/IJARCCE.2026.15572
Abstract: The growing demand for intelligent programming assistants has led to the development of agentic AI systems that can autonomously plan and execute multi-step development tasks. This paper presents Coder Buddy, an agentic AI application designed to assist Python developers with three core capabilities: automated code review, prompt-based code generation, and complete mini project building. The system combines rule-based static analysis with Large Language Models (LLMs) to deliver accurate, context-aware feedback and generate production-ready Python code. A key feature of the system is its ability to auto-detect user intent and dynamically generate either CLI or GUI (Streamlit-based) projects with proper folder structure, dependency files, and runnable output. The application is built using Python and Streamlit, and maintains session-based history for all interactions. Evaluation across simulated development scenarios demonstrates the system's effectiveness in improving code quality, reducing development time, and supporting learning and rapid prototyping.
Keywords: Agentic AI, Code Review, Code Generation, Large Language Models, Python, Streamlit, AST Analysis, Mini Project Builder, Automated Software Development
Keywords: Agentic AI, Code Review, Code Generation, Large Language Models, Python, Streamlit, AST Analysis, Mini Project Builder, Automated Software Development
Abstract
MCP-Based Context-Aware System Monitoring and Threat Detection Agent
G. Priyadharshini, M.E., Balaji A, Vishnu S, Mohamed Noufal M
DOI: 10.17148/IJARCCE.2026.15573
Abstract: This paper presents an MCP-Based Context-Aware System Monitoring and Threat Detection Agent, an intelligent, real-time cybersecurity monitoring platform leveraging the Model Context Protocol (MCP) to deliver context-aware threat detection and automated response capabilities. Traditional Security Information and Event Management (SIEM) systems rely on static rule-based engines that produce false-positive rates of 25–45%, suffer from alert fatigue, and fail to detect multi-stage Advanced Persistent Threats (APTs). The proposed system integrates a FastAPI backend, PostgreSQL 16 storage, WireGuard VPN encryption, and a Bootstrap 5 web dashboard to provide unified, real-time visibility across network traffic, system logs, and behavioral metrics. The MCP AI agent maintains a rolling context window over incoming security events, enabling temporal correlation, multi-stage attack detection, lateral movement identification, and significant reduction of false positives through composite threat scoring. Validation results demonstrate a 67% reduction in false positives, sub-3-second automated mitigation response, throughput exceeding 6,200 concurrent events per second, an AUC of 0.94 on the ROC curve, and 60–75% reduction in operational costs versus commercial SIEM solutions. All three functional modules—Data Collection & Traffic Monitoring, Threat Analysis & Context Awareness, and Alerting & Secure Notification—have been implemented and validated in a prototype environment over a 30-day test period.
Keywords: Model Context Protocol (MCP), Cybersecurity, Real-Time Threat Detection, Context-Aware AI, SIEM, FastAPI, WireGuard VPN, Anomaly Detection, Automated Incident Response.
Keywords: Model Context Protocol (MCP), Cybersecurity, Real-Time Threat Detection, Context-Aware AI, SIEM, FastAPI, WireGuard VPN, Anomaly Detection, Automated Incident Response.
Abstract
AI-BASED VERIFICATION OF LLM RESPONSE
Mrs. Asha Sattigeri, Lavanya R, Pavan Gowda S
DOI: 10.17148/IJARCCE.2026.15574
Abstract: Large Language Models(LLMs) now handle tasks like question answering, summarisation, code generation and dialogue with impressive results. Yet they still suffer from a key issue: hallucination happens when a model generates text that reads well but is factually wrong or not grounded in real evidence. The risk is higher in domains like healthcare, law, finance and research, where inaccurate outputs can lead to real damage. This survey focuses on how to detect hallucination verification in LLMs. We review 5 core detection approaches: retrieval-based, uncertainty-based, embedding-based, learning-based and self-consistency methods. We also cover current mitigation techniques, popular benchmarks such as Truthful QA and HaluEval, common evaluation metrics and verification tools such as xVerify and CompassVerifier. This paper closes by discussing open challenges and future directions for building more reliable, truthful LLMs
Keywords: Large Language Models, Hallucination Detection, Hallucination Mitigation, Factuality, Retrieval- Augmented Generation, TruthfulQA, HaluEval, Answer verification, Claim verification, Internal states
Keywords: Large Language Models, Hallucination Detection, Hallucination Mitigation, Factuality, Retrieval- Augmented Generation, TruthfulQA, HaluEval, Answer verification, Claim verification, Internal states
Abstract
Controlling Ethical Hacking: Operating System Security
Dipali Girhe, R.V. Daund
DOI: 10.17148/IJARCCE.2026.15575
Abstract: The rapid advancement of technology has increased the risk of cyberAttacks, making ethical hacking a critical tool for securing information systems. Ethical hacking involves authorized testing of computer systems to identify vulnerabilities before malicious hackers exploit them. This research focuses on controlling ethical hacking through the use of operating systems such as Windows, Kali Linux, and Linux-based security distributions. Data is collected from secondary sources, including journals, research papers, official OS documentation, and cyber security reports, to analyse hacking tools, techniques, and OS-level security features. The study highlights how operating systems provide built-in protections, access controls, logging, auditing, and monitoring mechanisms that can prevent unauthorized access while enabling safe ethical hacking. Additionally, the research explores emerging trends and best practices for secure implementation of ethical hacking. The findings aim to provide a comprehensive understanding of how operating systems can strengthen cyber security defenses and control ethical hacking activities effectively.
Keywords: Ethical Hacking, Operating System, Security Features, Vulnerability Assessment, etc.
Keywords: Ethical Hacking, Operating System, Security Features, Vulnerability Assessment, etc.
Abstract
AI-Based Health Monitoring System
Ummi Habiba, Vijetha S P, C B Shekhara, Prajwal V, Anita Patil
DOI: 10.17148/IJARCCE.2026.15576
Abstract: Continuous and real-time health monitoring has emerged as one of the most pressing challenges in modern healthcare, particularly in the context of ageing populations, rising prevalence of chronic diseases, and the growing demand for remote patient care. This paper presents the design and implementation of an Artificial Intelligence (AI)- based health monitoring system capable of acquiring, processing, and analysing multiple physiological parameters — including heart rate, blood oxygen saturation (SpO2), body temperature, and blood pressure — in real time. The proposed system integrates low-cost wearable sensors with a microcontroller-driven edge computing unit and a cloud-connected dashboard, forming an end-to-end pipeline from data acquisition to clinical decision support. A hybrid machine learning model combining a Long Short-Term Memory (LSTM) network for temporal pattern recognition with a Random Forest classifier for anomaly labelling is trained on publicly available physiological datasets. The model achieves a classification accuracy of 94.7% in detecting critical health events such as tachycardia, hypoxia, and hypertensive episodes. Automated alert notifications are dispatched to caregivers and physicians whenever abnormal readings are detected, enabling timely intervention. Experimental evaluation demonstrates that the system maintains end-to-end latency below 1.8 seconds and sustains reliable operation across a 24-hour continuous monitoring window. The results confirm that integrating AI with wearable sensing technology offers a scalable and cost-effective approach to preventive healthcare.
Keywords: Artificial Intelligence, Health Monitoring, Wearable Sensors, LSTM, Random Forest, IoT in Healthcare, Remote Patient Monitoring, Anomaly Detection, Edge Computing, Physiological Signals.
Keywords: Artificial Intelligence, Health Monitoring, Wearable Sensors, LSTM, Random Forest, IoT in Healthcare, Remote Patient Monitoring, Anomaly Detection, Edge Computing, Physiological Signals.
Abstract
Iot Based Patient Health Monitoring System
Basavanagowda B S, Basavaraju Bailaannavar, Gagangowda, Vijaykumar Hovale, Prof. Padmavathi N*
DOI: 10.17148/IJARCCE.2026.15577
Abstract: The proposed system is an advanced Internet of Things (IoT)-based health monitoring solution designed to provide continuous and real-time tracking of a patient’s vital parameters.The system is built around the ESP32 microcontroller, which acts as the central processing unit and communication hub. It integrates multiple biomedical sensors, including a temperature sensor, heart rate sensor, ECG sensor, and blood pressure sensor, to collect accurate physiological data from the patient. The acquired data is processed by the ESP32 and displayed on an LCD screen for immediate local monitoring. In addition to local display, the system utilizes Wi-Fi connectivity to transmit the data to an IoT platform, enabling remote access through a caregiver’s smartphone. This allows doctors or caretakers to monitor thepatient’s health condition from any location in real time. The system also incorporates abuzzer for emergency alerts and a manual button that can be used by the patient to signal distress. These features enhance the safety and responsiveness of the system during criticalsituations. By combining sensor technology, wireless communication, and IoT capabilities,the proposed system offers a reliable, cost-effective, and efficient solution for remote health monitoring. It is particularly useful for elderly patients, individuals with chronic diseases, and situations where continuous medical supervision is required.
Abstract
Emotion-Aware Adaptive Learning Systems: A Comprehensive Survey on Artificial Intelligence-Based Personalized Education
A Rashmi, A Keerthi, Harika K, Likitha K, Dr. Muhibur Rahman T.R
DOI: 10.17148/IJARCCE.2026.15578
Abstract: The increasing adoption of digital learning platforms and intelligent educational technologies has transformed modern education systems. However, conventional e-learning environments often fail to adapt to the emotional and cognitive states of learners, resulting in reduced engagement, low motivation, and ineffective personalized learning experiences. To address these limitations, recent advancements in Artificial Intelligence (AI), affective computing, and adaptive learning systems have enabled the development of emotion-aware educational platforms capable of dynamically responding to student emotions and behavioral patterns. This survey presents a comprehensive review of Emotion-Aware Adaptive Learning Systems that utilize AI techniques for personalized education and intelligent learner interaction. The study systematically examines the evolution of affective computing in education, including emotion recognition methods based on facial expressions, speech analysis, physiological signals, eye tracking, and behavioral analytics. Furthermore, this survey analyzes the integration of Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, and multimodal emotion recognition techniques in adaptive educational environments.
Through comparative analysis of recent studies, this paper evaluates the effectiveness of AI-driven adaptive learning systems in improving student engagement, learning efficiency, concentration, and academic performance. A four-layer taxonomy is proposed to classify existing systems into emotion detection, learner modeling, adaptive decision-making, and intelligent feedback mechanisms. The survey also highlights significant challenges including privacy concerns, emotional data reliability, ethical considerations, computational complexity, and real-time adaptability limitations. Additionally, emerging research trends such as explainable AI, multimodal affective computing, virtual intelligent tutors, and emotionally responsive educational agents are explored to identify future research directions. By consolidating existing research contributions and technological advancements, this survey aims to provide a structured understanding of emotion-aware AI learning systems and their potential to revolutionize personalized digital education. The findings of this study contribute toward the development of intelligent, human-centered educational technologies capable of enhancing learner experience and improving adaptive teaching methodologies in future smart learning environments.
Through comparative analysis of recent studies, this paper evaluates the effectiveness of AI-driven adaptive learning systems in improving student engagement, learning efficiency, concentration, and academic performance. A four-layer taxonomy is proposed to classify existing systems into emotion detection, learner modeling, adaptive decision-making, and intelligent feedback mechanisms. The survey also highlights significant challenges including privacy concerns, emotional data reliability, ethical considerations, computational complexity, and real-time adaptability limitations. Additionally, emerging research trends such as explainable AI, multimodal affective computing, virtual intelligent tutors, and emotionally responsive educational agents are explored to identify future research directions. By consolidating existing research contributions and technological advancements, this survey aims to provide a structured understanding of emotion-aware AI learning systems and their potential to revolutionize personalized digital education. The findings of this study contribute toward the development of intelligent, human-centered educational technologies capable of enhancing learner experience and improving adaptive teaching methodologies in future smart learning environments.
Abstract
Automated Threat Hunting Using AI: AI-Driven Defence Strategy
Vikram G.D, K. Sharath
DOI: 10.17148/IJARCCE.2026.15579
Abstract: The exponential digitization of global infrastructures, coupled with the proliferation of cloud computing, Internet of Things (IoT) devices, and decentralized workforces, has inadvertently created a vast and highly vulnerable cyber attack surface. Concurrently, adversarial tactics have evolved with alarming sophistication. Modern cybercriminals and state-sponsored actors frequently deploy zero-day exploits, advanced persistent threats (APTs), fileless malware, and polymorphic ransomware that are expressly designed to circumvent traditional, perimeter-based security architectures [1], [2]. Historically, cybersecurity has relied heavily on reactive paradigms—such as signature-based Intrusion Detection Systems (IDS) and standard firewalls—which require prior knowledge of an attack vector to mount a defense. This reactive posture is fundamentally insufficient in an era where the velocity and novelty of cyber threats outpace human response capabilities. To neutralize these stealthy incursions, the cybersecurity industry must pivot toward proactive threat hunting: the iterative, aggressive process of searching through networks, endpoints, and datasets to uncover latent malicious activities that have successfully evaded initial automated defenses.
However, the sheer volume and complex dimensionality of telemetry data generated by modern IT ecosystems render manual threat hunting physically impossible and highly susceptible to analyst burnout and alert fatigue. This paper presents a comprehensive framework for Automated Threat Hunting driven by Artificial Intelligence (AI) and Machine Learning (ML), positioning it as the indispensable core of modern cyber defense strategies [2], [3]. By integrating AI into Security Operations Centers (SOCs), organizations can transcend the limitations of human capacity. This research explores the deployment of advanced AI mechanisms, specifically focusing on User and Entity Behavior Analytics (UEBA) for establishing baseline operational norms, deep learning neural networks for structural payload analysis without relying on known signatures, and natural language processing (NLP) to autonomously ingest and correlate global threat intelligence feeds [6], [9].
Furthermore, this document examines how AI-driven systems leverage continuous contextual analysis to connect seemingly disparate, low-level alerts across vast network topologies, unearthing coordinated, slow-moving attacks before data exfiltration or encryption occurs. We also detail the integration of AI with Security Orchestration, Automation, and Response (SOAR) platforms to execute instantaneous, autonomous remediation protocols, drastically reducing both the Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) [10]. Finally, this paper critically assesses the practical implementation challenges—including high false-positive rates, data privacy constraints, and the emergence of adversarial AI [7], [11]—while forecasting the future scope of fully autonomous, self-healing networks governed by federated machine learning models [14].
Keywords: Automated Threat Hunting, Artificial Intelligence, Cybersecurity, Machine Learning, User and Entity Behavior Analytics (UEBA), Proactive Defense, Advanced Persistent Threats (APTs), Security Orchestration and Automation (SOAR), Anomaly Detection.
However, the sheer volume and complex dimensionality of telemetry data generated by modern IT ecosystems render manual threat hunting physically impossible and highly susceptible to analyst burnout and alert fatigue. This paper presents a comprehensive framework for Automated Threat Hunting driven by Artificial Intelligence (AI) and Machine Learning (ML), positioning it as the indispensable core of modern cyber defense strategies [2], [3]. By integrating AI into Security Operations Centers (SOCs), organizations can transcend the limitations of human capacity. This research explores the deployment of advanced AI mechanisms, specifically focusing on User and Entity Behavior Analytics (UEBA) for establishing baseline operational norms, deep learning neural networks for structural payload analysis without relying on known signatures, and natural language processing (NLP) to autonomously ingest and correlate global threat intelligence feeds [6], [9].
Furthermore, this document examines how AI-driven systems leverage continuous contextual analysis to connect seemingly disparate, low-level alerts across vast network topologies, unearthing coordinated, slow-moving attacks before data exfiltration or encryption occurs. We also detail the integration of AI with Security Orchestration, Automation, and Response (SOAR) platforms to execute instantaneous, autonomous remediation protocols, drastically reducing both the Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) [10]. Finally, this paper critically assesses the practical implementation challenges—including high false-positive rates, data privacy constraints, and the emergence of adversarial AI [7], [11]—while forecasting the future scope of fully autonomous, self-healing networks governed by federated machine learning models [14].
Keywords: Automated Threat Hunting, Artificial Intelligence, Cybersecurity, Machine Learning, User and Entity Behavior Analytics (UEBA), Proactive Defense, Advanced Persistent Threats (APTs), Security Orchestration and Automation (SOAR), Anomaly Detection.
Abstract
Medicinal Plant Identification and Classification
Anushree N N, Deepika V M
DOI: 10.17148/IJARCCE.2026.15580
Abstract: Medicinal plants play a significant role in healthcare systems due to their therapeutic and healing properties. Accurate identification of medicinal plants is essential for their proper utilization in traditional and modern medicine. However, manual identification requires expert botanical knowledge and is often time-consuming and error-prone because many plant species possess visually similar leaf structures. This project proposes an intelligent Medicinal Plant Identification and Classification system using deep learning techniques to automate the plant recognition process.
The proposed system utilizes Convolutional Neural Networks (CNNs) to analyze leaf images and classify medicinal plants based on visual features such as shape, texture, vein patterns, and color characteristics. Image preprocessing techniques including resizing, normalization, and augmentation are applied to improve model performance and handle variations in lighting, orientation, and background conditions. The system integrates Firebase Firestore for storing medicinal plant information such as scientific names, medicinal uses, and safety considerations.
Experimental evaluation demonstrates that the CNN-based approach provides high classification accuracy and reliable prediction results compared to conventional machine learning techniques. The proposed system reduces manual effort, improves identification speed, and provides an efficient and user-friendly solution for students, researchers, farmers, and healthcare enthusiasts. Overall, the project highlights the effective application of artificial intelligence and deep learning in botanical research and medicinal plant preservation.
Keywords: Medicinal Plants, Deep Learning, Convolutional Neural Network, Image Classification, Plant Identification, Artificial Intelligence, Firebase Firestore.
The proposed system utilizes Convolutional Neural Networks (CNNs) to analyze leaf images and classify medicinal plants based on visual features such as shape, texture, vein patterns, and color characteristics. Image preprocessing techniques including resizing, normalization, and augmentation are applied to improve model performance and handle variations in lighting, orientation, and background conditions. The system integrates Firebase Firestore for storing medicinal plant information such as scientific names, medicinal uses, and safety considerations.
Experimental evaluation demonstrates that the CNN-based approach provides high classification accuracy and reliable prediction results compared to conventional machine learning techniques. The proposed system reduces manual effort, improves identification speed, and provides an efficient and user-friendly solution for students, researchers, farmers, and healthcare enthusiasts. Overall, the project highlights the effective application of artificial intelligence and deep learning in botanical research and medicinal plant preservation.
Keywords: Medicinal Plants, Deep Learning, Convolutional Neural Network, Image Classification, Plant Identification, Artificial Intelligence, Firebase Firestore.
Abstract
Adaptive Federated Threat Detection
Khushi D Kunte, Ranganath S R
DOI: 10.17148/IJARCCE.2026.15581
Abstract: Food safety has become a major concern due to the increasing consumption of packaged and processed food products containing harmful additives and chemicals. Consumers often find it difficult to understand ingredient labels and identify the health risks associated with food preservatives, artificial colours, and additives. Conventional food awareness systems provide limited real-time analysis and lack intelligent recommendation mechanisms. This project proposes Caution Bites, an AI-powered food safety and ingredient analysis system that helps users identify harmful food additives and make healthier dietary decisions. The system allows users to scan or enter food ingredients, after which the application analyses the content using intelligent data processing and AI-based risk evaluation. It provides detailed information regarding additives, preservatives, allergens, and potential health impacts. The framework also generates safer food recommendations and health alerts based on detected ingredients. Experimental evaluation demonstrates improved consumer awareness, fast ingredient analysis, and effective health risk identification while maintaining a user- friendly and scalable architecture.
Keywords: Food Safety, AI-Based Ingredient Analysis, Health Risk Detection, Additive Identification, Intelligent Recommendation System, Consumer Health Monitoring
Keywords: Food Safety, AI-Based Ingredient Analysis, Health Risk Detection, Additive Identification, Intelligent Recommendation System, Consumer Health Monitoring
Abstract
Skin Disease Classification Using CNN
Amritha R, Dr. H K Madhu
DOI: 10.17148/IJARCCE.2026.15582
Abstract: Skin diseases represent a significant global healthcare challenge, with certain malignant lesions such as melanoma requiring early diagnosis to improve patient survival rates. Traditional dermatological diagnosis primarily depends on visual examination by specialists, which may be subjective, time-consuming, and limited by the availability of experienced dermatologists. This paper presents a deep learning–based multi-class skin disease classification system using Convolutional Neural Networks (CNNs) for automated analysis of dermoscopic skin images. The proposed framework classifies skin lesions into nine distinct disease categories using a publicly available dermoscopic image dataset. Image preprocessing techniques including resizing, normalization, and augmentation are applied to improve model generalization and classification performance. To enhance interpretability, Explainable Artificial Intelligence (XAI) techniques based on Gradient-weighted Class Activation Mapping (Grad-CAM) are incorporated to visualize important lesion regions influencing prediction outcomes. The trained CNN model is integrated into a Streamlit-based web application that enables real-time image upload and disease prediction along with confidence visualization. Experimental evaluation demonstrates that the proposed system achieves reliable multi-class classification performance with a validation accuracy of approximately 87%. The proposed framework serves as an intelligent decision-support tool for educational and preliminary diagnostic assistance and highlights the potential of explainable deep learning techniques in medical image analysis.This paper discusses the system architecture, methodology, implementation, and performance evaluation of the proposed solution.
Keywords: Skin Disease Classification, Deep Learning, Convolutional Neural Network, Explainable AI, Grad-CAM, Dermoscopic Image Analysis, Streamlit
Keywords: Skin Disease Classification, Deep Learning, Convolutional Neural Network, Explainable AI, Grad-CAM, Dermoscopic Image Analysis, Streamlit
Abstract
Cisix: Technology for Net-Zero Carbon Ecosystem
Darshan D Kolmi, Goutham E, Hrishikesh Bodapatti, Javeed Hussain P, Dr. Anita Patil, Vijay Kumar
DOI: 10.17148/IJARCCE.2026.15583
Abstract: Farmers represent one of the largest untapped sources of carbon sequestration potential globally, yet they remain almost entirely excluded from carbon markets. This paper presents Cisix, a technology-mediated carbon trading platform designed to bridge the structural gap between rural agricultural producers, Sustainable project Developers and Carbon credit buyers. Unlike conventional carbon market infrastructure — which demands technical literacy, legal capacity, and upfront capital that most smallholder farmers simply do not have, Cisix embeds farmer training and certification directly into the trading pipeline, treating knowledge transfer not as a peripheral service but as a core market function. Drawing on platform design principles and inclusive finance frameworks, we examine how Cisix addresses three compounding barriers to farmer participation: awareness deficits around carbon credit mechanisms, absence of accessible verification pathways, and the intermediary-heavy structures that erode farmer earnings. The platform operationalizes a four-stage model — assessment, training, credit generation, and direct marketplace access of ours — supported by multilingual outreach and website which supports at local village areas. Early deployment data suggest that co-locating financial participation with structured capacity building significantly improves both onboarding rates and long-term farmer retention in carbon programs. We argue that carbon market inclusion is not merely a technical challenge but a design and governance problem, and that platforms which treat farmers as informed economic agents — rather than passive land managers — are better positioned to deliver durable climate and livelihood outcomes.
Keywords: Carbon credits, Carbon Trading platform, Voluntary Carbon Market, AgriTech, Farmer Inclusion, Sustainable Projects, Rural Capacity Building, Climate Action, Indian Carbon Trading Platform.
Keywords: Carbon credits, Carbon Trading platform, Voluntary Carbon Market, AgriTech, Farmer Inclusion, Sustainable Projects, Rural Capacity Building, Climate Action, Indian Carbon Trading Platform.
Abstract
Recommendation system for cloud service on Trust management services
Deepak BN, Anusha J, Krishnaveni A, Karthik R
DOI: 10.17148/IJARCCE.2026.15584
Abstract: The trust management is challenging issue in cloud computing now a days. The main obstacle for growth and adoption of cloud computing is trust. Though many works has been proposed, the determination of credibility for trust feedbacks is ignored. In this paper, the Cloud Armor, a reputation and recommendation trust management is proposed in which set of functions are provided to delivery of Trust as a Service. Cloud administration faces significant challenge. The Service Level Agreements (SLAs) are facing difficulty in finding trust between cloud service customers and cloud service providers. Buyers’ input can be sensible to the cloud administration characteristic to survey.
Keywords: Trust Management,RecommendationTrust,ReputationTrust,CloudService,TrustResults.
Keywords: Trust Management,RecommendationTrust,ReputationTrust,CloudService,TrustResults.
Abstract
Intelligent Crop Disease Detection Systems: A Review of Deep Learning Approaches
Siddessh K S, Anita Patil
DOI: 10.17148/IJARCCE.2026.15585
Abstract: Agriculture plays a crucial role in global food production, but crop diseases continue to cause major losses in yield and quality each year. Traditional disease detection methods depend on manual inspection by agricultural experts, which is time-consuming, costly, and often ineffective for large-scale farming. Recent advancements in Artificial Intelligence (AI), Deep Learning, Computer Vision, and Internet of Things (IoT) technologies have enabled the development of intelligent crop disease detection systems capable of identifying plant diseases automatically and accurately. This paper presents a comprehensive review of AI-based crop disease detection approaches using Convolutional Neural Networks (CNN), image processing techniques, mobile applications, and drone-based monitoring systems. The study examines commonly used datasets, preprocessing methods, deep learning architectures, and deployment platforms in modern smart agriculture applications. A four-tier taxonomy is proposed to classify crop disease detection systems based on their level of automation and intelligence. Performance metrics such as accuracy, precision, recall, F1-score, and computational efficiency are also analyzed. Comparative analysis shows that while deep learning models provide high detection accuracy, challenges such as dataset imbalance, varying environmental conditions, internet dependency, and scalability still remain unresolved. Finally, the paper identifies major research gaps and discusses future directions toward intelligent AI-powered precision agriculture systems.
Abstract
Intelligent Fingerprint Storage and Management System for Authentication Applications on Cloud Storage
Vaasthava Sree Sai Reddy N, Subhamay Parya, Spoorti Patil, Vaishnavi Reddy, Dr.SoniaMariaD'souza
DOI: 10.17148/IJARCCE.2026.15586
Abstract: Data security has become highly necessary in our digital era. This paper proposes a Fingerprint Storage System (FSS) on cloud storage that provides users a secure way to access their files through fingerprint authentication and not password-based authentication. The OFR is used to recognize and authenticate users. Files stored are also encrypted via AES-256 algorithm in order to prevent any unauthorized access and data breach. This paper eliminates the threat of password thefts, phishing, and unauthorized access since passwords would no longer be necessary. The proposed solution is intended to operate in practical applications such as banking, health care, educational institutes, and enterprises.
Keywords: Cloud Technology, Fingerprint accessibility, AES-256 encryption, Biometric Security,Secure Cloud Storage
Keywords: Cloud Technology, Fingerprint accessibility, AES-256 encryption, Biometric Security,Secure Cloud Storage