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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
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