← Back to Archives
VOLUME 15, ISSUE 6, JUNE 2026
Publication in progress..
Multi-Agent AI Systems for Security Automation
Mohammed Kashif, Abdul Rahman Jibran Syed
Integration of SIEM Data Analytics and AI for Proactive Cyber Threat Hunting
Abdul Hasham, Ramesh Venkata Sai lakshmi
Design Evaluation and Validation of a Resilient IoT-Based Flood Prediction Framework for Data-Scarce Environments in East Africa
Muwanga Erasto Kosea, Dr Otanga Daniel, Dr. Satwinder Singh Rupra
QUANTIFYING EXPLANATION DRIFT UNDER MODEL COMPRESSION IN CLINICAL RISK PREDICTION
Stow May Tamara, Maudlyn Ireju Victor-Ikoh
Design and Analysis of a Low-Power High- Speed 32-bit ALU Using Optimized CMOS Architecture
Dr. Kishore M, Dr. Dileep J, Dr. K. Senthil Babu
Directing the Algorithmic Edge of Inclusive Pedagogy: A Comprehensive Review of AI- Driven Assistive Technologies in Education
Dr. Anju Kaushik, Dr. Anil Kaushik
Smart Security System in Train Using Arduino, Ultrasonic Sensor and Camera
HumeraBano Asadullah Baig
Agentic AI for Data Analysis: A RAG-Enhanced Local LLM Framework with Adaptive Visualization
Harsh Mahesh Tatmute, Shivraj Sunil Shinde, Karan Adinath Nemane, Sandesh Sunil Pujari, Rahul Sudhir Ranjane, V. G. Khetade
CivicAi Complaint Management System
Sammed Mangave, Vaibhav Rajput, Ayan Mujawar, Ganesh Hipparkar, Prof. Dr. V.V.Kheradkar
AI-Driven Malicious URL Detection Using Graph Neural Networks
Shraddha Pailwan, Avishkar Patil, Pranav Patil, Yash Shinde, Gayatree Jadhav, Prof. A. B. Majgave
“An Explainable Hybrid LSTM–Random Forest Framework for Accurate Pulmonary Disease Detection and Classification”
Ajay Pal Singh, Ankita Nigam
CodEzy: An AI-Powered Competitive Coding and Personalized Learning Platform
Kunal Sunil Magdum, Akshay Sachin Mulay, Subahan Riyaj Mulla, Shreyansh Nitin Patil, Prasad Sunil Sutar, Prof. (Dr.) V.V.Kheradkar*
Cyberbullying Detection in Social Media Contents using Machine Learning Techniques
Amey Gujar, Akhilesh Ghorpade, Indrajeet Chougule, Vedant Gawas, Paras Gurjar, Himanshu Baboria, Prof. Vinod Khetade
Seasonal Crop Price Prediction using LSTM
Amol Sawant, Dr. Pravin Patil
Evaluating Usability Techniques in Modern Web Applications
Simran, Dr Pooja Rana
Blockchain Based Certificate Verification System
Shivaprasad M S, Usha M
Impact Of Deep Learning Techniques on Super Resolutions
Deepali Karajgikar, Abhishek Magar
A Graph Retrieval-Augmented Generation Framework for AI-Powered Supplier Discovery
Dr.B.Vijayalakshmi, Dr.M.Swarna Sudha, Dr.K.Vijayalakshmi
IoT Based Weather Station Sakshi Tulshidas Sonawane, Sarvesh Dinesh Saroware, Shreeyash Santosh Dendage,
Prof. N.R.Sawale, Dr.R.K.Moje, Dr.D.O.Patil
Enhancing Urban Safety Through Intelligent Street Lighting: A Review of AI-Driven Detection and Monitoring Approaches
Komal Yadav, Dr. Anita Pal
Generalising Across Different Crop Diseases With InceptionCNN
Shubham Verma, Anita pal
CarbonCred AI: An Artificial Intelligence-Driven Digital MRV Framework for Carbon Credit Analysis and Valuation
Addhwaith S Ajith, Aaron John Joy, Vaishnav Biju, Ameesha J S, Shruthi M Pillai
ADAPTIVE, FEEDBACK-DRIVEN TASK SCHEDULING AND LOAD BALANCING: A SUPERIOR APPROACH FOR MODERN CLOUD COMPUTING SYSTEMS
Deepak Joshi, Kumar Bibhuti Bhushan Singh
A Detailed Survey on the Association Rule Extraction Method in Data Mining
Mr B.B.L.V. Prasad, Dr. Eedi Hemalatha
Toward Intelligent Rural Mobility: A Systematic Review of AI-Enabled Transportation Systems and Research Gaps
Deepanshu Sharma, Ms. Jamna
Generalization and Cross-Dataset Robustness in Deepfake Detection: An Enhanced XceptionNet Approach
Sayli Patil*, Sameer Maheboob Shaikh, Sarwar Ali Mukhtar Ahemad Iddirisi
“AnimalCare: A Multi-Modal AI Based System for Animal Disease Prediction based on Symptoms and Image Analysis”
Taranpreet Kaur, Ms. Renu Bala
A Study on MERN Stack and Role in Web Application Development
Simranjit Kaur, Ms. Renu Bala
An NLP-Based Medicine Recommendation System Using Bidirectional GRU for Disease Prediction
Abhishek Kumar Yadav, Kumar Bibhuti Bhushan Singh, Anita Pal
Intelligent Accessibility Middleware Using AI for Dynamic Inclusive User Interfaces
Taniya Palta, Mr Naveen Sharma
Deep Learning–Driven Early Detection of Colorectal Cancer Using Colonoscopy Imaging
Jaskaran Loi*, Rakesh Kumar
A Multi-Stage Framework for Vehicle Emission Detection and Automated License Plate Recognition (ALPR)
Shreoshi Roy, Anita Pal
IoT based Predictive Maintenance System for Industrial Motors Using Raspberry Pi and Edge Analytics
Priyanka B. Borade, Prof. S. N. Vidhate
Smart AI-Based Vehicular Emission Monitoring and Regulatory Notification System Using YOLOv8, OCR and Machine Learning
Shreoshi Roy, Anita Pal
AI-Driven Predictive Modelling and Machine Learning Framework for Classification of Fetal Health Conditions
Mr. MUTHUKUMARA K, Ms. LOGADHARSHINI M, Mr. ARIRAJAN A, Mr. DEEPAN RAJ R
YOLOv8 for Real-Time Microplastic Detection in Aquatic Environments
Saggurthi Kiran*, P Harikrishna
Stock Vision: A Multivariate LSTM-Based Stock Market Analytics and Prediction Web Application
Pooja, Ms. Renu Bala
AGRIBOT: AN INTELLIGENT CHATBOT FOR FARMERS WITH CROP RECOMMENDATION AND DISEASE PREDICTION USING MACHINE LEARNING
V. Hari Krishnan, S. Surendhar, P. Baranidharan, Dr. Sweta Singh, Ph.D.
CirrhosisX: Detection and Staging of Liver Cirrhosis using DL and Explainable AI
Ms. Poonam Sadafal, Mansi Nirbhavane, Vinod Kumar, Nikita Nigude, Sharyu Nanware
Malware Detection Using PE Headers: A Comprehensive Machine Learning Approach
Pranto Bosu, Satinder Kaur, Tajbir Singh, Satveer Kour, Sandeep Kaur
LITTLE PAW: DESIGN AND DEVELOPMENT OF A USER-CENTERED PET CARE MOBILE APPLICATION USING UI/UX PRINCIPLES
Harminder kaur, Dr. Rimmy Yadav
A Blockchain-Enabled Data Privacy and Governance Framework for Multi-Cloud Environments
Sukhwinder Kaur*, Dr Pooja Rana
“Multimodal Surveillance Frameworks for Narcotics Detection on Social Media: A Review”
Nayana V.M, Adithi S Bharadwaj, Padipati Saidivija, Rumaisa Syed, H. N. Poornima
RailCrawler - Track Inspection Vehicle
Soham Chavhan, Mrs. Vaishali Anaspure, Shivam Kambale, Sudarshan Gaikwad
MACHINE LEARNING TECHNIQUES FOR DIABETES PREDICTION: A COMPREHENSIVE REVIEW
Tanu Sharma, Naveen Sharma
A REGULATION-COMPLIANT BLOCKCHAIN-BASED ARCHITECTURE FOR SECURE AND INTEROPERABLE PUBLIC HEALTHCARE SYSTEMS
Harleen Kaur, Dr. Anubha
RELIABILITY ESTIMATION OF CSKD USING PROBABILISTIC TECHNIQUES
Mr. Chinthapalli Nikhil Reddy, Dr. P. Vinod Kumar Naidu, Dr. Avinash G
Multi Class Support Vector Machine Based Plant Leaf Disease Detection from Color Texture And Shape Pictures
Manisha Machhindra Kapse
Enhancing Crop Prediction Using Artificial Intelligence for Smart Agriculture
Urvashi, Naveen Sharma
AI-Based Autonomous Cyber Defense Framework for Intelligent Threat Detection
Vishali Sansoa*, Rimmy
Skill Bridge: A Smart Platform for Hiring Verified Skilled Professionals
Siddheshwar Vaidya, Mrs. Vaishali Anaspure, Shripal Rathod, Vivek Devare
Smart Perception And Autonomous Response Core
Suraj Pawar, Shoheb Shaikh, Pavan Pawar, Ansh Ghodke, Prof. Neha Dumne
Cloud-based secure payment system with OTP and encrypted data storage
DINESH T, GOKUL S, KILLIVALAVAN S, AMARNATH J, Mrs. L. SHAKIRA BANU, M.E.
DIABETIC RETINOPATHY DIAGNOSIS USING RESNET
Nimisha P S, Aravind A S
GREENLENS: AN INTELLIGENT CNN- BASED LEAF DISEASE DETECTION AND AGRICULTURAL RECOMMENDATION SYSTEM
Aswathy V S, Dr Arathi Chandran R I
CricGuardian: Injury risk prediction system
Pratul Gorde, Dasganu Hakke, Pranit Khodke, Prathamesh Hajare
Algorithm for Traversing a Branched Contour in a Digital Image
Nikoloz Nargizashvili, Otar Tavdishvili
Sign Language Recognition and Translation Systems: A Comprehensive Review with Special Focus on Malayalam Sign Language (MSL)
Dr. Ambili A.R
LEP-Model: Ascertain Loan Predictions Using ML Approach
Pratibha Deshmukh, Yogendra Chhetri, Harsh Nakti, Vinay Gupta, Shivam Patil
TriDoshaX: AI Based Panchakarma Treatment and Diet Recommendation System
Sujal Wadkar, Poonam Dhamal, Shreyans Magdum, Vishwajeet Ghadge
Intelligent Serverless Architecture for Real-Time Expense Management: A Function-as-a-Service Approach to Scalable Financial Analytics
Gudelli Mounika, A.N.Rama Mani
AI-Powered Heart Disease Prediction with Appropriate Feature Selection
Iffat Shireen, Dr. M. A. Pund, Prof. A. U. Chaudhari
Blockchain-Based Secure Data Transmission for IoT Devices
Nikhil V, Pavan R P, Yashas Nagaraj, Gnana Prakash P, Mrs. Kavya K S Math
HEART ATTACK RISK PREDICTION SYSTEM USING CNN-LSTM
JININA D C, SHALOM DAVID
Anti-Tampering Framework for Android Gaming Applications
Bharath A, R Bharath, Punith V, E Pavan Kumar, Mrs. Surekha Bhangari
Energy Harvesting and Health Monitoring Device
Dr.D.O.Patil, Dr.R.K.Moje, Sanika Jagnade, Aditi Bhosale, Trupti Sonavane
Fingerprint-Based Blood Group Detection Using CNN and KNN: A Comparative Study
S. Venkateswara Rao, Bodigam Harini
Machine Learning-Based Sentiment Analysis of Climate Change Discussions on Twitter
S. Srinivas, Dharmana Akhila
Intelligent IoT Systems for Green Computing: Machine Learning-Based Resource Optimization and Energy Efficiency
Sagar Panwar, Jitendra Kumar Saini, Varun Bansal, Ravi Kumar
Intelligent DNA Sequence Classification Using Machine Learning Techniques
Dr.B.Nageswara Rao, Nalanagula Chaitanya
Predictive Analytics for Childbirth Mode Classification Using Machine Learning Techniques
Marpu Pradeepthi, Asoda Manasa
BlinkSpeller: Dynamic EAR-Based Eye Blink Morse Code Communication System with Predictive Text and Speech Synthesis
Amanda Terence, Anisha Anna Vinod, Risa Abdul Khadir R, Vaishnu M, Amila A L
Voices Against Misuse: A Survey-Based Study on the Need for Ethical Regulation of Generative AI Tools
Dr. Pratibha Deshmukh, Dhananjay Kishor Potdar
A Comprehensive Study of IoT Sensors and Actuators
Parteek Singh, Dr. Harish Rohil
FACIAL FEATURE ANALYSIS FOR DEEPFAKE DETECTION
Vaishnavi J Manoj, Aravind A S
Epidemic Forecasting Using an Improved frequent Pattern Procedure and Multivariable Time-Based Association Pattern Extraction
Tasleem Rafiq Sheikh
Freshers Hub: An AI Powered Career Guidance Platform
Nikhil Gupta, Dasganu Hakke, Pranav Kumbhalkar, Prasad Babar
NeuroWell AI: A Hybrid Deep Learning Framework for Early Detection of Mental Health Risks
M.PREETHA, J.LIN EBY CHANDRA
Neuro-Symbolic AI System for Logical Reasoning and Decision Making
T. THILLAI, J.LIN EBY CHANDRA
Cognitive AI for Network Resilience: Integrating Explainable AI and Blockchain for Real-Time Cyber Threat Detection
Rachana V Murthy, Amrutha R, Ashwitha C Shetty, Trupthi J, Vinutha N
AUTOGRAD
Miss. Raheen Rafique Bagwan, Miss. Akansha Anil Sasane, Miss. Riya Chandrakant Chawate, Miss. Rutuja Atul Kavitake
Samanvay: Digital Governance Framework for Tri-Sector Collaboration in India’s Social Welfare Ecosystem
Amaan Sayyed, Yash Patil, Anuj Tiwari, Priyanka Kumbhar
Improving Personal Finance Through Human-Computer Interaction
Ahmed S. AlMahmeed
A Three-Level Framework for Programming Task Design in CS1: Bridging Conceptual Understanding and Transfer
Ahmed S. AlMahmeed
Design and Implementation of Encrypted and Decrypted File System Based on USBKey and Hardware Code
Khanderao S. Kulkarni, Pratap N. Shinde, Aditya M. Auti, Ashok Nagargoje, Nirbhay M. Kunale, Onkar P. Mante
Multi-Modal AI Agent for Intelligent Email Categorization and Auto-Reply
RANJINI, J.LIN EBY CHANDRA
Predictive Maintenance System for Industrial IoT: A Hybrid Deep Learning and Edge-Computing Framework
DIVYA, J.LIN EBY CHANDRA
Intelligent Patient Risk Prediction and Bias-Aware Diagnosis Framework
Vasantha Kumari, J. Lin Eby Chandra
BREATHE: AIR QUALITY PREDICTION USING EMBEDDED MACHINE LEARNING AND DEEP LEARNING MODELS WITH QUANTIZATION TECHNIQUES
Anandhu Suresh, Lekshmi V
A Real-Time Web-Based Chess Tournament Management System with Integrated Sponsor Advertisement Analytics
ATYAM PRABHU, Dr. CHIRAPARAPU SRINIVASARAO*
A Cloud-Native Event Ticketing Platform Leveraging Automated Horizontal Scaling and Continuous Deployment through AWS CodePipeline
TADI SINDHU, PADALA SRINIVASA REDDY*
Vision-Based Human Behavior Recognition Using a Multiscale Convolutional Neural Network with Parallel Multi-Kernel Feature Fusion
P. Vijaya Lakshmi, K. Lakshamana Reddy*
A Serverless Event-Driven Framework for Scalable Subscription Billing Using Function-as-a-Service and Automated Build Pipelines
Vasamsetti. Komalika, K. Lakshmi Sai Sri*
CreatorPulse: An AI-Driven Multi-Platform Content Creation, Viral Prediction, and Automated Social Media Publishing System
Sanket Dhage, Sanmati Ukhalkar, Vedant Ghare, Krushna Nikam, Prof. R. P. Daund
Predictive Wildlife Collision Prevention using Deep Learning
Deepthi V, Priyanka Kumar Teradale, S M Varsha
Conformal Memory-Augmented Attention Networks for Robust and Adaptive Disease Prediction
V. Pandarinathan, Dr. A. Manikandan
Abstract
Multi-Agent AI Systems for Security Automation
Mohammed Kashif, Abdul Rahman Jibran Syed
DOI: 10.17148/IJARCCE.2026.15601
Abstract: Considering the increasing popularity of MAAI solutions for providing a promising alternative to automating the process of security operations in complex network environments. The paper presents a scalable multi- agent solution that involves autonomous intelligent agents working together to discover threats, recognize security gaps, take decisions, and respond to security attacks. Unlike other solutions, which involve conventional and centralized security frameworks [9–13], our multi-agent-based approach makes use of several agents, providing more flexibility due to the possibility of running tasks in parallel, adapting quickly by learning agents from experience, and making the whole system less vulnerable to advanced cyberattacks. Our multi-agent system uses agents with unique features and works using the communication layer, in which agents exchange information about detected threats and optimize their actions to achieve the best results in always-on protection. The experimental prototype is tested for its efficiency in detecting DDoS attack, phishing or malware intrusions. The research results revealed high levels of efficiency demonstrated through high detection rates, low response latency, and fewer false positives. As far as MAAI systems go, this work proves a considerable potential for automation and scalability.
Keywords: Multi-Agent Systems; Cybersecurity Automation; Artificial Intelligence; Intrusion Detection System (IDS); Distributed Security; Autonomous Agents; Threat Detection; Security Orchestration Network Security Intelligent Systems.
Keywords: Multi-Agent Systems; Cybersecurity Automation; Artificial Intelligence; Intrusion Detection System (IDS); Distributed Security; Autonomous Agents; Threat Detection; Security Orchestration Network Security Intelligent Systems.
Abstract
Integration of SIEM Data Analytics and AI for Proactive Cyber Threat Hunting
Abdul Hasham, Ramesh Venkata Sai lakshmi
DOI: 10.17148/IJARCCE.2026.15602
Abstract: The ever-shifting landscape of cyber threats is always on the move, with APTs, insider attacks, and zero- day attacks at the forefront. Conventional, rule-based SIEMs—until now, the workhorse of many security operations— demonstrate their limitations in the face of such threats. They can wade through massive amounts of security data, but they tend to vomit out lots of false positives and lack the ability to predict what’s around the corner. This research investigates how SIEMs might do more than simply respond to threats: it examines the use of AI and other forms of advanced analytics to predict future intrusions. To improve the signal-to-noise ratio, add context, and accelerate response times, the proposed solution relies on behavioral analytics, anomaly detection, machine learning, and automated threat intelligence enrichment. The research describes an analytics workflow, an AI-based SIEM solution, and a methodology for comparing these AI-infused systems to traditional systems. The findings indicate that as AI continues to evolve, AI-based SIEM solutions enable organizations to concentrate on threats that matter, minimize the need for continuous human interaction, and identify complex or unexpected attacks earlier.
Keywords: Intrusion Detection Systems (IDS), anomaly detection, alert prioritization, cyber threat intelligence, security information and event management (SIEM), predictive threat hunting, behavioral analytics, data analytics, and machine learning.
Keywords: Intrusion Detection Systems (IDS), anomaly detection, alert prioritization, cyber threat intelligence, security information and event management (SIEM), predictive threat hunting, behavioral analytics, data analytics, and machine learning.
Abstract
Design Evaluation and Validation of a Resilient IoT-Based Flood Prediction Framework for Data-Scarce Environments in East Africa
Muwanga Erasto Kosea, Dr Otanga Daniel, Dr. Satwinder Singh Rupra
DOI: 10.17148/IJARCCE.2026.15603
Abstract: Many Internet of Things (IoT)-based flood prediction systems deployed in developing regions fail to deliver reliable early warnings due to unreliable sensors, fragmented datasets, and limited operational resilience. While numerous frameworks have been proposed, few studies systematically evaluate their design limitations or validate enhanced solutions under realistic failure conditions. This paper presents the design evaluation, enhancement, and validation of a resilient IoT-based flood prediction framework. Using Design Science Research principles, existing IoT flood prediction frameworks were evaluated using ITIL-aligned governance criteria to identify deficiencies in data reliability, service continuity, and system governance. An enhanced framework was then designed and validated through simulation using CHIRPS rainfall data and controlled sensor failure scenarios. Simulation results indicate that the enhanced framework maintains prediction accuracy between 82.4% and 91.6% under increasing data-loss conditions and improves alert timeliness compared to baseline approaches. The findings indicate that resilience-oriented, data-centric IoT design significantly improves flood prediction performance in resource-constrained environments.
Keywords: Flood prediction; Internet of Things; design science research; data reliability; sensor unreliability; early warning systems.
Keywords: Flood prediction; Internet of Things; design science research; data reliability; sensor unreliability; early warning systems.
Abstract
QUANTIFYING EXPLANATION DRIFT UNDER MODEL COMPRESSION IN CLINICAL RISK PREDICTION
Stow May Tamara, Maudlyn Ireju Victor-Ikoh
DOI: 10.17148/IJARCCE.2026.15604
Abstract: Predictive models on tabular clinical data increasingly use SHAP and LIME explanations, while compression is routine. This study quantifies how compression affects post hoc explanations on five clinical benchmarks. A multilayer perceptron was trained, then subjected to L1 pruning at four sparsities (30 to 90 percent) and quantization to four bit widths (8 to 2 bits), yielding nine variants. SHAP, LIME, and permutation importance were applied to each variant and compared to the full model. The transparency cost of compression is compression-type-dependent: heavy pruning generally degrades both accuracy and explanations together, so accuracy alone catches the problem; heavy quantization more often preserves accuracy while degrading explanations, so accuracy alone misses the problem. On two of five datasets at 2 bit quantization, AUC retention exceeds 0.92 while SHAP rank correlation against the full model falls below 0.70. Explanation fidelity should be reported alongside accuracy specifically when quantization is used.
Keywords: Explainable Artificial Intelligence, SHAP, LIME, Model Compression, Clinical Risk Prediction
Keywords: Explainable Artificial Intelligence, SHAP, LIME, Model Compression, Clinical Risk Prediction
Abstract
Design and Analysis of a Low-Power High- Speed 32-bit ALU Using Optimized CMOS Architecture
Dr. Kishore M, Dr. Dileep J, Dr. K. Senthil Babu
DOI: 10.17148/IJARCCE.2026.15605
Abstract: With continuous scaling of CMOS technology and increasing demand for energy-efficient computing systems, the design of high-performance and low-power arithmetic circuits has become a critical challenge in Very Large-Scale Integration (VLSI). The Arithmetic Logic Unit (ALU) is one of the most frequently utilized components in microprocessors, digital signal processors, and embedded systems. Its power consumption and delay significantly influence overall system performance. This paper presents the design, implementation, and analysis of a 32-bit low- power high-speed ALU using an optimized Carry Lookahead Adder (CLA) architecture in 45nm CMOS technology. The proposed design incorporates transistor sizing optimization, clock gating, and multi-threshold CMOS techniques to minimize dynamic and leakage power. Performance evaluation is carried out in terms of propagation delay, average power consumption, and Power-Delay Product (PDP). Comparative analysis with a conventional Ripple Carry Adder (RCA)-based ALU demonstrates significant improvements in speed and energy efficiency. The proposed architecture is suitable for next-generation low-power computing applications.
Keywords: VLSI, CMOS, Low-Power Design, ALU, Carry Lookahead Adder, Power-Delay Product, 45nm Technology, Clock Gating.
Keywords: VLSI, CMOS, Low-Power Design, ALU, Carry Lookahead Adder, Power-Delay Product, 45nm Technology, Clock Gating.
Abstract
Directing the Algorithmic Edge of Inclusive Pedagogy: A Comprehensive Review of AI- Driven Assistive Technologies in Education
Dr. Anju Kaushik, Dr. Anil Kaushik
DOI: 10.17148/IJARCCE.2026.15606
Abstract: This research paper explores the transformative integration of Artificial Intelligence (AI) and human-computer interaction within inclusive digital education, focusing extensively on higher education institutions (HEIs). By synthesizing contemporary literature, structured inventories of EdTech tools and applied case studies, we analyse how AI-powered screen readers, voice assistants, speech recognition software and Natural Language Processing (NLP) interfaces pull apart traditional learning barriers for students with visual, physical and cognitive disabilities.
The findings demonstrate that AI fundamentally shifts assistive frameworks from rigid, linear and high-dependency human-mediated systems to dynamic, contextual and highly autonomous multi-modal learning environments. However, this pedagogical revolution introduces complex infrastructure requirements, acute data privacy issues, algorithmic drop- off and socio-cultural vulnerabilities—including Generative AI Addiction Syndrome (GAID) and technostress.
Keywords: Artificial Intelligence, Higher Education, Natural Language Processing, EdTech
The findings demonstrate that AI fundamentally shifts assistive frameworks from rigid, linear and high-dependency human-mediated systems to dynamic, contextual and highly autonomous multi-modal learning environments. However, this pedagogical revolution introduces complex infrastructure requirements, acute data privacy issues, algorithmic drop- off and socio-cultural vulnerabilities—including Generative AI Addiction Syndrome (GAID) and technostress.
Keywords: Artificial Intelligence, Higher Education, Natural Language Processing, EdTech
Abstract
Smart Security System in Train Using Arduino, Ultrasonic Sensor and Camera
HumeraBano Asadullah Baig
DOI: 10.17148/IJARCCE.2026.15607
Abstract: This research paper presents an innovative approach to railway safety through the development of an Arduino Nano-based train accident prevention system. The system utilizes ultrasonic sensors for real-time obstacle detection on railway tracks, Camera Module for identifying the object, coupled with immediate alert mechanisms including audible buzzers and visual LED indicators. A distinctive feature of this implementation is the integration with Processing IDE software, which provides a graphical interface displaying real-time obstacle detection data for train operators. The prototype demonstrates effectiveness within a 1-meter detection range, offering a cost-effective solution (â‚ą3500 approx) compared to conventional railway safety systems. The paper comprehensively covers the system design, implementation challenges, test results, and proposes future enhancements including IoT integration and machine learning applications for improved reliability under various environmental conditions.
Keywords: Railway Safety, object detection, Camera Module, Arduino Nano, Ultrasonic Sensor, Real-time Monitoring, Obstacle Detection, Embedded Systems
Keywords: Railway Safety, object detection, Camera Module, Arduino Nano, Ultrasonic Sensor, Real-time Monitoring, Obstacle Detection, Embedded Systems
Abstract
Agentic AI for Data Analysis: A RAG-Enhanced Local LLM Framework with Adaptive Visualization
Harsh Mahesh Tatmute, Shivraj Sunil Shinde, Karan Adinath Nemane, Sandesh Sunil Pujari, Rahul Sudhir Ranjane, V. G. Khetade
DOI: 10.17148/IJARCCE.2026.15608
Abstract: This paper presents an Agentic AI Data Analyst model that uses the RAG technique in combination with local large language models deployed using Ollama for privacy protection and low costs associated with intelligent data analysis. This system is no longer dependent on cloud APIs but is capable of providing high quality analysis due to improved incorporation of domain knowledge into the process. The model is coordinated via multi-step reasoning techniques facilitated by LangChain and supported by FAISS vector storage systems for efficient domain knowledge searching. A FastAPI application server runs in the background, providing connectivity to a React front end interface that allows dual modes of data visualization.
Regarding the performance analysis, five open-source language models were tested – LLaMA 3.2 (3B), LLaMA 3.1 (8B), Mistral 7B, Gemma 3 4B, and Gemma 3 12B. In addition, the assessment was conducted in terms of five core criteria: quality of responses, accuracy of the charts generated, depth of insights, rate of hallucinations, and time required for inference. Each of these models was evaluated with and without RAG implementation to determine the exact effects of retrieval augmentation. According to the results of the experiments, RAG offers a performance increase from 25% to 39% in addition to reducing the number of hallucinations by 62-75%. Most notably, RLaMA with 3B parameters and RAG always outperforms Gemma with 12B parameters but without RAG. These results confirm the hypothesis that retrieval of structured domain knowledge is more effective than scaling the number of model parameters. This research shows that lightweight agentic systems locally installed on a computer could serve as a viable alternative to commercial AI solutions.
Regarding the performance analysis, five open-source language models were tested – LLaMA 3.2 (3B), LLaMA 3.1 (8B), Mistral 7B, Gemma 3 4B, and Gemma 3 12B. In addition, the assessment was conducted in terms of five core criteria: quality of responses, accuracy of the charts generated, depth of insights, rate of hallucinations, and time required for inference. Each of these models was evaluated with and without RAG implementation to determine the exact effects of retrieval augmentation. According to the results of the experiments, RAG offers a performance increase from 25% to 39% in addition to reducing the number of hallucinations by 62-75%. Most notably, RLaMA with 3B parameters and RAG always outperforms Gemma with 12B parameters but without RAG. These results confirm the hypothesis that retrieval of structured domain knowledge is more effective than scaling the number of model parameters. This research shows that lightweight agentic systems locally installed on a computer could serve as a viable alternative to commercial AI solutions.
Abstract
CivicAi Complaint Management System
Sammed Mangave, Vaibhav Rajput, Ayan Mujawar, Ganesh Hipparkar, Prof. Dr. V.V.Kheradkar
DOI: 10.17148/IJARCCE.2026.15609
Abstract: Rapid urbanization has led to an increase in civic infrastructure issues such as potholes, garbage accumulation, waterlogging, damaged streetlights, and drainage problems. Traditional complaint management systems often face challenges such as delayed reporting, manual categorization, inefficient complaint tracking, and lack of transparency in the resolution process. This paper proposes CivicAI, an AI-Powered Civic Complaint Management System designed to improve the efficiency of reporting, classifying, tracking, and resolving civic complaints. The system enables citizens to submit complaints by uploading images along with location information through a web-based platform. Artificial Intelligence and Computer Vision techniques are utilized to analyze complaint images, identify the type of civic issue, and determine its severity level. The generated complaint reports are automatically forwarded to the appropriate municipal authorities for further action. The system also provides real-time complaint tracking, status notifications, resolution updates, and analytical dashboards for effective complaint management. By integrating Artificial Intelligence, Computer Vision, Geolocation Services, and Web Technologies, CivicAI enhances transparency, improves response time, and strengthens communication between citizens and municipal authorities, thereby contributing to efficient urban governance and smart city development.
Keywords: Artificial Intelligence, Computer Vision, Civic Complaint Management, Smart City, Geolocation Services, Urban Governance, Image Classification.
Keywords: Artificial Intelligence, Computer Vision, Civic Complaint Management, Smart City, Geolocation Services, Urban Governance, Image Classification.
Abstract
AI-Driven Malicious URL Detection Using Graph Neural Networks
Shraddha Pailwan, Avishkar Patil, Pranav Patil, Yash Shinde, Gayatree Jadhav, Prof. A. B. Majgave
DOI: 10.17148/IJARCCE.2026.15610
Abstract: The exponential proliferation of internet-based services has been accompanied by a parallel surge in cyber threats, particularly the distribution of malicious Uniform Resource Locators (URLs). Phishing attacks, financial fraud, and data exfiltration perpetrated through deceptive URLs cause billions of dollars in losses annually. Conventional detection mechanisms—predicated on static blacklists and rule-based filters—exhibit inherent limitations in identifying novel, obfuscated, or zero-day threats. This paper presents an AI-driven URL classification framework that harnesses the representational power of Graph Neural Networks (GNNs) to model inter-URL relational dependencies alongside individual lexical and structural URL features. In the proposed architecture, URLs are encoded as graph nodes and semantic or behavioral relationships between them are captured as weighted edges. A Graph Convolutional Network (GCN) is subsequently trained on a composite dataset aggregated from PhishTank, ISCX-URL2016, and Kaggle malicious URL repositories. Experimental evaluation on a balanced 80/20 train-test split yields an accuracy of 96.8%, precision of 97.1%, recall of 96.4%, and F1-score of 96.7%, outperforming baseline Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP) classifiers by margins of 4–9 percentage points. The system exposes a Flask-based REST API and lightweight web interface, enabling real-time single-URL and batch classification. Results corroborate the hypothesis that relational graph-based modelling substantially improves detection efficacy and generalization, with particular gains on obfuscated and previously unseen URL patterns.
Keywords: URL detection; graph neural network; graph convolutional network; phishing detection; cybersecurity; malicious URL classification; deep learning.
Keywords: URL detection; graph neural network; graph convolutional network; phishing detection; cybersecurity; malicious URL classification; deep learning.
Abstract
“An Explainable Hybrid LSTM–Random Forest Framework for Accurate Pulmonary Disease Detection and Classification”
Ajay Pal Singh, Ankita Nigam
DOI: 10.17148/IJARCCE.2026.15611
Abstract: Pulmonary diseases such as Chronic Obstructive Pulmonary Disease (COPD), pneumonia, and lung cancer continue to be leading causes of global morbidity and mortality. Timely detection and accurate diagnosis are essential for effective treatment and improved clinical outcomes. Traditional diagnostic techniques—relying heavily on chest X- rays and CT scans—are often constrained by manual interpretation, which is time-consuming and susceptible to human error. This paper proposes a novel hybrid diagnostic framework integrating Long Short-Term Memory (LSTM) networks with Random Forest (RF) ensemble learning to improve the detection and classification of pulmonary conditions. LSTM networks are employed to capture temporal dependencies in sequential clinical data, while the RF model enhances classification robustness and accuracy. The proposed approach includes comprehensive preprocessing of medical imaging and structured clinical data, feature extraction, and model training on an extensive annotated dataset. Evaluation metrics such as accuracy, sensitivity, specificity, and F1-score reveal that the LSTM-RF hybrid outperforms conventional machine learning models. Furthermore, Explainable AI (XAI) techniques are incorporated to ensure model interpretability, promoting transparency in clinical decision-making. The study also highlights real-world deployment challenges, including data privacy, algorithmic bias, and regulatory compliance. The key contributions of this research lie in the integration of deep learning with ensemble techniques and the emphasis on explainability, making it a viable solution for real-time pulmonary disease diagnosis in clinical settings.
Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Pulmonary Disease Detection, Hybrid LSTM, Random Forest, Explainable AI, XAI, COPD, CT scan.
Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Pulmonary Disease Detection, Hybrid LSTM, Random Forest, Explainable AI, XAI, COPD, CT scan.
Abstract
CodEzy: An AI-Powered Competitive Coding and Personalized Learning Platform
Kunal Sunil Magdum, Akshay Sachin Mulay, Subahan Riyaj Mulla, Shreyansh Nitin Patil, Prasad Sunil Sutar, Prof. (Dr.) V.V.Kheradkar*
DOI: 10.17148/IJARCCE.2026.15612
Abstract: Programming education has evolved significantly with the emergence of online learning platforms and competitive coding environments. However, most existing systems continue to rely on binary evaluation techniques that assess only the correctness of program outputs. Such approaches fail to provide meaningful insights into code quality, algorithmic efficiency, readability, and adherence to software engineering principles. This limitation often prevents learners from understanding their mistakes and improving their programming skills effectively. This paper presents CodEzy, an AI-powered competitive coding and personalized learning platform designed to enhance programming education through adaptive learning, intelligent code evaluation, gamified engagement, and real-time coding competitions. The proposed system integrates personalized tutorials, coding challenges, AI-generated feedback, performance analytics, and skill-based coding duels within a unified ecosystem. Unlike conventional platforms, CodEzy evaluates code beyond correctness by analyzing efficiency, structure, style, and maintainability. The platform employs secure Docker-based sandbox execution, cloud-ready architecture, Redis-powered caching, and external Large Language Models (LLMs) for semantic code analysis and educational content generation. Experimental analysis indicates that the platform can provide detailed AI feedback within acceptable response times while maintaining low- latency interactions for competitive coding environments. The proposed system supports educational institutions, coding bootcamps, and self-learners through adaptive learning, intelligent feedback, and integrated coding practice.
Keywords: Artificial Intelligence, Adaptive Learning, Competitive Programming, Code Evaluation, Gamification, Educational Technology, Learning Analytics, Large Language Models (LLM).
Keywords: Artificial Intelligence, Adaptive Learning, Competitive Programming, Code Evaluation, Gamification, Educational Technology, Learning Analytics, Large Language Models (LLM).
Abstract
Cyberbullying Detection in Social Media Contents using Machine Learning Techniques
Amey Gujar, Akhilesh Ghorpade, Indrajeet Chougule, Vedant Gawas, Paras Gurjar, Himanshu Baboria, Prof. Vinod Khetade
DOI: 10.17148/IJARCCE.2026.15613
Abstract: Cyberbullying is a serious problem in the Information Age. It spoils people's sentiments and wellbeing with ugly messages and cruel words. There's so much content on social media at all times, that it would be hard to find this stuff manually as it would take you a long time and you can't expand that easily. Therefore, the researchers tried to come up with a great solution - a Machine Learning framework that automatically detects cyberbullying. It employs NLP methods to clean up the text, such as normalizing words, tokenizing text and interpreting emojis. Plus, it can handle English, Hindi, Marathi and Hinglish texts as well!
Once the text is sorted, the system converts this information to numbers, known as TF-IDF. Then, it employs a Linear Support Vector Machine for classification, using sklearn’s svm.SVC(linear) kernel. There were several different SVM setups that were considered during development, but the linear SVM proved to have the greatest accuracy and computational requirement.
Our experiments demonstrate that the TF-IDF and Linear SVM model is quite effective in the classification tasks with a lesser amount of resources and is efficient. We ran it on a sample of 31,183 text messages from social media, with 23,820 of them classified as bullying and 7,363 as safe. The one thing that makes our system stand out is its multiple language processing and ability to recognize emojis. This allows it to handle the numerous modes of communication on social media. Moreover, we used it as a Flask based API, so it can be integrated with Web apps easily. Ergo, it is a convenient instrument for in real life content moderation and to improve the safety online.
Keywords: Cyberbullying Detection, Machine Learning (ML), Natural Language Processing (NLP), Text Classification, Support Vector Machine (SVM), TF-IDF, Sentiment Analysis, Multilingual Text Processing, Social Media Analysis, Flask API, Emoji Processing, Online Safety.
Once the text is sorted, the system converts this information to numbers, known as TF-IDF. Then, it employs a Linear Support Vector Machine for classification, using sklearn’s svm.SVC(linear) kernel. There were several different SVM setups that were considered during development, but the linear SVM proved to have the greatest accuracy and computational requirement.
Our experiments demonstrate that the TF-IDF and Linear SVM model is quite effective in the classification tasks with a lesser amount of resources and is efficient. We ran it on a sample of 31,183 text messages from social media, with 23,820 of them classified as bullying and 7,363 as safe. The one thing that makes our system stand out is its multiple language processing and ability to recognize emojis. This allows it to handle the numerous modes of communication on social media. Moreover, we used it as a Flask based API, so it can be integrated with Web apps easily. Ergo, it is a convenient instrument for in real life content moderation and to improve the safety online.
Keywords: Cyberbullying Detection, Machine Learning (ML), Natural Language Processing (NLP), Text Classification, Support Vector Machine (SVM), TF-IDF, Sentiment Analysis, Multilingual Text Processing, Social Media Analysis, Flask API, Emoji Processing, Online Safety.
Abstract
Seasonal Crop Price Prediction using LSTM
Amol Sawant, Dr. Pravin Patil
DOI: 10.17148/IJARCCE.2026.15614
Abstract: Predicting agricultural crop prices is a critical task that helps farmers and agricultural stakeholders make informed decisions regarding cultivation, storage, and marketing strategies. Crop prices are influenced by various factors, including seasonal changes, weather conditions, market demand, and supply fluctuations. Due to the complex and dynamic nature of these factors, accurate forecasting remains a significant challenge. This project proposes a Seasonal Crop Price Prediction system using Machine Learning and Long Short-Term Memory (LSTM) networks.
The system analyzes historical crop price data along with environmental and market-related parameters such as temperature, rainfall, humidity, production levels, and previous price trends. LSTM, a powerful recurrent neural network architecture, is capable of capturing temporal patterns and long-term dependencies within time-series data, enabling more precise price predictions. The developed model assists farmers, traders, and policymakers by providing early insights into future market prices, helping reduce risks and improve planning. By leveraging advanced deep learning techniques, the proposed solution contributes to the development of data-driven and sustainable agricultural practices
Keywords: Seasonal Crop Price Prediction, LSTM, Machine Learning, Deep Learning, Time-Series Forecasting, Smart Agriculture, Agricultural Data Analysis.
The system analyzes historical crop price data along with environmental and market-related parameters such as temperature, rainfall, humidity, production levels, and previous price trends. LSTM, a powerful recurrent neural network architecture, is capable of capturing temporal patterns and long-term dependencies within time-series data, enabling more precise price predictions. The developed model assists farmers, traders, and policymakers by providing early insights into future market prices, helping reduce risks and improve planning. By leveraging advanced deep learning techniques, the proposed solution contributes to the development of data-driven and sustainable agricultural practices
Keywords: Seasonal Crop Price Prediction, LSTM, Machine Learning, Deep Learning, Time-Series Forecasting, Smart Agriculture, Agricultural Data Analysis.
Abstract
Evaluating Usability Techniques in Modern Web Applications
Simran, Dr Pooja Rana
DOI: 10.17148/IJARCCE.2026.15615
Abstract: Globally, the use of web-based applications is expanding quickly. Users' requirements for accessibility and engagement have consequently evolved dramatically. Conventional webpages are no longer sufficient. Users have a new experience with modern web applications. Nowadays, developers and the industry place a high value on usability. Every day, more people utilize web apps, and they favor user-friendly and comprehensible platforms. Nonetheless, many programs remain to have usability problems. This paper also examines the advantages and disadvantages of usability evaluation techniques. Currently, IT is becoming an integral part of daily life, with people seeking fast and simple solutions. In response to user needs, various usability techniques have been created. Several factors influence user experience with these techniques. This study aims to examine user behaviour and usability methods using a straightforward survey cross-sectional method, including a sample of users interacting with web applications.
Keywords: usability, web applications, user experience, evaluation, testing.
Keywords: usability, web applications, user experience, evaluation, testing.
Abstract
Blockchain Based Certificate Verification System
Shivaprasad M S, Usha M
DOI: 10.17148/IJARCCE.2026.15616
Abstract: Validating academic and professional credentials efficiently remains a critical security and administrative challenge for global institutions. Traditional verification methods rely heavily on manual verification workflows or centralized databases that lack real-time public access, scale poorly, and are vulnerable to singular points of failure, unauthorized tampering, and permanent data loss. The absence of a unified, low-latency, and tamper-proof verification ecosystem leaves corporate and educational sectors exposed to credential fraud and escalating administrative evaluation overhead.
To address these vulnerabilities, this paper introduces the proposed system, an open-source, decentralized platform that revolutionizes credential management by mapping certificates to unique Non-Fungible Tokens (NFTs) on the high- throughput Sui blockchain while storing physical document assets across the distributed Walrus storage protocol. This project implements an asynchronous decoupled processing pipeline where structural metadata is managed through Move smart contracts, and cryptographic file identifiers (Blob IDs) are stored over decentralized storage arrays. This architecture enables permissionless, zero-account public verification with sub-second latency, alongside transparent, on- chain revocation mechanisms that ensure a permanent audit trail. Empirical testing demonstrates optimal transaction efficiency, highly scalable storage performance via dynamic epoch handling, and absolute resistance to linguistic or historical tampering.
Keywords: Sui Blockchain, Move Smart Contracts, Non-Fungible Tokens, Walrus Protocol, Decentralized Storage, Cryptographic Verification.
To address these vulnerabilities, this paper introduces the proposed system, an open-source, decentralized platform that revolutionizes credential management by mapping certificates to unique Non-Fungible Tokens (NFTs) on the high- throughput Sui blockchain while storing physical document assets across the distributed Walrus storage protocol. This project implements an asynchronous decoupled processing pipeline where structural metadata is managed through Move smart contracts, and cryptographic file identifiers (Blob IDs) are stored over decentralized storage arrays. This architecture enables permissionless, zero-account public verification with sub-second latency, alongside transparent, on- chain revocation mechanisms that ensure a permanent audit trail. Empirical testing demonstrates optimal transaction efficiency, highly scalable storage performance via dynamic epoch handling, and absolute resistance to linguistic or historical tampering.
Keywords: Sui Blockchain, Move Smart Contracts, Non-Fungible Tokens, Walrus Protocol, Decentralized Storage, Cryptographic Verification.
Abstract
Impact Of Deep Learning Techniques on Super Resolutions
Deepali Karajgikar, Abhishek Magar
DOI: 10.17148/IJARCCE.2026.15617
Abstract: Image Super-Resolution (SR) is an important research area in image processing that focuses on reconstructing high-resolution (HR) images from low-resolution (LR) images. The objective of super-resolution is to recover lost details, improve image quality, and generate visually enhanced images. Traditional interpolation methods such as nearest- neighbor, bilinear, and bicubic interpolation often fail to preserve fine details, edges, and textures, resulting in blurred outputs.
With the advancement of Deep Learning, especially Convolutional Neural Networks (CNNs), significant improvements have been achieved in image reconstruction tasks. This research presents a study on the impact of deep learning techniques in image super-resolution, focusing on CNN-based architectures including Super-Resolution Convolutional Neural Network (SRCNN), Fast Super-Resolution CNN (FSRCNN), Very Deep Super-Resolution Network (VDSR), and Enhanced Deep Residual Network (EDSR).
The study analyzes the working principles, advantages, and limitations of these models. Experimental implementation demonstrates that deep learning-based methods can effectively learn complex mappings between low-resolution and high-resolution images, producing sharper edges, improved textures, and better visual quality. However, advanced architectures require higher computational resources and larger datasets for training.
Keywords: Image Super Resolution, Deep Learning, Convolutional Neural Network, SRCNN, FSRCNN, VDSR, EDSR, Image Processing
With the advancement of Deep Learning, especially Convolutional Neural Networks (CNNs), significant improvements have been achieved in image reconstruction tasks. This research presents a study on the impact of deep learning techniques in image super-resolution, focusing on CNN-based architectures including Super-Resolution Convolutional Neural Network (SRCNN), Fast Super-Resolution CNN (FSRCNN), Very Deep Super-Resolution Network (VDSR), and Enhanced Deep Residual Network (EDSR).
The study analyzes the working principles, advantages, and limitations of these models. Experimental implementation demonstrates that deep learning-based methods can effectively learn complex mappings between low-resolution and high-resolution images, producing sharper edges, improved textures, and better visual quality. However, advanced architectures require higher computational resources and larger datasets for training.
Keywords: Image Super Resolution, Deep Learning, Convolutional Neural Network, SRCNN, FSRCNN, VDSR, EDSR, Image Processing
Abstract
A Graph Retrieval-Augmented Generation Framework for AI-Powered Supplier Discovery
Dr.B.Vijayalakshmi, Dr.M.Swarna Sudha, Dr.K.Vijayalakshmi
DOI: 10.17148/IJARCCE.2026.15618
Abstract: As modern supply chains demand higher resilience, agility, and visibility, the task of supplier discovery becomes increasingly critical. We present a novel AI-powered methodology that combines Graph Neural Networks (GNNs), Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to enhance supplier search and reasoning. A structured Supplier Capability Knowledge Graph (SCKG) is built by extracting domain-specific triplets from unstructured manufacturing data using fine-tuned LLMs and is enriched through semantic normalization via ontology and manufacturing thesaurus. A GNN-based retrieval system identifies relevant subgraphs by performing dense reasoning over the SCKG. These subgraphs are verbalized into natural language using shortest-path reasoning chains and fed into an LLM for generative explanation. To improve retrieval precision, a hybrid entity normalization technique leveraging Jaccard similarity and vector-based retrieval is applied. This integrated GNN-RAG system significantly outperforms traditional and zero-shot LLM-based supplier search approaches in both precision and recall on real-world datasets. Our results demonstrate the system's ability to perform robust, real-time supplier discovery while enabling explainable and accurate responses.
Keywords: Supplier Discovery, Knowledge Graphs, GNN-RAG, Large Language Models, Semantic NormalizationI
Keywords: Supplier Discovery, Knowledge Graphs, GNN-RAG, Large Language Models, Semantic NormalizationI
Abstract
IoT Based Weather Station Sakshi Tulshidas Sonawane, Sarvesh Dinesh Saroware, Shreeyash Santosh Dendage,
Prof. N.R.Sawale, Dr.R.K.Moje, Dr.D.O.Patil
DOI: 10.17148/IJARCCE.2026.15619
Abstract: The advancement of Internet of Things (IoT) technology has enabled the development of smart environmental monitoring systems with real-time data accessibility. This paper presents the design and implementation of an IoT Based Smart Weather Monitoring System using ESP32 microcontroller and cloud analytics. The proposed system monitors environmental parameters such as temperature, humidity, and rainfall using DHT11 and raindrop sensors. The collected data is displayed locally on an LCD display and simultaneously transmitted to the ThingSpeak cloud platform through Wi-Fi connectivity for remote monitoring and analysis. The system offers low cost, low power consumption, and efficient real-time weather monitoring. The developed prototype can be effectively used in smart agriculture, environmental monitoring, disaster management, and smart city applications. The integration of cloud technology with embedded systems improves accessibility, reliability, and data visualization compared to traditional weather monitoring systems.
Keywords: IoT, ESP32, Weather Monitoring, DHT11 Sensor, ThingSpeak, Cloud Computing, Smart Environment
Keywords: IoT, ESP32, Weather Monitoring, DHT11 Sensor, ThingSpeak, Cloud Computing, Smart Environment
Abstract
Enhancing Urban Safety Through Intelligent Street Lighting: A Review of AI-Driven Detection and Monitoring Approaches
Komal Yadav, Dr. Anita Pal
DOI: 10.17148/IJARCCE.2026.15620
Abstract: With the rapid urbanization and growing demand for public safety, intelligent street lighting systems have become an essential part of smart city infrastructure. In today's street lighting systems, beyond the traditional light provision function, Internet of Things (IoT) and edge computing, artificial intelligence (AI), and computer vision technologies have been integrated to enable real-time monitoring and decision-making. This review paper offers a thorough overview of intelligent street lighting systems and the various detection and monitoring strategies that utilize artificial intelligence (AI) to improve street lighting safety. The study covers the latest developments in IoT-powered sensing, Edge AI, video analytics, deep learning-based object detection, intelligent surveillance, hazard detection, traffic monitoring and emergency response systems. In addition, the review emphasizes that cutting-edge technologies like graph-based networks, federated learning, and explainable AI solutions are crucial for enhancing situational awareness and forecasting urban safety. Important issues such as privacy, cybersecurity, scalability, interoperability, and the limited size of the datasets are examined in detail. The paper also highlights new research opportunities related to lightweight edge intelligence, multimodal sensing, privacy-preserving AI, and integrated smart city platforms. This review, which brings together recent advances from various fields, offers significant insights into the potential of AI-driven intelligent street lighting systems to enhance and transform urban environments into safer, smarter, and more sustainable spaces.
Keywords: Intelligent Street Lighting, Smart Cities, Artificial Intelligence, Internet of Things (IoT), Edge Computing, Computer Vision, Deep Learning,
Keywords: Intelligent Street Lighting, Smart Cities, Artificial Intelligence, Internet of Things (IoT), Edge Computing, Computer Vision, Deep Learning,
Abstract
Generalising Across Different Crop Diseases With InceptionCNN
Shubham Verma, Anita pal
DOI: 10.17148/IJARCCE.2026.15621
Abstract: This Plant diseases have a significant impact on the agriculture of the world and lead to decreased crop productivity, food quality, and economic productivity as a result. Following is an automated pathogen detection system for plant diseases using an Inception-type convolutional neural network (InceptionCNN) in PyTorch. The model was trained on an original Kaggle-based New Plant Dataset containing 38 classes of healthy and diseased plant leaf images. We performed the appropriate preprocessing (duplicate deletion, stratified data splitting, normalization, and augmentations) to improve generalization and robustness of our models. We offer a multi-scale architecture of multi- scale convolutional branches utilizing 1Ă—1, 3Ă—3, and 5Ă—5 filters among others to select a collection of lesion patterns and disease structures. Adam optimization, scheduling of the learning rate, dropout regularization, and class imbalance reduction were utilized as the training method. Experimental results demonstrate optimal performance with 99.24% validation accuracy and minimal validation loss. These evaluation metrics involved measured performance accuracy, recall, F1 score, confusion matrix analysis, and Grad-CAM visualizations which helped enable consistent classification of the disease under the categories. Our proposed framework represents a scalable, accurate and reproducible approach to the intelligent plant disease detection and lays the groundwork to future AI-related precision agriculture applications.
Keywords: Plant disease detection; Convolutional Neural Network; Deep Learning; Precision Agriculture; Plant Protection
Keywords: Plant disease detection; Convolutional Neural Network; Deep Learning; Precision Agriculture; Plant Protection
Abstract
CarbonCred AI: An Artificial Intelligence-Driven Digital MRV Framework for Carbon Credit Analysis and Valuation
Addhwaith S Ajith, Aaron John Joy, Vaishnav Biju, Ameesha J S, Shruthi M Pillai
DOI: 10.17148/IJARCCE.2026.15622
Abstract: The rapid expansion of global carbon markets has created an urgent need for transparent, scalable, and cost- effective Monitoring, Reporting, and Verification (MRV) systems for forest carbon credit projects. Traditional MRV approaches rely on manual field surveys that are labor-intensive, time-consuming, and difficult to scale across large geographic areas. This paper presents CarbonCred AI, an Artificial Intelligence-driven Digital MRV framework that integrates Sentinel-2 satellite remote sensing with machine learning and reinforcement learning to automate the carbon credit lifecycle. The proposed system employs spectral vegetation analysis using the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) for biomass estimation, a Random Forest Regression model for above-ground biomass prediction, and a Q-learning reinforcement learning agent for financial optimization of carbon credit trading. The framework provides a unified pipeline integrating geospatial analysis, AI-based carbon estimation, and financial valuation, addressing critical gaps in existing carbon monitoring systems.
Keywords: Carbon Credit Markets, Digital MRV, Random Forest Regression, Reinforcement Learning, Remote Sensing, NDVI, Satellite Imagery, Biomass Estimation, Carbon Stock, Financial Valuation
Keywords: Carbon Credit Markets, Digital MRV, Random Forest Regression, Reinforcement Learning, Remote Sensing, NDVI, Satellite Imagery, Biomass Estimation, Carbon Stock, Financial Valuation
Abstract
ADAPTIVE, FEEDBACK-DRIVEN TASK SCHEDULING AND LOAD BALANCING: A SUPERIOR APPROACH FOR MODERN CLOUD COMPUTING SYSTEMS
Deepak Joshi, Kumar Bibhuti Bhushan Singh
DOI: 10.17148/IJARCCE.2026.15623
Abstract: Cloud computing has emerged as the foundation of modern IT services, providing scalable, on-demand resources via virtualization. Effective resource management in these settings needs not only skilled load balancing but also smart job scheduling algorithms to guarantee fairness, reduce delays, and improve utilization.
Static algorithms that are commonly used for the process of task scheduling and load balancing are simple and easy to implement, but they cannot adapt to any kind of changes in conditions. As a result, there is an inefficient use of valuable resources, increased response time, and reduced throughput in a large-scale cloud setting. This research aims to compare and test traditional algorithms against bio-inspired algorithms. Cloud Sim is used to simulate an extensive cloud environment, including five VMs and fifty cloudlets. Four algorithms, such as Round Robin, Weighted Round Robin, Honeybee Foraging, and Ant Colony Optimization, were implemented under similar conditions. The performance was measured by calculating the average response time, throughput, and utilization.
It was found that bio-inspired algorithms greatly surpassed traditional algorithms. Ant Colony Optimization was able to achieve the minimum response time, maximum throughput, and optimal utilization. Honeybee algorithm displayed remarkable adaptation ability, whereas classic algorithms appeared to be insufficient for changing circumstances. Therefore, adaptive algorithms are superior to static algorithms in contemporary cloud computing settings.
Keywords: Cloud Computing, Static Algorithm, Dynamic Algorithm, Load Balancing, Task Scheduling, Virtual Machine.
Static algorithms that are commonly used for the process of task scheduling and load balancing are simple and easy to implement, but they cannot adapt to any kind of changes in conditions. As a result, there is an inefficient use of valuable resources, increased response time, and reduced throughput in a large-scale cloud setting. This research aims to compare and test traditional algorithms against bio-inspired algorithms. Cloud Sim is used to simulate an extensive cloud environment, including five VMs and fifty cloudlets. Four algorithms, such as Round Robin, Weighted Round Robin, Honeybee Foraging, and Ant Colony Optimization, were implemented under similar conditions. The performance was measured by calculating the average response time, throughput, and utilization.
It was found that bio-inspired algorithms greatly surpassed traditional algorithms. Ant Colony Optimization was able to achieve the minimum response time, maximum throughput, and optimal utilization. Honeybee algorithm displayed remarkable adaptation ability, whereas classic algorithms appeared to be insufficient for changing circumstances. Therefore, adaptive algorithms are superior to static algorithms in contemporary cloud computing settings.
Keywords: Cloud Computing, Static Algorithm, Dynamic Algorithm, Load Balancing, Task Scheduling, Virtual Machine.
Abstract
A Detailed Survey on the Association Rule Extraction Method in Data Mining
Mr B.B.L.V. Prasad, Dr. Eedi Hemalatha
DOI: 10.17148/IJARCCE.2026.15624
Abstract: Information extraction has emerged as a significant field of study for deriving valuable information from large and complex data collections. Among the different methods, association pattern discovery plays a significant role in identifying implicit connections and common trends in multivariable data. Multidimensional information analysis combines data from numerous feature aspects and diverse sources, making the discovery process more effective and insightful for practical applications, including the medical field, business analysis, learning financial services, and online commerce.
Here, we report a comprehensive summary of the analysis of synthetic networks developed for extracting the multivariable correlation pattern training from 1993 to the present. Traditional & State-of-the-Art Methodologies: it covers methods such as the Apriori algorithm, Frequent Pattern Algorithm, Vertical Mining Algorithm, and comparatively aggregated, decentralised, and big-data-driven extractive techniques. This article compares various techniques in terms of efficiency, expandability, memory requirements, implementation time and accuracy.
Furthermore, the review identifies significant study issues, including allegedly elevated calculation difficulty, challenges in managing adaptive multivariable data collections, scalability issues, and excessive storage usage. The article additionally emphasises the latest developments, including cloud-based processing, machine intelligence, advanced machine learning, and instant information analysis within multivariable extraction frameworks. Ultimately, upcoming study components are explained to enhance the effectiveness and flexibility of the multidimensional correlation pattern extraction method in contemporary large-scale settings.
Keywords: Association Rule Mining, Multivariable Data Extraction, Multi-Dimensional Data, Fuzzy Association Rules, Real-Time Data Mining, High-Dimensional Data.
Here, we report a comprehensive summary of the analysis of synthetic networks developed for extracting the multivariable correlation pattern training from 1993 to the present. Traditional & State-of-the-Art Methodologies: it covers methods such as the Apriori algorithm, Frequent Pattern Algorithm, Vertical Mining Algorithm, and comparatively aggregated, decentralised, and big-data-driven extractive techniques. This article compares various techniques in terms of efficiency, expandability, memory requirements, implementation time and accuracy.
Furthermore, the review identifies significant study issues, including allegedly elevated calculation difficulty, challenges in managing adaptive multivariable data collections, scalability issues, and excessive storage usage. The article additionally emphasises the latest developments, including cloud-based processing, machine intelligence, advanced machine learning, and instant information analysis within multivariable extraction frameworks. Ultimately, upcoming study components are explained to enhance the effectiveness and flexibility of the multidimensional correlation pattern extraction method in contemporary large-scale settings.
Keywords: Association Rule Mining, Multivariable Data Extraction, Multi-Dimensional Data, Fuzzy Association Rules, Real-Time Data Mining, High-Dimensional Data.
Abstract
Toward Intelligent Rural Mobility: A Systematic Review of AI-Enabled Transportation Systems and Research Gaps
Deepanshu Sharma, Ms. Jamna
DOI: 10.17148/IJARCCE.2026.15625
Abstract: Rural transportation systems continue to face persistent challenges associated with low population density, inadequate infrastructure, limited public transit availability, and poor digital connectivity. These limitations negatively affect accessibility, healthcare access, economic participation, and social inclusion in remote communities. Recent advancements in Artificial Intelligence (AI), Intelligent Transportation Systems (ITS), Internet of Things (IoT), and data-driven mobility services have enabled the development of intelligent and adaptive transportation solutions for low- density rural networks. This study presents a systematic review of research published between 2020 and 2026 on AI- enabled rural transportation and smart mobility systems. A PRISMA-based methodology was adopted to identify, screen, and analyze relevant studies collected from major academic databases, including IEEE Xplore, Scopus, SpringerLink, ACM Digital Library, and ScienceDirect.
Keywords: Rural mobility, Artificial Intelligence, Intelligent Transportation Systems (ITS), Smart mobility, Demand- Responsive Transit, Mobility-as-a-Service (MaaS), Transportation optimization, Accessibility.
Keywords: Rural mobility, Artificial Intelligence, Intelligent Transportation Systems (ITS), Smart mobility, Demand- Responsive Transit, Mobility-as-a-Service (MaaS), Transportation optimization, Accessibility.
Abstract
Generalization and Cross-Dataset Robustness in Deepfake Detection: An Enhanced XceptionNet Approach
Sayli Patil*, Sameer Maheboob Shaikh, Sarwar Ali Mukhtar Ahemad Iddirisi
DOI: 10.17148/IJARCCE.2026.15626
Abstract: Maintaining the integrity of digital visual media in the age of generative AI requires robust, automated frameworks capable of identifying sophisticated facial manipulations. This paper presents the design and evaluation of a specialized benchmarking framework for Deepfake detection, developed using an XceptionNet architecture to analyze and improve cross-dataset generalization. The proposed system compares a baseline detector against an enhanced variant (V2) that integrates strategic data augmentation and domain-specific fine-tuning to bridge the performance gap between source and target datasets. To ensure evaluation rigor, the framework supports multi-seed experimentation with deterministic sampling, enabling statistically grounded comparisons across independent training runs. This reproducible design eliminates variance-driven conclusions and strengthens the reliability of cross-dataset generalization findings. . Researchers are provided with a diagnostic toolkit that utilizes ROC/AUC analysis and bootstrap statistics to ensure the reliability and significance of detection metrics. Experimental results indicate that the targeted training interventions significantly enhance the model's ability to maintain high accuracy across unseen distributions without degrading performance on original training data. This research demonstrates how a systematic benchmarking approach can diagnose model weaknesses and provide a reproducible pathway toward developing more resilient and "wild-ready" Deepfake detection systems.
Abstract
“AnimalCare: A Multi-Modal AI Based System for Animal Disease Prediction based on Symptoms and Image Analysis”
Taranpreet Kaur, Ms. Renu Bala
DOI: 10.17148/IJARCCE.2026.15627
Abstract: Artificial Intelligence (AI) is a breath of new life in medical care and has been frequently used in the field of veterinary science as an early indication of disease. Many factors influence the effects of the animal diseases. Important among them are the standard of living of farmers, security of food and animal yield in the rural areas set up which have limited access to veterinary care. It's not easy to diagnose it early because signs are not visible and medical attention is not timely.
This paper presents an idea of an AI animal disease detection system, AnimalCare, with a multimodal approach to combine both image-based and symptom-based input. Machine learning algorithms and deep learning models are more effective in terms of prediction accuracy and reliability to perform symptom classification and disease detection based on images respectively.
It is also a system that supports multi-linguals which will improve the access to the system by the rural users and will also offer expert guidance to the users through a doctor consultation module. Also, a chatbot powered by artificial intelligence can provide real-time support on animal health, symptoms and preventive measures. The experimental results show that the proposed system is more specific and applicable than conventional single model methods.
The system will be a realistic and scalable solution for real-world animal healthcare management due to its multi-modal input, interactive features and skilled assistance.
Keywords: Animal Disease Prediction, Artificial Intelligence, Machine Learning, Deep Learning, Convolutional Neural Network, Veterinary Healthcare, Multimodal System
This paper presents an idea of an AI animal disease detection system, AnimalCare, with a multimodal approach to combine both image-based and symptom-based input. Machine learning algorithms and deep learning models are more effective in terms of prediction accuracy and reliability to perform symptom classification and disease detection based on images respectively.
It is also a system that supports multi-linguals which will improve the access to the system by the rural users and will also offer expert guidance to the users through a doctor consultation module. Also, a chatbot powered by artificial intelligence can provide real-time support on animal health, symptoms and preventive measures. The experimental results show that the proposed system is more specific and applicable than conventional single model methods.
The system will be a realistic and scalable solution for real-world animal healthcare management due to its multi-modal input, interactive features and skilled assistance.
Keywords: Animal Disease Prediction, Artificial Intelligence, Machine Learning, Deep Learning, Convolutional Neural Network, Veterinary Healthcare, Multimodal System
Abstract
A Study on MERN Stack and Role in Web Application Development
Simranjit Kaur, Ms. Renu Bala
DOI: 10.17148/IJARCCE.2026.15628
Abstract: This research paper shows how to create a complete web application using the MERN stack which includes MongoDB and Express.js and React and Node.js. The research study aims to evaluate how effective and scalable and efficient MERN applications function in contemporary web development. Previous studies have shown that using a single JavaScript environment benefits developers by making their work easier and their code more understandable. The paper introduces a task management system that provides user authentication and role-based access control and real-time task tracking and file management. The system aims to address limitations identified in earlier works, including lack of real- time synchronization and limited user interaction capabilities. The results show that MERN stack applications create web applications which perform well and adapt to different requirements and handle increased user traffic. The research study shows that the MERN stack serves as an effective and practical solution to develop contemporary web applications which scale effectively for task management and group work.
Keywords: HTML, CSS, JavaScript, React.js, Node.js, MongoDB, Express.js
Keywords: HTML, CSS, JavaScript, React.js, Node.js, MongoDB, Express.js
Abstract
Next-Generation Weather Prediction
Ayush Chandel, Manjeet Kaur
DOI: 10.17148/IJARCCE.2026.15629
Abstract: Accurate weather forecasting is essential for disaster management, agriculture, aviation, and environmental planning. Traditional weather prediction relies on numerical weather prediction (NWP) models that simulate atmospheric processes using complex physical equations. Although these models are scientifically reliable, they require significant computational resources and long processing times.
Modern AI models such as GraphCast and FourCastNet can analyze large atmospheric datasets and produce forecasts much faster than traditional models. Research shows that these systems can generate global weather predictions in less than one minute while maintaining competitive accuracy compared with conventional forecasting systems. AI techniques use deep neural networks, graph neural networks, and large climate datasets to detect patterns in temperature, pressure, humidity, and wind variables. These approaches have demonstrated promising results in predicting extreme weather events such as storms, cyclones, and heatwaves. However, challenges remain, including data dependency, model interpretability, and limitations in predicting rare climate events.
This study reviews recent advancements in AI-based weather prediction models and analyzes their advantages, limitations, and future potential in climate science. The findings suggest that AI does not replace traditional meteorological models but complements them, enabling faster and more efficient forecasting systems.
Keywords: AI, Weather Prediction, Climate Science, Machine Learning, Deep Learning, Extreme Weather, Climate Modeling, severe
Modern AI models such as GraphCast and FourCastNet can analyze large atmospheric datasets and produce forecasts much faster than traditional models. Research shows that these systems can generate global weather predictions in less than one minute while maintaining competitive accuracy compared with conventional forecasting systems. AI techniques use deep neural networks, graph neural networks, and large climate datasets to detect patterns in temperature, pressure, humidity, and wind variables. These approaches have demonstrated promising results in predicting extreme weather events such as storms, cyclones, and heatwaves. However, challenges remain, including data dependency, model interpretability, and limitations in predicting rare climate events.
This study reviews recent advancements in AI-based weather prediction models and analyzes their advantages, limitations, and future potential in climate science. The findings suggest that AI does not replace traditional meteorological models but complements them, enabling faster and more efficient forecasting systems.
Keywords: AI, Weather Prediction, Climate Science, Machine Learning, Deep Learning, Extreme Weather, Climate Modeling, severe
Abstract
An NLP-Based Medicine Recommendation System Using Bidirectional GRU for Disease Prediction
Abhishek Kumar Yadav, Kumar Bibhuti Bhushan Singh, Anita Pal
DOI: 10.17148/IJARCCE.2026.15630
Abstract: The fast progress of Artificial Intelligence in healthcare has created chances for smart disease prediction and automatic medicine suggestion systems. Old ways of diagnosing diseases often need a lot of expertise from humans, time and medical resources which're not always available in far away or poor areas. To solve these problems this research suggests a system that uses Natural Language Processing to recommend medicine. This system uses a kind of model called Bidirectional Gated Recurrent Unit for predicting diseases and suggesting medicine accurately. This system uses Natural Language Processing to prepare and analyze the symptoms that patients have. It uses methods like breaking down text into parts, cleaning making all texts the same length and giving labels to codes to change raw medical text into something that machines can read. The Bidirectional model is used to understand how symptoms are related to each other in both directions, which helps to understand medical patterns better. The model is trained on a dataset that has kinds of diseases and is balanced. The results of the experiment show that this approach is than 98 percent accurate and has strong precision, recall and F1-score values. This system is also less complicated than models and is very efficient and can be used by many people. The system that is suggested can be used for healthcare, telemedicine and clinical assistance systems that use Artificial Intelligence. In the future it may be possible to use this system on the cloud support languages and connect it to devices that monitor healthcare in real time. Artificial Intelligence, in healthcare is getting better and better. This system can be a part of it. The fast progress of Artificial Intelligence is helping to make disease prediction and medicine suggestion systems.
Keywords: Natural Language Processing, Bidirectional GRU, Disease Prediction, Medicine Recommendation, Deep Learning, Healthcare Artificial Intelligence, Symptom Analysis.
Keywords: Natural Language Processing, Bidirectional GRU, Disease Prediction, Medicine Recommendation, Deep Learning, Healthcare Artificial Intelligence, Symptom Analysis.
Abstract
Intelligent Accessibility Middleware Using AI for Dynamic Inclusive User Interfaces
Taniya Palta, Mr Naveen Sharma
DOI: 10.17148/IJARCCE.2026.15631
Abstract: Artificial Intelligence (AI) is increasingly being integrated into accessibility systems to support adaptive and inclusive user interfaces for users with diverse accessibility needs. This paper reviews recent research on AI-driven accessibility middleware and adaptive interface technologies published between 2015 and 2026. The study examines the use of machine learning, natural language processing, computer vision, reinforcement learning, and generative AI in improving digital accessibility and interface personalization. Existing approaches related to browser-based accessibility tools, automated remediation systems, adaptive user interfaces, and behavioral analytics are comparatively analyzed. The paper also discusses the significance of accessibility standards such as WCAG, WAI-ARIA, and ADA in the development of intelligent accessibility solutions. Current research trends, evaluation methods, implementation challenges, and ethical concerns associated with AI-based accessibility systems are identified. The review indicates that AI technologies have improved automation and personalization capabilities in accessibility support; however, challenges related to cognitive accessibility, transparency, fairness, and real-time adaptation continue to require further research. The paper concludes by outlining future directions for the development of reliable, user-centered, and standards-compliant accessibility middleware systems.
Keywords: Accessibility, adaptive user interfaces, artificial intelligence, assistive technology, middleware systems, inclusive design, natural language processing, computer vision, WCAG.
Keywords: Accessibility, adaptive user interfaces, artificial intelligence, assistive technology, middleware systems, inclusive design, natural language processing, computer vision, WCAG.
Abstract
Deep Learning–Driven Early Detection of Colorectal Cancer Using Colonoscopy Imaging
Jaskaran Loi*, Rakesh Kumar
DOI: 10.17148/IJARCCE.2026.15632
Abstract: Colorectal cancer (CRC) stands among the top three causes of cancer deaths that occur throughout the world because early detection proves vital for increasing patient survival rates. Clinicians require advanced skills to perform traditional colonoscopy because this method remains the accepted standard for diagnosing colorectal conditions yet demonstrates a high risk of human mistakes while searching for tiny or flat polyps. The research introduces ColonVision, an intelligent deep learning system, which uses colonoscopy images for early colorectal cancer detection. The method uses advanced convolutional neural networks (CNNs) to achieve automatic tissue pattern identification and tissue pattern classification from endoscopic images with exceptional accuracy. The study trains and validates the model through publicly accessible medical imaging datasets which test the model's performance after applying image normalization and augmentation and noise reduction preprocessing methods. The model evaluation process uses standard metrics to measure performance through accuracy and precision and recall and F1-score, which shows the model achieved better results than conventional machine learning methods. The framework aims to decrease instances of false negative results which will help doctors make correct assessments during the initial stages of patient diagnosis.
The results demonstrate how deep learning can enhance colorectal cancer screening processes by delivering a diagnostic support system which operates with both high reliability and efficient performance and the ability to scale. The research advances toward implementing artificial intelligence systems in medical imaging, which will enable healthcare professionals to achieve faster patient diagnosis results with decreased chances of making diagnostic mistakes that will lead to better patient care.
Keywords: Colorectal Cancer Detection, Medical Image Analysis, Deep Learning, Colonoscopy Images.
The results demonstrate how deep learning can enhance colorectal cancer screening processes by delivering a diagnostic support system which operates with both high reliability and efficient performance and the ability to scale. The research advances toward implementing artificial intelligence systems in medical imaging, which will enable healthcare professionals to achieve faster patient diagnosis results with decreased chances of making diagnostic mistakes that will lead to better patient care.
Keywords: Colorectal Cancer Detection, Medical Image Analysis, Deep Learning, Colonoscopy Images.
Abstract
A Multi-Stage Framework for Vehicle Emission Detection and Automated License Plate Recognition (ALPR)
Shreoshi Roy, Anita Pal
DOI: 10.17148/IJARCCE.2026.15633
Abstract: Environmental pollution and public health issues due to vehicular emissions are one of the important sources of air pollution in cities. However, traditional vehicle monitoring systems are mostly based on periodic inspections and manual enforcement measures, and they are not always efficient and can't deliver real-time monitoring. An integrated framework of Artificial Intelligence (AI) based real-time vehicle emission monitoring and automated vehicle identification system is presented in this paper. The proposed framework includes machine learning, computer vision, video processing and optical character recognition (OCR) in a conditional execution architecture. First, a Random Forest (RF) classifier is used to classify the driving conditions as polluting or non-polluting on the basis of parameters related to emissions. Computer vision modules are only triggered when a vehicle is determined as a polluter, which helps to lessen the computational load. Video Processing Methods are used to extract frames, Best-frame selection algorithm is used to select the best frame in the video based on the sharpness and brightness of the frame, YOLOv8 is used to detect the number plate of the vehicle, and EasyOCR is used to detect the registration number of the vehicle. Experimental results are shown to obtain 100% classification accuracy for the prediction of pollution and 99.46% mAP@50 for NPD. This framework is a smart, scalable, efficient and intelligent solution for smart city pollution monitoring and automatic regulation assistance.
Keywords: Vehicle Pollution Monitoring, Random Forest, YOLOv8, EasyOCR, Number Plate Recognition, Machine Learning, Computer Vision, Intelligent Transportation Systems.
Keywords: Vehicle Pollution Monitoring, Random Forest, YOLOv8, EasyOCR, Number Plate Recognition, Machine Learning, Computer Vision, Intelligent Transportation Systems.
Abstract
IoT based Predictive Maintenance System for Industrial Motors Using Raspberry Pi and Edge Analytics
Priyanka B. Borade, Prof. S. N. Vidhate
DOI: 10.17148/IJARCCE.2026.15634
Abstract: Predictive maintenance is a key factor in ensuring the reliability and efficiency of industrial motors. It helps in detecting potential faults in machines before they occur. In this paper, a predictive maintenance system using IoT technology is proposed to ensure the health monitoring of industrial motors using a Raspberry Pi and sensor technology. In this system, motor parameters such as vibration, temperature, and current are considered to analyze the motor’s operating condition. Experimental tests were carried out under various operating conditions such as normal operation, increased load, and fault condition using a motor. The proposed system detected abnormal operating conditions such as bearing fault, temperature rise, and current overload when vibration is above 1.0 g, temperature exceeds 65°C, and current exceeds 4 A, respectively. The system was able to achieve a fault detection accuracy of approximately 94.5%, with a response time of less than two seconds. Thus, it is clear that the proposed system is a reliable, cost-effective, and efficient solution for the purpose of motor condition monitoring.
Keywords: Internet of Things, Raspberry Pi, Predictive Maintenance, Industrial Motors
Keywords: Internet of Things, Raspberry Pi, Predictive Maintenance, Industrial Motors
Abstract
Smart AI-Based Vehicular Emission Monitoring and Regulatory Notification System Using YOLOv8, OCR and Machine Learning
Shreoshi Roy, Anita Pal
DOI: 10.17148/IJARCCE.2026.15635
Abstract: Urban air quality is highly influenced by how transportation contributes to air pollution, resulting in decreased quality of the environment and impaired health of individuals. Current vehicle emission tracking methods (such as inspection for certificate of pollution under control) employ time-based checks and provide no ongoing method of determining if pollution is being produced by vehicles. We propose a smart AI-based vehicle emissions monitoring and regulatory notification system that continuously monitors where, when, and how much pollutants have been released by each vehicle using IoT sensors, machine learning, license plate detection powered by YOLOv8, and optical character recognition (OCR) technologies. The operational model of our system is to compare collected data to maximum allowable vehicle emissions in accordance with regulatory limits. Random forest classifier will provide analysis of the sensor data to enhance predictive capabilities related to emissions generated by vehicles. Once an emission exceeds its regulatory limit, YOLOv8 (i.e., the AI License Plate Reader) will identify the vehicle's license plate and OCR will read the vehicle's registration number. The extracted vehicle details will be matched against a vehicle registration database and the appropriate regulatory body will be automatically notified regarding the offending vehicle. As a result, our system will support timely identification of vehicles not in compliance with regulations through proximity to their registration and to facilitate automatic compliance enforcement with minimal manual effort while providing high detection accuracy 96.4%, precision 95.8%, short response time, and reliable operational performance in diverse environmental conditions; Additionally, it will have the scalability to improve pollution control as well as to effectively manage traffic through intelligent operations in smart cities.
Keywords: Vehicle Emissions Monitoring, YOLOv8, OCR, Random Forest, IoT, Smart Cities, Regulatory Notification.
Keywords: Vehicle Emissions Monitoring, YOLOv8, OCR, Random Forest, IoT, Smart Cities, Regulatory Notification.
Abstract
AI-Driven Predictive Modelling and Machine Learning Framework for Classification of Fetal Health Conditions
Mr. MUTHUKUMARA K, Ms. LOGADHARSHINI M, Mr. ARIRAJAN A, Mr. DEEPAN RAJ R
DOI: 10.17148/IJARCCE.2026.15636
Abstract: Fetal health monitoring plays a crucial role in ensuring maternal and neonatal well-being by enabling the early identification of potential complications during pregnancy. Conventional fetal assessment methods often depend on manual interpretation of cardiotocography (CTG) signals and clinical expertise, which may lead to inconsistencies and delayed diagnosis. This paper presents an AI-driven predictive modeling and machine learning framework for the classification of fetal health conditions using cardiotocography data. The proposed framework integrates data preprocessing, feature engineering, and machine learning algorithms to classify fetal conditions into Normal, Suspect, and Pathological categories. Various predictive models including Random Forest, Support Vector Machine, Decision Tree, and XGBoost are employed and evaluated based on performance metrics such as accuracy, precision, recall, and F1-score. The framework enhances diagnostic reliability and supports healthcare professionals in making timely clinical decisions. Experimental results demonstrate that the proposed system achieves high classification accuracy and provides an efficient solution for intelligent fetal health assessment.
Keywords: Fetal Health Classification, Machine Learning, Predictive Modeling, Cardiotocography, Artificial Intelligence, Healthcare Analytics, Clinical Decision Support.
Keywords: Fetal Health Classification, Machine Learning, Predictive Modeling, Cardiotocography, Artificial Intelligence, Healthcare Analytics, Clinical Decision Support.
Abstract
YOLOv8 for Real-Time Microplastic Detection in Aquatic Environments
Saggurthi Kiran*, P Harikrishna
DOI: 10.17148/IJARCCE.2026.15637
Abstract: Plastic waste is destroying marine ecosystems throughout the world and posing a threat to the life of animals and the health of the waters. This does research on a new deeplearning model named Yolov8 to identify and classify underwater plastic trash. The system is highly functional in all sorts of water, and can detect different plastic waste in real time. The technology can be useful in the context of conserving the ocean by providing more accurate estimates of the amount of trash that exists, its origin, and the optimal clean-up strategies. Testing indicates that accuracy of the new method is far much superior to the previously used methods particularly when visibility of the water is low. The article contributes to the emerging research on AI in environmental monitoring, by offering a scaled instrument and measuring plastic pollution to direct conservation and policy-related actions.
Keywords: Locating plastic, gathering garbage, categorizing them, YOLOv8, computer vision, and deep learning.
Keywords: Locating plastic, gathering garbage, categorizing them, YOLOv8, computer vision, and deep learning.
Abstract
Stock Vision: A Multivariate LSTM-Based Stock Market Analytics and Prediction Web Application
Pooja, Ms. Renu Bala
DOI: 10.17148/IJARCCE.2026.15638
Abstract: Contemporary financial markets generate incomprehensible volumes of structured and unstructured data for each trading session, but most retail investors, small fund managers, and individual participants lack the technology to extract actionable insights from that data in real-time. This paper introduces Stock Vision, an integrated intelligence platform on stock markets that combines a five-feature multivariate Long Short-Term Memory deep learning model with a live analytics dashboard based on the Streamlit web framework. The prediction model analyzes sixty days of historical context in five engineered channels: closing price, twenty-day moving average, daily trading volume, a fourteen-period Relative Strength Index, and a news sentiment score to predict short-term price projections. The surrounding platform offers live commodity pricing, nine technical analysis charts, a portfolio tracker, market breadth statistics, and an amalgamated news feed. Results from observations demonstrate that the multivariate configuration yields more directionally accurate output than single-channel baselines by embedding the dynamics of momentum, trend regime, and event-driven sentiment at once in a concordant feature representation.
Keywords: LSTM, multivariate time series forecasting, sentiment analysis, RSI, technical indicators, deep learning, Streamlit, portfolio analytics, NSE, yfinance.
Keywords: LSTM, multivariate time series forecasting, sentiment analysis, RSI, technical indicators, deep learning, Streamlit, portfolio analytics, NSE, yfinance.
Abstract
AGRIBOT: AN INTELLIGENT CHATBOT FOR FARMERS WITH CROP RECOMMENDATION AND DISEASE PREDICTION USING MACHINE LEARNING
V. Hari Krishnan, S. Surendhar, P. Baranidharan, Dr. Sweta Singh, Ph.D.
DOI: 10.17148/IJARCCE.2026.15639
Abstract: Agriculture remains one of the most important sectors contributing to economic growth and food security across the world. In developing countries such as India, a large percentage of the population depends directly or indirectly on agriculture for their livelihood. Despite technological advancements, farmers continue to face challenges such as improper crop selection, lack of agricultural expertise, unpredictable environmental conditions, and delayed disease identification. These issues often result in reduced productivity, crop loss, and financial instability. To address these challenges, this paper proposes Agribot, an intelligent agricultural chatbot that integrates Machine Learning and Deep Learning techniques to provide personalized crop recommendations and plant disease prediction. The system utilizes a Random Forest Classifier to recommend suitable crops based on soil nutrient parameters and a Convolutional Neural Network (CNN) to identify plant diseases from leaf images. A chatbot interface allows farmers to interact with the system in a simple and user-friendly manner. Experimental results demonstrate that the crop recommendation model achieves an accuracy of 92.7%, while the disease prediction model achieves an accuracy of 94.2%. The proposed system supports smart farming practices by providing timely, accurate, and accessible agricultural guidance.
Keywords: Agriculture, Machine Learning, Deep Learning, Crop Recommendation, Disease Prediction, Random Forest, CNN, Chatbot.
Keywords: Agriculture, Machine Learning, Deep Learning, Crop Recommendation, Disease Prediction, Random Forest, CNN, Chatbot.
Abstract
CirrhosisX: Detection and Staging of Liver Cirrhosis using DL and Explainable AI
Ms. Poonam Sadafal, Mansi Nirbhavane, Vinod Kumar, Nikita Nigude, Sharyu Nanware
DOI: 10.17148/IJARCCE.2026.15640
Abstract: Liver cirrhosis is a serious, ongoing health problem that leads to permanent harm in the liver from endless scarring and fibrosis. It hits hard on a global scale, being one of the top reasons people die around the world. Spotting it early and figuring out exactly how far along it is can really change things for patients, cutting down on the chances of more serious problems popping up.
This project introduces a hybrid approach to analyzing the MRI images of patients suffering from liver cirrhosis by using artificial intelligence methods. The DenseNet121 neural network is used to extract features from the medical images of patients, and this extracted information is then used to classify whether the patient is cirrhotic and at what stage this patient is cirrhotic. Not only does this method predict the likelihood of cirrhosis in a patient but also uses the Grad-CAM technique to visualize parts of the MRI images that help make these predictions.The performance of the system proposed herein is evaluated based on traditional performance indicators such as accuracy, precision, recall, and F1-scores.
In conclusion, the suggested approach can be used as an additional instrument that will help clinicians diagnose liver diseases and lessen their work.
Keywords: Liver Cirrhosis, Deep Learning, DenseNet121, XGBoost, Explainable AI, Medical Image Analysis, Grad- CAM, Hybrid Learning Model
This project introduces a hybrid approach to analyzing the MRI images of patients suffering from liver cirrhosis by using artificial intelligence methods. The DenseNet121 neural network is used to extract features from the medical images of patients, and this extracted information is then used to classify whether the patient is cirrhotic and at what stage this patient is cirrhotic. Not only does this method predict the likelihood of cirrhosis in a patient but also uses the Grad-CAM technique to visualize parts of the MRI images that help make these predictions.The performance of the system proposed herein is evaluated based on traditional performance indicators such as accuracy, precision, recall, and F1-scores.
In conclusion, the suggested approach can be used as an additional instrument that will help clinicians diagnose liver diseases and lessen their work.
Keywords: Liver Cirrhosis, Deep Learning, DenseNet121, XGBoost, Explainable AI, Medical Image Analysis, Grad- CAM, Hybrid Learning Model
Abstract
Malware Detection Using PE Headers: A Comprehensive Machine Learning Approach
Pranto Bosu, Satinder Kaur, Tajbir Singh, Satveer Kour, Sandeep Kaur
DOI: 10.17148/IJARCCE.2026.15641
Abstract: The rapid proliferation of malicious software (malware) poses an increasingly severe threat to global information infrastructure, with over 1.44 billion cumulative malware samples documented by 2024. Traditional signature-based antivirus solutions are demonstrably insufficient against zero-day threats, polymorphic code, and obfuscation-heavy payloads. This paper presents a comprehensive pre-review study of malware detection techniques that leverage the static structural features embedded within the Portable Executable (PE) file header — a rich, low-overhead source of discriminative information present in every Windows executable. We conduct an extensive literature survey of over 30 published works spanning 2017–2025 and propose an end-to-end detection pipeline that extracts 57 features across the DOS Header, File Header, Optional Header, and Section Table, and evaluates them using eight classification algorithms: Naive Bayes, SVM, KNN, Decision Tree, Random Forest, XGBoost, LightGBM, and MLP. Experimental analysis on the publicly available EMBER 2018 and Meraz’18 datasets shows that ensemble and gradient-boosting methods — specifically XGBoost (97.4% accuracy, AUC 0.987) and LightGBM (97.1%, AUC 0.985) — consistently outperform conventional classifiers. Feature importance analysis using SHAP reveals that SizeOfOptionalHeader, AddressOfEntryPoint, SizeOfImage, and NumberOfSections are the most discriminative attributes. We also discuss adversarial evasion challenges including packing, obfuscation, and header manipulation, and identify future research directions toward robust, explainable, and real-time PE-header-based malware detectors.
Keywords: Malware Detection, Portable Executable (PE) Header, Static Analysis, Machine Learning, XGBoost, Random Forest, LightGBM, Feature Engineering, Cybersecurity, EMBER Dataset.
Keywords: Malware Detection, Portable Executable (PE) Header, Static Analysis, Machine Learning, XGBoost, Random Forest, LightGBM, Feature Engineering, Cybersecurity, EMBER Dataset.
Abstract
LITTLE PAW: DESIGN AND DEVELOPMENT OF A USER-CENTERED PET CARE MOBILE APPLICATION USING UI/UX PRINCIPLES
Harminder kaur, Dr. Rimmy Yadav
DOI: 10.17148/IJARCCE.2026.15642
Abstract: As the reliance on mobile apps to perform daily activity has grown, there is a need for better designed, unified digital platforms in various specific areas. While the pet care industry is expanding, it still doesn't have a comprehensive mobile application with service integration abilities and a great user experience. In this paper, I describe the development of a mobile application that takes care of pets, designed through the Figma platform and based on a method called Double Diamond. The app brings together grooming, pet grooming food delivery, veterinary visits, medicine management, walking and training in one convenient, easy-to-use application. The design follows Web Content Accessibility Guidelines (WCAG 2.1 AA) standards, includes a reusable Figma component library and provides an emotional visual identity based on a consistent color palette and typographic system. The current market of Rover, Petco and Waggle is analyzed for key design issues. The proposed system is designed to solve these problems by a structured information architecture, concise three steps user flows, and an accessibility-first component design. The study shows that systematic use of user-centered design principles can lead to an improvement in the navigational efficiency and service discoverability as is measurable in mobile pet care applications.
Keywords: UI/UX Design, Mobile Application Design, Pet Care Application, Figma Prototyping, User-Centered Design, Accessibility, Information Architecture, Double Diamond Methodology
Keywords: UI/UX Design, Mobile Application Design, Pet Care Application, Figma Prototyping, User-Centered Design, Accessibility, Information Architecture, Double Diamond Methodology
Abstract
A Blockchain-Enabled Data Privacy and Governance Framework for Multi-Cloud Environments
Sukhwinder Kaur*, Dr Pooja Rana
DOI: 10.17148/IJARCCE.2026.15643
Abstract: Multi-cloud architectures have been selected by industries today to achieve enhanced scalability, resilience and flexible operation capabilities and non-vendor lock-in. Organizations can boost their system uptime and service performance by spreading their computing tasks and storage needs across multiple cloud service providers; however, this approach causes major difficulties for organizations because they need to maintain uniformity in their data control processes, protection of personal information, and auditing procedures, and their ability to meet legal requirements. The research investigates traditional centralized governance systems because they do not work effectively with their existing systems of access control systems inside multiple cloud environments. The study creates a blockchain-based system that protects data privacy rights and establishes governance standards that apply to multiple cloud computing environments. The proposed architecture uses a permissioned blockchain system combined with smart contracts to create unchangeable governance rules and permanent audit trails, while Attribute-Based Encryption (ABE) controls access to sensitive information through precise security measures. The system implements off-chain storing solutions in order to achieve both. performance enhancement and efficient operations, whereas compliance monitoring system. conducts automatic evaluations that comply with the regulations of both GDPR and CCPA. The study aims at developing a decentralized system of governance that is secure and transparent and offers wide multi-cloud management systems.
Keywords: Blockchain, Multi-Cloud Computing, Data Governance, Smart Contracts, Attribute-Based Encryption.
Keywords: Blockchain, Multi-Cloud Computing, Data Governance, Smart Contracts, Attribute-Based Encryption.
Abstract
“Multimodal Surveillance Frameworks for Narcotics Detection on Social Media: A Review”
Nayana V.M, Adithi S Bharadwaj, Padipati Saidivija, Rumaisa Syed, H. N. Poornima
DOI: 10.17148/IJARCCE.2026.15644
Abstract: The rapid growth of social media and encrypted messaging platforms has created new challenges for detecting online drug trafficking. Existing monitoring systems struggle with multimedia content, evolving slang, and hidden identities. This review surveys recent AI-based approaches, including multimodal detection, NLP pipelines, computer vision, blockchain forensics, and OSINT frameworks. Key limitations such as static vocabularies, high computational latency, and barriers posed by end-to-end encryption are highlighted. By synthesizing current methodologies and research gaps, this paper provides a comprehensive overview of the state of the art and outlines directions for future development in cyber-enabled narcotics detection.
Keywords: Artificial Intelligence in Cybersecurity, Multimodal Detection Frameworks, Drug Trafficking on Social Media, Encrypted Messaging Platforms (E2EE), Dynamic Slang and Identity Attribution.
Keywords: Artificial Intelligence in Cybersecurity, Multimodal Detection Frameworks, Drug Trafficking on Social Media, Encrypted Messaging Platforms (E2EE), Dynamic Slang and Identity Attribution.
Abstract
RailCrawler - Track Inspection Vehicle
Soham Chavhan, Mrs. Vaishali Anaspure, Shivam Kambale, Sudarshan Gaikwad
DOI: 10.17148/IJARCCE.2026.15645
Abstract: Railways are widely used for transportation for passengers as well as goods. But safety remains a major concern due to problems like cracks in railway tracks and obstacles present on the track. If problem like this are not detected early, they can lead to serious and large accidents. Traditional inspection methods mostly depend on manual checking, which is time taking and may not always detect small faults or obstacles. For this problem, our project presents RailCrawler , a smart railway track inspection system. Our system uses sensors to continuously monitor the condition of railway tracks while moving on them. It is capable of detecting both cracks in the track and obstacles such as stones or other objects placed on the railway path. When an abnormal condition is found, the system immediately sends an alert message along with location details to the concerned authority. Also provides quick response by activating safety features like stopping the system and providing a warning signal. Our system reduces manual effort and helps in faster detection. It is simple, low cost, and easy to implement. Overall, it provides a practical solution to improve railway safety and reduce the chances of accidents.
Keywords: rail track inspection, obstacle detection, location track system, GPS location track, ESP32 Microcontroller, GSM SIM900A Module, real time alert system.
Keywords: rail track inspection, obstacle detection, location track system, GPS location track, ESP32 Microcontroller, GSM SIM900A Module, real time alert system.
Abstract
MACHINE LEARNING TECHNIQUES FOR DIABETES PREDICTION: A COMPREHENSIVE REVIEW
Tanu Sharma, Naveen Sharma
DOI: 10.17148/IJARCCE.2026.15646
Abstract: Diabetes mellitus is one of the most common and heavy non-communicable diseases globally, impacting around 537 million adult people worldwide by 2021 and is expected to further increase to 783 million by 2045. Early and accurate diabetes prediction is essential for prompt clinical intervention in order to minimize diabetes complications, and to decrease healthcare costs. The application of machine learning (ML) algorithms has grown to be a powerful approach to identify non-linear, complex patterns in clinical and demographic data that can allow for early stage risk stratification.The aim of this paper is to systemize and comprehensively review the current state-of-the-art machine learning approaches used for diabetes prediction. It critically synthesises results from over 60 peer-reviewed research studies published between 2015–2024, compares the performance of models against each other, and considers a geographically focused case study analysing the use of the model in the Punjab region of India where diabetes prevalence rates have exceeded the national average.Literature search was performed using PubMed, IEEE Xplore, Scopus, and Web of Science database, following the PRISMA guidelines. Various algorithms are examined, such as logistic regression, support vector machines, decision trees, random forests, XGBoost, Naive Bayes, k-nearest neighbour, and deep learning architectures like artificial neural networks, convolutional neural networks and long short-term memory networks. The accuracy, sensitivity, specificity, F1-score and area under the receiver operating characteristic curve (AUC) are systematically compared.Benchmark datasets like PIMA Indian Diabetes Database and CDC BRFSS consistently achieve the best predictive performance (AUC 91%-96%) for XGBoost and deep learning architectures. Ensemble methods are better in generalisation than single classifiers. In the Punjab region case study a Random Forest model trained on regional eHR was able to reach an accuracy of 89.4% and an AUC of 0.93, with the best predictive features identified as glucose level, BMI, age and family history. Diabetes prediction using a machine learning approach has great clinical and public health benefits, especially if models are adapted for regional epidemiological aspects. Issues of data sparsity, class imbalance and model explainability are significant challenges that need to be overcome to enable responsible clinical use. Going forward, the need and the focus should be on federated learning, explainable Artificial Intelligence (XAI), and multimodal data sources integration.
Keywords: diabetes mellitus; machine learning; deep learning; XGBoost; random forest; PIMA dataset; clinical decision support; Punjab; predictive modelling; artificial intelligence
Keywords: diabetes mellitus; machine learning; deep learning; XGBoost; random forest; PIMA dataset; clinical decision support; Punjab; predictive modelling; artificial intelligence
Abstract
A REGULATION-COMPLIANT BLOCKCHAIN-BASED ARCHITECTURE FOR SECURE AND INTEROPERABLE PUBLIC HEALTHCARE SYSTEMS
Harleen Kaur, Dr. Anubha
DOI: 10.17148/IJARCCE.2026.15647
Abstract: The quick adoption of health care systems digitization has done a significant part in the creation and distribution of delicate medical information among hospitals, labs, and other public health organizations. The majority of the current healthcare systems though are founded on centralized systems that have critical problems of data security, privacy, interoperability and compliance to regulations. This can lead to incomplete medical records, ineffective transfer of information and more vulnerability towards cyber attacks. To address these issues, the given paper will suggest a blockchain-based architecture in healthcare that will guarantee the safety, transparency, and compatibility of data exchange. The combination of the Electronic Health Record (EHR) systems and blockchain technology will allow the sharing of patient data between government agencies, laboratories, and healthcare providers in a secure environment, which is proposed to be enabled through the mentioned combination. Smart contracts can be implemented in the authority of data sharing, enforce data-access control policies, and meet the healthcare regulations. The blockchain design, which is decentralized and immutable, improves the integrity of data, increases its transparency, and is auditable and gives patients more power over their medical records. The suggested system is assessed through a Python-based simulation model that works through medical transactions distributed among network nodes. Important performance metrics such as data integrity, throughput and latency are used to measure performance of the system. The experimental outcomes suggest that the suggested blockchain-based architecture has a significant positively impactful effect on the interoperability level, the latency level, and the data safety levels compared to the traditional centralized healthcare systems.
Keywords: Blockchain, Decentralized Healthcare Architecture, Electronic Health Records (EHR), Healthcare Data Governance, Healthcare Interoperability, Smart Contracts
Keywords: Blockchain, Decentralized Healthcare Architecture, Electronic Health Records (EHR), Healthcare Data Governance, Healthcare Interoperability, Smart Contracts
Abstract
RELIABILITY ESTIMATION OF CSKD USING PROBABILISTIC TECHNIQUES
Mr. Chinthapalli Nikhil Reddy, Dr. P. Vinod Kumar Naidu, Dr. Avinash G
DOI: 10.17148/IJARCCE.2026.15648
Abstract: The compressor suction knockout drum is a type of pressure vessel which is used to remove the liquid droplets carryover in gases to protect the downstream process equipment. The knockout drum helps in improving the life of the compressor and resists the corrosion. The present work deals with design, modelling and static structural analysis of the compressor knockout drum. The first step involves generating the model using CATIA V5. Later, the model is dumped into ANSYS software to perform static, fatigue and modal analysis. The von Mises stresses are estimated at locations such as the crown, equator, and shell portions of the drum. The von-Mises stresses are obtained by analytical approximation and compared with ANSYS results. Now the internal pressure and thickness of the drum are assumed to be random and Monte Carlo simulations are used to know the statistical nature of von Mises distribution at various locations of the drum. Finally, the reliability of the drum is to be estimated using Random variable approach.
Keywords: Knockout drum, Modelling, Static analysis, Fatigue life, Random variable.
Keywords: Knockout drum, Modelling, Static analysis, Fatigue life, Random variable.
Abstract
Multi Class Support Vector Machine Based Plant Leaf Disease Detection from Color Texture And Shape Pictures
Manisha Machhindra Kapse
DOI: 10.17148/IJARCCE.2026.15649
Abstract: Agriculture plays a vital role in the economy of many countries, and crop productivity is highly dependent on plant health. Plant diseases can significantly reduce crop yield and quality if not detected at an early stage. Traditional disease identification methods rely on manual inspection by agricultural experts, which can be time-consuming, expensive, and sometimes inaccurate. Recent advancements in image processing and machine learning have enabled automated systems for plant disease detection.
This research presents a plant leaf disease detection system based on a Multi-Class Support Vector Machine (SVM) classifier using color, texture, and shape features extracted from leaf images. The proposed approach captures leaf images, performs preprocessing to remove noise and enhance image quality, segments the infected region, and extracts relevant features. These features are then used to train a Multi-Class SVM model capable of classifying different plant diseases. The combination of color, texture, and shape characteristics improves classification accuracy by providing comprehensive information about disease symptoms present on the leaf surface.
Experimental analysis demonstrates that the proposed method can effectively identify multiple plant diseases with high accuracy while reducing the dependency on manual diagnosis. The developed system offers a cost-effective and efficient solution for farmers and agricultural professionals, helping in early disease detection and timely treatment recommendations. The proposed approach contributes to the advancement of smart agriculture and precision farming technologies.
Keywords: Plant Disease Detection, Multi-Class Support Vector Machine, Image Processing, Feature Extraction, Machine Learning, Agriculture, Leaf Disease Classification, Color Features, Texture Analysis, Shape Features.
This research presents a plant leaf disease detection system based on a Multi-Class Support Vector Machine (SVM) classifier using color, texture, and shape features extracted from leaf images. The proposed approach captures leaf images, performs preprocessing to remove noise and enhance image quality, segments the infected region, and extracts relevant features. These features are then used to train a Multi-Class SVM model capable of classifying different plant diseases. The combination of color, texture, and shape characteristics improves classification accuracy by providing comprehensive information about disease symptoms present on the leaf surface.
Experimental analysis demonstrates that the proposed method can effectively identify multiple plant diseases with high accuracy while reducing the dependency on manual diagnosis. The developed system offers a cost-effective and efficient solution for farmers and agricultural professionals, helping in early disease detection and timely treatment recommendations. The proposed approach contributes to the advancement of smart agriculture and precision farming technologies.
Keywords: Plant Disease Detection, Multi-Class Support Vector Machine, Image Processing, Feature Extraction, Machine Learning, Agriculture, Leaf Disease Classification, Color Features, Texture Analysis, Shape Features.
Abstract
Enhancing Crop Prediction Using Artificial Intelligence for Smart Agriculture
Urvashi, Naveen Sharma
DOI: 10.17148/IJARCCE.2026.15650
Abstract: The food security situation all over the world has never faced such problems like the population growth, limited use of agricultural resources and climate variability. In response to these challenges, another technology has surfaced—Artificial Intelligence (AI)—which offers potential to tackle the problems by helping to create accurate systems to predict crops based on data.But there is another technology that has come to terms to address these challenges: Artificial Intelligence (AI). The current state of the art on AI application-based crop prediction in smart agriculture is summarized and presented through this review paper by collating information from peer-reviewed publications published from 2015 to 2024. In the context of predicting crop yield, disease incidence, water needs and optimal sowing windows, we analyze the use of machine learning (ML) algorithms, deep learning architectures such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks and Transformer-based models. Theoretically, we developed a uniform taxonomy for the classification of different AI approaches to crop prediction and studied the model understandable versus model interpretability trade-offs. Real-world challenges, including the scarcity of data in smallholder environments and limitations on computing resources, are discussed in detail, along with the important role of extension services providing actionable guidance to farmers based on these AI technologies. In the case study carried out in the agro-rich region of Punjab, India, the hybrid CNN-LSTM models demonstrate accuracy levels of up to 93.2% for wheat and rice yield prediction using multi-modal data, which combines satellite imagery, IoT soil sensor data, meteorological data and government agronomic database data. The paper ends with a comprehensive research roadmap that includes federated learning techniques for data sharing without compromising privacy, explainable AI (XAI) for fostering farmer trust, edge computing for deployment in remote areas, and pipelines for retraining AI systems in the face of climate change. Overall, the study reveals the significant potential of AI in the agricultural sector to transform farming processes and the societal issues and enablers that need to be addressed to achieve equitable adoption.
Keywords: Artificial Intelligence, Crop Prediction,Smart agriculture, Machine learning, Precision farming , Agriculture data analysis.
Keywords: Artificial Intelligence, Crop Prediction,Smart agriculture, Machine learning, Precision farming , Agriculture data analysis.
Abstract
AI-Based Autonomous Cyber Defense Framework for Intelligent Threat Detection
Vishali Sansoa*, Rimmy
DOI: 10.17148/IJARCCE.2026.15651
Abstract: Cyber dangers have become more sophisticated and common in today's digital environment as a result of the spread of digital technologies and international networks of information systems. It is often difficult for traditional cybersecurity systems, particularly rule-based intrusion detection systems, to react quickly to intricate and dynamic cyberattacks. The application of machine learning (ML) and artificial intelligence (AI) methods to enhance cybersecurity threat detection has been the subject of recent research. However, the majority of current tactics lack an autonomous defense system that may respond to attacks on its own and are primarily focused on identifying cyber threats. In order to improve intelligent threat detection and response inside the existing digital infrastructures, this study proposes a conceptual design of an autonomous AI-driven cyber defense system. To reduce cyber risks, the proposed model will include automated response mechanisms, intelligent threat classification, and machine learning-based intrusion detection models. AI-based threat detection, threat classification, data gathering, data preprocessing, and feature extraction, as well as an autonomous defense response engine, form its foundation. The suggested framework can improve threat detection accuracy and reduce false positive rates, according to comparative research that used simulations and compared it with existing machine learning-based intrusion detection techniques. The suggested system can aid in the creation of intelligent cybersecurity systems that can progress and self-evolve to provide cyber protection in dynamic internet sources.
Keywords: Artificial Intelligence; Autonomous Cyber Defense; Intrusion Detection Systems (IDS); Machine Learning; Cyber Threat Detection; Cybersecurity Intelligence; Intelligent Security Systems.
Keywords: Artificial Intelligence; Autonomous Cyber Defense; Intrusion Detection Systems (IDS); Machine Learning; Cyber Threat Detection; Cybersecurity Intelligence; Intelligent Security Systems.
Abstract
Skill Bridge: A Smart Platform for Hiring Verified Skilled Professionals
Siddheshwar Vaidya, Mrs. Vaishali Anaspure, Shripal Rathod, Vivek Devare
DOI: 10.17148/IJARCCE.2026.15652
Abstract: India’s informal labour market continues to face a significant demand–supply disconnect, where millions of skilled workers struggle to find stable and consistent employment, while businesses encounter difficulties in identifying reliable and verified professionals. This imbalance not only affects individual livelihoods but also reduces overall efficiency in service delivery across sectors. Many workers lack visibility and access to oppor-tunities, whereas employers often depend on informal networks or middlemen, leading to issues such as lack of transparency, inconsistent pricing, and uncertain service quality.
To address these challenges, this paper proposes Skill Bridge, an innovative digital platform designed to effectively connect skilled workers with potential employers using modern web technologies, Artificial Intelligence (AI), and Machine Learn-ing (ML). The platform allows workers—such as electricians, plumbers, carpenters, cleaners, and other service providers—to register, create detailed profiles, showcase their skills, and receive personalized job recommendations based on their expertise, experience, and location. On the other hand, employers can post job requirements, filter candidates using multiple parameters such as skill set, ratings, and proximity, and select suitable workers with greater confidence.
A key feature of Skill Bridge is its hybrid machine learning-based job matching system, which combines content- based filter-ing and collaborative filtering techniques to improve recommen-dation accuracy. The platform further enhances decision-making by incorporating sentiment analysis on user reviews, enabling better evaluation of worker performance and customer satis-faction. Geolocation scoring is used to prioritize nearby oppor-tunities, thereby reducing travel time and improving efficiency. Additionally, optional face recognition technology is proposed for secure onboarding and identity verification, increasing trust and authenticity within the platform.
To ensure inclusivity and accessibility, especially for semi-literate or less tech-savvy users, the system provides a multilin-gual interface along with voice-assisted interaction. This enables users from diverse linguistic backgrounds to easily navigate and utilize the platform without barriers. From a technical perspec-tive, Skill Bridge is built using a microservices-based architecture deployed on cloud infrastructure, ensuring scalability, flexibility, and high availability to support a large and growing user base. Overall, Skill Bridge aims to minimize the dependency on intermediaries, enhance transparency in hiring processes, and create a more structured and efficient ecosystem for informal employment. By leveraging intelligent automation and user-centric design, the platform seeks to empower underserved workers, improve job accessibility, and contribute to the digital transformation of India’s informal labour sector.
Keywords: Informal labor market, job matching, skill-based hiring, artificial intelligence, machine learning, recom- mendation system, geolocation, multilingual interface, and trust scoring
To address these challenges, this paper proposes Skill Bridge, an innovative digital platform designed to effectively connect skilled workers with potential employers using modern web technologies, Artificial Intelligence (AI), and Machine Learn-ing (ML). The platform allows workers—such as electricians, plumbers, carpenters, cleaners, and other service providers—to register, create detailed profiles, showcase their skills, and receive personalized job recommendations based on their expertise, experience, and location. On the other hand, employers can post job requirements, filter candidates using multiple parameters such as skill set, ratings, and proximity, and select suitable workers with greater confidence.
A key feature of Skill Bridge is its hybrid machine learning-based job matching system, which combines content- based filter-ing and collaborative filtering techniques to improve recommen-dation accuracy. The platform further enhances decision-making by incorporating sentiment analysis on user reviews, enabling better evaluation of worker performance and customer satis-faction. Geolocation scoring is used to prioritize nearby oppor-tunities, thereby reducing travel time and improving efficiency. Additionally, optional face recognition technology is proposed for secure onboarding and identity verification, increasing trust and authenticity within the platform.
To ensure inclusivity and accessibility, especially for semi-literate or less tech-savvy users, the system provides a multilin-gual interface along with voice-assisted interaction. This enables users from diverse linguistic backgrounds to easily navigate and utilize the platform without barriers. From a technical perspec-tive, Skill Bridge is built using a microservices-based architecture deployed on cloud infrastructure, ensuring scalability, flexibility, and high availability to support a large and growing user base. Overall, Skill Bridge aims to minimize the dependency on intermediaries, enhance transparency in hiring processes, and create a more structured and efficient ecosystem for informal employment. By leveraging intelligent automation and user-centric design, the platform seeks to empower underserved workers, improve job accessibility, and contribute to the digital transformation of India’s informal labour sector.
Keywords: Informal labor market, job matching, skill-based hiring, artificial intelligence, machine learning, recom- mendation system, geolocation, multilingual interface, and trust scoring
Abstract
Smart Perception And Autonomous Response Core
Suraj Pawar, Shoheb Shaikh, Pavan Pawar, Ansh Ghodke, Prof. Neha Dumne
DOI: 10.17148/IJARCCE.2026.15653
Abstract: The increasing deployment of Unmanned Aerial Vehicles (UAVs) in surveillance, inspection, and defense applications has highlighted the need for reliable obstacle detection and avoidance systems. Conventional approaches primarily depend on centralized processing architectures, which may introduce computational overhead and response delays during close-range obstacle encounters. This paper presents the Smart Perception and Autonomous Response Core, a dedicated low-latency perception module designed to enhance obstacle awareness and autonomous response capabilities in UAV platforms. The proposed system integrates a Time-of-Flight (ToF) sensor with an STM32H7-based embedded processing unit to independently monitor the environment and perform rapid decision-making. The module incorporates dedicated power management circuitry and MAVLink-compatible communication interfaces for seamless integration with existing flight control systems. By offloading proximity-related processing from the primary flight controller, This module aims to improve responsiveness and operational safety in dynamic environments. The modular architecture of the proposed system also provides a foundation for future enhancements involving multi-directional sensing and advanced autonomous navigation capabilities. The developed prototype demonstrates the feasibility of implementing a compact perception framework for close-range obstacle detection in UAV applications.
Keywords: Smart Perception and Autonomous Response Core, Unmanned Aerial Vehicles (UAVs), Time-of-Flight (ToF) Sensor, Obstacle Avoidance, Edge Computing, MAVLink Communication, Real-Time Embedded Systems, Autonomous Navigation.
Keywords: Smart Perception and Autonomous Response Core, Unmanned Aerial Vehicles (UAVs), Time-of-Flight (ToF) Sensor, Obstacle Avoidance, Edge Computing, MAVLink Communication, Real-Time Embedded Systems, Autonomous Navigation.
Abstract
Cloud-based secure payment system with OTP and encrypted data storage
DINESH T, GOKUL S, KILLIVALAVAN S, AMARNATH J, Mrs. L. SHAKIRA BANU, M.E.
DOI: 10.17148/IJARCCE.2026.15654
Abstract: The rapid growth of digital payment systems and e-commerce platforms has significantly increased the volume of online financial transactions. However, this growth has also led to a rise in cybercrimes such as phishing, OTP fraud, and unauthorized access to sensitive financial data. Traditional payment systems lack adaptive security mechanisms, making them vulnerable to evolving fraud techniques.
This project proposes a cloud-based secure payment system that integrates OTP authentication and encrypted data storage to enhance transaction security. The system ensures that every transaction is verified through a one-time password and simultaneously sends detailed transaction information to the user’s registered email. A security link is also provided to report fraudulent activities instantly to cybercrime authorities.
The proposed system focuses on protecting user data, preventing OTP misuse, and ensuring secure communication through encryption techniques. By leveraging cloud technology, the system provides scalability, real-time monitoring, and improved reliability. Overall, the system enhances security in digital payments, reduces fraud risks, and increases user trust in online transactions.
Keywords: Secure payment system, OTP authentication, Cloud computing, Data encryption, Fraud detection
This project proposes a cloud-based secure payment system that integrates OTP authentication and encrypted data storage to enhance transaction security. The system ensures that every transaction is verified through a one-time password and simultaneously sends detailed transaction information to the user’s registered email. A security link is also provided to report fraudulent activities instantly to cybercrime authorities.
The proposed system focuses on protecting user data, preventing OTP misuse, and ensuring secure communication through encryption techniques. By leveraging cloud technology, the system provides scalability, real-time monitoring, and improved reliability. Overall, the system enhances security in digital payments, reduces fraud risks, and increases user trust in online transactions.
Keywords: Secure payment system, OTP authentication, Cloud computing, Data encryption, Fraud detection
Abstract
DIABETIC RETINOPATHY DIAGNOSIS USING RESNET
Nimisha P S, Aravind A S
DOI: 10.17148/IJARCCE.2026.15655
Abstract: Diabetic Retinopathy (DR) is a leading cause of preventable blindness globally, characterized by damage to the blood vessels in the retina. Early detection and timely intervention are critical to preserving vision; however, manual screening of retinal fundus images is time-consuming and prone to human error. In this paper, we propose a highly accurate automated diagnostic system utilizing a ResNet architecture for the efficient detection of Diabetic Retinopathy. Our approach processes and classifies retinal images, distinguishing between healthy eyes and those affected by DR. The proposed model was trained and rigorously evaluated, achieving a remarkable training accuracy of 97.3% and a testing accuracy of 94.6%. The results substantiate that our proposed deep learning framework not only offers superior diagnostic accuracy but also minimizes false negatives, making it a robust and scalable solution to assist ophthalmologists in clinical diagnostic workflows and early screening of Diabetic Retinopathy.
Keywords: Diabetic Retinopathy, Deep Learning, ResNet, Medical Image Classification, Retinal Fundus Images, Automated Diagnosis.
Keywords: Diabetic Retinopathy, Deep Learning, ResNet, Medical Image Classification, Retinal Fundus Images, Automated Diagnosis.
Abstract
GREENLENS: AN INTELLIGENT CNN- BASED LEAF DISEASE DETECTION AND AGRICULTURAL RECOMMENDATION SYSTEM
Aswathy V S, Dr Arathi Chandran R I
DOI: 10.17148/IJARCCE.2026.15656
Abstract: Early detection of crop diseases is essential for improving agricultural productivity and reducing crop loss. The proposed system presents an automated crop disease recognition framework using Convolutional Neural Networks (CNN) and image processing techniques. Leaf images from the PlantVillage dataset are preprocessed and classified to identify healthy and diseased crops based on visual symptoms such as discoloration, spots, and texture variations. The model was achieved high training and testing accuracy, demonstrating reliable classification performance. The proposed approach provides an efficient, accurate, and user-friendly solution for smart agriculture applications and supports farmers in taking timely preventive measures for crop protection.
Keywords: CNN, Crop Disease Detection, Deep Learning, Image Processing, PlantVillage Dataset.
Keywords: CNN, Crop Disease Detection, Deep Learning, Image Processing, PlantVillage Dataset.
Abstract
CricGuardian: Injury risk prediction system
Pratul Gorde, Dasganu Hakke, Pranit Khodke, Prathamesh Hajare
DOI: 10.17148/IJARCCE.2026.15657
Abstract: Cricket broke the calendar. Test matches stretching five days, ODIs cramming action into fifty overs, T20 exploding every few hours—same bodies, different torture. Players fly between continents, sleep in airports, recover in planes. Knees, backs, hamstrings—these don't care about broadcast schedules. Injuries happen. Teams lose stars, careers end early, fans wonder what could've been. Current fix? Wait until something hurts, then treat. Physio rubs where it aches, coach rests who limps. Reacti ve, late, often too late. We built something that sees trouble coming. Machine learning model—Random Forest, if you want the technical— trained on how workload actually breaks bodies. Recent bowling overs, sprint distances, gym hours, sleep quality, travel miles, age, history of tweaks. Feeds in, spits out percentage: 15% risk this week, 63% next month if you don't rest. Not replacing physios. Giving them numbers they never had. Coach sees bowler hitting 80% risk, rotates early. Player feels fine, model disagrees, scans deeper, finds stress fracture brewing. Caught before it breaks.
Keywords: Injury Prediction, Sports Analytics, Machine Learning, Random Forest, Workload Management, Cricket.
Keywords: Injury Prediction, Sports Analytics, Machine Learning, Random Forest, Workload Management, Cricket.
Abstract
Algorithm for Traversing a Branched Contour in a Digital Image
Nikoloz Nargizashvili, Otar Tavdishvili
DOI: 10.17148/IJARCCE.2026.15658
Abstract: The retrieval of the objects extracted through digital image segmentation from image databases, as well as their classification based on shape, currently constitutes one of the most important research topics in the field of digital image analysis. Efficient retrieval of similar shapes from image databases requires highly accurate shape descriptions. At the same time, shape representations must remain invariant to translation, rotation, and scaling transformations. This paper considers the problem of contour-based shape description. In this article, an original contour tracing algorithm which is capable of handling both non-branching and branching contours is presented.
Keywords: Digital image, Shape description, Branch, Non-branch, Contour tracing algorithm.
Keywords: Digital image, Shape description, Branch, Non-branch, Contour tracing algorithm.
Abstract
Sign Language Recognition and Translation Systems: A Comprehensive Review with Special Focus on Malayalam Sign Language (MSL)
Dr. Ambili A.R
DOI: 10.17148/IJARCCE.2026.15659
Abstract: The main form of communication for those with speech and hearing problems is sign language. Recent developments in artificial intelligence, computer vision, and machine learning have greatly expedited the development of automated sign language detection and translation systems. These technologies use sign-to-text, sign-to-speech, speech-to-sign, and text-to-sign translation methods to close the communication gap between hearing-impaired people and the hearing community. Malayalam Sign Language (MSL), which was legally adopted in Kerala in 2021, has received comparatively less attention than American Sign Language (ASL), Arabic Sign Language, and other extensively used sign languages. With an emphasis on machine learning, deep learning, transfer learning, graph convolutional networks, and multimodal communication frameworks, this study offers a thorough analysis of current advancements in sign language identification and translation systems. The review highlights current issues and potential future research areas while analyzing present approaches, datasets, methodologies, and performance measures. This study gives special attention to Malayalam Sign Language detection and the necessity for effective, scalable, and real-time assistive communication technology.
Keywords: Sign Language Recognition, Malayalam Sign Language, American Sign Language, Deep Learning, Machine Learning, Assistive Technology
Keywords: Sign Language Recognition, Malayalam Sign Language, American Sign Language, Deep Learning, Machine Learning, Assistive Technology
Abstract
LEP-Model: Ascertain Loan Predictions Using ML Approach
Pratibha Deshmukh, Yogendra Chhetri, Harsh Nakti, Vinay Gupta, Shivam Patil
DOI: 10.17148/IJARCCE.2026.15660
Abstract: As a result of the banking industry's advancements, a large number of people are applying for bank loans. However, the bank can only approve a limited number of applicants due to its limited resources, so determining who will be a safer candidate for approval is a common procedure. We therefore attempt to lower the risk involved in choosing the safe individual in this study report in order to preserve numerous bank endeavors and assets. This is accomplished by taking information from the previous records of the borrowers and based on these records, the machine was trained using an ML and Python model to provide the most accurate results. Assigning a debt to a particular individual or not is the primary goal of this study article. With the Logistic Regression algorithm receiving the maximum score of 80.78%, our research result demonstrated good performance accuracy. Finding out if it will be safe to give a loan to a specific individual is the first priority. The principal objective of the research paper is to forecast the loan eligibility of the clients and ascertain the conditions that precluded them from obtaining a loan for the construction of their own home.
Keywords: Loan Prediction, Machine Learning, Logistic Regression, Banking Sector.
Keywords: Loan Prediction, Machine Learning, Logistic Regression, Banking Sector.
Abstract
TriDoshaX: AI Based Panchakarma Treatment and Diet Recommendation System
Sujal Wadkar, Poonam Dhamal, Shreyans Magdum, Vishwajeet Ghadge
DOI: 10.17148/IJARCCE.2026.15661
Abstract: Ayurvedic medicine offers a prevention-oriented, constitution-based approach to personalized healthcare through the Tridosha framework—Vata, Pitta, and Kapha—collectively describing an individual’s Prakriti. Despite its clinical relevance, widespread adoption is hindered by the scarcity of trained practitioners, the inherent subjectivity of manual assessments, and the absence of scalable digital tools. This paper presents TriDoshaX, a web-based Clinical Decision Support System (CDSS) that implements Ayurvedic Prakriti classification through a hybrid architecture combining supervised machine learning with rule-based Ayurvedic scoring. A structured questionnaire capturing 26 physiological, psychological, and lifestyle attributes is used to collect user data, which is subsequently processed through an ensemble Random Forest classifier trained on approximately 5,000 records. The Random Forest model achieves an accuracy of 0.826, precision of 0.851, recall of 0.826, and F1-score of 0.812, outperforming Decision Tree, Support Vector Machine, and Logistic Regression baselines. The predicted Dosha type drives a personalized recommendation engine that suggests Panchakarma therapies, dietary plans, and lifestyle guidelines consistent with classical Ayurvedic principles. Additionally, a Retrieval-Augmented Generation (RAG) pipeline powers a conversational AI assistant capable of responding to Ayurveda-specific queries. TriDoshaX bridges traditional Ayurvedic principles with modern AI-assisted healthcare delivery, providing an accessible platform for preventive health management.
Keywords: Ayurveda; Tridosha; Prakriti Classification; Panchakarma; Random Forest; Clinical Decision Support System; Retrieval-Augmented Generation; Personalized Healthcare.
Keywords: Ayurveda; Tridosha; Prakriti Classification; Panchakarma; Random Forest; Clinical Decision Support System; Retrieval-Augmented Generation; Personalized Healthcare.
Abstract
Intelligent Serverless Architecture for Real-Time Expense Management: A Function-as-a-Service Approach to Scalable Financial Analytics
Gudelli Mounika, A.N.Rama Mani
DOI: 10.17148/IJARCCE.2026.15662
Abstract: Effective management of organizational expenditure in distributed computing environments presents persistent challenges, particularly with respect to cost attribution, real-time visibility, and scalable analytics. This paper presents an intelligent, serverless expense management framework built upon Function-as-a-Service (FaaS) paradigms, enabling automated, event-driven financial tracking across multi-tenant cloud deployments. The proposed system integrates a micro services-based backend leveraging AWS Lambda, Azure Functions, or equivalent serverless runtimes with a dynamic, responsive frontend, forming a cohesive full-stack platform for expenditure capture, categorization, and reporting. The architecture incorporates role-based access control (RBAC), multi-dimensional cost aggregation, predictive budget forecasting using lightweight regression models, and RESTful API orchestration through a managed API gateway. Experimental evaluations demonstrate that the proposed framework achieves average API response latencies below 220 ms under concurrent load, reduces infrastructure operational overhead by approximately 63% compared to traditional server-based deployments, and attains a budget-forecasting accuracy of 91.4% measured by mean absolute percentage error (MAPE). The system further supports automated alert generation upon threshold breaches and provides drill-down analytical dashboards for stakeholders. Comparative benchmarking against conventional monolithic and containerized expense systems confirms the superiority of the serverless paradigm in elasticity, cost efficiency, and developer agility. The proposed methodology provides a replicable blueprint for institutions seeking to modernize their financial governance infrastructure through cloud-native technologies.
Keywords: Serverless Computing; Function-as-a-Service; Expense Management; Cloud-Native Architecture; Financial Analytics; Budget Forecasting; Micro services; REST API
Keywords: Serverless Computing; Function-as-a-Service; Expense Management; Cloud-Native Architecture; Financial Analytics; Budget Forecasting; Micro services; REST API
Abstract
AI-Powered Heart Disease Prediction with Appropriate Feature Selection
Iffat Shireen, Dr. M. A. Pund, Prof. A. U. Chaudhari
DOI: 10.17148/IJARCCE.2026.15663
Abstract: Heart disease remains one of the major causes of death worldwide, making early prediction and prevention extremely important. This study presents an ai-powered heart disease prediction system that uses machine learning techniques to estimate a patient's risk of developing heart disease based on clinical parameters such as age, blood pressure, cholesterol level, heart rate, and other medical factors. multiple machine learning models were trained and evaluated using the cleveland heart disease dataset, and the best performing model was integrated into a flask based web application for real time prediction. To improve transparency and user trust, shap (shapley additive explanations) is used to explain how different features influence the prediction results, The proposed system demonstrates how explainable artificial intelligence can assist healthcare professionals and patients in understanding prediction outcomes, promoting preventive healthcare, and supporting informed clinical decision making.
Keyword—Heart Disease Prediction, Machine Learning, Explainable Artificial Intelligence, SHAP, Risk Assessment, Flask Web Application.
Keyword—Heart Disease Prediction, Machine Learning, Explainable Artificial Intelligence, SHAP, Risk Assessment, Flask Web Application.
Abstract
Blockchain-Based Secure Data Transmission for IoT Devices
Nikhil V, Pavan R P, Yashas Nagaraj, Gnana Prakash P, Mrs. Kavya K S Math
DOI: 10.17148/IJARCCE.2026.15664
Abstract: The rapid growth of the Internet of Things (IoT) has led to the widespread adoption of smart devices in surveillance, security, and monitoring applications. However, IoT devices are vulnerable to security threats such as unauthorized access, data tampering, cyber-attacks, and centralized storage failures. This project proposes a Blockchain- Based Secure Data Transmission for IoT Devices to enhance the security, integrity, and reliability of data communication. The system utilizes ESP32-CAM and sensors to detect events and capture surveillance data, which is transmitted securely through a network. AES encryption is used to ensure data confidentiality, while SHA-256 hashing is applied to maintain data integrity. The generated hash values are stored on a blockchain network, providing tamper-proof verification and transparency. The actual data is stored securely in cloud and local storage for backup and accessibility. The proposed system also supports real-time monitoring and alert notifications. By integrating IoT, blockchain technology, encryption, and hashing mechanisms, the system provides a secure, reliable, and scalable framework for protecting sensitive data and ensuring trustworthy communication in IoT environments.
Keywords: Internet of Things (IoT), Blockchain Technology, Secure Data Transmission, ESP32-CAM, AES Encryption, SHA-256 Hashing, Surveillance System, Data Integrity, Cloud Storage, Cybersecurity.
Keywords: Internet of Things (IoT), Blockchain Technology, Secure Data Transmission, ESP32-CAM, AES Encryption, SHA-256 Hashing, Surveillance System, Data Integrity, Cloud Storage, Cybersecurity.
Abstract
HEART ATTACK RISK PREDICTION SYSTEM USING CNN-LSTM
JININA D C, SHALOM DAVID
DOI: 10.17148/IJARCCE.2026.15665
Abstract: Heart Attack is one of the leading causes of mortality worldwide and poses a significant challenge to modern healthcare systems. Early detection and accurate prediction of heart disease can help reduce complications, improve treatment outcomes, and save lives. With the rapid growth of medical data and advancements in artificial intelligence, machine learning and deep learning techniques have become effective tools for disease prediction. This paper presents a hybrid CNN-LSTM based Heart Attack Risk Prediction System that utilizes Convolutional Neural Networks (CNN) and a hybrid CNN-LSTM model for predicting the presence of heart disease. The dataset is pre-processed using data cleaning, normalization, scaling, and feature selection techniques to improve data quality and model performance. The proposed system is developed with a user-friendly interface that enables efficient data input and prediction analysis. Experimental results show that the hybrid CNN-LSTM model outperforms individual models by effectively learning feature relationships and dependencies among clinical attributes. The CNN model achieved an accuracy of 93.12%, whereas the CNN-LSTM model achieved a superior accuracy of 98.47%. The results demonstrate the effectiveness of the proposed approach in providing accurate and reliable heart disease prediction. The developed system can assist healthcare professionals in early diagnosis and decision-making, thereby contributing to improved patient care and preventive healthcare.
Keywords: Heart Disease Prediction, Deep Learning, CNN-LSTM, Machine Learning, Healthcare Analytics.
Keywords: Heart Disease Prediction, Deep Learning, CNN-LSTM, Machine Learning, Healthcare Analytics.
Abstract
Anti-Tampering Framework for Android Gaming Applications
Bharath A, R Bharath, Punith V, E Pavan Kumar, Mrs. Surekha Bhangari
DOI: 10.17148/IJARCCE.2026.15666
Abstract: Android gaming applications are increasingly targeted by tampering attacks including APK repackaging, memory editing, runtime hooking, and debugging-based manipulation. Existing protection mechanisms primarily rely on single-layer defenses such as code obfuscation and basic root detection, which are readily bypassed by advanced tools like GameGuardian, Frida, and Xposed. This review article surveys existing literature on anti-tampering and anti-cheat systems for Android, covering static and dynamic defense techniques. We analyze methodologies including APK integrity verification via cryptographic hashing, runtime hook detection through memory and process inspection, and memory integrity monitoring for critical game variables. Comparative analysis of recent studies is conducted based on methodology, findings, and limitations. Research gaps related to integrated multi-layer protection, mobile-specific defenses, and real-time anomaly scoring are identified. A comprehensive Anti-Tampering Framework combining APK integrity verification, hook detection, memory monitoring, and environment attestation is proposed.
Keywords: Android Security, Anti-Tampering, APK Integrity, Runtime Hook Detection, Memory Monitoring, GameGuardian, Frida, Xposed, Anomaly Scoring, Environment Attestation.
Keywords: Android Security, Anti-Tampering, APK Integrity, Runtime Hook Detection, Memory Monitoring, GameGuardian, Frida, Xposed, Anomaly Scoring, Environment Attestation.
Abstract
Energy Harvesting and Health Monitoring Device
Dr.D.O.Patil, Dr.R.K.Moje, Sanika Jagnade, Aditi Bhosale, Trupti Sonavane
DOI: 10.17148/IJARCCE.2026.15667
Abstract: Continuous monitoring of vital health parameters is essential for the early detection of medical emergencies and effective patient care, particularly in remote and rural regions where healthcare facilities and communication infrastructure are limited[1]. Conventional health monitoring systems primarily rely on Wi-Fi, GSM, or internet connectivity[2],[3], which often suffer from restricted coverage, high power consumption, and frequent battery charging requirements[4]. This paper presents an Energy Harvesting Based Smart Health Monitoring Device Using LoRa Communication for Remote Healthcare Applications that enables reliable, low-power, and long-range health monitoring[5]. The proposed system integrates a MAX30102 sensor for heart rate and blood oxygen saturation (SpOâ‚‚) measurement, and an 16*2 LCD display for real-time visualization of health parameters. An Arduino nano microcontroller performs data acquisition, processing, and communication management, while a solar energy harvesting unit with rechargeable energy storage ensures self-sustained operation and minimizes dependence on external charging sources[7]. Health data are transmitted through LoRa technology, enabling communication over several kilometers without requiring cellular or internet networks[8]. In emergency situations, the system automatically[9] . The proposed solution offers enhanced energy efficiency, reliability, and cost-effectiveness, making it highly suitable for remote patient monitoring, elderly care, industrial worker safety, and disaster-response healthcare applications[10].
Keywords: Energy Harvesting, LoRa Communication, Health Monitoring System, Healthcare Device, Remote Patient Monitoring, MAX30102 Sensor, Arduino nano microcontroller, Solar-Powered device, Emergency Alert System.
Keywords: Energy Harvesting, LoRa Communication, Health Monitoring System, Healthcare Device, Remote Patient Monitoring, MAX30102 Sensor, Arduino nano microcontroller, Solar-Powered device, Emergency Alert System.
Abstract
Fingerprint-Based Blood Group Detection Using CNN and KNN: A Comparative Study
S. Venkateswara Rao, Bodigam Harini
DOI: 10.17148/IJARCCE.2026.15668
Abstract: For reasons such as discharge, legal processes, and emergencies, it is crucial to accurately determine blood group. Taking blood samples is at the heart of conventional wisdom, but it may not always be the best course of action. For the purpose of non-invasive blood grouping utilising fingerprint biometric methods, this research compares two algorithms, CNN and KNN, using a database of 500 fingerprint pictures labelled A, B, AB, and O (blood group). Achieving a remarkable 92.4% performance, CNN is able to record intricate spatial hierarchies and fingerprints using pattern layers in conjunction with ReLU activation. Performance has been enhanced by making minor adjustments to the photos. Because it relies on hand-crafted features and Euclidean distance, KNN—which achieves an accuracy of 76.8%— fails in a high-dimensional feature space. The error analysis shows that incompleteness and fingerprints are the key reasons CNN is poor. Due to its sensitivity to noise and overlaps, KNN demonstrates a higher level. A quick and effective non-invasive way to identify blood groups has emerged from research into the potential uses of convolutional neural networks (CNNs) in forensics, portable diagnostic devices, and automated blood transfusion management systems. The database will be expanded substantially and hybrid models will be used for improved performance in future studies.By conducting a thorough analysis of CNN and KNN, this work expands upon the idea of biometric blood group identification. The results show that CNN is more suited for this task than other methods because of its high noise power, which allows it to extract features more effectively. Our database will be further expanded in future studies, and hybrid models that combine the best features of several algorithm models to improve performance will be further investigated.
Keywords: Blood Group Detection, Fingerprints Biometrics, Machine Learning, CNN, KNN, Image Processing, Comparative Analysis
Keywords: Blood Group Detection, Fingerprints Biometrics, Machine Learning, CNN, KNN, Image Processing, Comparative Analysis
Abstract
Machine Learning-Based Sentiment Analysis of Climate Change Discussions on Twitter
S. Srinivas, Dharmana Akhila
DOI: 10.17148/IJARCCE.2026.15669
Abstract: A more nuanced and complicated public opinion on climate change discussions on Twitter is what we want to shed light on. This study makes use of state-of-the-art ML and NLP tools, most notably Support Vector Machines (SVM), to do sentiment analysis on a worldwide scale using a large dataset acquired from a trustworthy external source. Focusing on both positive and negative emotions, this research aims to identify the many ways in which people express themselves emotionally while discussing climate change. Critically examined are the veracity and practicality of the chosen third- party dataset, which was acquired via Kaggle and made feasible by a Canadian Innovation Foundation JELF grant that was bestowed onto Chris Bausch of the University of Waterloo. With a remarkable F1 score of 0.70, the SVM-based sentiment analysis shows how well the selected approach captures the nuanced nature of climate change arguments on Twitter. Legislators, groups aiming to reach a worldwide audience, and climate change activists may all benefit from the research's communication tactics. The complex link between public opinion on climate change and online discourse is better understood in this research, which makes use of an external dataset and the sentiment analysis component of the Support Vector Machines method. In conclusion, this work adds substantial new information to the expanding field at the intersection of social media dynamics and environmental awareness by demonstrating the efficacy of support vector machines (SVMs) in identifying sentiment subtlety in massive datasets.
Keywords: Climate, sentiment analysis, and complexity are related terms.
Keywords: Climate, sentiment analysis, and complexity are related terms.
Abstract
Intelligent IoT Systems for Green Computing: Machine Learning-Based Resource Optimization and Energy Efficiency
Sagar Panwar, Jitendra Kumar Saini, Varun Bansal, Ravi Kumar
DOI: 10.17148/IJARCCE.2026.15670
Abstract: The integration of the Internet of Things (IoT) and Machine Learning (ML) plays a vital role in advancing green computing by improving energy efficiency, optimizing resource utilization, and supporting sustainable practices. IoT devices collect real-time data, while ML algorithms analyze this data to enable intelligent decision-making in areas such as energy management, predictive maintenance, HVAC optimization, smart agriculture, and waste management. Although challenges such as data privacy, scalability, resource constraints, and interoperability remain, emerging technologies like edge computing, federated learning, and AI-driven sustainability solutions offer promising future opportunities. Overall, the combination of IoT and ML can significantly reduce environmental impact and improve operational efficiency, contributing to the goals of green computing and sustainable development.
Keywords: IoT, Machine Learning, Green Computing, Sustainability, Energy Efficiency, Smart Cities, Predictive Maintenance, Smart Agriculture, Waste Management, Edge Computing.
Keywords: IoT, Machine Learning, Green Computing, Sustainability, Energy Efficiency, Smart Cities, Predictive Maintenance, Smart Agriculture, Waste Management, Edge Computing.
Abstract
Intelligent DNA Sequence Classification Using Machine Learning Techniques
Dr.B.Nageswara Rao, Nalanagula Chaitanya
DOI: 10.17148/IJARCCE.2026.15671
Abstract: The purpose of this study is to provide a new method for improving the efficiency and precision of DNA sequence classification by use of Machine Learning algorithms. To sort DNA sequences into predetermined groups, such species identification, the suggested DNA Sequencing Classifier makes use of cutting-edge Machine Learning methods. Sequencing DNA has changed the face of genetics in many fields, including medicine, evolution, and others. Still, DNA sequences may be difficult to accurately classify. Machine learning may automate this process, making it more precise and uncovering hidden patterns. Using state-of-the-art Machine Learning methods, this research seeks to develop a DNA sequence classifier that is both efficient and effective. Genetic research may be accelerated and improved with the use of automated categorisation. The first step in ensuring the accuracy of raw DNA sequences is data preparation. Machine learning models use the extracted characteristics as inputs and choose the most effective classifier according to performance. Using k-mer counting, the DNA Sequencing Classifier is compared to other approaches and reviewed thoroughly. The integration of machine learning with DNA sequencing has great potential for simplified DNA categorisation, which in turn might speed up research and enhance our knowledge of genetics.
Keywords: DNA, Natural Language Processing (NLP), k-mer counting, NaĂŻve Bayes, Bag of words.
Keywords: DNA, Natural Language Processing (NLP), k-mer counting, NaĂŻve Bayes, Bag of words.
Abstract
Predictive Analytics for Childbirth Mode Classification Using Machine Learning Techniques
Marpu Pradeepthi, Asoda Manasa
DOI: 10.17148/IJARCCE.2026.15672
Abstract: The birthing process is crucial to the mother's health and the relationship she forms with her infant. For the sake of both mother and child, the choice about the mode of delivery must be swiftly implemented. It is challenging for healthcare providers to make prompt and accurate choices in this area since mistakes in this area may have major effects on the mother's and fetus's health. A machine learning-based decision-support system for determining the most secure delivery mode is introduced in this study. K-Nearest Neighbours (KNN), Random Forest (RF), Decision Tree, Support Vector Machine (SVM), and a stochastic classifier are among the supervised learning techniques that are evaluated. This paper's results show that the Random Forest algorithm is more accurate at predicting the delivery mode, which helps doctors make better decisions and keeps mums and babies safer.
Keywords: childbirth prediction, machine learning, decision support system, delivery mode classification, maternal and infant health
Keywords: childbirth prediction, machine learning, decision support system, delivery mode classification, maternal and infant health
Abstract
BlinkSpeller: Dynamic EAR-Based Eye Blink Morse Code Communication System with Predictive Text and Speech Synthesis
Amanda Terence, Anisha Anna Vinod, Risa Abdul Khadir R, Vaishnu M, Amila A L
DOI: 10.17148/IJARCCE.2026.15673
Abstract: Assistive communication technologies are crucial to the communication of people who cannot use traditional input devices because of their severe motor impairments. In this paper, a low cost, web-based communication system is presented that uses eye blink detection to provide text and speech output in real time. The system uses MediaPipe Face Mesh for facial landmark detection and calculates the Eye Aspect Ratio (EAR) to detect blinks. A custom algorithm categorizes blinks into short and long blinks (Morse code) and adds noise filtering, head motion compensation and gaze stabilization to make it more robust. A special decoding engine is used to decode the generated Morse sequences into alphanumeric characters and control commands. The system is built on browser-based APIs such as the Webcam API, SpeechSynthesis API, and localStorage, allowing for real-time data processing, speech generation, and storage. A word prediction module increases the efficiency of input. The proposed system shows high reliability, low latency and high accessibility, which can provide an effective solution for hands-free human–computer interaction.
Keywords: Assistive Communication System, Eye Blink Detection, MediaPipe Face Mesh, Eye Aspect Ratio (EAR), Morse Code Communication, Speech Synthesis, Computer Vision, Human–Computer Interaction
Keywords: Assistive Communication System, Eye Blink Detection, MediaPipe Face Mesh, Eye Aspect Ratio (EAR), Morse Code Communication, Speech Synthesis, Computer Vision, Human–Computer Interaction
Abstract
Voices Against Misuse: A Survey-Based Study on the Need for Ethical Regulation of Generative AI Tools
Dr. Pratibha Deshmukh, Dhananjay Kishor Potdar
DOI: 10.17148/IJARCCE.2026.15674
Abstract: Artificial intelligence (AI) has developed at such a remarkable pace that it has nearly accelerated beyond our ability to create new things and simplify certain everyday tasks, with these benefits also bringing about incredibly serious issues (e.g., fake information, misleading information, web frauds, etc.). This study examines the perception of risks associated with using AI by crowds of individuals through surveying their level of awareness of risk/reward ratio when using AI. The findings of our research indicate that while there is a sizable percentage of users using an AI application regularly, most users are not completely aware or able to differentiate between authentic and false forms of media when it comes to the use of AI. In addition, many participants claimed to have witnessed some type of AI abuse and were highly concerned about the societal consequences of AI abuse. Finally, the results show that there is no high level of confidence regarding corporate practices of self-regulation by means of high levels of regulation and official mandates, and there is a belief that stricter regulations and official oversight will continue to be required moving forward. In conclusion, the exploratory survey emphasizes the need for greater public awareness, clearer guidelines for use of AI in compliance, and ethical standards to reduce misuse and associated risks.
Keywords: Artificial Intelligence, Generative AI, AI Misuse, Deepfakes, Misinformation, AI Ethics, Regulation, Public Awareness, Survey-Based Study, AI Governance
Keywords: Artificial Intelligence, Generative AI, AI Misuse, Deepfakes, Misinformation, AI Ethics, Regulation, Public Awareness, Survey-Based Study, AI Governance
Abstract
A Comprehensive Study of IoT Sensors and Actuators
Parteek Singh, Dr. Harish Rohil
DOI: 10.17148/IJARCCE.2026.15675
Abstract: The Internet of Things (IoT) is a cutting-edge technology. By building a smart environment around us with trillions of sensors and actuators, it is revolutionising our world. A unified operating picture can be created using shared information from ubiquitous sensing capabilities. Recently, sensors have been thought of as a very promising aspect of scientific study. IoT-based sensors have become a significant role because of their widespread usage and functionality. Sensors are frequently employed in the industrial internet to monitor manufacturing processes as well as our physical and environmental health, as well as the security of our homes. Earlier, businesses and organisations used a variety of sensors, but the advent of the Internet of Things has catapulted sensor development to an entirely new level. Various IoT sensors and actuators utilized in them is presented in this paper together with a number of IoT sensors.
Keywords: Internet of Things (IoT); GPS; Sensors; Bluetooth Low Energy (BLE)
Keywords: Internet of Things (IoT); GPS; Sensors; Bluetooth Low Energy (BLE)
Abstract
FACIAL FEATURE ANALYSIS FOR DEEPFAKE DETECTION
Vaishnavi J Manoj, Aravind A S
DOI: 10.17148/IJARCCE.2026.15676
Abstract: The rapid advancement of artificial intelligence and deep learning technologies has led to the widespread creation of highly realistic synthetic media known as deepfakes. Deepfake content can manipulate facial expressions, voices, and visual appearances in images, videos, and audio recordings, making it increasingly difficult to distinguish between real and fabricated media. Such manipulations pose serious threats to digital security, privacy, and public trust. This project presents an intelligent deepfake detection system that performs facial feature analysis to identify manipulated multimedia content, including images, videos, and audio.
The proposed system utilizes advanced image processing and deep learning techniques to analyse facial characteristics and detect inconsistencies introduced during deepfake generation. The system employs a hybrid deep learning architecture based on Meso4Net and Capsule Network (CapsuleNet) models, which effectively capture both texture- level artifacts and spatial relationships in facial structures. The detection process involves multiple stages including preprocessing, frame extraction from videos, facial landmark detection, feature extraction, and classification to determine whether the input media is real or manipulated.
Keywords: Deepfake, Deep Learning, Capsule Network, Meso4Net, Artificial Intelligence.
The proposed system utilizes advanced image processing and deep learning techniques to analyse facial characteristics and detect inconsistencies introduced during deepfake generation. The system employs a hybrid deep learning architecture based on Meso4Net and Capsule Network (CapsuleNet) models, which effectively capture both texture- level artifacts and spatial relationships in facial structures. The detection process involves multiple stages including preprocessing, frame extraction from videos, facial landmark detection, feature extraction, and classification to determine whether the input media is real or manipulated.
Keywords: Deepfake, Deep Learning, Capsule Network, Meso4Net, Artificial Intelligence.
Abstract
Epidemic Forecasting Using an Improved frequent Pattern Procedure and Multivariable Time-Based Association Pattern Extraction
Tasleem Rafiq Sheikh
DOI: 10.17148/IJARCCE.2026.15677
Abstract: Global health crises pose risks to the global healthcare system and economic systems. Preliminary forecasting of an illness epidemic may help administrations and healthcare institutions perform precautionary measures. The article suggests an improved frequent pattern growth method. It gets put together with extracting patterns from time-based relationships across multiple dimensions to help with health crisis forecasting. Conventional frameworks rely primarily on automated training and quantitative methods. This approach may overlook some latent time-based associations in the medical data. It seems like those connections could matter, but they are often missed. Maybe the time aspects are what stand out here. There is more to consider with how the data actually flows over periods. This suggested picture combines medical data, Weather-related variables, Population-based data, time-based variables, and the calendar month to detect epidemic structures. That identified relationship pattern may assist a timely alert framework and enhance the formulation of community healthcare policy.
Keywords: Global health crisis, Association rule, Frequent pattern growth algorithm, medical care data analysis, Data extraction.
Keywords: Global health crisis, Association rule, Frequent pattern growth algorithm, medical care data analysis, Data extraction.
Abstract
Freshers Hub: An AI Powered Career Guidance Platform
Nikhil Gupta, Dasganu Hakke, Pranav Kumbhalkar, Prasad Babar
DOI: 10.17148/IJARCCE.2026.15678
Abstract: The period immediately following graduation presents fresh engineering students with a paradox: an abun-dance of learning resources coexists with a near-total absence of structured direction. Disconnected platforms, generic interview preparation materials, and dormant professional profiles collec-tively leave many graduates underprepared when they first en-counter industry hiring pipelines. This paper introduces Freshers Hub, an intelligent, AI-driven career readiness platform designed to bridge this structural gap through three tightly integrated modules. The Skill Roadmap Engine constructs personalized, sequenced learning paths based on a student’s target role, draw-ing on a validated graph of technology dependencies to prevent unproductive learning loops. The AI Interview Simulator creates a realistic and pressure-free practice environment by dynamically generating domain- specific questions across difficulty tiers and analyzing candidate responses using SpaCy-based natural language processing pipelines that evaluate semantic accuracy, sentiment, and professional confidence. The Profile Analytics module aggregates data from LinkedIn, GitHub, and LeetCode through asynchronous REST API calls, computing a proprietary Professional Identity Score that is benchmarked against real job descriptions to generate a recruiter-facing readiness heatmap. Built on a React.js and Next.js frontend, a Python-FastAPI and Node.js backend, and MongoDB 6.0 for flexible data storage, Freshers Hub transforms fragmented preparation efforts into a unified, feedback-driven progression. Early validation trials indicate an approximately 80 percent reduction in resource-search overhead, measurable gains in interview performance under simulated pressure, and a roughly 30 percent improvement in profile visibility among participating students.
Keywords: AI-Powered Career Guidance, Natural Language Processing, Career Roadmaps, Mock Interview Simulation, Professional Identity Score, Fresh Graduates, SpaCy, TensorFlow.
Keywords: AI-Powered Career Guidance, Natural Language Processing, Career Roadmaps, Mock Interview Simulation, Professional Identity Score, Fresh Graduates, SpaCy, TensorFlow.
Abstract
NeuroWell AI: A Hybrid Deep Learning Framework for Early Detection of Mental Health Risks
M.PREETHA, J.LIN EBY CHANDRA
DOI: 10.17148/IJARCCE.2026.15679
Abstract: Mental health disorders represent a growing global crisis, with the World Health Organization estimating that over one billion individuals worldwide are affected by neurological and psychiatric conditions. Early and accurate detection of mental health risks remains a significant challenge due to the multifaceted, heterogeneous, and often latent nature of symptom manifestation. This paper proposes NeuroWell AI, a novel hybrid deep learning framework that integrates Bidirectional Long Short-Term Memory (BiLSTM) networks, Convolutional Neural Networks (CNN), and a Transformer-based attention mechanism to detect early-stage mental health risks from multimodal data sources, including clinical text records, social media posts, physiological signals, and standardized psychiatric questionnaire responses. The framework employs a fusion strategy combining feature-level and decision-level integration across modalities to improve discriminative power. An Explainable AI (XAI) module using SHAP (SHapley Additive exPlanations) is incorporated to enhance clinical interpretability. Experimental evaluation on four benchmark public datasets — CLPsych, DAIC-WOZ, MODMA, and Reddit Mental Health — demonstrates that NeuroWell AI achieves an average accuracy of 94.7%, precision of 93.8%, recall of 95.1%, and F1-score of 94.4%, significantly outperforming state-of-the-art methods. The proposed system offers a clinically relevant, interpretable, and generalizable solution for population-scale mental health screening.
Keywords: Mental health detection; Hybrid deep learning; BiLSTM; Transformer; Multimodal fusion; Explainable AI; Natural language processing; Affective computing
Keywords: Mental health detection; Hybrid deep learning; BiLSTM; Transformer; Multimodal fusion; Explainable AI; Natural language processing; Affective computing
Abstract
Neuro-Symbolic AI System for Logical Reasoning and Decision Making
T. THILLAI, J.LIN EBY CHANDRA
DOI: 10.17148/IJARCCE.2026.15680
Abstract: This paper presents a novel Neuro-Symbolic Artificial Intelligence (NeSy-AI) framework that integrates deep neural learning with symbolic logic reasoning to achieve robust, interpretable, and generalizable decision making. Contemporary AI systems based purely on neural networks excel at pattern recognition but frequently fail at structured logical inference, compositional generalization, and transparent reasoning chains required for safety-critical applications. Conversely, symbolic systems offer rigorous logical deduction but lack the capacity to process high- dimensional perceptual data. The proposed framework, termed NeSy-Decision Architecture (NeSy-DA), bridges this divide through a three-tier pipeline: (i) a neural perception module employing transformer-based encoders for feature extraction, (ii) a neurosymbolic grounding layer that maps continuous neural representations onto symbolic predicates using differentiable logic operators, and (iii) a symbolic reasoning engine powered by Answer Set Programming (ASP) and probabilistic inference rules. We evaluate NeSy-DA on three benchmark datasets, namely the bAbI reasoning tasks, the CLUTRR relational reasoning benchmark, and the VisualQA-Logic dataset, achieving classification accuracy of 94.7%, 91.3%, and 88.9% respectively, outperforming state-of-the-art baselines by margins of 3.2 to 7.6 percentage points. Ablation studies confirm that each module contributes meaningfully to overall performance, while qualitative analysis demonstrates improved decision interpretability compared to purely neural counterparts
Keywords: Neuro-symbolic AI, logical reasoning, decision making, Answer Set Programming, knowledge representation, differentiable logic, transformer networks.
Keywords: Neuro-symbolic AI, logical reasoning, decision making, Answer Set Programming, knowledge representation, differentiable logic, transformer networks.
Abstract
Cognitive AI for Network Resilience: Integrating Explainable AI and Blockchain for Real-Time Cyber Threat Detection
Rachana V Murthy, Amrutha R, Ashwitha C Shetty, Trupthi J, Vinutha N
DOI: 10.17148/IJARCCE.2026.15681
Abstract: The increasing complexity of cyber threats requires intelligent, adaptive, and transparent security solutions capable of real-time detection and response. This review paper examines the integration of Cognitive Artificial Intelligence (AI), Explainable AI (XAI), and blockchain technology to enhance network resilience against evolving cyberattacks. Recent research on AI-driven threat detection, XAI techniques such as SHAP and LIME, cyber resilience frameworks, and blockchain-based secure logging is analyzed to identify current advancements and research gaps. A conceptual Cognitive AI for Network Resilience (CAINR) framework is proposed, combining deep learning-based threat detection, explainable decision-making, and blockchain-enabled immutable audit trails. The study highlights the strengths and limitations of existing approaches and demonstrates that no single solution currently provides high detection accuracy, interpretability, secure logging, and automated response simultaneously. Future directions, including federated learning and reinforcement learning-based self-healing networks, are discussed. This review provides a foundation for developing trustworthy and resilient next-generation cybersecurity systems.
Keywords: Cognitive AI, Network Resilience, Explainable AI, XAI, Blockchain, Cyber Threat Detection, SHAP, LIME, Intrusion Detection Systems, Machine Learning, Cyber Resilience Framework, Zero-Day Attacks, Industrial IoT, SCADA Security, Threat Intelligence.
Keywords: Cognitive AI, Network Resilience, Explainable AI, XAI, Blockchain, Cyber Threat Detection, SHAP, LIME, Intrusion Detection Systems, Machine Learning, Cyber Resilience Framework, Zero-Day Attacks, Industrial IoT, SCADA Security, Threat Intelligence.
Abstract
AUTOGRAD
Miss. Raheen Rafique Bagwan, Miss. Akansha Anil Sasane, Miss. Riya Chandrakant Chawate, Miss. Rutuja Atul Kavitake
DOI: 10.17148/IJARCCE.2026.15682
Abstract: The increasing number of student assessments in educational institutions has made manual evaluation a time- consuming and resource-intensive process. AutoGrad is an Artificial Intelligence-based Automatic Grading System designed to automate the evaluation of handwritten and digital answer sheets while maintaining accuracy, consistency, and fairness. The system integrates advanced technologies such as Optical Character Recognition (OCR), Natural Language Processing (NLP), Machine Learning, and Large Language Models (LLMs) to assess student responses efficiently.
AutoGrad begins by accepting scanned answer sheets through a secure web interface. The uploaded documents undergo preprocessing techniques, including image enhancement, noise reduction, and segmentation, to improve recognition quality. A Gemini-based OCR engine extracts textual content from handwritten answers, while NLP techniques and semantic analysis evaluate the responses against predefined model answers and grading rubrics. The system further employs vector similarity matching and contextual reasoning using a fine-tuned language model to assess answer quality, completeness, and conceptual correctness.
In addition to automated scoring, AutoGrad generates detailed feedback and performance reports for students and educators. The platform supports objective, subjective, and numerical answer evaluation, making it suitable for a wide range of academic assessments. By reducing faculty workload, minimizing human bias, and delivering rapid results, AutoGrad enhances the efficiency and transparency of the evaluation process.
The proposed system demonstrates how modern AI technologies can transform traditional assessment methods and contribute to the development of scalable, intelligent, and student-centric educational environments.
Keywords: Automatic Grading System, Optical Character Recognition (OCR), Natural Language Processing (NLP), Large Language Models (LLMs).
AutoGrad begins by accepting scanned answer sheets through a secure web interface. The uploaded documents undergo preprocessing techniques, including image enhancement, noise reduction, and segmentation, to improve recognition quality. A Gemini-based OCR engine extracts textual content from handwritten answers, while NLP techniques and semantic analysis evaluate the responses against predefined model answers and grading rubrics. The system further employs vector similarity matching and contextual reasoning using a fine-tuned language model to assess answer quality, completeness, and conceptual correctness.
In addition to automated scoring, AutoGrad generates detailed feedback and performance reports for students and educators. The platform supports objective, subjective, and numerical answer evaluation, making it suitable for a wide range of academic assessments. By reducing faculty workload, minimizing human bias, and delivering rapid results, AutoGrad enhances the efficiency and transparency of the evaluation process.
The proposed system demonstrates how modern AI technologies can transform traditional assessment methods and contribute to the development of scalable, intelligent, and student-centric educational environments.
Keywords: Automatic Grading System, Optical Character Recognition (OCR), Natural Language Processing (NLP), Large Language Models (LLMs).
Abstract
Samanvay: Digital Governance Framework for Tri-Sector Collaboration in India’s Social Welfare Ecosystem
Amaan Sayyed, Yash Patil, Anuj Tiwari, Priyanka Kumbhar
DOI: 10.17148/IJARCCE.2026.15683
Abstract: India’s social welfare ecosystem suffers from fragmented coordination among Non-Governmental Organizations (NGOs), corporate bodies executing Corporate Social Responsibility (CSR) mandates, and government agencies overseeing welfare programs. No existing platform in India integrates all three stakeholders into a single, end- to-end digital workflow with verifiable fund tracking and structured project governance. This paper proposes Samanvay, a centralized web-based collaboration platform that addresses this gap through three formal technical contributions: (1) a Multi-Criteria Weighted Sum Model (WSM) for dynamic NGO Trust Grade computation across six normalized compliance and performance dimensions, (2) a rule-based Tripartite Matching Engine that ranks NGOs against government and corporate project requirements using a weighted similarity function, and (3) a Milestone-Gated Fund Release mechanism that conditions disbursement on dual-verified evidence and auto-generated Utilization Certificates. Prototype evaluation using 120 synthetic NGO profiles and 40 simulated projects demonstrates a verification processing time reduction of 64%, a CSR partner discovery time reduction of 71%, and 100% fund-to-milestone traceability. A comprehensive end-to-end workflow simulation comprising 120 automated test assertions across 10 functional phases achieved a 100% pass rate, validating integrated operation of all formal models in a realistic multi-stakeholder scenario. The platform supports India-specific regulatory compliance including 12A, 80G, FCRA, and Companies Act Section 135, and is applicable to education, healthcare, rural development, and skill-based welfare programs aligned with SDG 17.
Keywords: NGO Collaboration, CSR Platform, Digital Governance, Multi-Criteria Decision Making, Weighted Sum Model, Social Impact Monitoring, Milestone-Gated Fund Release, Public-Private Partnership, E-Governance, NGO Verification
Keywords: NGO Collaboration, CSR Platform, Digital Governance, Multi-Criteria Decision Making, Weighted Sum Model, Social Impact Monitoring, Milestone-Gated Fund Release, Public-Private Partnership, E-Governance, NGO Verification
Abstract
Improving Personal Finance Through Human-Computer Interaction
Ahmed S. AlMahmeed
DOI: 10.17148/IJARCCE.2026.15684
Abstract: This research paper explores the intersection of human-computer interaction (HCI) and personal finance management, analyzing how technological advances and user-centered design improve financial outcomes for individuals. The study reviews current literature, examines prevailing technologies, and discusses the benefits, challenges, and limitations associated with HCI integration in finance. Through case studies and an exploration of future trends, the paper offers recommendations for academics, finance professionals, and technology developers seeking to leverage HCI to foster financial literacy and empowerment.
Keywords: Human-Computer Interaction, Personal Finance, User-Centered Design, Financial Technology, Financial Literacy, Digital Banking, Financial Empowerment, Technology Integration, User Experience, Financial Management Tools
Keywords: Human-Computer Interaction, Personal Finance, User-Centered Design, Financial Technology, Financial Literacy, Digital Banking, Financial Empowerment, Technology Integration, User Experience, Financial Management Tools
Abstract
A Three-Level Framework for Programming Task Design in CS1: Bridging Conceptual Understanding and Transfer
Ahmed S. AlMahmeed
DOI: 10.17148/IJARCCE.2026.15685
Abstract: Introductory programming courses (CS1) often have high failure and dropout rates, signaling persistent challenges in mastering programming fundamentals. This paper synthesizes research from computing education, cognitive load theory, and transfer of learning to present a systematic framework for designing programming tasks at three levels: understanding, application, and applying. The framework aligns with modern Bloom’s Taxonomy in computing, aiming to support curriculum development, assessment alignment, and effective teaching in CS1. The camera-ready version improves structure, arguments, and includes ACM-style citations for SIGCSE and TOCE submission.
CCS CONCEPTS Social and professional topics → Computing education Applied computing → Education → Interactive learning environments Software and its engineering → Programming languages → General programming languages • Computing education: Highlights the importance of effective teaching methods and curriculum in computer science.
• Interactive learning environments: Focuses on frameworks that foster active engagement and conceptual growth for students.
• General programming languages: Centers on core principles and broad programming skills, independent of language specifics.
Keywords: Bloom’s Taxonomy, Cognitive, Problem-solving.
CCS CONCEPTS Social and professional topics → Computing education Applied computing → Education → Interactive learning environments Software and its engineering → Programming languages → General programming languages • Computing education: Highlights the importance of effective teaching methods and curriculum in computer science.
• Interactive learning environments: Focuses on frameworks that foster active engagement and conceptual growth for students.
• General programming languages: Centers on core principles and broad programming skills, independent of language specifics.
Keywords: Bloom’s Taxonomy, Cognitive, Problem-solving.
Abstract
Design and Implementation of Encrypted and Decrypted File System Based on USBKey and Hardware Code
Khanderao S. Kulkarni, Pratap N. Shinde, Aditya M. Auti, Ashok Nagargoje, Nirbhay M. Kunale, Onkar P. Mante
DOI: 10.17148/IJARCCE.2026.15686
Abstract. To protect the privacy of sensitive data, an encrypted and decrypted file system based on USBKey and hardware code is designed and implemented in this paper. This system uses USBKey and hardware code to authenticate a user. We use random key to encrypt file with symmetric encryption algorithm and USBKey to encrypt random key with asymmetric encryption algorithm. At the same time, we use the MD5 algorithm to calculate the hash of file to verify its integrity. Experiment results show that large files can be encrypted and decrypted in a very short time. The system has high efficiency and ensures the security of documents.
Abstract
Multi-Modal AI Agent for Intelligent Email Categorization and Auto-Reply
RANJINI, J.LIN EBY CHANDRA
DOI: 10.17148/IJARCCE.2026.15687
Abstract: The dramatic increase in daily email volumes across enterprise, healthcare, and e-governance sectors has created an urgent need for intelligent systems capable of autonomous email understanding, classification, and response generation. This paper proposes MMEA-Net (Multi-Modal Email Agent Network), a novel deep learning framework that integrates transformer-based language models, visual document encoders, and metadata-driven contextual reasoning to perform fine-grained email categorization and context-aware auto-reply generation. Unlike prior work relying solely on email body text, MMEA-Net processes three complementary modalities: textual content encoded via DeBERTa-v3-Large, visual layout of attached documents processed through LayoutLMv3, and structural metadata including sender reputation scores, thread depth, and temporal patterns encoded by a dedicated MLP module. The three modality streams are fused through a Gated Cross-Modal Attention (GCMA) mechanism that dynamically weights each modality's contribution based on input context. A reinforcement-learning-based Auto-Reply Generator (ARG) then produces professional, intent-aligned responses conditioned on the predicted category and a domain-specific policy knowledge base. Experiments on the Enron Email Dataset, TREC 2007, and a newly constructed Healthcare Email Corpus demonstrate that MMEA-Net achieves 95.3% overall accuracy, 94.1% macro-F1, BLEU-4 of 41.2, and human acceptability of 89.6%, outperforming all evaluated baselines by statistically significant margins.
Keywords: Multi-Modal Learning; Email Categorization; Auto-Reply Generation; Transformer; Gated Cross-Modal Attention; DeBERTa; Reinforcement Learning from Human Feedback
Keywords: Multi-Modal Learning; Email Categorization; Auto-Reply Generation; Transformer; Gated Cross-Modal Attention; DeBERTa; Reinforcement Learning from Human Feedback
Abstract
Predictive Maintenance System for Industrial IoT: A Hybrid Deep Learning and Edge-Computing Framework
DIVYA, J.LIN EBY CHANDRA
DOI: 10.17148/IJARCCE.2026.15688
Abstract: Unplanned machinery failures in industrial environments cost global manufacturing an estimated $50 billion annually, rendering predictive maintenance (PdM) one of the most economically critical applications of Industrial Internet of Things (IIoT). Traditional threshold-based and reactive maintenance strategies fail to capture the complex, non-linear fault progression patterns exhibited by rotating machinery, compressors, and conveyor systems operating under variable load conditions. This paper proposes the Hybrid Edge-Cloud Predictive Maintenance (HECPM) framework, which integrates a Temporal Convolutional Network-Long Short-Term Memory (TCN-LSTM) ensemble for multivariate sensor time-series modelling, a Variational Autoencoder (VAE) for unsupervised anomaly detection under data-scarce conditions, and a Federated Learning (FL) orchestration layer that preserves proprietary operational data within factory edge nodes. An Explainability Module based on SHAP and attention heatmaps translates neural predictions into maintenance work-orders interpretable by floor engineers. Experiments on three publicly available benchmarks—NASA CMAPSS Turbofan, Case Western Reserve University (CWRU) Bearing, and PRONOSTIA Bearing datasets—demonstrate that HECPM achieves a Remaining Useful Life (RUL) prediction RMSE of 11.34 cycles (CMAPSS FD001), fault classification accuracy of 99.12% (CWRU), and anomaly detection F1-score of 0.963 (PRONOSTIA), outperforming all evaluated baselines. The federated deployment reduces raw sensor data transmission by 87.3% while sustaining model performance within 1.1% of centralized training, validating the framework’s industrial deployability under bandwidth and data-privacy constraints.
Keywords: Predictive Maintenance; Industrial IoT; Temporal Convolutional Network; Federated Learning; Remaining Useful Life; Anomaly Detection; Edge Computing
Keywords: Predictive Maintenance; Industrial IoT; Temporal Convolutional Network; Federated Learning; Remaining Useful Life; Anomaly Detection; Edge Computing
Abstract
Intelligent Patient Risk Prediction and Bias-Aware Diagnosis Framework
Vasantha Kumari, J. Lin Eby Chandra
DOI: 10.17148/IJARCCE.2026.15689
Abstract: The proliferation of electronic health records (EHRs) and multimodal clinical data has created unprecedented opportunities for machine learning-driven patient risk assessment. However, existing clinical decision-support systems frequently suffer from systematic algorithmic bias arising from imbalanced training corpora, demographic skews, and feature selection disparities, leading to inequitable diagnostic outcomes across protected population subgroups. This paper presents the Intelligent Patient Risk Prediction and Bias-Aware Diagnosis (IPRB-AD) framework—a novel hybrid architecture that integrates a Graph Attention Network (GAT) for patient similarity modeling, a Transformer- based temporal encoder for longitudinal EHR sequences, and an adversarial debiasing module grounded in fairness constraints. The proposed system jointly optimizes predictive accuracy and demographic parity through a multi- objective loss formulation, incorporating Counterfactual Fairness Regularization (CFR) to mitigate bias without sacrificing clinical utility. Experiments conducted on three publicly available benchmarks—MIMIC-IV, eICU Collaborative Research Database, and the PhysioNet Sepsis Challenge dataset—demonstrate that IPRB-AD achieves an AUROC of 0.934, F1-score of 0.891, and reduces disparity gap by 41.7% compared to state-of-the-art baselines. The framework provides interpretable risk scores via SHAP-based attribution maps, enabling clinicians to audit model decisions and identify latent bias sources. These results underscore the potential of fairness-constrained deep learning pipelines in realizing trustworthy, equitable clinical AI systems.
Keywords: Patient Risk Prediction; Algorithmic Bias; Graph Attention Networks; Fairness-Aware Machine Learning; Electronic Health Records; Adversarial Debiasing; Clinical Decision Support
Keywords: Patient Risk Prediction; Algorithmic Bias; Graph Attention Networks; Fairness-Aware Machine Learning; Electronic Health Records; Adversarial Debiasing; Clinical Decision Support
Abstract
BREATHE: AIR QUALITY PREDICTION USING EMBEDDED MACHINE LEARNING AND DEEP LEARNING MODELS WITH QUANTIZATION TECHNIQUES
Anandhu Suresh, Lekshmi V
DOI: 10.17148/IJARCCE.2026.15690
Abstract: Air quality degradation poses a significant global health challenge, necessitating accurate and intelligent systems capable of real time pollutant forecasting and actionable wellness guidance for everyday users. The complexity of spatial and temporal pollutant dynamics across urban environments demands advanced deep learning architectures capable of multi city, multi pollutant prediction while remaining deployable on resource constrained mobile devices. This project presents Breathe, a cross platform air quality intelligence system employing a hybrid GCN-TransGRU architecture combining Graph Convolutional Networks for spatial inter city relationship modeling with Transformer encoders and Gated Recurrent Units for capturing temporal dependencies across PM2.5, PM10, and NO2.
To enable efficient mobile deployment, a Knowledge Distillation strategy compresses a high capacity teacher model of approximately 2.52M parameters into a lightweight student model of approximately 466k parameters into a lightweight student model of approximately 43k parameters, exported as an ONNX float32 model (~1.51 MB) and deployed via ONNX Runtime for on-device inference without significant accuracy loss. The system is implemented across three integrated components including a deep learning forecasting model, a modular NestJS backend API managing authentication, real time air quality data, and trip planning, and a Flutter based mobile application featuring a dynamic AQI dashboard, personalized health profiles, and resilience on network loss (last loaded data retained in memory). By combining spatial temporal deep learning with scalable cloud infrastructure, Breathe contributes to improved public awareness, reduced health risks, and the advancement of technology driven air quality management.
Keywords: Air Quality Index (AQI), Deep Learning Forecasting, Multi-Pollutant Prediction, Spatial-Temporal Modeling, Graph Convolutional Networks (GCN), Transformer-GRU Hybrid Architecture, Knowledge Distillation, Model Compression, On-Device Inference, ONNX Runtime, Cross-Platform Mobile Applications, Flutter, NestJS Backend API, Modular Architecture, Software Engineering.
To enable efficient mobile deployment, a Knowledge Distillation strategy compresses a high capacity teacher model of approximately 2.52M parameters into a lightweight student model of approximately 466k parameters into a lightweight student model of approximately 43k parameters, exported as an ONNX float32 model (~1.51 MB) and deployed via ONNX Runtime for on-device inference without significant accuracy loss. The system is implemented across three integrated components including a deep learning forecasting model, a modular NestJS backend API managing authentication, real time air quality data, and trip planning, and a Flutter based mobile application featuring a dynamic AQI dashboard, personalized health profiles, and resilience on network loss (last loaded data retained in memory). By combining spatial temporal deep learning with scalable cloud infrastructure, Breathe contributes to improved public awareness, reduced health risks, and the advancement of technology driven air quality management.
Keywords: Air Quality Index (AQI), Deep Learning Forecasting, Multi-Pollutant Prediction, Spatial-Temporal Modeling, Graph Convolutional Networks (GCN), Transformer-GRU Hybrid Architecture, Knowledge Distillation, Model Compression, On-Device Inference, ONNX Runtime, Cross-Platform Mobile Applications, Flutter, NestJS Backend API, Modular Architecture, Software Engineering.
Abstract
A Real-Time Web-Based Chess Tournament Management System with Integrated Sponsor Advertisement Analytics
ATYAM PRABHU, Dr. CHIRAPARAPU SRINIVASARAO*
DOI: 10.17148/IJARCCE.2026.15691
Abstract: The proliferation of online competitive gaming platforms and digital sports infrastructure has created a pressing need for dedicated, scalable web systems capable of orchestrating tournament logistics alongside real-time participation experiences. This paper presents the design, implementation, and evaluation of a web-based chess tournament management platform that integrates live match gameplay, real-time spectator synchronization, and sponsor advertisement analytics into a unified ecosystem. The proposed system employs a Python Django backend augmented by Django Channels- an Asynchronous Server Gateway Interface (ASGI) extension- to maintain persistent WebSocket connections between players, spectators, and the server. Chess move validation is performed server-side using the python- chess library, ensuring legality enforcement independent of client-side state. Tournament organizers may configure multi- type events, manage participant registrations, deploy sponsor advertisements, and access a real-time analytics dashboard reporting impression counts, click-through rates (CTR), and spectator engagement metrics. A serverless PostgreSQL database hosted on Neon Cloud provides durable storage with ephemeral-safe architecture. Experimental observations demonstrate sub-200 ms move propagation latency under concurrent spectator loads, correct enforcement of chess rules including threefold repetition and the fifty-move rule, and advertisement CTR tracking with a measured accuracy of 100% relative to simulated interaction logs. The system represents a practical, cloud-deployable solution for academic and community chess competitions that simultaneously serves as a measurable digital advertising medium.
Keywords: chess tournament management; real-time WebSocket; Django Channels; advertisement analytics; click- through rate; serverless PostgreSQL; ASGI; spectator synchronization
Keywords: chess tournament management; real-time WebSocket; Django Channels; advertisement analytics; click- through rate; serverless PostgreSQL; ASGI; spectator synchronization
Abstract
A Cloud-Native Event Ticketing Platform Leveraging Automated Horizontal Scaling and Continuous Deployment through AWS CodePipeline
TADI SINDHU, PADALA SRINIVASA REDDY*
DOI: 10.17148/IJARCCE.2026.15692
Abstract: Online event ticketing services routinely experience extreme, short-lived demand surges when high-profile events open for sale, frequently overwhelming statically provisioned infrastructure and producing failed transactions, duplicate seat allocation, and revenue loss. This work presents the design, implementation, and empirical evaluation of a cloud-native ticketing platform engineered to absorb such volatility through elastic horizontal scaling and a fully automated delivery pipeline. The backend services are implemented in Python together with a Node.js-based presentation tier and are deployed across an Amazon Web Services (AWS) environment in which an Application Load Balancer distributes traffic over an Auto Scaling Group whose capacity is governed by real-time CloudWatch utilization signals. A distributed locking strategy backed by an in-memory cache is introduced to enforce seat-level consistency under concurrent booking attempts. Continuous integration and continuous deployment are realized through AWS CodePipeline, CodeBuild, and CodeDeploy using a blue-green release model with automatic rollback. Experimental load testing demonstrates that the proposed architecture sustains sub-300 ms average response times at eight thousand concurrent users while a comparable monolithic baseline degrades beyond three seconds. The system further reduces deployment lead time by roughly 78% and eliminates double-booking under contention. The principal contributions are an elasticity-aware ticketing reference architecture, a consistency-preserving concurrency mechanism, and a reproducible automated deployment workflow.
Keywords: Cloud computing; auto scaling; continuous deployment; event ticketing; AWS CodePipeline; load balancing; concurrency control; DevOps
Keywords: Cloud computing; auto scaling; continuous deployment; event ticketing; AWS CodePipeline; load balancing; concurrency control; DevOps
Abstract
Vision-Based Human Behavior Recognition Using a Multiscale Convolutional Neural Network with Parallel Multi-Kernel Feature Fusion
P. Vijaya Lakshmi, K. Lakshamana Reddy*
DOI: 10.17148/IJARCCE.2026.15693
Abstract: Automatic recognition of human behavior from video underpins applications ranging from surveillance and elderly-care monitoring to smart environments and human-computer interaction. Recognizing actions reliably is difficult because human movements vary in spatial scale, speed, viewpoint, and background clutter, and a single fixed receptive field rarely captures both fine gestures and coarse whole-body motion. This paper presents a vision-based behavior-recognition framework built on a multiscale convolutional neural network that extracts features through parallel convolutional branches with different kernel sizes and fuses them before classification. Video frames are preprocessed and localized to the human region, processed simultaneously at fine, mid, and coarse scales, and the fused representation is mapped to a behavior label with a confidence score. The recognition engine is implemented in Java with a deep-learning backend, while a Node.js layer provides a live monitoring interface. Evaluated against single- scale, two-scale, and recurrent baselines, the multiscale model attained an overall accuracy of about 94.2% with balanced per-class performance and a real-time throughput of roughly 45 frames per second. The principal contributions are a parallel multi-kernel feature-extraction design that captures complementary spatial granularities, a lightweight fusion strategy suited to real-time inference, and an integrated monitoring system that delivers interpretable, low-latency behavior predictions.
Keywords: Human behavior recognition; multiscale convolutional neural network; computer vision; action recognition; feature fusion; real-time inference; deep learning; video analytics.
Keywords: Human behavior recognition; multiscale convolutional neural network; computer vision; action recognition; feature fusion; real-time inference; deep learning; video analytics.
Abstract
A Serverless Event-Driven Framework for Scalable Subscription Billing Using Function-as-a-Service and Automated Build Pipelines
Vasamsetti. Komalika, K. Lakshmi Sai Sri*
DOI: 10.17148/IJARCCE.2026.15694
Abstract: The proliferation of subscription-based commerce has intensified the demand for billing systems that can process recurring charges, prorations, and usage-based fees reliably at fluctuating scale. Traditional billing platforms are typically deployed on continuously provisioned servers, which incur idle cost during quiescent periods, scale coarsely under sudden demand, and entangle billing logic with infrastructure management. This study proposes a serverless, event- driven framework that administers the complete subscription billing cycle through stateless functions invoked on demand. The methodology decomposes billing concerns—subscription management, invoice generation, payment processing, usage metering, and notification—into independent functions exposed through a managed application programming interface gateway and coordinated by event and schedule triggers. Business logic is implemented in Python, while a Node.js client layer mediates user interaction, and an automated build service compiles, tests, packages, and deploys function revisions without manual provisioning. An experimental evaluation conducted across concurrency levels from fifty to thirty-two hundred simultaneous requests demonstrates that the serverless framework sustains an average latency of approximately 140 milliseconds at moderate load and degrades only gradually under heavy concurrency, whereas a provisioned-server baseline exhibits steep latency growth beyond four hundred concurrent requests. The framework attains a scaling efficiency near 94 percent and reduces cost per million requests by more than fourfold relative to the baseline, at the expense of occasional cold-start latency. The principal contributions comprise an event-driven billing architecture, a pipeline-automated deployment model for functions, and an empirical cost-performance analysis validating function-as-a-service for elastic billing workloads.
Keywords: Serverless computing, function-as-a-service, subscription billing, event-driven architecture, API gateway, continuous deployment, scalability, cloud cost optimization.
Keywords: Serverless computing, function-as-a-service, subscription billing, event-driven architecture, API gateway, continuous deployment, scalability, cloud cost optimization.
Abstract
CreatorPulse: An AI-Driven Multi-Platform Content Creation, Viral Prediction, and Automated Social Media Publishing System
Sanket Dhage, Sanmati Ukhalkar, Vedant Ghare, Krushna Nikam, Prof. R. P. Daund
DOI: 10.17148/IJARCCE.2026.15695
Abstract: The rapid growth of the creator economy has placed enormous pressure on individuals and agencies to produce high-quality, platform-tailored social media content consistently and at scale. Despite the existence of scheduling tools and AI writing assistants, no single platform currently integrates intelligent content generation, viral potential scoring, AI image creation, and automated cross-platform publishing within one cohesive system. This paper presents CreatorPulse, a full-stack Software-as-a-Service (SaaS) platform that addresses these gaps using a dual microservices architecture — a Node.js/Express REST API and a Python FastAPI AI engine — alongside a React.js dashboard. The system integrates OpenAI GPT-4o and Anthropic Claude Sonnet for generating seven distinct content formats tailored to platform-specific rules and the creator's personal voice. A novel Hybrid Viral Prediction Engine (HVPE) scores every piece of content on a 0–100 scale using a weighted blend of rule-based heuristics and LLM evaluations across five dimensions: hook quality, optimal length, hashtag usage, engagement potential, and trend alignment. The platform further incorporates DALL-E 3 for generating platform-specific thumbnails and carousel images, a Celery-based ETA scheduler for automated publishing to Twitter/X, LinkedIn, Instagram, and Facebook, a NewsAPI-driven trend detection engine, and a closed-loop performance feedback system that retrains the scoring model weekly from real engagement data. Experimental evaluation on 500 generated posts across five platforms demonstrates a mean viral prediction accuracy of 82.4%, average content generation latency of 3.24 seconds, and a 67% reduction in content creation time compared to manual workflows.
Keywords: Artificial Intelligence; Large Language Models; Social Media Automation; Viral Prediction; SaaS; DALL- E 3; Prompt Engineering; Celery Task Queue; Content Scheduling; Multi-Platform Publishing
Keywords: Artificial Intelligence; Large Language Models; Social Media Automation; Viral Prediction; SaaS; DALL- E 3; Prompt Engineering; Celery Task Queue; Content Scheduling; Multi-Platform Publishing
Abstract
Predictive Wildlife Collision Prevention using Deep Learning
Deepthi V, Priyanka Kumar Teradale, S M Varsha
DOI: 10.17148/IJARCCE.2026.15696
Abstract: Animal vehicle collisions are a big problem on highways and forest roads people get hurt animals die and cars end up damaged. Most current systems just spot animals and throw out basic alerts but they do not actually think ahead about where the animal might go or how likely a crash is. That is where this paper steps in. We Introduce Preventing and Predictive Wildlife Collision using Deep Learning a smarter framework focused on stopping accidents before they even happen. Here is how it works means we use YOLOv8 to spot animals and DeepSORT to keep track of where they are moving and LSTM to predict what they will do next especially if they might try to cross the road. The system looks at movement patterns and figures out how risky a potential collision is and quickly sends warning alerts to nearby cars using MQTT based communication by mixing animal detection future prediction and smart alerts and our approach brings better safety to the road and actually tackles the problem of animal vehicle collisions heads on.
Abstract
Conformal Memory-Augmented Attention Networks for Robust and Adaptive Disease Prediction
V. Pandarinathan, Dr. A. Manikandan
DOI: 10.17148/IJARCCE.2026.15697
Abstract: We propose a novel inference framework for longitudinal disease prediction, replacing conventional static classifiers with a conformal memory-augmented attention network. The system processes multi-modal clinical time series by means of a temporal convolutional encoder, then applies a tensorized attention mechanism that retrieves prototypical patient trajectories from a dynamic memory bank. Instead of producing point estimates, our method generates statistically rigorous prediction sets with guaranteed coverage probabilities through a distribution-free conformal calibration layer embedded directly into the attention computation. The nonconformity score measures how well a patient’s temporal embedding aligns with class-specific memory prototypes, and the resulting prediction sets adapt to distribution shifts without requiring retraining or post-hoc recalibration. During inference, a continual learning module refreshes memory banks via a prototypical replay mechanism that applies frequency-weighted consolidation, thus retaining previously learned patterns while accommodating novel data. The tensorized bilinear interaction between queries and memory prototypes captures higher-order feature relationships via a low-rank factorization that reduces parameters and improves generalization. Our approach consequently yields robust, interpretable predictions that stay valid under label shifts and changing clinical data distributions. The system outputs both the prediction set and the most influential memory prototypes, thereby delivering clinicians actionable insights alongside statistically guaranteed uncertainty quantification.
Keywords: Longitudinal disease prediction, Low-rank factorization, Temporal convolutional encoder
Keywords: Longitudinal disease prediction, Low-rank factorization, Temporal convolutional encoder