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VOLUME 15, ISSUE 9, SEPTEMBER 2026
Publication in progress..
AI-Powered Business Intelligence and Profit Management System for Small Retail Businesses: A Review
Piyush Pandit Ubale, Shivraj Nagesh Budale, Omkar Sharad Bedarkar, Gudakesh Dyaneshwar Pind, Aakash Prabhakar Bhosale, Prof. Aishwarya Hosale
A Machine Learning-Based Framework for Intelligent Cybersecurity Threat Detection and Prevention
Akash jain, Dr. Pradeep Yadav
A Scalable AI Recruitment Framework Using RAG and Explainable Multi-Agent Decision-Making
Kudupudi Rajesh
Computing Disciplines Competency Framework for the Digital Age
Bostley Muyembe Asenahabi
Lightweight and Scalable Real-Time Phishing URL Detection Models: A Comparative Review and Proposed Framework
Abhishek Kumar, Anand Kumar
Fusion of Fractal Color Watermarking with RSA
Maninder Kaur, Manish Sachdeva
Assessing AI Awareness, Utilization, and Perceived Benefits Among College Students at Wesleyan University Philippines
Kim Colene S. Dela Cruz and Jovith G. Nabua
Next Generation Authentication Using a Blockchain-Distributed Secure Ledger
Mrs. Manjula K, Niveditha G M
A Unified Analysis of Digital Forensics and Machine Learning in Cybercrime Investigation
Muzamil Amin, Yatham Thirumalanayudu
DESIGN AND DEVELOPMENT OF A FULL-STACK LEARNING MANAGEMENT SYSTEM WITH SECURE AUTHENTICATION, ONLINE PAYMENT, AND COURSE PROGRESS TRACKING
Akshata Mahishawadagi, Padma Y Yadahalli, Dr. Dayanand G. Savakar
A Survey on Handwriting Disorder Detection in Individuals with Disabilities
Ramya Deshinamoorthy, Dr.F.Kurus Malai Selvi
Lightweight and Explainable Real-Time Detection of Social Media-Borne Phishing URLS: A Critical Review for Indian Context
Pushpam Kumari
Adaptive Ensemble Machine Learning Framework for Robust Prediction, Classification, and Intelligent Decision Support Across Heterogeneous Data Domains
M. Sasikumar, Addepalli Keerthika, N Saranya
Next-Generation Deep Learning: A Comprehensive Survey on Explainable, Efficient, Privacy-Preserving and Multimodal Artificial Intelligence
K.Ramalakshmi, S.Prema, M.Ajithaveni, M.Ramya
AI-BASED STUDENT PREFORMANCE PREDICTION
Dr.B. Rajesh Kumar, S. Shailesh, Mohammed Aadhil.I
AI-Based Chatbot for Education: Design, Architecture, and Empirical Evaluation
Dr. B. Rajesh Kumar, Abishek.V, Sujan.R, Abishek N
YojanaMitra: An AI-Assisted Framework for Government Scheme Eligibility Matching and Guidance
Chinta Lavanya
Noble Poly Shapes: An AI-Assisted Interactive Interface for the Enumeration of Noble Polyhedra
Akshat Wahal, Anupam Andrew Maxton
Smart Classroom Management Software for Enhanced Learning Environments.
Ms. Sakshi Umakant Chaudhari, Asst. Prof. Sapana A. Fegade
ARMOR: An Extensible Multi-Provider Secret Validation Framework for Automated Credential Verification in DevSecOps Pipelines
Siddharth Gupta, Poonam Bhartiya & Shailendra Shriwastava
A. ISSN (O) 2278-1021, ISSN (P) 2319-5940IJARCCE XplainGuard: Dynamic Policy Enforcement in Cloud Infrastructure as Code: Integrating Generative AI for Auto-Remediation and Explainability
Aadrika Khare, Poonam Bhartiya & Shailendra Shriwastava
Enhancing the ELLANGA Traditional Cascade Tank-Village System in Sri Lanka: Proposal for a Smart IT Based Solution to Enhance Predictability and Sustainability
Rathnayake R.M.N.B Mahesh Hapugoda
AI-Driven Network Intrusion Detection System Using Machine Learning
Rashmi, Abhinandan Raje Urs M B, Appu Gowda M P, Aravind Kumar P N, Keerthan K
Identification and Analysis of Problems Faced by Stakeholders in the Existing Grain Market System
Parteek Singh, Dr. Harish Rohil
A Machine Learning and NLP Framework for Automatic Title Similarity Detection and Preliminary Verification
Miss. Suvarna Bhoi, Dr. Sarkarsinha H. Rajput, Dr. Shital A. Patil, Dr. Dinesh D. Puri
Alumni Association Platform for the University/Institute
Mr. Hemant Bodade, Dr. Dinesh Puri
Women Safety Analytics – Protecting Women From Safety Threats
Miss. Gayatri Vijay Amode, Dr. Sarkarsinha H. Rajput, Dr. Shital A. Patil, Dr. Dinesh D. Puri
Biomechanics of the Kho-Giving (Seated Exchange) and Pole-Turn Technique in Kho-Kho
Dr. Pathan Yusufkhan Hidayatkhan
Generative AI in Education: Opportunities, Challenges, and Future Perspectives
Geetha, N, Lathika, S, Vikasini, S
Child Vaccination Tracking System
Jeevitha. N, Janani. B, Lakshana. B, Priyadarshini. S. S, Dr. Premavathi. C
Heart Disease Prediction Using Machine Learning and Deep Learning Techniques: A Comprehensive Review
Geetha. N, Janasuruthi. M, Sripavanya. K
Fairlytics: AI-Based Intelligent Dynamic Pricing With Discount Authenticity Validation
Palak Shukla, Soukhya Raghavendra Yadawad, Spurthi D S, Rashmi, Lavanya N L
AI AND IOT APPLICATIONS IN SMART AGRICULTURE
Dr.Geetha, N, Harshini, S, Jeevitha, V
Smart Collaborative Coding Environment with AI Assistance
Anitha L, Bhumika A R, Deeksha J, Hithashree N M, Jeevitha B V
Cardiovascular Risk Prediction From Retinal Images Using Vision Transformer
Sai Sireesha Pakki, Y. Sudheer Kumar
Self-Supervised Learning With Multibranch Consistency for Few-Shot PolSAR Image Classification
S Sehar Tasneem, Saboor Saniya, Siddarth S, Sadanand, Prof. Gnanamani H
Blockchain-Based Decentralized Identity Verification and Management System
Sneha. S, Swathi. N, Alice Steffi. S, Kavya. R, Anuradha.C. R
Data-Driven Approach for Crop Yield Prediction Using Machine Learning Techniques
Janhavi Jawalkar, Janhavi Jari, Tejaswini Ghule, Arpita Solanke, Atharv Sune, V B Gadicha
Smart Education: The Role of Artificial Intelligence and Digital Technologies in Modern Learning
Mr. Hansraj Chaudhari, Dr. Dinesh D. Puri
Chaos-Based Image Encryption with 5D Keystreams and 3D Voxel Scrambling: A Survey and Reference-Implementation Study
Vittala S, Sooraj N J, Vinay B S, Teja N R, and Anuradha C R
Rapid Colorimetric and Artificial Intelligence- Based Methods for Determining the Microbial Quality of Raw Milk, Processed Milk, and Milk Products
Mr. Gaurav Ahire, Dr. Dinesh D Puri
AN INTEGRATED AI AND GIS FRAMEWORK FOR TRACKING GOVERNMENT FUNDS FROM BUDGET ALLOCATION TO GROUND-LEVEL PROJECT COMPLETION
Pavan S. Badgujar, Assoc. Prof. Dinesh D. Puri
Voice-Controlled Gaming Tools for Enhanced Learning in The Skill Ecosystem
Miss. Ashwini Vijay Adhav, Dr. Dinesh D. Puri
An Efficient Urban Digital Video Broadcasting (DVB) from Aerial Platforms using Smart Antenna Arrays
Yasser Albagory
Empowering Safety: A Study of Smart Technologies in The Next Frontier of Women’s Safety Devices
Dr. Ambili A.R, Malavika Pradeep, Dr. Sreevidya P., Beenu Riju
AI-Enhanced Career Guidance System For Personalized Career Pathways
Miss.Vaishnavi S. Badgujar, Asso. Prof. Dr. Dinesh D Puri
Conversational Image Recognition Chatbot
Miss. Khushi D. Danej, Asst. Prof. Sapana A. Fegade
Low Power Mesh Network System
Mr. Nitin Jagdish Chaudhari, Sapana Fegade
A Real-Corridor SUMO and ns-3 Testbed for VANET Traffic Congestion Studies on NH-16, Visakhapatnam
Gunde Neha
Improving Android Security: Application Security for Mobile App on Android 14 or Higher
Miss. Hemlata Chaudhari, Asst. Prof. Sapana Fegade
Adaptive DDoS Protection Architecture for Cloud Servers: A Custom Traffic Filtering Tool
Mr. Yash Narendra Date, Asst. Prof. Sapana Fegade
SOFTWARE SOLUTION TO IDENTIFY USERS BEHIND TELEGRAM, WHATSAPP AND INSTAGRAM BASED DRUG- TRAFFICKING
Miss. Amrapali B Baviskar, Prof. Dr. Dinesh D Puri
Enhancing Navigation for Railway Station Facilities and Locations
Mr. Shubham Pandurang Chaudhari, Asst. Prof. Ms. Sapana Fegade
SolarQuest: Innovate to Capture More Sunlight and Boost Energy
Miss. Riya Swapnil Chaudhari, Asst. Prof. Sapana A. Fegade
Beyond the Aggregate: Provenance, Communication and Scope in Machine-Learning FDI Forecasting for Policy
Ofierohor Ufuoma Earnest
Abstract
ANALYSIS OF SOCIAL MEDIA IMPACT ON STUDENT ACADEMIC PERFORMANCE
Lina Patil, MS. Rucha Mohite
Abstract: Social media is now a common part of student life. Students use social media for communication, entertainment, news, and study-related activities. However, too much use of social media may affect students' study time, sleep, concentration, and academic performance. This research studies the impact of social media use on academic performance of students. Data was collected from students using a Google Form. The collected data was cleaned and analyzed using data analysis and machine learning techniques. Logistic Regression, Decision Tree, and Random Forest models were used to study and predict academic performance. The model results showed that Logistic Regression and Decision Tree achieved 47.62% accuracy, while Random Forest achieved 42.86% accuracy. The study also analyzed social media usage time, late-night social media use, distraction while studying, sleep hours, and academic performance. The findings help to understand how different social media habits are related to students' academic performance.
Keywords: Social Media, Academic Performance, Students, Machine Learning, Data Analysis.
Keywords: Social Media, Academic Performance, Students, Machine Learning, Data Analysis.
Abstract
AI-Powered Business Intelligence and Profit Management System for Small Retail Businesses: A Review
Piyush Pandit Ubale, Shivraj Nagesh Budale, Omkar Sharad Bedarkar, Gudakesh Dyaneshwar Pind, Aakash Prabhakar Bhosale, Prof. Aishwarya Hosale
Abstract: Small retail businesses generate significant amounts of data through sales, purchases, inventory, expenses, customer payments, and supplier activities. However, many small retailers still depend on manual records and basic software, making it difficult to obtain timely insights about business performance and profitability. Artificial Intelligence (AI), Machine Learning (ML), and Business Intelligence (BI) technologies provide opportunities to transform operational data into useful information for decision-making. This review examines existing research on AI-enabled business intelligence, retail analytics, inventory management, demand forecasting, and profit-oriented decision support systems, with particular emphasis on small and medium-sized businesses. Recent studies indicate that machine learning can support demand prediction, inventory optimization, and business forecasting, while BI dashboards can provide accessible visual representations of business information. However, existing solutions frequently focus on individual functions such as forecasting, inventory, or analytics rather than providing an integrated and affordable system designed specifically for small retailers. The review identifies challenges including limited historical data, technical expertise, implementation cost, model reliability, and data quality. Based on these findings, an integrated AI-powered business intelligence and profit management system is proposed as a potential approach for combining sales, inventory, expenses, and profit analysis with AI-based insights.
Keywords: Artificial Intelligence, Business Intelligence, Machine Learning, Retail Analytics, Profit Management, Inventory Management
Keywords: Artificial Intelligence, Business Intelligence, Machine Learning, Retail Analytics, Profit Management, Inventory Management
Abstract
A Machine Learning-Based Framework for Intelligent Cybersecurity Threat Detection and Prevention
Akash jain, Dr. Pradeep Yadav
Abstract: The increasing sophistication of cyberattacks poses critical challenges to modern network infrastructure security. Traditional signature-based intrusion detection systems (IDS) are becoming obsolete against zero-day exploits, polymorphic malware, and advanced persistent threats (APTs). This paper presents a comprehensive Machine Learning (ML)-based framework for intelligent cybersecurity threat detection and prevention capable of identifying eight distinct attack categories including Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), User-to-Root (U2R), Botnet, Brute Force, SQL Injection, and Cross-Site Scripting (XSS). We design and evaluate a multi-tier classification pipeline comprising Random Forest (RF), Gradient Boosting (GB), and Logistic Regression (LR) algorithms on a synthetic, NSL-KDD-inspired dataset of 4,506 labeled network traffic samples with 35 engineered features. Our framework achieves 100% accuracy in binary threat classification (normal vs. attack) and 90.91% accuracy in multiclass attack-type classification, with a weighted F1-score of 0.9114. Feature importance analysis reveals that behavioral indicators such as num_compromised, src_bytes, num_file_creations, and packet anomaly flags are the strongest predictors of malicious activity. The proposed framework further incorporates a real-time prevention module with adaptive firewall rules, automated incident response, and feedback-driven model retraining. Comparative analysis demonstrates statistically significant improvements over traditional SVM and neural network baselines. This research provides a scalable, explainable, and deployable ML security pipeline suitable for enterprise-grade network environments.
Keywords: Cybersecurity, Intrusion Detection System, Machine Learning, Random Forest, Network Traffic Classification, Threat Prevention, Deep Learning, Anomaly Detection, Feature Engineering, Zero-Day Attacks
Keywords: Cybersecurity, Intrusion Detection System, Machine Learning, Random Forest, Network Traffic Classification, Threat Prevention, Deep Learning, Anomaly Detection, Feature Engineering, Zero-Day Attacks
Abstract
A Scalable AI Recruitment Framework Using RAG and Explainable Multi-Agent Decision-Making
Kudupudi Rajesh
Abstract: Recruitment processes increasingly rely on Artificial Intelligence (AI) to automate resume screening, candidate-job matching, skill-gap identification, and candidate evaluation. However, conventional AI and Large Language Model (LLM)-based recruitment systems may generate responses that are insufficiently grounded in the actual information contained in resumes and job descriptions, limiting their transparency, reliability, and explainability. This research presents a scalable Retrieval-Augmented Generation (RAG) framework for explainable AI-based recruitment that integrates semantic retrieval with LLM-based response generation. Candidate resumes and job descriptions are processed into overlapping text chunks and converted into semantic embeddings using all-MiniLM-L6-v2, which are indexed in ChromaDB for efficient similarity-based retrieval. For a given recruitment query, the system retrieves the most relevant evidence using different Top-K configurations (K ∈ {1, 3, 5, 10}) and provides the retrieved context to Qwen3-14B Instruct for evidence-grounded response generation. The framework supports resume-based question answering, job-requirement analysis, candidate-job matching, skill-gap identification, and evidence-based explanations. A recruitment benchmark of 500 queries, distributed across five categories (Resume Information, Job Requirements, Candidate-Job Matching, Skill-Gap Identification, and Explainability) is used to evaluate the framework. Retrieval performance is assessed using Precision@K, Recall@K, Mean Reciprocal Rank (MRR), and NDCG, while generated responses are evaluated using Context Precision, Context Recall, and Answer Relevance. The experimental analysis demonstrates that retrieval depth substantially influences response quality, with lower Top-K configurations providing stronger contextual precision and more consistent answer relevance. The proposed framework therefore provides a systematic approach for integrating retrieval, generation, and evidence tracing to support more context-aware, transparent, and explainable AI-assisted recruitment.
Keywords: Retrieval-Augmented Generation, Explainable AI, AI-Based Recruitment, Large Language Models, Semantic Retrieval.
Keywords: Retrieval-Augmented Generation, Explainable AI, AI-Based Recruitment, Large Language Models, Semantic Retrieval.
Abstract
Scalable and Quantum-Resistant Authentication for the Blockchain of Medical Things
Meruga Vijaya Sri
Abstract: The rapid proliferation of the Internet of Medical Things (IoMT) has revolutionized remote patient monitoring but introduces severe data privacy and security challenges. Integrating Blockchain technology (BIoMT) provides immutable auditing, yet classical cryptographic signatures (e.g., RSA, ECC) are highly vulnerable to the looming threat of quantum computing via Shor’s algorithm. Furthermore, storing individual digital signatures on a distributed ledger incurs massive storage overhead, limiting scalability. This paper proposes a Post-Quantum Aggregate Blind Signature (PQ-ABS) framework tailored for BIoMT. Our architecture leverages Module Learning With Errors (Module-LWE) to guarantee quantum-resistant security. To ensure patient privacy, we employ blind signatures that allow medical authorities to authenticate health data without exposing the plaintext payload. Signatures from multiple sources are mathematically aggregated into a single, constant-size proof. We validate the architecture by deploying the verification logic within a Hyperledger Fabric smart contract. Furthermore, we extend base security models by formalizing PQ-ABS in the Random Oracle Model and analyzing its resistance against Grover’s quantum search algorithm.
Keywords: Post-Quantum Cryptography, Internet of Medical Things, Blockchain, Hyperledger Fabric, Blind Signatures, Aggregate Signatures, Module-LWE
Keywords: Post-Quantum Cryptography, Internet of Medical Things, Blockchain, Hyperledger Fabric, Blind Signatures, Aggregate Signatures, Module-LWE
Abstract
Computing Disciplines Competency Framework for the Digital Age
Bostley Muyembe Asenahabi
DOI: 10.17148/IJARCCE.2026.15901
Abstract: The field of computing has rapidly evolved from the 1960s into distinct disciplines: Computer Science, Information and Communication Technology. While this diversity enriches the profession, it has also created a problem. The overlapping competencies and blurred boundaries make it difficult for learners to choose appropriate pathways, trainers to impart discipline-specific skills, and employers to interpret graduate profiles. This leads to mismatched expectations, skill gaps, and inefficiencies in the workforce, thus weakening the ability of computing education to meet organizational and societal demands. This study clarifies the uniqueness of each computing discipline, highlighting their philosophies, competencies, and graduate profiles. The study employed a desktop research method, drawing on secondary data from academic publications, professional guidelines, and industry reports. Sources were selected based on credibility, relevance, and recognition by established bodies. The analysis involved systematically reviewing literature to identify recurring themes, disciplinary distinctions, and competency frameworks. Sources were cross-checked for were synthesized by comparing perspectives across disciplines, highlighting overlaps, and distinguishing unique competencies. This triangulated approach ensured that conclusions were both academically robust and practically relevant. By distinguishing these pathways, the study ensures that learners acquire targeted skills aligned with career aspirations, trainers deliver curricula that reflect discipline-specific competencies, and employers can interpret graduate profiles accurately to reduce skill mismatches. Ultimately, society benefits from graduates whose competencies are tailored to solving real-world problems through computing technologies. This study contributes to bridging the gap between education and practice by offering clarity on computing disciplines. It empowers learners to make informed choices, trainers to design effective programs, employers to align talent with organizational needs, and policymakers to strengthen curricula that support national and global development. In doing so, the paper positions computing education as a driver of innovation, efficiency, and societal progress.
Keywords: computing disciplines, computing curriculum pathways, unique competencies, graduate profiles, digital transformation, ICT
Keywords: computing disciplines, computing curriculum pathways, unique competencies, graduate profiles, digital transformation, ICT
Abstract
Lightweight and Scalable Real-Time Phishing URL Detection Models: A Comparative Review and Proposed Framework
Abhishek Kumar, Anand Kumar
DOI: 10.17148/IJARCCE.2026.15902
Abstract: Phishing URL detection has advanced considerably through machine learning and deep learning, with recent published systems reporting accuracy figures that frequently exceed 95%. However, a close reading of this literature shows that high accuracy is often achieved using large feature sets, deep sequence architectures, or ensembles comprising a dozen or more classifiers — approaches that are difficult to justify on resource-constrained platforms such as browser extensions, mobile applications, or edge gateways, where real-time response and a small memory footprint matter as much as raw detection accuracy. This paper reviews six recent studies on machine-learning-based phishing URL detection, comparing their algorithms, feature counts, and reported accuracy, and identifies a consistent gap: none of the reviewed works measure deployment cost — inference latency, model size, or throughput under concurrent load — alongside their accuracy claims. Building on this analysis, the paper proposes a tentative methodology for a lightweight, scalable, real-time phishing-detection framework that restricts features to those computable from the URL string alone, benchmarks compact classical classifiers against the heavier baselines identified in the review, and treats latency, model size, and throughput as first-class evaluation metrics rather than afterthoughts. The proposed direction is intended to narrow the gap between laboratory-grade accuracy and field-deployable, real-time phishing protection.
Abstract
Fusion of Fractal Color Watermarking with RSA
Maninder Kaur, Manish Sachdeva
DOI: 10.17148/IJARCCE.2026.15903
Abstract: Now day’s security is an important aspect of life. We require security in every part of communication to transmit our information safely. Here our main aim is to secure the important information hidden in the images, while communicating we are hiding watermark inside the image by using RSA algorithm. To obtain very high image quality we use Fractal color image watermarking. Firstly we encrypt the watermark using RSA and then this encrypted watermark is used to hide inside the image using Fractal color image watermarking. To achieve watermarking we convert RGB into space. Also we are trying to achieve better parameters like PSNR (power signal to noise ratio), BER (Bit error rate) and MSE (Mean square Error).
Keywords: DCT, DWT, HVS, CIE, SCDFT etc
Keywords: DCT, DWT, HVS, CIE, SCDFT etc
Abstract
Assessing AI Awareness, Utilization, and Perceived Benefits Among College Students at Wesleyan University Philippines
Kim Colene S. Dela Cruz and Jovith G. Nabua
DOI: 10.17148/IJARCCE.2026.15904
Abstract: Artificial intelligence (AI) has become increasingly integrated into higher education, influencing how students access information, complete academic tasks, and support their learning. This study assessed the awareness, utilization,
respondents’ demographic profiles, identified AI tools used for studying and their purposes, and determined students’ levels of AI awareness, utilization, and perceived benefits. A quantitative research design was employed using a researcher-made, expert-validated, and pilot-tested survey questionnaire. Purposive sampling was used to select 129 undergraduate students from different departments who were familiar with AI tools. Data were analyzed using frequency, percentage, and weighted mean.Findings revealed that most respondents were female and fourth-year students. ChatGPT was the most commonly used AI tool, while writing assistance and research and information gathering were the leading purposes for AI use. Respondents demonstrated a high level of AI awareness, with an overall weighted mean of 3.57, indicating that they were fully aware of AI concepts, applications, limitations, and related concerns. AI utilization obtained an overall weighted mean of 3.19, indicating that students sometimes used AI, particularly for accessing AI tools and searching complex topics. Perceived benefits obtained an overall weighted mean of 3.23, interpreted as moderately agree, with reduced educational expenses, time savings, and improved productivity among the notable benefits. The study concludes that AI serves as a valuable academic support tool, although responsible utilization, critical evaluation of AI-generated information, and strengthened AI literacy remain important for students in higher education.
Keywords: Artificial Intelligence, AI Awareness, AI Utilization, Perceived Benefits, Higher Education, College Students, Academic Support
respondents’ demographic profiles, identified AI tools used for studying and their purposes, and determined students’ levels of AI awareness, utilization, and perceived benefits. A quantitative research design was employed using a researcher-made, expert-validated, and pilot-tested survey questionnaire. Purposive sampling was used to select 129 undergraduate students from different departments who were familiar with AI tools. Data were analyzed using frequency, percentage, and weighted mean.Findings revealed that most respondents were female and fourth-year students. ChatGPT was the most commonly used AI tool, while writing assistance and research and information gathering were the leading purposes for AI use. Respondents demonstrated a high level of AI awareness, with an overall weighted mean of 3.57, indicating that they were fully aware of AI concepts, applications, limitations, and related concerns. AI utilization obtained an overall weighted mean of 3.19, indicating that students sometimes used AI, particularly for accessing AI tools and searching complex topics. Perceived benefits obtained an overall weighted mean of 3.23, interpreted as moderately agree, with reduced educational expenses, time savings, and improved productivity among the notable benefits. The study concludes that AI serves as a valuable academic support tool, although responsible utilization, critical evaluation of AI-generated information, and strengthened AI literacy remain important for students in higher education.
Keywords: Artificial Intelligence, AI Awareness, AI Utilization, Perceived Benefits, Higher Education, College Students, Academic Support
Abstract
Next Generation Authentication Using a Blockchain-Distributed Secure Ledger
Mrs. Manjula K, Niveditha G M
DOI: 10.17148/IJARCCE.2026.15905
Abstract: The increasing demand for digital services has created an insatiable demand for authentication systems that are trustworthy, safe, and tamper-proof. Traditional centralized authentication systems could potentially expose the identity of users to the risks of data breaches, unauthorized modification, and insider attacks. In contrast, blockchain technology offers distributed data integrity and tamper-proof data. However, traditional blockchain-based authentication solutions could also add new difficulties in infrastructure, computation, and deployment. In this paper, the author presents a lightweight distributed authentication model that stems from the idea of blockchain and makes use of three synchronized MySQL databases. In the proposed model, AES-256 encryption is used to protect user information, while BCrypt is used to hash passwords, SHA256 is used for integrity verification, and majority voting is applied to distinguish rightful and faulty replicas. The proposed model is made even stronger owing to the use of JWT-based authentication and RBAC. In addition to that, the results of experiments that involved 1000 simulated users and 200 provoked tampering events are shown, as it refers to 100% tampering detection accuracy and consistency of the replication of data during the experiments. In addition to that, an average recovery time for resolving corrupted records proved to
Keywords: authentication based on blockchain technology; distributed ledger; AES-256 encryption; BCrypt hashing; SHA-256 integrity check; majority voting; tampering definition; backup recovery; JWT authentication; access control based on the role.
Keywords: authentication based on blockchain technology; distributed ledger; AES-256 encryption; BCrypt hashing; SHA-256 integrity check; majority voting; tampering definition; backup recovery; JWT authentication; access control based on the role.
Abstract
A Unified Analysis of Digital Forensics and Machine Learning in Cybercrime Investigation
Muzamil Amin, Yatham Thirumalanayudu
DOI: 10.17148/IJARCCE.2026.15906
Abstract: The rapid increase in cybercrime and the growing volume of digital evidence have made digital forensics an essential component of modern cyber investigations. Digital forensic tools play a crucial role in identifying, acquiring, preserving, analyzing, and presenting digital evidence from computers, mobile devices, cloud platforms, networks, and Internet of Things (IoT) environments. Over the years, numerous commercial and open-source forensic tools have been developed to address a wide range of investigative requirements. Meanwhile, machine learning (ML) techniques have emerged as effective approaches for improving the efficiency of forensic analysis through automated evidence classification, malware detection, anomaly detection, user behavior analysis, and event reconstruction. This paper presents an analysis of digital forensic tools, and their functions, and the application of machine learning techniques to digital evidence analysis throughout the investigation process. The study also examines the application of different ML algorithms across forensic domains based on the characteristics of the available evidence and extracted features. In addition, the study experimentally evaluates selected forensic tools and proposes a generalized architecture for integrating digital forensic tools with machine learning. The comparative analysis highlights how ML can assist in addressing challenges related to data volume, complexity, timeline analysis, evidence correlation, and analysis efficiency. The findings demonstrate that the integration of digital forensic tools with machine learning can enhance the efficiency and scalability of forensic investigations while maintaining the need for evidence validation and expert interpretation.
Keywords: Digital Forensics, Cybercrime Investigation, Digital Forensic Tools, Machine Learning, Digital Evidence, Forensic Analysis, Evidence Classification.
Keywords: Digital Forensics, Cybercrime Investigation, Digital Forensic Tools, Machine Learning, Digital Evidence, Forensic Analysis, Evidence Classification.
Abstract
Enhanced Intellects-Masses Optimizer Routing Strategy to improve the performance of MANET
Hamela K
DOI: 10.17148/IJARCCE.2026.15907
Abstract: Mobile Ad Hoc Networks (MANETs) consist of infrastructure-less, dynamically configured mobile nodes that communicate through wireless links. The frequent changes in network topology, limited energy resources, and vulnerability to misbehaving nodes pose significant challenges to reliable and secure routing. Optimized Link State Routing (OLSR) is a proactive routing protocol that employs Multipoint Relays (MPRs) to reduce routing overhead and improve communication efficiency. However, the selection of MPR nodes may result in excessive energy consumption, while misbehaving MPR nodes can drop data packets to conserve their energy, thereby degrading network performance and security. To address these challenges, this research proposes an Energy and Security-aware Optimized Link State Routing (ESOLSR) model based on an Enhanced Intellects-Masses Optimizer (EIMO). The proposed approach optimizes the MPR selection and route discovery process by considering available bandwidth, queue occupancy, and node lifetime as willingness parameters. In addition, misbehaving probability, power factor, and forwarding behavior are incorporated into a Composite Eligibility Index (CEI) to identify reliable and energy-efficient nodes for routing. The proposed EIMO- based routing mechanism aims to minimize energy consumption while improving network security and communication reliability. Simulation results demonstrate that the proposed ESOLSR approach outperforms existing energy-efficient routing schemes in terms of energy consumption, remaining network lifetime, average network lifetime, and variance of energy consumption. The results indicate that integrating security-aware node selection with optimization-based routing can significantly enhance the overall performance and reliability of MANETs.
Keywords: Mobile Ad Hoc Network, Optimized Link State Routing, Multipoint Relay, MPR, Energy Efficiency, Network Security, Enhanced Intellects-Masses Optimizer.
Keywords: Mobile Ad Hoc Network, Optimized Link State Routing, Multipoint Relay, MPR, Energy Efficiency, Network Security, Enhanced Intellects-Masses Optimizer.
Abstract
DESIGN AND DEVELOPMENT OF A FULL-STACK LEARNING MANAGEMENT SYSTEM WITH SECURE AUTHENTICATION, ONLINE PAYMENT, AND COURSE PROGRESS TRACKING
Akshata Mahishawadagi, Padma Y Yadahalli, Dr. Dayanand G. Savakar
DOI: 10.17148/IJARCCE.2026.15908
Abstract: This paper presents the design and development of a full-stack Learning Management System (LMS) intended to provide an integrated platform for course management, digital learning, authentication, online enrollment, payment processing, and learner progress tracking. The system is implemented using the MERN technology stack, with React.js and Vite on the client side, Node.js and Express.js on the server side, and MongoDB Atlas for persistent data storage. Clerk is used for user authentication, while Stripe supports online payments and webhook-based payment confirmation. Cloudinary is used for course thumbnail media storage. The platform supports educator-oriented course creation with chapters and lectures and student-oriented course discovery, purchase, enrollment, video learning, and progress management. RESTful APIs connect the client and server, while database models maintain course, purchase, enrollment, and progress information. Functional testing of the implemented workflow demonstrates that the major learning cycle— from authentication and course publication through payment, enrollment, lecture access, and progress tracking—can be handled within a unified web application. The work demonstrates how contemporary full-stack web technologies and third-party services can be combined to construct a practical and deployable LMS.
Keywords: Learning Management System, MERN Stack, React.js, MongoDB Atlas, Clerk Authentication, Stripe Payment, Webhook, Course Progress Tracking
Keywords: Learning Management System, MERN Stack, React.js, MongoDB Atlas, Clerk Authentication, Stripe Payment, Webhook, Course Progress Tracking
Abstract
A Survey on Handwriting Disorder Detection in Individuals with Disabilities
Ramya Deshinamoorthy, Dr.F.Kurus Malai Selvi
DOI: 10.17148/IJARCCE.2026.15909
Abstract: Handwritten writing is a complex biometric marker, which reflects both neurometer skill and cognitive processing abilities. The growing occurrence of writing and learning difficulties such as dysgraphia and dyslexia along with neurological conditions such as Parkinson's disease (PD), Multiple Sclerosis (MS), essential tremor (ET), and Alzheimer's disease (AD). There has been significant interest in automated handwritten based diagnostic and assessment systems. This research conducted a detailed systematic assessment of AI and Machine Learning approaches applied to handwritten analysis for prediction severity classification and monitoring of these issues. The comprehensive studies involve a broad range of approaches including CNN, transfer learning, ensemble methods, multimodal fusion frameworks, and data augmentation techniques. All analyses consistently compare the main Key performance measures including accuracy, precision, recall F1-score area under the curve of ROC sensitivity and specificity. The H2FCD model, CNN and SVM hybrid achieved highest accuracy of 99.26% and 99.33% as a result. The research gap is identified highlighting the lack of Multilanguage and multiclass severity datasets, absence of real world clinical deployment pipelines and the underrepresentation of diverse populations in training data. This review supports AI- based handwriting analysis for disability evaluation.
Keywords: Dysgraphia, Handwriting Analysis, Machine Learning, CNN, Feature Extraction
Keywords: Dysgraphia, Handwriting Analysis, Machine Learning, CNN, Feature Extraction
Abstract
Lightweight and Explainable Real-Time Detection of Social Media-Borne Phishing URLS: A Critical Review for Indian Context
Pushpam Kumari
DOI: 10.17148/IJARCCE.2026.15910
Abstract: Cybercrime in India has undergone a fundamental transformation in the last five years. Phishing, which was once limited to fraudulent emails, has now shifted aggressively to social media platforms such as WhatsApp, Telegram, Instagram and Facebook. Cybercriminals exploit trust by circulating deceptive links that promise free mobile recharge, government jobs, scholarship schemes, KYC updates and lottery prizes. Academic researchers have responded with a large number of machine learning based detection systems that report very high accuracy, often exceeding 98 percent. However, these systems are developed in isolation from real-world deployment constraints. Critical practical metrics such as inference latency, model file size, throughput and explainability for non-technical users are completely ignored. Most models depend on external services such as WHOIS lookup, DNS query and PageRank fetching, which require network calls and introduce significant delay. This paper presents a comprehensive critical theoretical review of six prominent recent studies published between 2025 and 2026.
Keywords: Phishing Detection, Social Media Security, Lightweight ML, Explainable AI, Real-Time Detection
Keywords: Phishing Detection, Social Media Security, Lightweight ML, Explainable AI, Real-Time Detection
Abstract
Adaptive Ensemble Machine Learning Framework for Robust Prediction, Classification, and Intelligent Decision Support Across Heterogeneous Data Domains
M. Sasikumar, Addepalli Keerthika, N Saranya
DOI: 10.17148/IJARCCE.2026.15911
Abstract: The increasing diversity and complexity of data across different application domains create significant challenges for conventional machine learning models in achieving robust prediction, classification, and intelligent decision support. This paper proposes an Adaptive Heterogeneous Dynamic Ensemble for Intelligent Decision Support (AHDE-DS) framework that integrates multiple heterogeneous machine learning and deep learning models within a unified adaptive architecture. The framework performs data preprocessing and feature representation, followed by parallel learning using complementary classifiers. A dynamic competence evaluation mechanism assesses each learner according to predictive performance, local competence, confidence, and uncertainty. Based on these measures, adaptive weights are assigned to the most competent models, and their outputs are fused through a stacking-based meta-learning mechanism. The final prediction is further processed by a confidence-aware decision-support layer. Experimental evaluation demonstrates that AHDE-DS achieves 98.73% accuracy, 98.61% precision, 98.54% recall, and 98.57% F1- score, outperforming the considered existing ensemble approaches. The results demonstrate the framework's effectiveness, adaptability, and robustness for heterogeneous data-driven intelligent applications.
Keywords: Adaptive Ensemble Learning, Heterogeneous Data, Dynamic Ensemble Selection, Machine Learning, Deep Learning, Stacking, Adaptive Weighting, Robust Prediction, Classification, Intelligent Decision Support
Keywords: Adaptive Ensemble Learning, Heterogeneous Data, Dynamic Ensemble Selection, Machine Learning, Deep Learning, Stacking, Adaptive Weighting, Robust Prediction, Classification, Intelligent Decision Support
Abstract
Next-Generation Deep Learning: A Comprehensive Survey on Explainable, Efficient, Privacy-Preserving and Multimodal Artificial Intelligence
K.Ramalakshmi, S.Prema, M.Ajithaveni, M.Ramya
DOI: 10.17148/IJARCCE.2026.15912
Abstract: The continuous evolution of deep learning has significantly expanded the capabilities of artificial intelligence, enabling intelligent systems to solve increasingly complex problems across healthcare, computer vision, natural language processing, cybersecurity, finance, autonomous systems, and smart environments. Beginning with artificial neural networks and progressing through convolutional and recurrent networks to Transformers, Graph Neural Networks, Vision Transformers, and Large Language Models, deep learning has achieved remarkable improvements in feature representation, prediction accuracy, and knowledge transfer. Nevertheless, the growing complexity of these models introduces several challenges, including limited model transparency, high computational and energy requirements, data privacy risks, and the effective utilization of heterogeneous multimodal information. Unlike conventional surveys that primarily classify deep learning according to network architectures or application domains, this work presents a capability-oriented taxonomy that organizes recent developments into five major research directions: Explainable Deep Learning, Efficient Deep Learning, Privacy-Preserving Deep Learning, Green Deep Learning and Multimodal Deep Learning. Based on this framework, representative architectures are critically examined with respect to their operating principles, strengths, limitations, and suitability for diverse real-world applications. The survey also analyses emerging application areas, identifies unresolved challenges related to fairness, robustness, scalability, and trustworthy artificial intelligence, and discusses promising research opportunities involving Federated Large Language Models, Edge AI, Green AI, Self-supervised Learning, and Multimodal Foundation Models. The proposed framework provides a structured understanding of current advances while offering practical insights for the design of transparent, efficient, secure, and sustainable next-generation deep learning systems.
Keywords: Deep Learning, Explainable Artificial Intelligence, Privacy-Preserving Learning, Green Deep Learning, Multimodal Learning, Foundation Models.
Keywords: Deep Learning, Explainable Artificial Intelligence, Privacy-Preserving Learning, Green Deep Learning, Multimodal Learning, Foundation Models.
Abstract
AI-BASED STUDENT PREFORMANCE PREDICTION
Dr.B. Rajesh Kumar, S. Shailesh, Mohammed Aadhil.I
DOI: 10.17148/IJARCCE.2026.15913
Abstract: Education is increasingly generating large amounts of digital data through examinations, assignments, attendance systems, online learning platforms, and internal assessments. This data can provide valuable information about student learning and academic progress.
Student Performance Prediction using Machine Learning is a system that analyzes historical student information and predicts the likely academic performance of students. The system can consider several factors, including attendance, previous examination marks, assignment performance, internal assessment marks, study hours, classroom participation, and other relevant attributes.
The proposed system consists of multiple stages, including data collection, data preprocessing, feature selection, model training, model testing, and performance prediction. Machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine can be evaluated for the prediction task.
The system can help teachers identify students who may require additional academic support. It can also help educational institutions understand patterns associated with student achievement and improve academic planning.
However, predictions should be treated as supporting information rather than as a final judgment about students. Privacy, fairness, transparency, and human supervision are important when applying machine learning to educational data.
Keywords: Student Performance, Machine Learning, Educational Data Mining, Prediction, Classification, Academic Analytics, Data Science.
Student Performance Prediction using Machine Learning is a system that analyzes historical student information and predicts the likely academic performance of students. The system can consider several factors, including attendance, previous examination marks, assignment performance, internal assessment marks, study hours, classroom participation, and other relevant attributes.
The proposed system consists of multiple stages, including data collection, data preprocessing, feature selection, model training, model testing, and performance prediction. Machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine can be evaluated for the prediction task.
The system can help teachers identify students who may require additional academic support. It can also help educational institutions understand patterns associated with student achievement and improve academic planning.
However, predictions should be treated as supporting information rather than as a final judgment about students. Privacy, fairness, transparency, and human supervision are important when applying machine learning to educational data.
Keywords: Student Performance, Machine Learning, Educational Data Mining, Prediction, Classification, Academic Analytics, Data Science.
Abstract
AI-Based Chatbot for Education: Design, Architecture, and Empirical Evaluation
Dr. B. Rajesh Kumar, Abishek.V, Sujan.R, Abishek N
DOI: 10.17148/IJARCCE.2026.15914
Abstract: Modern higher education is characterized by an exponential expansion of digital study materials; however, students continue to encounter friction in locating syllabus-aligned, context-aware academic resolutions in real time. Conventional academic support channels, such as scheduled office hours, physical libraries, and generic web search engines, are constrained by temporal availability, commercial clutter, and a lack of persistent pedagogical context. This paper presents the design, architectural formulation, and empirical evaluation of the AI-Based Chatbot for Education, an intelligent, interactive web platform engineered as an on-demand, non-judgmental digital academic companion. Built upon a modular stack incorporating Python 3.10+, Streamlit, and an embedded SQLite relational database, the system bridges conversational Natural Language Processing (NLP) models with structured syllabus navigation. The platform encapsulates four core modules: an AI Chat Module with session-aware state management, a Subjects Module for curriculum-scoped queries, a Study Tools Module for extractive summarization and synthetic quiz generation, and a Chat History Module providing persistent local auditability. Comprehensive verification across Unit, Integration, Functional, System, and User Acceptance levels demonstrated a 100% test pass rate across 45 executed test cases, maintaining sub-two-second query response latency and robust session recovery under adverse network conditions. Beyond the functional evaluation, this extended study reports a detailed breakdown of latency contributors, a structured usability survey administered to 42 undergraduate participants, a discussion of construct, internal, and external validity threats, and a roadmap for retrieval-augmented and multi-tenant extensions of the platform.
Keywords: Artificial Intelligence, Natural Language Processing, Educational Chatbot, Streamlit, SQLite, Pedagogical Assistants, Prompt Engineering, Human-Computer Interaction, Software Verification, Usability Evaluation.
Keywords: Artificial Intelligence, Natural Language Processing, Educational Chatbot, Streamlit, SQLite, Pedagogical Assistants, Prompt Engineering, Human-Computer Interaction, Software Verification, Usability Evaluation.
Abstract
YojanaMitra: An AI-Assisted Framework for Government Scheme Eligibility Matching and Guidance
Chinta Lavanya
DOI: 10.17148/IJARCCE.2026.15916
Abstract: Artificial Intelligence and Machine Learning are being utilised to automate eligibility assessment and provide personalisation in public service provision. They address longstanding issues faced by traditional systems such as fragmented scheme information, multi-condition eligibility criteria and limited citizen awareness. In this paper, we present YojanaMitra, a web application which provides access to 134 central and state government welfare schemes of India via a hierarchical and AI-driven matching pipeline. In the initial step, our matching pipeline uses a deterministic rule engine which supports both AND/OR logic of eligibility criteria to classify a citizen into three categories based on each welfare scheme: Eligible, Near Miss and Not Eligible. Only the schemes classified as Eligible are then ranked via a Random Forest classifier by engineering ten features from citizen-profile; finally, retrieval augmented conversational assistant provides citizen responses based on the matching scheme data alone. The application has been developed using Flask, SQLite database and Groq large language model API. It guarantees citizen security, structured profile-based matching and multilinguality. Automated testing shows accurate eligibility classification for all test cases; meanwhile, the accuracy of Random Forest ranking model is 0.597 with precision of 0.606, recall of 0.598, F1-score of 0.602 and ROC-AUC of 0.627 in the evaluation on held-out test set. We find that vulnerability and income margin are the two most important ranking features.
Keywords: Government Scheme Eligibility, Rule-Based Matching, Machine Learning Recommendation, Retrieval- Augmented Generation, Random Forest, E-Governance, Eligibility Prediction, Progressive Web Application, Flask, Multilingual Interface, User Engagement.
Keywords: Government Scheme Eligibility, Rule-Based Matching, Machine Learning Recommendation, Retrieval- Augmented Generation, Random Forest, E-Governance, Eligibility Prediction, Progressive Web Application, Flask, Multilingual Interface, User Engagement.
Abstract
Noble Poly Shapes: An AI-Assisted Interactive Interface for the Enumeration of Noble Polyhedra
Akshat Wahal, Anupam Andrew Maxton
DOI: 10.17148/IJARCCE.2026.15918
Abstract: We describe Noble Poly Shapes, a browser-based graphical interface for exploring, visualizing, and extracting the complete set of noble polyhedra enumerated by Hill [1]. Noble polyhedra are those polyhedra that are simultaneously vertex-transitive and face-transitive; Hill's computer-assisted proof establishes that, aside from the two known infinite families, exactly 146 such polyhedra exist. The accompanying data library, noble-tools-revised [2], contains the enumeration code and a full set of exported models. This document summarizes the relevant definitions and results of that work and describes the respects in which Noble Poly Shapes differs from the original repository: namely, in providing browser-native visualization, interactive exploration of the catalog, and direct extraction of shape and face data, without requiring the user to run the original enumeration software.
Abstract
Smart Classroom Management Software for Enhanced Learning Environments.
Ms. Sakshi Umakant Chaudhari, Asst. Prof. Sapana A. Fegade
DOI: 10.17148/IJARCCE.2026.15919
Abstract: Smart classroom technology is increasingly used to support teaching, attendance, communication, and classroom resources. However, many institutions still manage these activities through separate tools or manual procedures. This paper proposes Smart Classroom Management Software (SCMS) as a centralized platform for administrators, teachers, and students. The system combines attendance, timetable management, learning resources, announcements, student information, and basic analytics. Optional AI and IoT features can support attendance and classroom monitoring, while teacher verification and privacy controls remain important. Recent 2025–2026 research shows growing use of computer vision, IoT, AI-based behaviour analysis, and smart classroom interoperability. Based on this literature, the proposed system focuses on practical integration rather than a single feature. A design-oriented mixed- method approach is proposed, using questionnaires, interviews, task-based testing, and measures such as attendance accuracy, task completion time, usability, and response time. The paper presents a research-ready model and evaluation plan and does not invent experimental results that have not yet been collected.
Keywords: Smart classroom, classroom management software, artificial intelligence, Internet of Things, attendance, learning analytics, classroom monitoring, privacy, learning environment
Keywords: Smart classroom, classroom management software, artificial intelligence, Internet of Things, attendance, learning analytics, classroom monitoring, privacy, learning environment
Abstract
ARMOR: An Extensible Multi-Provider Secret Validation Framework for Automated Credential Verification in DevSecOps Pipelines
Siddharth Gupta, Poonam Bhartiya & Shailendra Shriwastava
DOI: 10.17148/IJARCCE.2026.15920
Abstract: Credential leakage through public repositories creates an urgent incident-response problem: detection tools identify candidate secrets but do not establish whether a credential remains active. This paper presents Armor, an open- source active credential-validation framework that issues read-only, idempotent probes to official provider endpoints. Armor supports seventeen credential types across cloud, source-code, AI/ML, communication, payment, and DevOps services, and returns a normalised five-state verdict schema: valid, invalid, no permission, failed validation, or nvalidjwt. Its five-layer pipeline combines a CLI, service facade, validator registry, data models, and provider-specific strategies, with an optional Groq LLaMA-3.3-70B triage agent for unstructured text. The local evaluation contains 126 passing unit tests, 100% branch coverage for tested validator modules, and 93.55% total package coverage.
Keywords: active credential validation, DevSecOps, secret scanning, cloud security, API authentication, JWT, Python.
Keywords: active credential validation, DevSecOps, secret scanning, cloud security, API authentication, JWT, Python.
Abstract
A. ISSN (O) 2278-1021, ISSN (P) 2319-5940IJARCCE XplainGuard: Dynamic Policy Enforcement in Cloud Infrastructure as Code: Integrating Generative AI for Auto-Remediation and Explainability
Aadrika Khare, Poonam Bhartiya & Shailendra Shriwastava
DOI: 10.17148/IJARCCE.2026.15921
Abstract: Infrastructure as Code (IaC) concentrates cloud security posture into text files, where one misconfigured attribute propagates to every environment where it is applied. Four gaps persist in prior tooling: static scanners such as Checkov and Trivy detect misconfigurations but generate no corrective code, leaving a detection-to-repair gap; generative repair methods report incorrect-code rates as high as 20.4%, precluding unattended use; automated fixes carry no security rationale, so practitioners cannot judge why a change is needed; and existing tools evaluate misconfigurations in isolation, missing compound attack paths formed by co-located resources. This paper presents XplainGuard, a command-line Terraform auditor that targets all four directly: model output is constrained to a strict JSON schema; every generated patch passes through a self-verification guardrail that executes terraform validate before the developer sees it, with one bounded correction attempt; each finding carries a structured four-element rationale (resource and attribute, attack vector, compound-risk context, compliance control). Across 21 runs on a seven-file fixture, every response parsed as valid JSON, and the guardrail reduced the observed incorrect code rate from 6.7% to 0%. Detection coverage was 100% on isolated vulnerabilities and 91.7% overall, and locally executed scans show Trivy 0.72.0 missing two of the four target categories that Checkov 3.3.8 detects, illustrating the specific gaps this work addresses across prior tooling rather than uniform superiority over any single tool. The evaluation is small; its statistical limits are stated explicitly.
Keywords: Infrastructure as Code, Terraform, dynamic policy enforcement, generative AI, large language models, auto- remediation, explainable AI, cloud security, self-verification.
Keywords: Infrastructure as Code, Terraform, dynamic policy enforcement, generative AI, large language models, auto- remediation, explainable AI, cloud security, self-verification.
Abstract
Enhancing the ELLANGA Traditional Cascade Tank-Village System in Sri Lanka: Proposal for a Smart IT Based Solution to Enhance Predictability and Sustainability
Rathnayake R.M.N.B Mahesh Hapugoda
DOI: 10.17148/IJARCCE.2026.15922
Abstract: The Ellanga Traditional Cascade -Village System (ETCTVS), practiced in Sri Lanka, becomes an important factor in the agriculture of this island nation through providing irrigation water to farmers and sustaining livelihoods of communities residing around these systems. However, its sustainability and stability are threatened as a result of specific factors such as climate change, water scarcity, and changing farming practices. The paper, therefore, discusses an agricultural information system application with regard to enhancing the sustainability and stability of the Ellanga Traditional Cascade -Village System (ETCTVS). In collecting data from farmers and other agents within the ETCTVS, a mixed-methods approach was used: survey, interview, and field observation. The information was later used in the GIS technology and analysis with the aid of a statistical analysis computer software. "This study shows that the implementation of an agricultural information system in the Ellanga Traditional Cascade -Village System (ETCTVS) will be very much efficient in making this system more stable and sustainable in nature. The study explains how, in this system, farmers will be able to have updated information with regard to the current status of climate, soil water content, crop handling practices, and market price of their products. The farmer will be able to access all this information sources which will aid them in taking prudent decisions in order to make maximum usage and achieve sustainability in agriculture," according to this research.
Keywords: Ellanga Cascade Water Tank System, IT Network System, Traditional Knowledge, Efficiency Enhancement
Keywords: Ellanga Cascade Water Tank System, IT Network System, Traditional Knowledge, Efficiency Enhancement
Abstract
AI-Driven Network Intrusion Detection System Using Machine Learning
Rashmi, Abhinandan Raje Urs M B, Appu Gowda M P, Aravind Kumar P N, Keerthan K
DOI: 10.17148/IJARCCE.2026.15923
Abstract: The Network Intrusion Detection technique is an essential component of cybersecurity for detecting malicious activities in computer networks. However, signature-based system may have limitations in detecting some new or modified attacks. In this paper, an AI-based Network Intrusion Detection System has been developed that applies supervised machine learning techniques for classifying network traffic. It performs data preprocessing and feature preparation and applies Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Extra Trees, and XGBoost algorithms to the CICIDS2017, CSE-CIC-IDS2018, NSL-KDD, and UNSW-NB15 datasets. The developed models are embedded in a web application that allows uploading of CSV files, traffic prediction, attack detection, and dashboard- based analysis of results.
Keywords: Network Intrusion Detection System, Machine Learning, Network Security, Cybersecurity, XGBoost, Random Forest, CICIDS2017, Network Traffic Classification.
Keywords: Network Intrusion Detection System, Machine Learning, Network Security, Cybersecurity, XGBoost, Random Forest, CICIDS2017, Network Traffic Classification.
Abstract
Identification and Analysis of Problems Faced by Stakeholders in the Existing Grain Market System
Parteek Singh, Dr. Harish Rohil
DOI: 10.17148/IJARCCE.2026.15926
Abstract: Agriculture markets remain essential interfaces between farmers, intermediaries, buyers and public market authorities, yet their operational performance is often constrained by manual handling, congestion, uneven infrastructure and incomplete use of digital trading services. This study identifies and compares the problems experienced by three stakeholder groups in the existing grain market system in Haryana and Punjab, India. A structured questionnaire survey produced 290 usable responses: 200 farmers, 60 commission agents/traders and 30 grain-market office staff. Responses were summarized through frequencies, percentages and cross-stakeholder comparisons. Strong agreement was observed on labour dependence (86.50–100.00%), time-consuming weighing and packing (90.50–100.00%), and congestion associated with inadequate storage and transportation (96.50–96.67%). Awareness of e-NAM was also widely viewed as a barrier to adoption (75.00–83.34%). Perceptions diverged on the complexity of the e-NAM sale process and online- payment delays: farmers and traders reported greater difficulty, whereas office staff largely rejected these concerns. Infrastructure and resource-management gaps were visible in responses concerning electricity, connectivity, quality- testing facilities, lighting and water management. The findings show that digitizing auction and payment alone does not remove physical-process bottlenecks in the market yard. The paper contributes a stakeholder-aligned problem taxonomy and identifies operational priorities for market modernization: mechanized handling, transparent digital weighing and assaying, queue and logistics management, assisted digital services, and monitored infrastructure. Because the study uses non-probability field access and aggregated self-reports from two states, the results should be interpreted as diagnostic rather than nationally representative.
Keywords: agricultural marketing; grain market; e-NAM; stakeholder analysis; farmers; commission agents; market infrastructure; digital agriculture
Keywords: agricultural marketing; grain market; e-NAM; stakeholder analysis; farmers; commission agents; market infrastructure; digital agriculture
Abstract
A Machine Learning and NLP Framework for Automatic Title Similarity Detection and Preliminary Verification
Miss. Suvarna Bhoi, Dr. Sarkarsinha H. Rajput, Dr. Shital A. Patil, Dr. Dinesh D. Puri
DOI: 10.17148/IJARCCE.2026.15927
Abstract: Title verification becomes difficult when a large collection of publication titles has to be checked for similarity before a new title is accepted. Exact string matching is not sufficient because titles may contain spelling variations, reordered words, prefixes or suffixes, periodicity changes, phonetic variations, or different words with similar meanings. This research proposes a practical Natural Language Processing (NLP) and Machine Learning framework for automatic preliminary title verification. The proposed approach first normalizes and preprocesses title text and then represents titles using Term Frequency–Inverse Document Frequency (TF-IDF). Cosine Similarity is used as the main lexical similarity measure to rank existing titles against a newly submitted title. Character-level and phonetic features can be added to improve detection of spelling and sound-based variations. Logistic Regression, Support Vector Machine (SVM), and Random Forest are considered for classifying title pairs as similar or non-similar. The system will return the most similar existing titles, similarity scores, and a verification recommendation for human review. Evaluation will consider accuracy, precision, recall, F1-score, false-positive and false-negative behaviour, and processing time. The study aims to provide an interpretable, scalable, and practical approach that reduces repeated manual comparison while keeping final verification under human control.
Keywords: Title Similarity Detection, Natural Language Processing (NLP), TF-IDF, Cosine Similarity, SBERT, Machine Learning, Semantic Similarity, Preliminary Verification.
Keywords: Title Similarity Detection, Natural Language Processing (NLP), TF-IDF, Cosine Similarity, SBERT, Machine Learning, Semantic Similarity, Preliminary Verification.
Abstract
Alumni Association Platform for the University/Institute
Mr. Hemant Bodade, Dr. Dinesh Puri
DOI: 10.17148/IJARCCE.2026.15928
Abstract: This research presents a centralized Alumni Association Platform for managing alumni information and engagement. The platform maintains accurate alumni records through a unique digital alumni ID linked to verified academic records. It enables alumni to create profiles, connect with batchmates, and interact with students and the institution. Features include an alumni directory, discussion boards, events, job notifications, and networking opportunities. Academic verification and role-based authentication help prevent fake profiles and unauthorized data access. The study evaluates registration accuracy, data consistency, response time, user engagement, and scalability. Platform security and performance are also analyzed under different usage conditions. The proposed system aims to provide secure, reliable, and long-term engagement between alumni and the institution.
Abstract
Women Safety Analytics – Protecting Women From Safety Threats
Miss. Gayatri Vijay Amode, Dr. Sarkarsinha H. Rajput, Dr. Shital A. Patil, Dr. Dinesh D. Puri
DOI: 10.17148/IJARCCE.2026.15929
Abstract: Women’s safety is an important issue in today’s society, as women may face different types of risks in public places, workplaces, educational institutions, transportation, and online platforms. The proposed Women Safety Analytics system aims to use technology and data analysis to help identify and understand such safety risks. The system collects safety-related information, analyzes incidents based on factors such as location and time, and identifies areas or situations where the risk may be higher. It can also provide alerts and useful recommendations when potential threats are detected. The main purpose of the system is to support prevention and quick response rather than replace emergency services or human decision-making. By using data analytics and machine learning techniques, the system can convert collected safety information into meaningful insights that can help women, security personnel, administrators, and authorities. The project also considers important issues such as privacy, data security, false alarms, scalability, and the responsible use of monitoring technologies. Overall, the proposed system aims to support safer environments by using technology to understand safety patterns and provide useful information for better prevention and response.
Keywords: Women Safety, Safety Analytics, Machine Learning, Risk Detection, Threat Analysis, Location Analytics, Emergency Alerts, Data Privacy.
Keywords: Women Safety, Safety Analytics, Machine Learning, Risk Detection, Threat Analysis, Location Analytics, Emergency Alerts, Data Privacy.
Abstract
Biomechanics of the Kho-Giving (Seated Exchange) and Pole-Turn Technique in Kho-Kho
Dr. Pathan Yusufkhan Hidayatkhan
DOI: 10.17148/IJARCCE.2026.15930
Abstract: Two technical actions unique to kho-kho -- the seated-to-standing “kho” exchange, in which a stationary chaser transfers pursuit to a teammate through a tap and vocal call, and the tight circular turn executed around the corner pole at the end of each lane -- have received almost no dedicated mechanical attention despite governing the tempo of the entire chasing phase. This paper analyses both actions using rotational and rate-of-force-development frameworks drawn from biomechanics research on rapid postural transitions and curved-path running. Twelve male state-level kho-kho players were filmed performing repeated kho exchanges and pole turns under match-simulated conditions, with synchronised high-speed video and an adjacent force platform capturing the seated-to-standing transition and, separately, marker-based tracking of trunk lean and foot placement during pole rounding. The kho exchange was decomposed into a reaction interval, an upper-limb tap phase, and a lower-limb rate-of-force-development phase converting a seated hip and knee position into the first sprint stride; the pole turn was decomposed into an approach, a banked circular phase, and an exit-acceleration phase. Results showed a mean total kho-exchange time of approximately 0.30 seconds from tap contact to first foot-strike, with rate of force development in the seated-start extensors measurably lower than published standing sprint-start values, indicating a genuine mechanical cost to the seated launch position. Pole-turn analysis showed that players who leaned the trunk approximately 15-20° into the turn and reduced turn radius through tighter foot placement completed the pole-rounding phase significantly faster than those adopting an upright, wide-radius path, consistent with the centripetal-force relationship between speed, turn radius, and lean angle. These findings offer the first quantitative mechanical description of two of kho-kho's most frequently repeated, yet previously unexamined, technical actions.
Abstract
Generative AI in Education: Opportunities, Challenges, and Future Perspectives
Geetha, N, Lathika, S, Vikasini, S
DOI: 10.17148/IJARCCE.2026.15931
Abstract: Generative Artificial Intelligence (GenAI) is rapidly transforming education by enabling systems to generate text, images, code, feedback, and other learning resources. Large language models have created opportunities for personalized learning, intelligent tutoring, automated feedback, content development, and accessibility. At the same time, GenAI introduces concerns regarding academic integrity, misinformation, bias, privacy, copyright, unequal access, and excessive learner dependence. This paper reviews recent research studies and synthesizes major findings on the educational potential and limitations of GenAI. A comparative literature survey of ten influential studies identifies recurring themes, including personalization, teacher support, assessment redesign, AI literacy, and ethical governance. The review reveals a research gap in long-term empirical evidence, particularly in diverse educational settings and in measuring actual learning outcomes. The paper argues that GenAI should be implemented as a human-centered educational assistant rather than a replacement for teachers. Future directions include multimodal adaptive learning, AI literacy, transparent assessment practices, inclusive access, privacy-preserving systems, and stronger institutional policies.
Keywords: Generative AI, Artificial Intelligence in Education, ChatGPT, Personalized Learning, AI Literacy, Educational Technology, Academic Integrity
Keywords: Generative AI, Artificial Intelligence in Education, ChatGPT, Personalized Learning, AI Literacy, Educational Technology, Academic Integrity
Abstract
Child Vaccination Tracking System
Jeevitha. N, Janani. B, Lakshana. B, Priyadarshini. S. S, Dr. Premavathi. C
DOI: 10.17148/IJARCCE.2026.15932
Abstract: Childhood vaccination is one of the most effective methods for preventing vaccine-preventable diseases and reducing child mortality. However, maintaining complete and timely vaccination records can be difficult because of missed appointments, misplaced vaccination cards, lack of awareness, migration, and limitations in manual record- keeping. This paper proposes a Child Vaccination Tracking System (CVTS), a digital health information system designed to maintain vaccination records and assist parents, healthcare workers, and administrators in monitoring children's immunization status. The proposed system stores child details, vaccination history, vaccine schedules, upcoming doses, and missed vaccinations in a centralized database. It can generate automatic reminders through SMS or application notifications and provide alerts when a vaccination becomes overdue. A QR-based identification mechanism can also be incorporated to quickly retrieve a child's authorized vaccination record at participating healthcare centers. The system provides dashboards and reports to help healthcare workers identify children who have missed vaccinations and support follow-up activities. Recent studies indicate that mobile reminders and electronic immunization registries can improve vaccination timeliness, data availability, and identification of under-immunized children [1]– [10]. The proposed system integrates these concepts into a child-centered platform with emphasis on usability, security, privacy, interoperability, and accessibility. The system can contribute to improved vaccination adherence, better record management, and efficient immunization monitoring.
Keywords: Child vaccination, immunization tracking, electronic immunization registry, mHealth, SMS reminder, digital health, vaccine schedule, QR code.
Keywords: Child vaccination, immunization tracking, electronic immunization registry, mHealth, SMS reminder, digital health, vaccine schedule, QR code.
Abstract
Heart Disease Prediction Using Machine Learning and Deep Learning Techniques: A Comprehensive Review
Geetha. N, Janasuruthi. M, Sripavanya. K
DOI: 10.17148/IJARCCE.2026.15933
Abstract: Heart disease remains one of the leading causes of mortality worldwide, accounting for millions of deaths annually despite significant advancements in healthcare technologies. Early prediction and timely diagnosis play a crucial role in reducing mortality rates and improving patient outcomes. Conventional diagnostic approaches rely heavily on clinical expertise, laboratory investigations, and medical imaging, which are often time-consuming, expensive, and susceptible to human interpretation errors. Recent developments in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized cardiovascular disease prediction by enabling automated analysis of large-scale clinical datasets with high accuracy and efficiency. Machine learning algorithms such as Logistic Regression, Decision Trees, Support Vector Machines, Random Forests, Naïve Bayes, K-Nearest Neighbors, Gradient Boosting, and Extreme Gradient Boosting have demonstrated remarkable capability in predicting heart disease using structured clinical data. Simultaneously, deep learning architectures including Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, Autoencoders, and Transformer-based models have shown superior performance in analyzing electrocardiograms, echocardiograms, cardiac MRI, CT scans, wearable sensor data, and multimodal healthcare information. This review comprehensively examines recent advancements in heart disease prediction. The study discusses traditional statistical approaches, modern machine learning algorithms, deep learning techniques, hybrid intelligent models, explainable artificial intelligence, federated learning, Internet of Medical Things (IoMT), cloud-based healthcare systems, and wearable monitoring technologies. Furthermore, this review summarizes the strengths, limitations, datasets, evaluation metrics, and prediction performance of recent studies through a comparative analysis. Existing research gaps, practical challenges, future research directions, and emerging opportunities are also highlighted to guide researchers toward developing reliable, interpretable, and clinically deployable heart disease prediction systems. The findings indicate that integrating explainable AI, multimodal learning, privacy-preserving federated learning, and real-time wearable monitoring represents the future direction of intelligent cardiovascular healthcare.
Keywords: Heart Disease Prediction, Machine Learning, Deep Learning, Cardiovascular Disease, Artificial Intelligence, Explainable AI, Internet of Medical Things, Federated Learning, Healthcare Analytics.
Keywords: Heart Disease Prediction, Machine Learning, Deep Learning, Cardiovascular Disease, Artificial Intelligence, Explainable AI, Internet of Medical Things, Federated Learning, Healthcare Analytics.
Abstract
Fairlytics: AI-Based Intelligent Dynamic Pricing With Discount Authenticity Validation
Palak Shukla, Soukhya Raghavendra Yadawad, Spurthi D S, Rashmi, Lavanya N L
DOI: 10.17148/IJARCCE.2026.15934
Abstract: E-commerce platforms increasingly rely on algorithmic pricing to remain competitive, yet two problems persist side by side: prices that fail to track real-time demand, and discount offers that mislead buyers through inflated “strike-through” reference prices, fake festival markdowns, and recycled coupon claims. This paper presents Fairlytics, an AI-based intelligent system that unifies dynamic pricing with automated discount authenticity validation in a single pipeline. Fairlytics estimates a fair, demand-responsive price for a product using historical sales, competitor prices, inventory levels, and seasonal signals, and simultaneously verifies whether an advertised discount is genuine by reconstructing the product's true historical price trajectory and comparing it against the claimed original price. A hybrid ensemble of regression and gradient-boosted tree models drives the pricing engine, while a time-series anomaly detector and rule-based authenticity scorer flag manipulated discounts. The proposed architecture is modular, combining data ingestion, feature engineering, dual prediction engines, and a fairness-scoring dashboard for administrators and consumers. This work is intended to improve pricing transparency, protect consumers from deceptive discounting, and give platform operators a defensible, explainable basis for dynamic pricing decisions.
Keywords: Dynamic Pricing, Discount Authenticity, Fake Discount Detection, Machine Learning, Price Fairness, E- Commerce, Anomaly Detection, Ensemble Learning, Consumer Trust
Highlights ● AI-based framework for intelligent dynamic pricing and discount validation.
Keywords: Dynamic Pricing, Discount Authenticity, Fake Discount Detection, Machine Learning, Price Fairness, E- Commerce, Anomaly Detection, Ensemble Learning, Consumer Trust
Highlights ● AI-based framework for intelligent dynamic pricing and discount validation.
Abstract
AI AND IOT APPLICATIONS IN SMART AGRICULTURE
Dr.Geetha, N, Harshini, S, Jeevitha, V
DOI: 10.17148/IJARCCE.2026.15935
Abstract: Smart agriculture is evolving from conventional, experience-based farming toward data-driven and automated decision making. The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), often termed Artificial Intelligence of Things (AIoT), enables continuous sensing, communication, prediction and control across the agricultural lifecycle. IoT devices collect soil moisture, temperature, humidity, nutrient, weather and crop- condition data, while AI models transform these observations into recommendations or automated actions. Recent studies show applications in precision irrigation, crop and yield prediction, soil and nutrient management, pest and disease detection, greenhouse and hydroponic control, weed management, livestock monitoring, post-harvest management and climate-smart farming. This paper reviews research published, Summarizes major AI-IoT approaches, identifies gaps in scalability, interoperability, data quality, connectivity, explainability, cyber security and smallholder adoption, and outlines future directions involving edge AI, federated learning, digital twins, 5G/6G, low-cost virtual sensing and multimodal foundation models. The evidence indicates that AI-IoT can improve resource efficiency and resilience, but practical deployment requires affordable, robust and farmer-centered systems rather than isolated laboratory prototypes.
Keywords: Artificial Intelligence, Internet of Things, AIoT, Smart Agriculture, Precision Agriculture, Machine Learning, Edge Computing, Crop Monitoring, Smart Irrigation
Keywords: Artificial Intelligence, Internet of Things, AIoT, Smart Agriculture, Precision Agriculture, Machine Learning, Edge Computing, Crop Monitoring, Smart Irrigation
Abstract
Smart Collaborative Coding Environment with AI Assistance
Anitha L, Bhumika A R, Deeksha J, Hithashree N M, Jeevitha B V
DOI: 10.17148/IJARCCE.2026.15936
Abstract: The Smart Collaborative Coding Environment with AI Assistance is a web-based platform designed to enable multiple users to write, edit, execute, and debug code collaboratively in real time. The system provides a shared coding workspace where users can work together from different locations with immediate synchronization of code changes. To improve the coding experience, the platform integrates Artificial Intelligence (AI) to analyze source code, identify syntax errors, and provide real-time debugging suggestions and code assistance. The proposed system combines real-time
to reduce the difficulties associated with remote programming collaboration, improve code quality, and increase developer productivity. The system can be useful for software development teams, students, coding interviews, and collaborative learning environments. By integrating real-time collaboration with intelligent coding assistance, the proposed platform provides a unified environment for efficient and interactive programming.
Keywords: Collaborative Coding, Real-Time Collaboration, Artificial Intelligence, AI-Assisted Programming, Code Editor, Error Detection, Debugging, Code Execution.
to reduce the difficulties associated with remote programming collaboration, improve code quality, and increase developer productivity. The system can be useful for software development teams, students, coding interviews, and collaborative learning environments. By integrating real-time collaboration with intelligent coding assistance, the proposed platform provides a unified environment for efficient and interactive programming.
Keywords: Collaborative Coding, Real-Time Collaboration, Artificial Intelligence, AI-Assisted Programming, Code Editor, Error Detection, Debugging, Code Execution.
Abstract
A CNN–BiLSTM–CTC Recognition System with Post-Processing Optimization for Handwritten English Prescription Digitization
Rijia Sultana
DOI: 10.17148/IJARCCE.2026.15937
Abstract: The digitization of handwritten medical prescriptions remains a critical challenge in healthcare informatics due to the prevalence of illegible handwriting, abbreviated terminology, and inconsistent writing styles. Errors in prescription interpretation contribute significantly to adverse drug events, medication misuse, and patient safety risks. This paper presents a robust offline handwritten prescription recognition framework based on a hybrid Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Connectionist Temporal Classification (CTC) architecture. The proposed system combines deep visual feature extraction using ResNet34 with sequential context modeling through BiLSTM layers and alignment-free transcription using CTC decoding. To improve clinical reliability, a post-processing module integrates fuzzy string matching and pharmaceutical contextual validation through Market Basket Analysis (MBA). The framework employs a modular segmentation strategy utilizing Single Shot Detector (SSD) based word localization and line-clustering heuristics to improve recognition quality. Experimental evaluation demonstrates a Character Error Rate (CER) of 8.5%, Character Recognition Rate (CRR) of 99.20%, and Word Recognition Rate (WRR) of 93.75% for pharmaceutical terms containing 7–10 characters. The proposed system achieves high recognition accuracy while maintaining computational efficiency, requiring substantially less memory than contemporary Vision Language Models (VLMs). The framework provides a practical, deployable solution for real-time prescription digitization and integration into healthcare information systems.
Keywords: Handwritten Text Recognition; Medical Prescription Recognition; CNN–BiLSTM–CTC; Deep Learning; Optical Character Recognition; Healthcare Informatics;
Keywords: Handwritten Text Recognition; Medical Prescription Recognition; CNN–BiLSTM–CTC; Deep Learning; Optical Character Recognition; Healthcare Informatics;
Abstract
Cardiovascular Risk Prediction From Retinal Images Using Vision Transformer
Sai Sireesha Pakki, Y. Sudheer Kumar
DOI: 10.17148/IJARCCE.2026.15938
Abstract: Cardiovascular diseases are among the leading causes of death worldwide, making early diagnosis essential for effective treatment and prevention. This paper presents a deep learning-based approach for predicting heart disease risk using retinal fundus images and a Vision Transformer (ViT) model. The proposed system utilizes the RFMiD2 retinal image dataset, where images are pre-processed through resizing, normalization, and binary classification. Unlike traditional Convolutional Neural Networks (CNNs), the Vision Transformer divides images into patches and employs a self-attention mechanism to capture both local and global retinal features. Transfer learning with a pre-trained ViT model improves feature extraction and enhances performance on a limited medical image dataset. The model is trained using the Adam optimizer and Cross-Entropy Loss for binary classification. A Streamlit-based web application is developed to provide an interactive interface for uploading retinal images and generating predictions with confidence scores. The proposed model achieved an accuracy of 98.62%, demonstrating its effectiveness in identifying retinal patterns associated with cardiovascular risk. This framework offers a non-invasive, cost-effective, and AI-assisted screening solution that can support healthcare professionals in the early detection of heart disease and highlights the potential of Vision Transformers in medical image analysis.
Keywords: Heart Disease Prediction, Retinal Fundus Images, Vision Transformer (ViT), Deep Learning, Transfer Learning, Medical Image Classification, Artificial Intelligence, Early Disease Detection, Loss ,Accuracy.
Keywords: Heart Disease Prediction, Retinal Fundus Images, Vision Transformer (ViT), Deep Learning, Transfer Learning, Medical Image Classification, Artificial Intelligence, Early Disease Detection, Loss ,Accuracy.
Abstract
Self-Supervised Learning With Multibranch Consistency for Few-Shot PolSAR Image Classification
S Sehar Tasneem, Saboor Saniya, Siddarth S, Sadanand, Prof. Gnanamani H
DOI: 10.17148/IJARCCE.2026.15940
Abstract: Polarimetric Synthetic Aperture Radar (PolSAR) images provide valuable information for land-cover classification and can capture surface characteristics even under challenging weather and lighting conditions. However, training deep learning models for PolSAR image classification generally requires a large number of labelled samples, which are often difficult and expensive to obtain. This limitation becomes more significant in few-shot classification, where only a small number of labelled samples are available for each class. This paper presents a Self-Supervised Learning with Multibranch Consistency (SSL-MBC) approach for few-shot PolSAR image classification. The proposed method first learns useful image representations from largely unlabelled PolSAR data using a self-supervised learning strategy. A shared feature encoder is combined with multiple convolutional branches using different kernel sizes to capture spatial and contextual information at different scales. The consistency between the representations generated from different views of the same PolSAR image is used to improve the quality and stability of the learned features. After pretraining, the learned representations are transferred to a classification network and fine-tuned using only a small number of labelled samples. The proposed approach is evaluated on PolSAR land-cover classes such as water, forest, urban, and cropland. Experimental results show that the method can achieve improved classification performance compared with conventional supervised deep learning models under limited-label conditions. The results demonstrate that combining self-supervised representation learning with multibranch consistency can reduce the dependence on labelled data while maintaining reliable classification performance. The proposed framework therefore provides a practical approach for PolSAR image classification in scenarios where obtaining large labelled datasets is difficult.
Keywords: Keywords: PolSAR Image Classification, Self-Supervised Learning, Few-Shot Learning, Multibranch Consistency, Deep Learning, Land-Cover Classification, Representation Learning, Synthetic Aperture Radar (SAR).
Keywords: Keywords: PolSAR Image Classification, Self-Supervised Learning, Few-Shot Learning, Multibranch Consistency, Deep Learning, Land-Cover Classification, Representation Learning, Synthetic Aperture Radar (SAR).
Abstract
Blockchain-Based Decentralized Identity Verification and Management System
Sneha. S, Swathi. N, Alice Steffi. S, Kavya. R, Anuradha.C. R
DOI: 10.17148/IJARCCE.2026.15941
Abstract: Digital identity has become a fundamental component of modern digital services, including banking, healthcare, education, government services, e-commerce, and online authentication. Traditional identity management systems generally depend on centralized authorities or identity providers for storing, managing, and verifying users' personal information. Although these systems provide convenient authentication and centralized control, they introduce important limitations such as single points of failure, privacy concerns, data breaches, limited user control, and poor portability of identity information. Blockchain technology provides an alternative approach through decentralized identity management systems (DIMS) and self-sovereign identity (SSI). In these systems, users can control their digital identities and selectively share verified credentials without depending entirely on a centralized authority. Blockchain provides decentralization, immutability, cryptographic security, transparency, and smart-contract-based automation, making it suitable for secure identity verification and credential management. This paper presents a comprehensive review of blockchain-based decentralized identity verification and management systems. It examines centralized, federated, self-sovereign, attribute-based, and Know Your Customer (KYC) approaches and compares their control models, advantages, and limitations. Existing commercial solutions such as uPort, Sovrin, ShoCard, and Connect.me are also examined. The study further analyzes major security threats, including private-key compromise, Sybil attacks, eclipse attacks, blockchain reorganizations, off-chain data tampering, oracle manipulation, social engineering, metadata leakage, and regulatory compliance issues.
Keywords: Blockchain, Decentralized Identity, Self-Sovereign Identity, Digital Identity Management,Verifiable Credentials, Zero-Knowledge Proofs.
Keywords: Blockchain, Decentralized Identity, Self-Sovereign Identity, Digital Identity Management,Verifiable Credentials, Zero-Knowledge Proofs.
Abstract
Data-Driven Approach for Crop Yield Prediction Using Machine Learning Techniques
Janhavi Jawalkar, Janhavi Jari, Tejaswini Ghule, Arpita Solanke, Atharv Sune, V B Gadicha
DOI: 10.17148/IJARCCE.2026.15942
Abstract: Agriculture plays a vital role in food security and economic development, making accurate crop yield prediction important for effective farm planning and resource management. This study proposes a data-driven web-based agricultural decision-support system that integrates crop yield prediction, crop recommendation, real-time weather information, plant disease detection, farm analytics, and government scheme assistance. Machine learning algorithms such as Random Forest, XGBoost, and Gradient Boosting are used for crop yield prediction, while a Random Forest classifier is used for crop recommendation based on soil and weather parameters. The system further provides agronomic explanations, spray dosage calculation, multilingual support, voice input, and WhatsApp-based information sharing to improve accessibility and usability for farmers. The proposed KrushiMitra platform combines predictive analysis and practical agricultural support features within a single farmer-friendly interface.
Keywords: Crop Yield Prediction, Machine Learning, Random Forest, Precision Agriculture, Agriculture Decision Support.
Keywords: Crop Yield Prediction, Machine Learning, Random Forest, Precision Agriculture, Agriculture Decision Support.
Abstract
Smart Education: The Role of Artificial Intelligence and Digital Technologies in Modern Learning
Mr. Hansraj Chaudhari, Dr. Dinesh D. Puri
DOI: 10.17148/IJARCCE.2026.15943
Abstract: Smart Education is an emerging approach to teaching and learning in which Artificial Intelligence (AI), digital technologies, data-driven tools, and online learning environments are integrated to make education more flexible, responsive, and learner-centred. Traditional educational models commonly deliver similar instruction to learners despite differences in prior knowledge, learning speed, interests, and academic needs. This paper examines how AI and digital technologies can address some of these differences, with particular attention to personalized learning, Intelligent Tutoring Systems (ITS), learning analytics, generative AI, automated assessment, and AI-based teaching assistance. The study follows an exploratory, qualitative, literature-based research design and synthesizes findings from academic literature and institutional reports, including systematic reviews and UNESCO guidance. The reviewed evidence indicates that AI- enabled tutoring and adaptive technologies can support learning and provide faster feedback, but the presence of AI alone does not guarantee improved educational outcomes. Effective instructional design, relevant curriculum content, appropriate assessment, teacher involvement, data quality, and continuous evaluation remain essential. Generative AI can extend educational assistance by producing explanations, examples, summaries, practice questions, and feedback through natural-language interaction. At the same time, it introduces concerns including inaccurate or fabricated information, privacy risks, algorithmic bias, academic integrity, unequal access, and overdependence on automated assistance. Based on the literature, this paper proposes a human-centred AI teaching assistant model in which AI supports routine academic assistance while teachers retain responsibility for mentoring, judgement, motivation, contextual understanding, and final oversight. The paper concludes that Smart Education is most meaningful when technology is selected according to educational objectives, implemented with appropriate safeguards, and evaluated using evidence of actual learning, usability, and equity.
Keywords: Smart Education, Artificial Intelligence, Intelligent Tutoring Systems, Personalized Learning, Generative AI, Learning Analytics, Automated Teaching Assistant, Digital Learning
Keywords: Smart Education, Artificial Intelligence, Intelligent Tutoring Systems, Personalized Learning, Generative AI, Learning Analytics, Automated Teaching Assistant, Digital Learning
Abstract
Chaos-Based Image Encryption with 5D Keystreams and 3D Voxel Scrambling: A Survey and Reference-Implementation Study
Vittala S, Sooraj N J, Vinay B S, Teja N R, and Anuradha C R
DOI: 10.17148/IJARCCE.2026.15944
Abstract: Digital images used in medical, financial, surveillance and cloud settings need protection that general-purpose ciphers provide inefficiently, because images are large and strongly redundant. Chaos-based ciphers address this with confusion–diffusion structures driven by keystreams from chaotic and hyperchaotic systems. This paper surveys recent work (2018–2026) on hyperchaotic image encryption, organizes it by chaotic source and cipher structure, and then studies one representative design in depth: 3D voxel scrambling of a 512×512 image combined with a keystream from a five- dimensional Lorenz-type system seeded by SHA-384. A complete Python reference implementation recovers all test images losslessly and gives cipher entropy of 7.9993 bits, adjacent-pixel correlation below 0.02 in magnitude, NPCR of 99.60–99.62 % and UACI of 33.47–33.51 %, all inside the 5 % critical intervals of Wu et al. Two findings are hidden by these headline metrics. First, the Lyapunov spectrum of the 5D system has a single positive exponent (+1.13), so the system is chaotic, not hyperchaotic. Second, the cipher is exactly affine over Z₂₅₆, which makes its differential behaviour independent of the plaintext and exposes it to chosen-plaintext attack. We conclude with concrete design recommendations and open problems.
Keywords: image encryption, hyperchaotic system, 3D pixel scrambling, Fisher–Yates shuffle, confusion–diffusion, NPCR, UACI, Lyapunov exponent
Keywords: image encryption, hyperchaotic system, 3D pixel scrambling, Fisher–Yates shuffle, confusion–diffusion, NPCR, UACI, Lyapunov exponent
Abstract
Rapid Colorimetric and Artificial Intelligence- Based Methods for Determining the Microbial Quality of Raw Milk, Processed Milk, and Milk Products
Mr. Gaurav Ahire, Dr. Dinesh D Puri
DOI: 10.17148/IJARCCE.2026.15945
Abstract: Milk is a nutrient-rich food that can support microbial growth when hygienic handling, storage, or processing conditions are inadequate. Conventional microbiological tests remain important reference methods, but they can require substantial time, laboratory resources, and skilled handling. This review examines rapid colorimetric approaches and artificial intelligence (AI)-based methods for screening microbial quality in raw milk, processed milk, and milk products. Particular attention is given to dye-reduction assays such as resazurin, objective RGB colour measurement, machine-learning classification, and sensor systems such as electronic noses. Published studies indicate that colourimetric sensing can convert biochemical activity into measurable optical changes, while machine learning can associate sensor patterns with reference microbial measurements. One recent study using an RGB colourimetric resazurin assay reported 100% prediction accuracy for a low-microbial-concentration class and 96% for a high- microbial-concentration class, with lower performance in the intermediate class. Another study combining electronic- nose measurements with an artificial neural network reported very strong agreement between predicted and reference total bacterial counts in its tested and validated subsets. These findings support the potential of integrated sensing and AI as rapid screening tools, while also showing the need for representative datasets, controlled measurement conditions, external validation, and explainable decision systems. The proposed framework combines rapid colour sensing, reference microbiology, machine learning, and quality classification to support faster dairy-quality monitoring.
Keywords: milk quality, microbial quality, colorimetric assay, resazurin, RGB sensing, artificial intelligence, machine learning, electronic nose, dairy safety
Keywords: milk quality, microbial quality, colorimetric assay, resazurin, RGB sensing, artificial intelligence, machine learning, electronic nose, dairy safety
Abstract
AN INTEGRATED AI AND GIS FRAMEWORK FOR TRACKING GOVERNMENT FUNDS FROM BUDGET ALLOCATION TO GROUND-LEVEL PROJECT COMPLETION
Pavan S. Badgujar, Assoc. Prof. Dinesh D. Puri
DOI: 10.17148/IJARCCE.2026.15946
Abstract: Government development projects generate financial, procurement, physical-progress, and geospatial records at different stages of implementation. Because these records are usually maintained in separate systems, it is difficult to judge whether expenditure reported for a project is reasonably consistent with the work reported on the ground. This paper presents and evaluates an Artificial Intelligence (AI) and Geographic Information System (GIS) framework that brings these records together through a common project record. A controlled synthetic benchmark of 3,000 project records was prepared because confidential government data were not available. Fifteen percent of the records contained four deliberately injected anomaly types: financial-physical mismatch, overspending, payment delay, and procurement irregularity. Isolation Forest, Local Outlier Factor (LOF), and a rule-based baseline were compared over five random seeds using precision, recall, F1-score, ROC-AUC, PR-AUC, and Precision@K. LOF gave the highest scores in the benchmark (F1 = 0.985; ROC-AUC = 0.9999), followed by the rule-based method (F1 = 0.874; ROC-AUC = 0.982) and Isolation Forest (F1 = 0.573; ROC-AUC = 0.890). For Isolation Forest, Precision@50 was 0.680 against a 0.150 no-skill baseline. A separate 30-seed Wilcoxon signed-rank test showed a consistent difference between the rule-based method and Isolation Forest (p = 1.86 × 10⁻⁹). SHAP analysis highlighted tender variance and the financial-physical gap among the strongest contributors to Isolation Forest scores. A Moran's I test on randomly assigned coordinates found no significant spatial clustering (I = −0.016, p = 0.067). These results show that the proposed analysis pipeline can be tested in a controlled setting, while the near-perfect LOF result should be treated as a best-case outcome because the simulated anomalies were relatively easy to separate. The framework is therefore intended as an early-warning and decision-support tool, not as an automated means of establishing corruption.
Keywords: Artificial Intelligence, Geographic Information System, Government Funds, E-Governance, Anomaly Detection, Explainable AI, Public Procurement, Project Monitoring, Design Science Research
Keywords: Artificial Intelligence, Geographic Information System, Government Funds, E-Governance, Anomaly Detection, Explainable AI, Public Procurement, Project Monitoring, Design Science Research
Abstract
Voice-Controlled Gaming Tools for Enhanced Learning in The Skill Ecosystem
Miss. Ashwini Vijay Adhav, Dr. Dinesh D. Puri
DOI: 10.17148/IJARCCE.2026.15947
Abstract: Voice-controlled games combine voice commands, educational activities, and skill development to make learning more natural and interesting. This research looks at how voice commands can be used in games to support activities such as quizzes, simulations, language practice, problem-solving, and skill testing.
The proposed system uses speech recognition to understand the learner’s voice commands, a game engine to control the game and its interactions, and a learning system to track performance and provide feedback. Using voice commands can reduce the need for keyboards and touchscreens, allow learners to interact hands-free, and make learning activities more engaging.
This report also covers the system requirements, development method, feasibility, possible risks, diagrams, and ways to evaluate the system. The project will follow a step-by-step development approach, where features such as voice input, game interaction, learner profiles, assessment, and performance tracking are developed and tested gradually.
Keywords: Voice Control, Speech Recognition, Educational Gaming, Skill Development, Gamification, Learning Analytics, Artificial Intelligence.
The proposed system uses speech recognition to understand the learner’s voice commands, a game engine to control the game and its interactions, and a learning system to track performance and provide feedback. Using voice commands can reduce the need for keyboards and touchscreens, allow learners to interact hands-free, and make learning activities more engaging.
This report also covers the system requirements, development method, feasibility, possible risks, diagrams, and ways to evaluate the system. The project will follow a step-by-step development approach, where features such as voice input, game interaction, learner profiles, assessment, and performance tracking are developed and tested gradually.
Keywords: Voice Control, Speech Recognition, Educational Gaming, Skill Development, Gamification, Learning Analytics, Artificial Intelligence.
Abstract
An Efficient Urban Digital Video Broadcasting (DVB) from Aerial Platforms using Smart Antenna Arrays
Yasser Albagory
DOI: 10.17148/IJARCCE.2026.15948
Abstract: In this paper, an efficient smart adaptive antenna technique is proposed for the provision of digital video broadcasting services from aerial platforms for urban areas. The proposed smart antenna array utilizes concentric circular arrays with adaptive feeding for maximizing the signal at ground users. The array footprint from aerial platforms is analysed over the whole coverage from aerial platforms showing the link performance in terms of bit-energy to noise power spectral density ratio. The concentric circular array is optimized for providing uniform coverage over wide areas with line-of-sight propagation quality like satellite DVB systems but at very close range for both fixed and mobile stations.
Keywords: DVB Broadcasting, Aerial Platforms, Adaptive Antenna Arrays.
Keywords: DVB Broadcasting, Aerial Platforms, Adaptive Antenna Arrays.
Abstract
Empowering Safety: A Study of Smart Technologies in The Next Frontier of Women’s Safety Devices
Dr. Ambili A.R, Malavika Pradeep, Dr. Sreevidya P., Beenu Riju
DOI: 10.17148/IJARCCE.2026.15949
Abstract: Throughout civilizations worldwide, women's safety has consistently ranked as a primary concern, prompting the development and refinement of numerous safety technologies aimed at empowering individuals and facilitating swift responses to potential threats. This paper delves into the domain of women's safety devices, with a particular emphasis on the diverse triggering mechanisms employed in these devices. By discussing the advantages, disadvantages, and current research gaps, this review aims to illuminate the spectrum of technological advancements, including smart technology, in women's safety technology. These advancements encompass a wide range, from conventional manual triggers like push buttons to cutting-edge features such as voice activation, fingerprint identification, and artificial intelligence (AI) integration. Despite ongoing progress, crucial areas still warrant further investigation. By highlighting these research gaps, this review aspires to catalyze subsequent developments capable of fundamentally transforming the landscape of women's safety technology.
Keywords: Women Safety Devices, Triggering Mechanism, Image, Voice, Button Activated
Keywords: Women Safety Devices, Triggering Mechanism, Image, Voice, Button Activated
Abstract
AI-Enhanced Career Guidance System For Personalized Career Pathways
Miss.Vaishnavi S. Badgujar, Asso. Prof. Dr. Dinesh D Puri
DOI: 10.17148/IJARCCE.2026.15950
Abstract: Career selection is an important decision for students and job seekers, but many individuals face difficulties in identifying suitable career options because of limited guidance, lack of awareness about current job trends, and differences in their skills, interests, and abilities. This research proposes an AI-Enhanced Career Guidance System for Personalized Career Pathways that uses Artificial Intelligence and Machine Learning techniques to provide personalized career recommendations.
The proposed system collects user information such as educational background, technical and soft skills, interests, aptitude, career preferences, and career goals. The collected data is processed and analyzed to identify the user's strengths, weaknesses, and skill gaps. AI/ML techniques are then used to match the user's profile with suitable career roles and generate ranked career recommendations. The system further provides a personalized pathway consisting of recommended skills, courses, certifications, projects, internships, and potential job roles.
The proposed approach aims to improve career decision-making, reduce uncertainty, identify skill gaps, and provide users with a clear roadmap for career development. By combining user-specific information with career and job-market data, the system can provide more relevant and adaptive guidance than traditional one-size-fits-all career counseling. The research demonstrates how AI can support personalized career planning and help individuals make informed decisions about their future careers.
Keywords: Artificial Intelligence, Machine Learning, Career Guidance, Personalized Recommendation, Career Pathways, Student Profiling, Skill Recommendation, Data Analytic
The proposed system collects user information such as educational background, technical and soft skills, interests, aptitude, career preferences, and career goals. The collected data is processed and analyzed to identify the user's strengths, weaknesses, and skill gaps. AI/ML techniques are then used to match the user's profile with suitable career roles and generate ranked career recommendations. The system further provides a personalized pathway consisting of recommended skills, courses, certifications, projects, internships, and potential job roles.
The proposed approach aims to improve career decision-making, reduce uncertainty, identify skill gaps, and provide users with a clear roadmap for career development. By combining user-specific information with career and job-market data, the system can provide more relevant and adaptive guidance than traditional one-size-fits-all career counseling. The research demonstrates how AI can support personalized career planning and help individuals make informed decisions about their future careers.
Keywords: Artificial Intelligence, Machine Learning, Career Guidance, Personalized Recommendation, Career Pathways, Student Profiling, Skill Recommendation, Data Analytic
Abstract
Conversational Image Recognition Chatbot
Miss. Khushi D. Danej, Asst. Prof. Sapana A. Fegade
DOI: 10.17148/IJARCCE.2026.15951
Abstract: The rapid development of Artificial Intelligence (AI), Computer Vision, and Natural Language Processing (NLP) has enabled the creation of intelligent systems capable of understanding both images and human language. This research presents a Conversational Image Recognition Chatbot, an AI-based system designed to identify and interpret objects, scenes, and visual information from uploaded images while allowing users to interact with the system through natural language. The main objective of the project is to provide an easy, interactive, and user-friendly platform that combines image recognition with conversational capabilities.
The proposed system uses computer vision and deep learning techniques for image analysis and NLP-based methods for understanding user queries and generating meaningful responses. Users can upload an image and ask questions about its contents, and the chatbot processes the image and provides relevant textual responses. The system can recognize common objects and visual elements and explain the detected information in a conversational manner.
The expected findings indicate that integrating image recognition with conversational AI can improve user interaction, accessibility, and the practical use of computer vision technologies. The proposed chatbot can be useful in education, customer support, accessibility applications, and information retrieval. The study concludes that conversational image recognition provides an effective approach for developing more intelligent and interactive AI applications.
Keywords: Artificial Intelligence (AI), Computer Vision, Natural Language Processing (NLP), Image Recognition, Deep Learning, Chatbot, Conversational AI.
The proposed system uses computer vision and deep learning techniques for image analysis and NLP-based methods for understanding user queries and generating meaningful responses. Users can upload an image and ask questions about its contents, and the chatbot processes the image and provides relevant textual responses. The system can recognize common objects and visual elements and explain the detected information in a conversational manner.
The expected findings indicate that integrating image recognition with conversational AI can improve user interaction, accessibility, and the practical use of computer vision technologies. The proposed chatbot can be useful in education, customer support, accessibility applications, and information retrieval. The study concludes that conversational image recognition provides an effective approach for developing more intelligent and interactive AI applications.
Keywords: Artificial Intelligence (AI), Computer Vision, Natural Language Processing (NLP), Image Recognition, Deep Learning, Chatbot, Conversational AI.
Abstract
Low Power Mesh Network System
Mr. Nitin Jagdish Chaudhari, Sapana Fegade
DOI: 10.17148/IJARCCE.2026.15952
Abstract: The rapid expansion of the Internet of Things (IoT) and Industrial IoT (IIoT) requires reliable, energy- efficient communication across resource-constrained wireless nodes. Traditional single-hop topologies often struggle with range limitations and power drain, while standard multihop protocols can introduce significant control overhead. This paper presents a Low-Power Mesh Network System engineered to optimize routing efficiency, extend network operational lifetime, and minimize overall energy consumption. By integrating lightweight communication protocols with dynamic power-management techniques and multihop routing strategies, the proposed architecture significantly reduces control signal overhead and packet loss during data transmission. Experimental evaluations and comparative analysis demonstrate that the proposed system achieves substantial power savings compared to standard multihop frameworks while maintaining low latency and high transmission reliability across dense sensor networks. The results confirm the viability and scalability of this low-power mesh network for real-time monitoring in smart agriculture, industrial automation, and smart environment deployments.
Keywords: Low-Power Wireless Networks, Wireless Mesh Networks (WMNs), Internet of Things (IoT), Energy Efficiency, Multihop Routing, Protocol Optimization.
Keywords: Low-Power Wireless Networks, Wireless Mesh Networks (WMNs), Internet of Things (IoT), Energy Efficiency, Multihop Routing, Protocol Optimization.
Abstract
A Real-Corridor SUMO and ns-3 Testbed for VANET Traffic Congestion Studies on NH-16, Visakhapatnam
Gunde Neha
DOI: 10.17148/IJARCCE.2026.15953
Abstract: Vehicular ad hoc network (VANET) applications for traffic congestion need to be tested on roads that look like the ones they are meant for, yet most simulation studies still rely on synthetic grids. This paper describes a simulation testbed built on a real Indian corridor: the 6.48 km stretch of NH-16 between Old Gajuwaka and NAD Junction in Visakhapatnam. The road network is taken from OpenStreetMap, and seven roadside units (RSUs) are placed at identifiable roadside sites such as bus stands. SUMO, controlled through TraCI, moves ten vehicles whose routes are weighted by segment congestion levels and applies a repeatable jam on one segment. The resulting mobility is replayed in ns-3, where on-board units and RSUs exchange IEEE 802.11p beacons. In the reference scenario, seven of the nine vehicles that entered the network reached the jam zone and dropped below 5 km/h. Their mean speed there was 3.8 km/h against 44.1 km/h elsewhere, the queue grew from five to seven vehicles as later arrivals drove into it, and all vehicles recovered within a few seconds of the jam being lifted. The testbed provides a realistic and repeatable base for studying congestion-aware VANET services on Indian roads.
Keywords: VANET, IEEE 802.11p, SUMO, NS-3, TraCI, Roadside Unit, Traffic Simulation, Traffic Congestion, OpenStreetMap
Keywords: VANET, IEEE 802.11p, SUMO, NS-3, TraCI, Roadside Unit, Traffic Simulation, Traffic Congestion, OpenStreetMap
Abstract
Improving Android Security: Application Security for Mobile App on Android 14 or Higher
Miss. Hemlata Chaudhari, Asst. Prof. Sapana Fegade
DOI: 10.17148/IJARCCE.2026.15954
Abstract: Mobile applications handle a large amount of sensitive information, making application security an important requirement for modern Android systems. This project, “Improving Android Security: Application Security for Mobile Apps on Android 14 and Higher,” focuses on improving the security of Android applications by integrating Android 14+ security features with secure development practices. The proposed approach includes requirement analysis, threat identification, secure application design, secure implementation, security testing, vulnerability analysis, deployment, and continuous maintenance. Security techniques such as least-privilege permissions, application sandboxing, secure authentication, data encryption, Android Key store, HTTPS/TLS communication, input validation, and protected application components are considered to reduce common security risks. Security testing methods such as static analysis, dynamic analysis, dependency scanning, API testing, and penetration testing can be used to identify potential vulnerabilities. The proposed defense-in-depth approach provides multiple layers of protection for user data and application resources and helps reduce the application's attack surface. Overall, the project demonstrates that integrating security throughout the complete Android application lifecycle can contribute to developing safer, more reliable, and better-protected mobile applications.
Keywords: Android Security, Android 14+, Mobile Application Security, Secure Application Development, Data Encryption, Android Key store, Authentication, Permission Management, HTTPS/TLS, Vulnerability Analysis, Security Testing, Application Protection.
Keywords: Android Security, Android 14+, Mobile Application Security, Secure Application Development, Data Encryption, Android Key store, Authentication, Permission Management, HTTPS/TLS, Vulnerability Analysis, Security Testing, Application Protection.
Abstract
Adaptive DDoS Protection Architecture for Cloud Servers: A Custom Traffic Filtering Tool
Mr. Yash Narendra Date, Asst. Prof. Sapana Fegade
DOI: 10.17148/IJARCCE.2026.15955
Abstract: Cloud computing architectures are highly susceptible to Distributed Denial of Service (DDoS) attacks that can exceed server processing capabilities. A volumetric DDoS attack overwhelms a targeted system with a massive volume of unwanted network traffic, causing severe CPU and memory exhaustion and preventing legitimate user requests from being processed. This research project presents a localized DDoS protection architecture designed for small to medium- size cloud environments. The proposed approach employs a custom C++ traffic filtering engine positioned at the network edge, utilizing a hash map data structure to track incoming IP addresses and their corresponding request rates. Controlled synthetic traffic is generated using an asynchronous Python script, and the protected and unprotected server states are compared using CPU utilization, memory consumption and request latency as primary evaluation metrics. The experimental results demonstrate that the filtering mechanism can reduce server CPU utilization from 98.7% to 22.4% while maintaining a legitimate request latency of 52 ms, thereby establishing the usefulness of lightweight edge-based filtering for volumetric DDoS protection.
Keywords: DDoS Mitigation, Cloud Security, Traffic Filtering, Hash Map, Edge Computing, Rate Limiting, Network Security.
Keywords: DDoS Mitigation, Cloud Security, Traffic Filtering, Hash Map, Edge Computing, Rate Limiting, Network Security.
Abstract
SOFTWARE SOLUTION TO IDENTIFY USERS BEHIND TELEGRAM, WHATSAPP AND INSTAGRAM BASED DRUG- TRAFFICKING
Miss. Amrapali B Baviskar, Prof. Dr. Dinesh D Puri
DOI: 10.17148/IJARCCE.2026.15956
Abstract: Drug trafficking is a serious social and security problem, and social media and instant messaging applications can be misused for illegal drug-related communication. Telegram, WhatsApp and Instagram provide features such as private communication, groups, temporary accounts, coded language, emojis and multiple online identities, which can make manual identification of suspicious activities difficult. This research proposes a software-based decision-support solution for identifying and analyzing potential drug-trafficking indicators using only publicly available or lawfully obtained digital data. The proposed approach combines Natural Language Processing (NLP), Machine Learning (ML), Cybersecurity, data analysis and Blockchain-based integrity mechanisms. NLP techniques can analyze text, keywords, coded terms, captions and comments, while Machine Learning (ML) techniques can be used to classify activities as Normal, Suspicious and High-Risk. Communication-pattern and cross-platform analysis can provide additional indicators of possible relationships among accounts. A risk score can help authorized investigators prioritize cases for further human review. Cybersecurity controls such as authentication, access control, encryption and secure storage are proposed to protect sensitive investigation data. Blockchain and hashing support evidence-integrity verification. The proposed solution is not intended to automatically identify a person as guilty; rather, it provides investigative leads and risk indicators that must be verified by authorized investigators. The research therefore focuses on supporting lawful, secure and human-verified analysis of potential drug-trafficking activities across multiple digital platforms.
Keywords: Drug Trafficking Detection, Social Media Analysis, Natural Language Processing (NLP), Machine Learning (ML), Cybersecurity, Blockchain, Risk Score, Digital Evidence, Telegram, WhatsApp, Instagram.
Keywords: Drug Trafficking Detection, Social Media Analysis, Natural Language Processing (NLP), Machine Learning (ML), Cybersecurity, Blockchain, Risk Score, Digital Evidence, Telegram, WhatsApp, Instagram.
Abstract
Enhancing Navigation for Railway Station Facilities and Locations
Mr. Shubham Pandurang Chaudhari, Asst. Prof. Ms. Sapana Fegade
DOI: 10.17148/IJARCCE.2026.15957
Abstract: Railway stations are large and complex public environments containing platforms, entrances, exits, ticket counters, waiting areas, food facilities, restrooms, enquiry desks and other passenger services. Finding a particular facility can be difficult for first-time visitors, elderly passengers, passengers with disabilities and people travelling under time pressure. This paper expands the SIH1710 problem into a structured research and prototype proposal for a software-based railway-station navigation system.
The proposed system maintains a digital representation of station locations and provides route guidance from a passenger’s current or selected starting point to a chosen destination. A mobile application can provide interactive maps and directions, while digital kiosks can offer an alternative interface. Accessibility is treated as a core design consideration through voice guidance, readable instructions, route preferences, multilingual assistance and optional augmented-reality cues.
The paper discusses the problem definition, objectives, related technologies, system architecture, navigation workflow, data entities, technology stack, testing approach, limitations and future scope. The work is presented as an academic prototype-oriented solution; practical deployment would require station-specific maps, positioning infrastructure, operational data, validation and coordination with railway authorities.
Keywords: Railway Station Navigation, GIS, Indoor Navigation, Mobile Application, Accessibility, Route Planning, Augmented Reality
The proposed system maintains a digital representation of station locations and provides route guidance from a passenger’s current or selected starting point to a chosen destination. A mobile application can provide interactive maps and directions, while digital kiosks can offer an alternative interface. Accessibility is treated as a core design consideration through voice guidance, readable instructions, route preferences, multilingual assistance and optional augmented-reality cues.
The paper discusses the problem definition, objectives, related technologies, system architecture, navigation workflow, data entities, technology stack, testing approach, limitations and future scope. The work is presented as an academic prototype-oriented solution; practical deployment would require station-specific maps, positioning infrastructure, operational data, validation and coordination with railway authorities.
Keywords: Railway Station Navigation, GIS, Indoor Navigation, Mobile Application, Accessibility, Route Planning, Augmented Reality
Abstract
SolarQuest: Innovate to Capture More Sunlight and Boost Energy
Miss. Riya Swapnil Chaudhari, Asst. Prof. Sapana A. Fegade
DOI: 10.17148/IJARCCE.2026.15958
Abstract: Every hour, the sun delivers more energy to Earth than humanity uses in a year, yet today's solar systems capture only a small fraction of it. SolarQuest asks a simple question: how can we get more electricity from the same sunlight? The project looks at where energy is lost, from reflection, heat, dust, and poor panel orientation, and at the technologies that recover it.Three areas of innovation are highlighted. First, capture: sun-tracking mounts, bifacial panels, and concentrators that gather more light across the day. Second, conversion: next-generation cells such as perovskite tandems and better anti-reflective coatings that turn more of that light into power. Third, control: AI-based forecasting, smart inverters, and automated cleaning and cooling that keep output high in real conditions.The findings suggest that no single upgrade is enough. Combining these approaches can raise annual energy yield by roughly 15-40%, depending on climate and site, while improving cost-effectiveness and land-use efficiency. SolarQuest concludes that steady, integrated innovation is key to making solar power more affordable and more reliable.
Keywords: Solar Energy, Sunlight Capture, Photovoltaic Efficiency, Solar Tracking, Bifacial Panels, Perovskite-Silicon Tandem, Anti-Reflective Coating, Soiling, Smart Inverter, AI Forecasting, Renewable Energy, Clean Energy Innovation
Keywords: Solar Energy, Sunlight Capture, Photovoltaic Efficiency, Solar Tracking, Bifacial Panels, Perovskite-Silicon Tandem, Anti-Reflective Coating, Soiling, Smart Inverter, AI Forecasting, Renewable Energy, Clean Energy Innovation
Abstract
Beyond the Aggregate: Provenance, Communication and Scope in Machine-Learning FDI Forecasting for Policy
Ofierohor Ufuoma Earnest
DOI: 10.17148/IJARCCE.2026.15959
Abstract: Machine-learning forecasts of national-aggregate FDI are increasingly offered to policymakers as decision support. This paper identifies three risks that accompany such forecasts and are rarely stated alongside the accuracy figures, and grounds each in a documented case. The first is provenance. A forecast assembled from central bank, statistical agency and multilateral sources inherits their definitional choices and revision practices, and downstream users usually cannot trace which source supplied which observation. In the case examined here, half the assembled series was linearly interpolated from annual figures and two further construction defects were present, none of which was documented or detectable from the published output. The second is communication. An out-of-sample R-squared says nothing about which quarters a model handles well; the best model in the companion forecasting study achieved 0.0257 and was the only one of twelve to beat a mean benchmark, a context that a headline figure omits. The third is aggregation. National-aggregate FDI in Nigeria is dominated by a small number of large transactions, so a forecast validated at that level supports no inference about sectoral or regional dynamics. We propose three safeguards: a provenance table including construction method, mandatory disclosure of failure modes alongside any accuracy figure, and explicit scope limits restricting aggregate forecasts to aggregate questions.
Keywords: Data Governance; Model Documentation; Forecast Communication; Policy Use; Nigeria.
Keywords: Data Governance; Model Documentation; Forecast Communication; Policy Use; Nigeria.