International Journal on Recent and Innovation Trends in Computing and Communication

A Study on Efficient Ai-Enabled Virtual Classroom

T. Parimalam, K. Hemalatha, P. Thenmozhi, P. Sathya — Thu, 10 Sep 2026 00:00:00 +0000

The global shift toward online and hybrid education following the COVID-19 pandemic has redefined the learning landscape. In India, the National Education Policy (NEP) 2020 champions the democratization of technology-enabled education. This vision aims to build an inclusive digital infrastructure across all higher education institutions nationwide. Despite these mandates, a significant digital divide remains a persistent reality for rural autonomous colleges. Statistics reveal that roughly 64% of these institutions struggle with systemic barriers to technological adoption. One major hurdle is the bandwidth deficit, where heavy video tools collapse under erratic 2G or 3G connectivity. High-definition streaming remains out of reach for many students in regions with limited internet infrastructure. Linguistic disenfranchisement also poses a psychological barrier for students from vernacular-medium backgrounds. English-centric interfaces create friction, preventing students from expressing complex ideas in their native tongues. Data privacy vulnerabilities further complicate the landscape as centralized servers process sensitive student information. Compliance with the Digital Personal Data Protection Act makes invasive video-based tracking a legal and ethical risk. Current academic literature and commercial products often fail to address these three critical dimensions simultaneously.

Predicting Student Academic Performance Using Combined Machine Learning Algorithms

P. Ramya — Thu, 10 Sep 2026 00:00:00 +0000

Accurate prediction of student academic outcomes supports early intervention strategies and enhances institutional planning. This study evaluates multiple machine learning algorithms—Random Forest, XGBoost, Support Vector Regression, Linear Regression, and Ridge Regression—using a comprehensive student dataset containing demographic, academic, behavioural, and lifestyle attributes. An ensemble voting regressor combining the five models shows improved accuracy over individual algorithms. Two sets of results are retained: the first demonstrating ensemble superiority with an R² of 0.32, and the second presenting regression coefficients and an alternative R² of 0.19. Across models, attendance, sleep patterns-habits, Behaviour, Regularity and previous grades emerge as significant predictors. The study highlights the importance of integrated modelling approaches and identifies key factors influencing academic performance.

An AI-Enabled Multi-Agent Reinforcement Learning Framework for Secure and Adaptive Routing in Vehicular Ad Hoc Networks

Infant Jansi I, G. Jagatheesh Kumar — Thu, 10 Sep 2026 00:00:00 +0000

Vehicular Ad Hoc Networks (VANETs) are an essential component of intelligent transportation systems, enabling communication among vehicles and roadside infrastructure for traffic safety and efficient transportation management. However, the highly dynamic topology, rapid mobility, and growing cybersecurity threats make secure and reliable routing a major challenge. Traditional routing protocols fail to adapt efficiently to changing network conditions and malicious attacks. To address these issues, this paper proposes an Artificial Intelligence (AI)-enabled secure adaptive routing framework using Multi-Agent Reinforcement Learning (MARL). In the proposed model, each vehicle acts as an intelligent autonomous agent capable of learning optimal routing decisions through continuous interaction with the environment. The framework integrates AI-based trust evaluation and anomaly detection mechanisms to identify malicious nodes and improve routing security. Reinforcement learning techniques optimize routing performance using parameters such as packet delivery ratio, delay, throughput, link stability, and trust values. Simulation results demonstrate that the proposed framework significantly improves packet delivery ratio, reduces end-to-end delay, increases throughput, and achieves high malicious node detection accuracy compared to conventional routing protocols. The study highlights the effectiveness of integrating AI and cybersecurity techniques for next-generation intelligent vehicular communication systems.

Next-Generation Data Mining Techniques for Healthcare, IoT, and Cybersecurity: A Unified Framework

Kiruthika P, C. Premavathi — Thu, 10 Sep 2026 00:00:00 +0000

The convergence of the Internet of Medical Things (IoMT), cloud computing, and pervasive healthcare systems has generated unparalleled volumes of real-time telemetry. However, this hyper-connectivity introduces critical vulnerabilities, making health networks premier targets for sophisticated cyber threats. Traditional data mining frameworks fail to balance real-time diagnostic mining with concurrent cryptographic security and threat detection. This paper proposes a unified, next-generation data mining framework that simultaneously extracts predictive clinical insights and neutralizes cybersecurity threats at the IoT edge. By integrating Federated Learning (FL), Generative Adversarial Networks (GANs) optimized via Adaptive Moment Estimation, and Homomorphic Encryption with Laplacian Differential Privacy, our system achieves high-accuracy diagnostic anomaly detection while maintaining a Zero-Trust cryptographic posture. Experimental simulations demonstrate a clinical pattern-extraction accuracy of 98.5% while keeping latency under critical thresholds for real-time IoMT deployment.

Hybrid Machine Learning-Based Data Mining Framework for Early Fraud Detection in Digital Transactions

R. Aravind, K. S. Kanya — Thu, 10 Sep 2026 00:00:00 +0000

The exponential growth of global electronic payment systems has simultaneously catalysed a dramatic surge in sophisticated digital transaction fraud. Traditional rule-based engines and standalone machine learning classifiers face critical bottlenecks: they either struggle with high false-alarm rates or lack real-time computational scalability when handling massively imbalanced streaming datasets. This paper presents a novel, two-stage HybridMachine Learning-Based Data Mining Framework designed specifically for the ultra-early isolation of fraudulent patterns. The first stage employs unsupervised Autoencoders to conduct low-latency feature extraction and isolate non-linear transaction anomalies from heavily skewed datasets. The second stage feeds these optimal feature mappings into an optimized ensemble pipeline featuring Light Gradient Boosting Machine (LightGBM) to execute precise fraud classification. Tested against the standard benchmark PaySim mobiletransaction dataset, our hybrid architecture achieves a fraud classification accuracy of 98.4% and an overall recall of 97.8%, while cutting transaction assessment latency to just 2.6milliseconds. These outcomes systematically outperform legacy standalone classifiers, presenting an adaptive, risk-aware security layer ideal for high-throughput digital banking ecosystems.

Energy-Efficient Cross-Layer Optimization Framework for Wireless Sensor Networks Using Adaptive Routing and MAC Coordination

R.Janaranjani — Thu, 10 Sep 2026 00:00:00 +0000

Wireless Sensor Networks (WSNs) are distributed communication networks composed of small sensor nodes capable of sensing, processing, and transmitting data wirelessly. These networks are widely used in applications such as environmental monitoring, military surveillance, disaster management, healthcare systems, industrial automation, smart cities, and agricultural monitoring. Each sensor node generally consists of sensing components, microcontrollers, transceivers, and limited power sources. Due to their compact size and low deployment cost, WSNs have gained immense importance in both academic research and industrial applications.

A Novel Intelligent Tracking Framework for Wireless Sensor Networks based on Sammon Regularization and Circumference-Reinforced Projection Pursuit

S. Vasanth Kumar, P. Balamuthukumar — Thu, 10 Sep 2026 00:00:00 +0000

In recent years, Wireless Sensor Networks (WSNs) used in extensive application ranging between military, surveillance, smart cities and so on. Target object tracking is one of the most fascinating applications in this domain of interest that specifically comprises of detecting the target object and keeps tracking of their movements. Several methods have been developed for target object tracking in WSN with minimal energy consumption. However, the accuracy level was not increased by existing tracking techniques. In order to address these problems, a novel targets object tracking method called Gaussian Distributive Sammon Regularization Policy (GG-DSRP) for efficient target object tracking in WSN is proposed. To make tracking more accurate another novel is also proposed called Circumference Reinforced Projection Pursuit and Adaptive Boosting (CRPP-AB) for tracking target object in WSN. The GG-DSRP method consists of three major processes namely reference node selection, target detection, and trajectory prediction. For this process Wilcoxon rank-sum test for identifying the reference node based on higher residual energy is applied in the first stage and in final stage, the target object trajectories are identified using Sammon projective on-policy learning algorithm to predict the target trajectories based on the state transition property. In CRPP-AB, node selection is done with Circumference Reinforced Acceleration and Adaptive Boost Target Object Classification is carried out to identify the target trajectory in WSN with Soft-Margin Support Vector Machine as the weak learners. Experimental evaluation is carried out on factors such as energy consumption and target object tracking accuracy with respect to different number of sensor nodes and data packets.

Artificial Intelligence in Urban Climate Prediction and Sustainability: A Systematic Review of Methods, Data, and Challenges.

Sivaranjani V, G. Jagatheeshkumar — Thu, 10 Sep 2026 00:00:00 +0000

Urban climate variability has become a critical concern due to rapid urbanization and its impact on environmental sustainability. Artificial Intelligence (AI) has emerged as a powerful tool for modeling and predicting complex urban climate patterns by leveraging large-scale heterogeneous data. This paper presents a systematic review of recent advancements in AI-driven urban climate analytics, focusing on machine learning and deep learning techniques applied to temperature prediction, air quality assessment, and microclimate modeling.

A structured review methodology was adopted to analyze studies published between 2018 and 2025 from major scientific databases, including IEEE, Springer, Elsevier, and Scopus. The selected studies are categorized based on modeling approaches, data sources, and application domains. The review highlights the increasing adoption of convolutional neural networks (CNNs) and hybrid models for capturing spatial–temporal dependencies.

Despite notable progress, challenges such as limited data integration, poor model generalization, and lack of interpretability remain. This paper identifies research gaps and proposes future directions including multi-modal data fusion and explainable AI for sustainable urban climate management.

Lifecycle-Aware Post-Quantum Cryptography with Provenance Verification for Secure Cloud Microservices

S. Nithya, S. Lohit — Thu, 10 Sep 2026 00:00:00 +0000

The transition from classical cryptographic schemes to post-quantum cryptography (PQC) in cloud microservices introduces significant challenges in maintaining performance, interoperability, and verifiable trust. This work presents Lifecycle-Aware Post-Quantum Cryptography with Provenance Verification for Secure Cloud Microservices (LAPQC), a novel framework that enables controlled and traceable cryptographic evolution across distributed service environments. Unlike existing approaches that rely on static replacement of cryptographic primitives, the proposed model introduces a lifecycle-aware transition mechanism combined with provenance-driven verification. The framework comprises two core modules. The Lifecycle-Aware PQC Transition Engine (LAPTE) facilitates adaptive switching between classical, hybrid, and PQC schemes based on operational context, ensuring seamless migration without service disruption. The Cryptographic Provenance and Verification Engine (CPVE) maintains verifiable records of cryptographic operations, enabling integrity validation, auditability, and protection against downgrade and substitution attacks. Experimental evaluation is conducted using metrics such as throughput and end-to-end latency under real-time workloads. Observed results indicate that while classical AES achieves maximum throughput, the proposed hybrid PQC scheme maintains competitive throughput levels while significantly improving quantum resilience. Furthermore, the hybrid approach demonstrates a balanced trade-off between performance and security compared to standalone lattice-based and hash-based PQC methods, which exhibit higher latency overheads. The integration of lifecycle-aware transition with cryptographic provenance introduces a new paradigm for secure and verifiable PQC adoption, making LAPQC a scalable and robust solution for next-generation cloud microservice architectures.

Automated Glaucoma Diagnosis Using Vision Transformer and Deep Learning Techniques on Retinal Fundus Images

S. Sangeetha — Thu, 10 Sep 2026 00:00:00 +0000

Glaucoma is one of the leading causes of irreversible blindness worldwide. Early diagnosis is critical to preventing permanent optic nerve damage. Traditional glaucoma diagnosis relies heavily on ophthalmologists’ expertise and manual examination of retinal fundus images, which can be time-consuming and subjective. This paper proposes a Novel Glaucoma Detection framework using Advanced Deep Learning and Vision Transformer (ViT)-based architectures for automated analysis of ophthalmic fundus images. The proposed system integrates image preprocessing, optic disc localization, data augmentation, and a hybrid Swin Transformer–CNN classification model for accurate glaucoma screening. Publicly available datasets such as REFUGE, ORIGA, and Drishti-GS are utilized for evaluation. Experimental results demonstrate superior performance compared with conventional CNN models. The proposed framework shows strong generalization capability and can assist ophthalmologists in early glaucoma diagnosis and large-scale screening programs.

IoT-Based Railway Track Fault Detection and Localization Using Acoustic Analysis

Sandra Jose, K. Sathishkumar — Thu, 10 Sep 2026 00:00:00 +0000

Railway transportation is one of the most efficient and widely used modes of transport worldwide; however, track faults remain a major cause of derailments and accidents. Traditional inspection techniques are manual, time-consuming, and prone to human error. This paper proposes an Internet of Things (IoT)-based system for railway track fault detection and localization using acoustic signal analysis. The proposed system employs distributed acoustic sensors integrated with microcontrollers to collect real-time sound signals from railway tracks. Machine learning techniques are utilized to classify faults based on extracted acoustic features. Experimental analysis demonstrates that the proposed system achieves high accuracy in identifying various track defects and enables real-time localization, thereby improving railway safety and maintenance efficiency.

Building Operational Resilience in Egyptian Banks through Information Security Governance: The Role of Cyber Resilience Culture and Top Management Support

Wed, 16 Sep 2026 00:00:00 +0000

The accelerating digitalization of banking services has increased both operational efficiency and exposure to sophisticated cyber threats. Although financial institutions continue to invest in cybersecurity technologies, the literature remains comparatively fragmented across technical security, risk management, business continuity, governance, and operational resilience. This conceptual paper develops the Banking Information Security Governance and Operational Resilience (BISGOR) Framework to explain how Information Security Governance (ISG) can strengthen Operational Resilience (OR) in Egyptian banks. The framework positions Cyber Resilience Culture (CRC) as a mediating mechanism and Top Management Support (TMS) as a moderating condition. Grounded in the Resource-Based View (RBV) and Dynamic Capability Theory (DCT), the study integrates peer-reviewed research on information security governance, cyber resilience, operational resilience, security culture, and executive support. It argues that ISG establishes strategic alignment, accountability, risk ownership, oversight, and cross-functional coordination; CRC translates these formal mechanisms into shared preparedness, reporting, collaboration, learning, and adaptive behavior; and TMS strengthens this translation through visible commitment, resources, authority, and strategic direction. Five hypotheses are developed and a 30-item proposed operationalization is provided for subsequent empirical validation. The paper contributes to Information Security Management research by shifting the unit of analysis from isolated technical protection to governance-driven continuity of critical banking services and by addressing a geographically underrepresented African banking context.

Develop an AI-Based Multilingual Tutor/Chatbot for Student Interaction

Naveen Kumar K.P., Mahesha D.M, Bhavya D.N, Sharath Kumar Y. H — Fri, 25 Sep 2026 00:00:00 +0000

The rapid growth of Artificial Intelligence (AI) and Natural Language Processing (NLP) has created new opportunities for developing intelligent and inclusive educational systems. However, language barriers continue to limit access to digital learning resources, particularly for students who are more comfortable learning in regional and native languages. This research proposes the development of an AI-based multilingual tutor/chatbot for personalized student interaction and academic support. The proposed system integrates multilingual Natural Language Processing, Machine Translation, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and speech technologies to enable students to interact with the tutor using English and Indian regional languages, with a primary focus on Kannada-English interaction.

The proposed tutor will be grounded in curriculum-specific resources such as syllabi, textbooks, lecture notes, question banks, and other approved educational materials. Student queries will be processed through language identification, semantic understanding, information retrieval, and AI-based response generation to provide contextually relevant and curriculum-aligned explanations. The system will additionally support personalized learning by identifying students' learning requirements, generating topic-wise explanations, practice questions and quizzes, and providing immediate feedback. Voice-based interaction through speech-to-text and text-to-speech technologies will further improve accessibility for students who prefer spoken communication. The proposed framework aims to address challenges associated with low-resource Indian languages, code-mixed language interaction, contextual understanding, and hallucination in generative AI-based educational systems. The effectiveness of the system will be evaluated using NLP performance measures, response relevance, translation quality, question-answering accuracy, user satisfaction, and student learning outcomes. The proposed multilingual AI tutor is expected to provide an accessible, scalable, and personalized learning environment and contribute towards inclusive, mother-tongue-supported and technology-enabled higher education.

The Interplay of Chromatic Contrast and Typographic Legibility: Enhancing Mobile Interface Accessibility for Seniors with Age-Related Visual and Cognitive Declines

Bhaskar Mani Tiwari, Swati V Chande — Wed, 07 Oct 2026 00:00:00 +0000

Age-related visual and cognitive impairments significantly affect seniors' ability to interact with mobile interfaces, yet many contemporary applications fail to address these accessibility challenges adequately. This research examines the synergistic relationship between chromatic contrast and typographic legibility in mobile interface design for older adults aged 65 and above. Through a comprehensive analysis of current accessibility standards, empirical studies, and age-related physiological changes, this paper establishes evidence-based design guidelines that accommodate declining contrast sensitivity, reduced visual acuity, and cognitive processing limitations. The findings reveal that implementing WCAG 2.2 Level AAA contrast ratios (7:1 for normal text) combined with font sizes of 18 points or larger significantly enhances readability and reduces visual fatigue for seniors. Furthermore, the research demonstrates that carefully selected color combinations particularly high-contrast schemes such as black text on white backgrounds combined with sans-serif typefaces can compensate for age-related visual processing deficits. This paper provides practical design recommendations for creating inclusive mobile interfaces that bridge the digital divide and promote technological independence among the aging population.

XAI-Driven Federated Learning Framework for Energy-Efficient, Secure and Real-Time Anomaly Detection in IoT-Driven Wireless Sensor Networks

J. Karthikeyini, K. S. Mohanasathiya — Fri, 16 Oct 2026 00:00:00 +0000

The rapid growth of IoT-driven Wireless Sensor Networks (WSNs) has enabled extensive real-time monitoring and data collection across diverse applications, from smart cities to industrial automation. These networks face critical challenges such as energy constraints, data security, and timely detection of anomalies caused by faulty or malicious nodes. Existing centralized machine learning approaches often compromise privacy and are unsuitable for resource-constrained WSN environments. To address these issues, this research proposes an Explainable Artificial Intelligence (XAI)-enabled Federated Learning (FL) framework for energy-efficient, secure, and real-time AD in IoT-driven WSNs. The methodology integrates federated learning to collaboratively train models across distributed sensor nodes without sharing raw data, ensuring privacy preservation, while XAI techniques provide transparency and interpretability of AI-driven decisions, allowing network administrators to understand and trust AD outcomes. This proposed framework also incorporates energy-aware routing and relay node optimization strategies to reduce power consumption, prolong network lifetime, and maintain real-time responsiveness. The primary objectives include enhancing AD accuracy, minimizing energy usage, preserving data privacy, and improving network reliability. Simulation results and performance evaluations are expected to demonstrate significant improvements in energy efficiency, detection speed, and interpretability compared to existing approaches, providing a robust and intelligent solution for next-generation IoT WSN management

SARG-MORL: Security-Aware Risk-Guided Multi-Objective Reinforcement Learning for Adaptive Routing in Software-Defined Networks

N. Senthilkumaran, R. Sankarasubramanian, K.Prabha — Sat, 17 Oct 2026 00:00:00 +0000

Software-Defined Networking (SDN) offers a programmable control plane and a network-wide view that can be exploited for adaptive traffic engineering. However, routing decisions based only on latency, throughput, congestion, or link utilization can select paths that are operationally efficient but exposed to anomalous traffic or attack conditions. Conversely, security mechanisms that operate independently of routing often detect a threat without allowing the routing policy to react to its spatial and temporal consequences. This paper proposes SARG-MORL, a security-aware risk-guided multi-objective reinforcement learning framework for adaptive SDN routing. The central idea is to transform security observations into a continuously updated path-risk field and expose that field to the routing agent as both a state feature and a safety constraint. A topology-aware graph encoder represents nodes, links, flows, and security indicators; a constrained multi-objective actor-critic agent then selects routes by jointly considering delay, packet loss, residual bandwidth, load variance, recovery cost, and security exposure. Unlike a conventional weighted reward formulation, the proposed design separates performance preferences from non-negotiable security constraints, preventing the agent from compensating for an unsafe path merely through gains in latency or throughput. The paper defines the architecture, mathematical formulation, learning procedure, threat model, Mininet/Ryu experimental design, baselines, ablation strategy, and reproducibility requirements. The framework is intended for evaluation under normal, bursty, congestion, link-failure, and attack-injected conditions.

The Impact of Social Media Marketing on Purchase Intention in Textile Industries

P. Megaladevi, Boomika.V, Ranjith.R, Swathi. S, Harikrishnan.S — Fri, 23 Oct 2026 00:00:00 +0000

Social media marketing has become an important tool for businesses to promote products and influence consumer behavior in the digital era. In the textile industry, social media platforms such as Facebook, Instagram, and YouTube play a significant role in increasing brand awareness, engaging customers, and encouraging purchase decisions. Tirupur, one of the major textile manufacturing hubs in India, has witnessed a growing use of social media marketing by textile businesses to reach a wider audience and improve their market presence. This study aims to analyze the impact of social media marketing on consumers’ purchase intention in the textile industry in Tirupur. The research focuses on understanding how social media activities such as online advertisements, product promotions, customer reviews, and influencer marketing influence consumer buying behavior. The study adopts a descriptive research design and collects primary data through a structured questionnaire from consumers in Tirupur. The collected data is analyzed using statistical tools such as percentage analysis and chi-square analysis to identify the relationship between social media marketing and purchase intention. The findings of the study indicate that social media marketing significantly influences consumers’ purchase intention by providing product information, enhancing brand awareness, and creating trust among customers. The study concludes that effective use of social media platforms can help textile businesses attract potential customers, strengthen customer relationships, and increase sales in the competitive textile market.

A Study on Payroll Processing and Employee Satisfaction in Construction Industry

S. Prabhavathi Theneesh, Hariharan.R, Sangavi.K, SathyaPriya.B, Menaka.S — Fri, 23 Oct 2026 00:00:00 +0000

Payroll processing is a fundamental function in any organization, as it directly influences employees’ financial stability, job satisfaction, and overall performance. It involves a series of structured activities such as maintaining employee records, monitoring attendance, calculating wages, processing overtime, and applying statutory deductions including Provident Fund (PF), Employee State Insurance (ESI), and income tax. In addition to these, payroll also includes bonuses, incentives, and other employee benefits. In labour-intensive industries like construction, payroll processing becomes more complex due to the presence of a diverse workforce, varying wage structures, and multiple project locations. Employees may include permanent staff, contract labourers, and daily wage workers, each requiring different methods of payment and calculation. This study aims to analyze payroll processing practices and their impact on employee satisfaction in URC Construction Private Limited. The research adopts a descriptive design and collects primary data from 146 employees using a structured questionnaire. Secondary data is obtained from books, journals, and company records. Statistical tools such as percentage analysis, regression, ANOVA, and Chi-square are used for data analysis. The study finds that payroll factors such as salary timing, accuracy, transparency, overtime payment, and HR support significantly influence employee satisfaction. However, issues such as delays in salary, lack of clarity in payslips, and ineffective grievance handling reduce employee trust. The study concludes that improving payroll systems through automation, transparency, and communication can enhance employee satisfaction and organizational efficiency.

The Impact of Artificial Intelligence on Accounting Practices in Modern Business

G. Sindhu — Fri, 23 Oct 2026 00:00:00 +0000

This study examines how artificial intelligence (AI) may reshape accounting practices in modern business. AI-enabled tools can support transaction processing, reconciliation, anomaly detection, financial forecasting and decision support. The article presents a research framework for assessing AI awareness and adoption among accounting professionals and examining its relationship with accounting efficiency and financial reporting quality. A structured questionnaire and quantitative methods including descriptive statistics, chi-square tests and regression analysis are proposed. Numerical results supplied in the initial draft are illustrative and must be verified or replaced using actual survey data before the article is submitted as empirical research. The article also considers implementation challenges such as data quality, privacy, explainability, professional judgement and governance. It recommends role-specific training, secure infrastructure, management support and regular evaluation of AI-enabled processes. The framework can assist organizations in investigating how AI can be adopted responsibly while preserving human oversight and professional accountability.