Scopus Index Publications
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/2162
This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
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Item Embargo Voice Agentic Smart Reader Application In Sinhala and Tamil For Visually Impaired People(Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Siriwardana B.J.A; Hewagama A.D; Aluwihare M.C; Sharumathan M; Weerasinghe, M; Abeywardhana, LThis paper proposes an intelligent smart reading system to improve access to documents, news articles, and printed content for visually impaired and reading-challenged users, particularly in low-resource languages such as Sinhala and Tamil. Although recent advances in computer vision, speech processing, and large language models have enabled effective document understanding, existing systems often operate independently and provide limited support for non-visual navigation, interactive information access, and expressive audio output. The proposed system integrates four main components into a unified framework. First, a voice-guided navigation module enables users to capture documents without visual assistance by providing real-time audio guidance, ensuring proper alignment and reliable image acquisition for text recognition. Second, a Tamil document understanding and question answering module processes document content and supports accurate, context-aware information retrieval using a retrieval-based approach, while also providing personalized content recommendations. Third, a voice-assisted Sinhala reading module allows users to navigate documents and access information using natural voice commands and semantic processing. Finally, an emotional text-to-speech module generates expressive speech in Sinhala and Tamil, improving the naturalness and clarity of audio output. Experimental results demonstrate that the proposed system improves document capture accuracy, navigation efficiency, information retrieval performance, and speech quality. Overall, the system provides a practical, accessible, and user-centered solution for intelligent document interaction in underrepresented languages.Item Embargo ML-Based System to Detect GPS Spoofing and Signal Jamming via Signal Logs(Institute of Electrical and Electronics Engineers Inc., 2026-08-04) Harshani S.U.E; Wickramasinghe V.D.A; Muthukuda M.A.D.H.N; Siriwardhane H.H.D.V.; Siriwardana, D; Wijesooriya, AThis paper describes the design, implementation, and analysis of a machine learning-based and forensic-grade desktop system to detect GPS spoofing attacks and signal jamming attacks. The system processes telemetry logs collected from Unmanned Aerial Vehicles (UAVs) and other GNSS-enabled systems to detect any malicious signal manipulations and disruptions. It utilizes a pipeline comprising modules: GPS logging, feature extraction, and an unsupervised machine learning detection engine based on Isolation Forest, One-Class SVM, and LSTM Autoencoder models. The system learns the normal behavioral patterns and is trained on actual GPS data, therefore, identifying the previously unknown attacks. One of the contributions is that forensic concepts, such as hash-SHA-256, chain-of-custody logging, and read-only processing, are factored into the human process, thus supporting evidence integrity and traceability. The system generates elaborate visual and textual reports, giving a user-friendly timeline of the attack with severity ratings. Through the experiment with real and synthetic interfered datasets, the system is found to be effective in predictably distinguishing between spoofing (jumping coordinates and unrealistic kinematics) and jamming (significantly lost signal and large drift variance) with substantial detection. This tool offers an essential feature to cybersecurity forensic investigators, drone operators, and other critical infrastructure defenders to diagnose and record GNSS susceptibility.Item Embargo An Ethical and Emotionally Intelligent Social Media Plugin Using Responsible & Explainable AI(Institute of Electrical and Electronics Engineers Inc., 2026-08-04) Wickremasinghe N.S.; Ullandupitiya U.P.L.I.; Jayawardena D.S; Jayanetti J.K.D.S.D; Rathnayake S.; Nawarathne M.Social media platforms encounter ongoing difficulties with fragmented moderation and interaction operations that do not collaborate effectively to deal with destructive content, inauthentic behavior, emotion-insensitive interaction, and opaque recommendations. This paper features an ethical and emotionally intelligent social media plugin using responsible and explainable AI implemented as a unified real-time service for social media applications, proven through implementation on the Open-Source Social Network (OSSN). The plugin incorporates four elements, namely multimodal cyberbullying detection, behavior-based social bot detection, explainable friend recommendation, and an emotion-aware reaction system. The cyberbullying module is a combination of transformer-based text analysis, image processing, OCR, and keyword fusion to moderate content in an explainable manner, and the fake account module uses temporal behavioral features to detect bot accounts regardless of content. The recommendation system offers interpretable recommendations with context, and the emotion-aware module provides empathetic interaction via emotion recognition and filtering. Experimental results indicate high performance across task-appropriate metrics such as accuracy for classification tasks and MCC/ROC-AUC for bot detection due to class imbalance, yielding 88.60% on text-based cyberbullying, 68.81% on image moderation, MCC 0.9704 and ROC-AUC 0.9981 on bot detection, and 73.10% F1 on sarcasm detection. The outcomes of deployments demonstrate the potential of a single, transparent, and responsible AI system to have safer and more meaningful social media interactionsItem Embargo Smart Heritage: A Blockchain and NFT Framework for Secure Authentication and Fractional Investment in Sri Lankan Handicrafts(Institute of Electrical and Electronics Engineers Inc., 2026-07-06) Manohara K.K.K; Dharmasena E.A.H.T.; Rajapaksha D.N; Bandara H.D; Chamara, D; Abeywardena, K.Y; Nismi, NThe Sri Lankan handicraft sector possesses significant cultural and economic value; however, counterfeit production, limited ownership traceability, and the absence of resale royalty mechanisms restrict its global scalability. Traditional systems fail to provide reliable provenance verification and sustainable financial benefits for artisans and previous owners. This paper proposes a blockchain-enabled ecosystem integrating Non-Fungible Tokens (NFTs), NFC-based authentication, fractional ownership, and a smart security box to enhance authenticity, transparency, and value creation. Each handicraft is linked to a unique NFT and a cryptographically secured NFC tag, creating a tamper-resistant connection between the physical artifact and its digital identity. Smart contracts enable transparent ownership tracking and automated lifetime royalty distribution, ensuring artisans receive royalties from every future resale without intermediaries. The proposed framework also introduces a fractional ownership model for culturally significant, non-commercial heritage artifacts, enabling collective digital ownership with tiered access to exclusive cultural content while distributing cascading royalties to previous shareholders upon resale. A smart security box with NFC further protects high-value physical artifacts. Prototype evaluation on an Ethereum-compatible test network achieved a 100% success rate across 24 smart contract unit tests, 100% NFC authentication accuracy for 12 handicraft items with average response times of 380-450 ms, and consistent royalty and Proof-of-Residency distribution across nine simulated resale transactions. The key contributions of this work are: (1) a cryptographic NFC-NFT binding mechanism resistant to tag cloning, (2) a smart contract-based lifetime royalty engine with previous-owner redistribution, and (3) a fractional community ownership model that supports global participation in preserving non-commercial cultural heritage. The proposed architecture demonstrates the potential of blockchain technology to preserve cultural heritage while creating a sustainable economic ecosystem for artisans and investors.Item Embargo Knowledge Graph-Based AI Framework for Predicting Nutritional and Health Impacts of Food Ingredients(Institute of Electrical and Electronics Engineers Inc., 2026-08-04) Dakshina P.D.S.D; Rupasighe W.A.R.K; Waduge N.P; Nimsitha M.V.T; Tissera, W; Rathnayake, S; Krishara, JThe increasing complexity of modern food products and dietary supplements has made it challenging for both consumers and healthcare professionals to interpret nutritional information and assess the potential health risks associated with these products. Modern food labeling schemes provide static and fragmented information and cannot effectively capture the relationships between different ingredients, nutrients and their health effects. In this study, a new AI-based framework named Food Health Risk Analyzer has been proposed that utilizes KGs, GNNs, RAG and a dose-response module based on consumption quantities to perform the dynamic, explainable and evidence-based prediction of food-related health risks. The model uses heterogeneous data in order to analyze the relationships between ingredients and diseases to predict potential health risks while generating scientifically supported explanations as well. The experimental evaluation has shown high prediction accuracy with a micro-F1 score of 0.88 and AUC of 0.85 which shows that the framework surpasses conventional machine learning baseline models. In addition to that, the use of RAG has helped in improving the interpretability of predictions through evidence-based natural language explanations whereas dose-response module improves the practical relevance of risk assessment by considering the consumption quantities of ingredients.Item Embargo CocoSense: AI-Powered Drone-Based System for Comprehensive Coconut Tree Health Monitoring and Yield Prediction(Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Subasinghe, M; Panditharathne, R; Pasanjith, R; Nadun, T; Samarakoon, U; Tissera, WCoconut cultivation is vital to Sri Lanka's agricultural economy, yet farmers face significant challenges in early pest detection, disease diagnosis, and yield prediction. This research presents CocoSense, an AI-powered mobile application integrated with IoT technology for automated coconut tree health monitoring using drone-captured imagery. The system comprises four modules: (1) pest detection using EfficientNetB0 (91.44% accuracy) and MobileNetV2 (96.08% accuracy) with a trilingual AI chatbot for treatment recommendations; (2) disease detection for leaf rot, leaf spot, and leaf dieback classification (98.69% accuracy); (3) health assessment for leaf (93.70%) and branch health (99.63%); and (4) coconut yield estimation (87.86% accuracy) using YOLOv8 with dual-view acquisition strategy. Additionally, a coconut bunch detection (88.96% accuracy) module is developed to support yield estimation by identifying fruit clusters within tree canopies. The system integrates IoT-based GPS tracking with Google Maps API for real-time plantation visualization. Experimental results demonstrate that CocoSense provides a robust, accessible solution for intelligent coconut plantation management in Sri Lanka.Item Embargo Sinhala-English Multilingual AI Call Center Bot with Sentiment-Aware Dialogue and Multimodal CSAT Prediction(Institute of Electrical and Electronics Engineers Inc., 2026-07-06) Kulathunga, T; Amarasinghe, B; Fernando, V; Lakruwani, P; Weerasinghe, M; Kasthurirathna, DThis paper presents a Sri Lanka focused, voice-based call center automation framework supporting Sinhala, English, and Sinhala-English code-mixed conversations in low-resource environments. The system adopts an end-to-end architecture integrating bilingual dataset construction, privacy-preserving speech processing, retrieval-grounded response generation, multimodal sentiment intelligence, and customer satisfaction (CSAT) estimation. A Sinhala-English call-center corpus is created using noise reduction, speaker diarization, and transcript alignment, combined with transcript-aligned PII redaction. During live interaction, language identification routes calls to unified processing pipelines. Real-time sentiment analysis with explainable risk scoring supports escalation decisions, while retrieval-augmented generation ensures factually grounded responses. Emotion-adaptive text-to-speech enhances conversational naturalness. The framework enables interaction-based CSAT estimation without relying solely on post-call surveys, providing scalable and privacy-aware automation tailored to multilingual Sri Lankan call center operationsItem Embargo PregAssist: Pregnancy Support Mobile Application for Pregnant Mothers and Doctors(Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Buddika, B. P; Jayasinghe N.; Perera A.N.M; Wijethunga D.N; Weerasinghe, M; Dunuwila, OPregnant care involves constant follow-ups, timely risk detection, and successful interactions between mothers and medics. This paper introduces PregAssist, a mobile integrated support system that is a synthesis of four products, including fetal health decision support, physical health risk prediction, AI-based mental health monitoring, and AR-driven emergency training. The fetal health module provides explainable and offline-capable cardiotocography (CTG) classification to assist clinical decision-making. XGBoost classifier with SHAP-based interpretability is used to analyze physical risks to generate individual recommendations and alerts. Mental health assessment integrates questionnaire-based indicators with CNN-driven facial emotion recognition, while federated learning preserves data privacy through on-device training. AR-based deterministic expert system offers protocol-adherent emergency simulations to improve preparedness activity when facing high-level of risk. Through the convergence of predictive analytics, explainable AI, privacy preserving learning, and training design, PregAssist assists in proactive maternal attention in urban and resource constrained environments via combining a hybrid mobile architecture.Item Embargo Secure Enhanced JWT Framework with Post-Quantum Cryptography(Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Sunera, A; Ransika, Y; Rasiru, M; Gunawardane, A; Abeywardena, K. Y; Senarathne, AJSON Web Tokens (JWTs) are widely used in distributed authentication, but many implementations still rely on classical algorithms such as RSA and provide limited support for key transparency, guarded validation, and secure revocation. These limitations reduce their suitability for future-ready security environments [1], [2], [3]. This research proposes a secure enhanced JWT framework built on four integrated components: a post-quantum signing and verification service, a transparency-driven key distribution service, a guard layer for policy enforcement, and a secure revocation service. The framework introduces ML-DSA-based post-quantum signatures while strengthening key integrity, token validation, and revocation control. A classical RS256-based authentication system was used as the baseline and compared against the integrated post-quantum system. The baseline recorded an average login latency of 77.891 ms, verification latency of 17.078 ms, protected endpoint latency of 14.608 ms, and an average token size of 519 bytes. The integrated post-quantum system achieved an overall average request latency of 67.245 ms, 97.667 ms p95 request latency, zero request failures, and 71.943 requests per second in the scoped live benchmark. The results show that the proposed framework maintains operational stability while delivering stronger security properties than conventional JWT systems, contributing a unified JWT security architecture that integrates post-quantum signatures, transparency-based key distribution, guarded validation, and revocation-aware trust enforcement into a single end-to-end authentication model.Item Embargo AgriSense LK: Weekly Automated Machine Learning for Sri Lankan Produce Prices with Business Continuity Plan, Market Opportunity Ranking, Cultivation Targeting, and Yield Quality Valuation(Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Matharaarachchi, Charaka J.; Samarasinghe, Ravindu T; Vidyasarani G.G.T.; Fasnas, M; Siriwardana, D; Wijesooriya, AIn Sri Lanka, agricultural decision-making remains largely traditional: decisions are often based on historical practices, informal consultation, and heuristic judgment. The primary barrier is that market price data is difficult to interpret without analytical expertise, resulting in unpredictable price volatility and suboptimal farmer income. AgriSense LK is a machine learning platform that converts historical price records into actionable recommendations for farmers and traders. The system comprises four components: business strategy classification, market opportunity ranking, cultivation targeting, and smartphone-based produce quality grading. The platform was trained on 123,985 real price records sourced from the Central Bank of Sri Lanka (CBSL), spanning 2017 to 2025. Key results include a MAPE of 0.7% and MAE of Rs. 1.86 on weekly price forecasting (a 98.1% improvement over the naive baseline), a ROC-AUC of 0.9056 on cultivation targeting, and 91.49% crop classification accuracy with 89.84% quality grade accuracy in the computer vision component. Direct price regression over a seven-day horizon proved unreliable; a binary profitability classifier was adopted instead and substantially outperformed the regression approach. While results are promising, further validation under real-world deployment conditions is required.