Recent Submissions
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, J
The 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.
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, W
Coconut 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.
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, D
This 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 operations
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, O
Pregnant 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.
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, A
JSON 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.
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The collection comprises the research output of SLIIT staff and postgraduate research students, including research publications, conference and symposium papers, books, book chapters, theses, and other scholarly materials. Access to full texts may be restricted depending on the access and licensing terms.