Faculty of Computing-Scopus

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Author: search.filters.author.Rathnayake, S

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    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.
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    Adaptive Voice Communication in Emotion-Aware Digital Companions
    (Institute of Electrical and Electronics Engineers Inc., 2025) Rathnayake, P; Rathnaweera, C; Jithma, U; Aththanayake, I; Rathnayake, S; Gunaratne, M
    This paper presents an adaptive voice communication system for emotion-aware digital companions that dynamically responds to users' affective states through expressive speech and synchronized 3D avatar animation. The system integrates real-time voice input, emotion recognition, and context-aware dialogue generation using GPT-3.5, followed by emotional text-to-speech synthesis via neural TTS. Lip-sync data is generated using phoneme alignment and rendered in sync with the avatar's facial expressions and gestures. To enhance user trust and engagement, the avatar visually mirrors the emotional tone of the speech. A cultural adaptation layer is introduced to align voice output and speech style with Sri Lankan communication norms, including tone, pacing, and formality. Implemented using a Node.js backend and React + Three.js frontend, the system demonstrates strong potential for emotionally intelligent, culturally adaptive AI interactions. This work contributes a modular pipeline for building empathetic voice agents capable of enhancing realism and trust in human-AI communication.
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    Hybrid Motion Prediction for Autonomous Vehicles using GNN-Transformer Architecture
    (Institute of Electrical and Electronics Engineers Inc., 2025) Akalanka, A; Athukorala, D; Ganepola, N; Tharindu, I; Rathnayake, S
    Accurate perception and scene understanding are pivotal in enabling autonomous vehicles to navigate safely and intelligently. This paper presents an integrated perception module comprising three core subcomponents: real-time object detection using YOLOv5, lane-keeping using a CNN-based steering predictor, and a novel motion prediction architecture based on a hybrid Graph Neural Network (GNN) and Transformer design. The system is deployed and validated within the CARLA simulation environment, with custom data generation pipelines designed to mimic real-world behavioral patterns of nearby agents. The novelty lies in the hybrid GNN-Transformer model, which effectively captures both spatial and temporal interactions of dynamic objects for behavior classification. Experimental results demonstrate a high accuracy of 98.75% in classifying behaviors into four categories: Going, Coming, Crossing, and Stopped. This paper details the architecture, dataset creation, training methodology, and performance evaluation, highlighting the hybrid model's potential to improve trajectory planning modules in autonomous systems.
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    Multimodal Knowledge Graph for Domain-Specific Intelligence
    (Institute of Electrical and Electronics Engineers Inc., 2025) Mohan, K; Munasinghe, M; Bandara, L; Wijesinghe, H; Rathnayake, S; Abeywardhana, L
    In the era of information abundance, transforming vast amounts of data into meaningful knowledge remains a critical challenge, especially in domains like medicine, engineering, and education, where visual and multimodal elements play a vital role. Traditional Knowledge Graphs (KGs) excel in organizing structured and textual data but struggle to incorporate multimodal information and implicit relationships, limiting their effectiveness. This paper explores the potential of Multimodal Knowledge Graphs (MMKGs) to address these limitations by integrating text, images, videos, and audio into a unified framework. We investigate how MMKGs enhance knowledge retrieval, comprehension, and interactive learning through advanced techniques, including Natural Language Processing and deep learning. Our findings demonstrate that MMKGs significantly improve knowledge retention and application in specialized fields, offering a foundation for more intuitive and effective domain-specific knowledge ecosystems.