Research Publications

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Author: search.filters.author.Seneviratne, O

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Now showing 1 - 4 of 4
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    PublicationOpen Access
    An Intelligent Risk Aware Navigation Framework for Accident Hotspot Prediction, Real-Time Traffic Analysis, and Safety-Oriented Route Planning
    (Sri Lanka Institute of Information Technology, 2026-05-21) Rathnapala, A; Seneviratne, O
    Road traffic accidents remain a major public safety issue, particularly in urban regions where increasing traffic density and complex road environments contribute to higher accident risk. Existing navigation systems primarily optimize routes based on travel time or distance, without considering accident risk, which can expose drivers to unsafe road segments. This study proposes an intelligent risk-aware navigation framework that integrates accident hotspot prediction with real-time traffic analysis for safety-oriented route planning. A supervised machine learning model was trained using historical accident records obtained from Sri Lanka Police data to estimate accident risk levels across road segments. These risk scores are combined with real-time traffic information retrieved from mapping services to evaluate alternative routes based on both safety and travel efficiency. Experimental results show that the proposed model achieves an accuracy of 0.93 in predicting accident risk levels. Furthermore, the system is able to recommend routes that reduce exposure to accident-prone areas while maintaining acceptable travel time. A mobile prototype was developed to visualize accident hotspots and provide safer route commendations. The results demonstrate that integrating predictive accident analytics with real-time traffic information can significantly enhance navigation systems by enabling safety-aware decision-making and improving overall road safety
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    PublicationOpen Access
    EcoSort: An Edge-Deployable Hybrid AI-IoT Framework with Decision Fusion for Automated Waste Sorting and Real-Time Bin Monitoring
    (Sri Lanka Institute of Information Technology, 2026-05-21) Liyanage, V; Seneviratne, O
    Abstract—Effective waste segregation remains a major challenge in urban environments because manual sorting, isolated sensor systems, and stand-alone vision models often fail to deliver the accuracy, integration, and operational visibility required for reliable deployment. This paper presents EcoSort, an edgedeployable hybrid AI-IoT framework that combines imagebased waste classification, sensor-assisted validation, decision fusion, automated sorting, and real-time bin monitoring within a single architecture. A MobileNetV3-based classifier performs lightweight visual recognition, while complementary sensor readings provide physical cues for validating ambiguous cases. The independent outputs are merged using a priority-based decision fusion layer that produces the final class label used to trigger the sorting actuator. The system is implemented as a lowcost prototype using embedded controllers, ultrasonic sensing, servo-based actuation, and a web dashboard for live fill-level visualization. In addition to end-to-end sorting, the monitoring layer generates threshold-based collection alerts when bin capacity approaches critical levels, improving operational responsiveness. The study contributes a unified design that addresses the fragmentation seen in prior waste management solutions, where classification, segregation, and monitoring are typically treated as separate subsystems. Prototype-level evaluation and implementation observations indicate that the hybrid pipeline improves classification dependability and sorting robustness compared with single-modality operation while remaining feasible for resource-constrained edge deployment. The proposed framework therefore offers a practical foundation for scalable, data-driven, and sustainable waste management in institutions and smart-city settings.
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    PublicationOpen Access
    Nutria: An AI-Driven Personalized Meal and Exercise Recommender System for Diabetes Management
    (SLIIT City UNI, 2025-07-08) Kumari, V.W.I.D; Seneviratne, O
    The prevalence of diabetes has led to a growing demand for personalized dietary management tools, leading to the development of Nutria, a web-based food recommendation system tailored for individuals with diabetes. Nutria application is leveraging artificial intelligence, machine learning, and image processing. Nutria analyzes individual health data to provide realtime meal suggestions. The system also features predicting blood glucose level, feature of a chatbot that supports user engagement by offering dietary advice, tracking user progress and exercise recommendation for control their disease condition. The inclusion of a chatbot serves as a vital component of Nutria, facilitating ongoing user engagement and support. Users can interact with the chatbot to receive personalized dietary advice, track their progress over time. This interactive feature not only helps users stay motivated but also fosters a sense of accountability in their dietary choices. Findings from the system evaluation revealed a high level of user satisfaction, with over 85% of participants reporting improved dietary awareness and adherence.
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    PublicationOpen Access
    Bliss2Glamour: An Artificial Intelligence Integrated Educational Platform for Skincare and Beautician Training
    (SLIIT CITY UNI, 2025-07-08) Ranthatige, N; Seneviratne, O
    This research paper represents Bliss2Glamour, an artificial intelligence (AI) based educational platform developed to assist all the NVQ Level 4 blooming beauticians, qualified lecturers, and beauty enthusiasts. Bliss2Glamour has an integrating Learning Management System (LMS), well trained AI chatbot, Selfaffirmations to keep the users motivated, calming music for the salon purposes, provide 24/7 skincare consultation from a highly qualified cosmetologist via WhatsApp website for beautician training, standard online quizzes for the trainee beauticians to get prepared for the exam aligned with the TVEC syllabus. This research paper highlights the motivation, methodology (Agile), implementation (FastAPI, React, React JS Query, fine-tuned QWEN 2.5 0.5B-Instruct AI model), and evaluation (via Weights & Biases). All objectives were met. The AI chatbot achieved 80% accuracy rate based on evaluation using Weights. These results confirm that Bliss2Glamour successfully combines educational content, AI technology, and holistic care into one user-friendly system.