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Author: search.filters.author.Seneviratne, OSubject: search.filters.subject.machine learning

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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
    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.