Faculty of Computing

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Author: search.filters.author.Rajapaksha, SSubject: search.filters.subject.Machine Learning

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    AI-Driven Integrated Caregiving and Health Monitoring Framework for Elderly Well-Being
    (Institute of Electrical and Electronics Engineers, 2026-07-22) Nugaliyadde, S; Nikeshi, N; Marasinghe, M; Rajapaksha, C; Rajapaksha, S; Thelijjagoda, S
    The rapid growth of the aging population has brought about some serious challenges, particularly in managing illnesses, feelings of loneliness, cognitive decline, and mental health issues. Traditional caregiving methods often depend on occasional assessments and hands-on supervision, which can fall short in providing the ongoing and adaptable support that’s really needed. This paper introduces an innovative caregiving and monitoring framework powered by AI, aimed at offering integrated, real-time, and comprehensive assistance for older adults. The system harnesses the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and health data analytics to combine physical health monitoring, nutrition planning, smart routine coaching, and therapist-led mental health support all in one platform. With features like voice-based conversations and journaling, it makes emotional expression and behavioral analysis more accessible, helping to gain a deeper insight into users’ mental well-being. Predictive analytics and anomaly detection are used to spot early signs of health risks and shifts in behavior, allowing for timely interventions. Plus, remote access means caregivers and healthcare professionals can keep an eye on users and offer informed advice. By shifting caregiving from a reactive approach to a proactive and preventive one, this system not only improves quality of life but also encourages independent living and eases the burden on caregivers.
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    Intelligent Traffic Management Using Fuzzy Logic and Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2026-06-12) Gunarathna R.P; Randima K.M.G.D; Tennakoon I.M.S.R.; Palihakkara P.I; Rajapaksha, S; Kahatapitiya, K
    fic violations, and inefficient signal con-trol. Conventional traffic management systems rely on manual monitoring and fixed signal timings, making them ineffective in handling dynamic real-time traffic conditions. This research proposes an Intelligent Traffic Management System (ITMS) that integrates real-time traffic monitoring, adaptive signal control, traffic violation detection, and accident risk prediction through a unified analytical dashboard. The system is designed for an IoT-based four-way junction where sensors and cameras detect vehicle density and dynamically prioritize lanes with higher traffic volume. Using video-based vehicle detection, the system measures vehicle speed in real time and identi-fies violations such as over-speeding, red-light violations, and illegal parking. Drivers receive notifications through a mobile application where they can check violation details and pay fines calculated based on predefined traffic rules. Additionally, a dynamic accident risk scoring mechanism combines real-time vehicle speed data with historical violation records to identify high-risk driving behavior. Analytical dashboards visualize traffic density, violations, and risk levels to support data-driven decision making. The proposed system demonstrates how real-time traffic monitoring, violation detection, and dynamic signal control can improve road safety and traffic efficiency, contributing to the development of advanced smart city traffic management solutions.
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    Interactive Sinhala Letter Learning Module for School Children (Grade 1 to 5)
    (Springer Science and Business Media Deutschland GmbH, 2026) Weerasooriya, K; Udana, I; Jayasinghe, L; Kasiwaththa, J; Rajapaksha, S; Kumari, S
    Sinhala is the native language of most people in Sri Lanka. However, most of the children find it difficult to write Sinhala letters fast and accurately, this may undermine their confidence and affect grades. The primary issue is that the parents usually lack their time in order to assist their children in their studying at home. Few interesting tools also exist to teach children how to write in Sinhala in an interesting and effective manner. To address these issues we have developed the ”Interactive Application of the Sinhala Language to School children (Grade 1 to 5) which is a web based application, to allow children studying in primary schools to enhance their knowledge of the Sinhala language. This app provides children an entertaining and effective method of learning how to write Sinhala letters. The system combines instructions in animation, touch tracing finger tools, hand writing recognition and immediate feedback such that kids can learn Sinhala writing, and the non touch screen users can post their written letters on paper to be analyzed individually as to feedback analysis. The system uses handwriting recognition to provide real-time feedback on accuracy and speed. The system also monitors progress and generates comprehensive reports to help children and parents in identifying areas requiring improvement. The application uses a combination of engaging letter tracing and intensive deep learning which are not present in other learning tools. Additionally, the system will aid parents to mentor their children in education even when they are in charged schedules and also enable children improve their skills in Sinhala writing. We offer to make the learning of Sinhala to school students in Sri Lanka easier, more relevant and interesting.