Research Publications
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Item Embargo 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, SThe 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.Item Embargo 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, Kfic 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.Item Embargo Sinhala Speech Recognition System for Speech-Based Autism Intervention in Children Using the NAO Robot(Institute of Electrical and Electronics Engineers Inc., 2026-03-26) Bopage, H; Pulasinghe, K; Rajapaksha, SThis research focuses on the development of a Sinhala speech recognition engine tailored to identify the language content of conversations with children. The engine leverages machine learning algorithms and natural language processing (NLP) techniques to transcribe and classify speech in Sinhala. Key features include an acoustic model optimized for the nuances of Sinhala phonetics and a language model trained on datasets encompassing texts of child-directed speech. The system evaluates linguistic aspects to assess the appropriateness of content and engagement levels in child-centric dialogues. By addressing challenges such as phoneme variation and informal conversational patterns, the system aims to enhance the understanding and facilitation of Sinhala-based child interactions, promoting effective communication and developmental support.Publication Open Access Novel mycelium-based composites with enhanced physico-mechanical properties, as sustainable alternatives for packaging applications(Taylor and Francis Ltd., 2026-07-17) Madusanka, C; Udayanga, D; Nilmini, R; Rajapaksha, S; Hewawasam, C; Manamgoda, D; Herath, I. SMycelium-based composites (MBCs) are produced through a combination of fungi and lignocellulosic materials. Identifying novel combinations of fungi and lignocellulosic waste is crucial for exploring new material properties. In this study, MBCs were prepared with strains of Ganoderma orbiforme and Lentinus squarrosulus from Sri Lanka, using three different types of locally sourced lignocellulosic substrates, including Cocos nucifera sawdust, Mangifera indica sawdust, and coir pith derived from coconut husk. Mycelium inoculum grown on rice seeds was introduced to organic substrates and incubated at 28 °C for 30 d. The resulting composites were separated from the container, dried at 80 °C, and characterised for physicochemical, and microscopic properties. Results indicated that the produced MBCs exhibit properties equivalent to or superior to those of expanded polystyrene (EPS). The Ashby chart generated revealed that MBCs possess properties comparable to cork and other low-density foams, making them suitable for insulating, cushioning, and packaging applications. Among the combinations tested, MBCs made with coir pith and coconut sawdust proved to be the most effective, eco-friendly alternatives to protective packaging.Publication Open Access Adaptive Path Planning for Mobile Robots Using a Hybrid PRM–GA Optimization Approach(John Wiley and Sons Ltd, 2026-04-10) Jathunga, T; Rajapaksha, S; Jayasinghe, S; Abeygunawardena, NThis study addresses the challenge of path planning in mobile robots, that requires efficient navigation in complex environments. Traditional approaches often struggle to meet the increasing demands of modern multi-robot systems operating in dynamic environments. To address these limitations, this study proposes an improved path planning technique by combining the probabilistic roadmap (PRM) with the genetic algorithm (GA), forming a hybrid PRM–GA approach designed to optimize the routes of mobile robots. Experiments were carried out for scenarios involving 2, 3, and 9 robots to analyze the performance of the proposed method under increasing complexity. The proposed PRM–GA method was compared with widely used path planning algorithms including (Formula presented.), Rapidly exploring random tree (RRT), and conventional PRM. Performance of each method was evaluated focusing on path efficiency and energy consumption. The enhanced fitness function within the GA evaluates robot paths based not only on distance but also on smoothness and turn count, promoting routes with fewer directional changes. The proposed PRM–GA method reduces robot energy consumption while improving navigation efficiency. Experimental results demonstrate that the PRM–GA hybrid method outperforms (Formula presented.), RRT, and PRM by encouraging smoother paths with fewer turns, thereby enhancing the operational efficiency of multi-robot systems. The effectiveness of the proposed approach highlights its potential for practical applications in sectors where efficient mobile robot navigation is essential.genetic algorithmItem Open Access Between Absence and Dreams: A Phenomenological Study on the Experiences of Malaiyaha Tamil Children with Migrant Mothers(Faculty of Humanities and Sciences, SLIIT, 2025-05-27) Wathna, S; Mahaliyanage, E; Senarath, K; Rajapaksha, S; Sethini, R; Kodikara, V; Subasinghe, V; Selvaratnam, N.D; Ponnamperuma, LMaternal labor migration has become a significant socioeconomic strategy among structurally marginalized communities in Sri Lanka, particularly within the Malaiyaha Tamil estate sector. While existing research has largely focused on economic and educational outcomes, there remains limited understanding of children’s lived experiences within these contexts. This study aimed to explore how Malaiyaha Tamil children make sense of their mothers’ migration for overseas employment. A qualitative design informed by Interpretative Phenomenological Analysis (IPA) was employed. Semi-structured interviews were conducted with adolescents aged 12-17 years residing in plantation communities. Data were analyzed ideographically before identifying shared experiential patterns across cases. The analysis generated three interrelated superordinate themes: (1) mother as emotional anchor and “absent presence,” (2) childhood reassigned through role reorganization and educational vulnerability, and (3)living between longing and aspiration within structural constraint. Findings indicate that maternalmigration is experienced as a relational paradox, where financial provision coexists with emotionaldisruption. Children described ambivalent attachment experiences, shifting family roles, and variededucational engagement shaped by available support systems. Despite these challenges, participantsarticulated future aspirations that reflected resilience within structurally constrained environments.Drawing on Attachment Theory, Ecological Systems Theory, and Ambiguous Loss Theory, the studyconceptualizes maternal migration as a relational and ecological process rather than a singular economicevent. The findings highlight the coexistence of vulnerability and agency and underscore the importanceof context-sensitive interventions for children in migration-affected communities.Item Embargo 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, SSinhala 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.Item Embargo Adaptive Robotic Voice Modulation for ASD Kids: Tailored Voice Pitch, Tone, and Speed(Institute of Electrical and Electronics Engineers Inc., 2025) Panduwawala, P; Pulasinghe, K; Rajapaksha, SChildren with Autism Spectrum Disorder (ASD) often experience sensory sensitivities, particularly auditory hypersensitivity, which can make interactions and communication challenging. This study explores the customization of the NAO robot's voice pitch, tone, and speech speed using the Kaldi Speech Recognition Toolkit to align with the preferences of children with ASD. Eight distinct voice profiles were created, offering a range of variations in pitch, tone, and speech speed. Parents or caretakers were asked to select the voice profile they felt would be most suitable for their child. Based on this feedback, we created a spectrum of voices tailored to each child's needs. Results indicate that medium-pitch and moderate-speed combinations are most effective in enhancing engagement, with Voice 2 emerging as the preferred profile. The findings underscore the potential of adaptive voice modulation in improving robotic interactions for ASD therapy and highlight opportunities for further research in real-time adaptability and long-term impact assessment.Item Embargo Intelligent Adaptive Lighting Control: Reinforcement Learning-Based Optimization for Smart Home Energy Efficiency(Institute of Electrical and Electronics Engineers Inc., 2025) Hewakapuge M.M; Gamage W.G.T; Surendra D.M.B.G.D; Thejan K.G.T; Rajapaksha, S; Rajendran, KThis study introduces a novel research paper outlining a behavioral-based adaptive lighting system that aims to revolutionise smart home lighting by integrating user behavior tracking to enhance energy efficiency and user comfort. Unlike traditional motion-sensor-based lighting, the novelty of this approach is the ability to adapt dynamically to evolving user behaviors through reinforcement learning. The system utilises Wi-Fi-based positioning, GPS and accelerometer data to monitor user movements and classify different areas of the house. Users initially calibrate the home layout through a mobile application, marking room locations and lighting configurations. The system then collects movement data over time to predict optimal lighting schedules based on user routines and refines the predictions and updates lighting adjustments accordingly, minimising energy wastage while maximising user convenience. A serverless backend architecture ensures scalability, cost-effectiveness, and seamless data processing. The adaptive framework continuously refines lighting automation, responding to evolving behavioral patterns.Publication Open Access Adding Common Sense to Robots Using Ontology(International Association of Computer Science and Information Technology, 2025-04-11) Ranathunga, R.A.A.L; Rajapaksha, SThis work investigates how ontological frameworks might improve robots’ ability to reason using common sense. The goal of the project was to enhance robot decision-making in dynamic real-world situations by developing an ontology-based model retraining technique. The researchers wanted to incorporate organized commonsense knowledge into robotic systems, so they built extensive ontologies that captured knowledge about the physical world and human interactions. The research compared the performance of robots with conventional models (control group) to those with ontology-enhanced models (experimental group) across various measures. The results indicate that this strategy may be used to develop more competent and user-friendly robotic helpers for a variety of sectors, including industry, healthcare, and education. Although the study has limitations related to data quality and experimental design, it does demonstrate the promise of ontology-based techniques to advance autonomous systems and human-robot interactions. Extending ontology databases, multidisciplinary cooperation, and investigating applications in other sectors are some of the future research goals.