Scopus Index Publications

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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.

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Now showing 1 - 6 of 6
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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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    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. S
    Mycelium-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.
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    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, S
    Children 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.
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    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, K
    This 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.
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    Intelligent Wheelchair with Emotion Analysis and Voice Recognition
    (IEEE, 2022-12-26) Perera, S; Gamage, S; Weerasinghe, C; Jayawardena, C; Pathinayake, K; Rajapaksha, S
    Intelligent wheelchairs are becoming more and more prevalent in contemporary life, and the peaceful interaction of humans with wheelchairs is one of the most popular research topics. The development of a voice recognition and emotion recognition based intelligent wheelchair framework is being addressed here for truly impaired/disabled people who are unable to operate the wheelchair by hand. The patient can operate the wheelchair using voice commands, and the wheelchair’s Emotion Analysis module recognizes the patient’s face and records the patient’s emotions before sending the information to a cell phone application. A portion of the intelligent wheelchair is made to gather crucial information given by other units and send out emergency calls or notifications to the caregivers. Face recognition technology uses image processing to identify facial expressions by detecting the patient’s face and facial expressions. This helps the other components collect and send data via Internet of Things technologies. Speech – to –Text and Text – to-Speech Methodology is used in the voice recognition module and it captures the voice command data set and extracts the features of the commands.The model is already built and trained to recognize the commands and to send action request to the relevant unit.The Responsive AI auto starts the timer when the patient moves away from the wheelchair, recognizes time and responses back. This unit auto also sends the alert and calls to the guardian when the user has no response.
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    Farming Through Technology Driven Solutions For Agriculture Industry Ceylon E-Agro mobile application-find technology based solutions for agricultural problems
    (Institute of Electrical and Electronics Engineers, 2022-09-16) Imalka, L.A.; Gunawardana, K.G.A.; Kodithuwakku, K.M.S.K; Arachchi, H.K.E; Harshanath, S.M.B; Rajapaksha, S
    Many developing countries are based on the agricultural sector. More than 60 percent of the population depends on this sector. This project is focused on maize cultivation. In agriculture, farmers play the most important role. Currently, farmers are facing many problems related to maize cultivation in Sri Lanka. This mobile application will help the maize farmers to overcome these difficulties and provide a good consumer demand for maize cultivation. Through this mobile application, the farmer can find solutions for pest & diseases in maize, fire threat in the farm field. AI based Agri Agent will be provide real-time solutions, bring the farmers and the buyers into the one platform, and provide price prophesying, price index feature. IoT based smart farming features will be provided to remain soil moisture and quality of soil for maize plantation.