Recent Submissions
Transforming Education And Therapy For Children On The Autism Spectrum with Machine Learning Solutions
(Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Jayawickrama Y.R.C.S; Kumarasiri O.A.K.U; Kurera W.N.K; De Silva J.H.J.A; Thelijjagoda, S; Hathurusinghe, S
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects cognitive, social, behavioral, and sensory development. Early diagnosis and intervention are crucial but remain challenging due to cultural, environmental, and diagnostic limitations, particularly in Sri Lanka. This research proposes a machine learning-driven web-based system to assess and support children with ASD across four critical domains: behavioral observation, cognitive skills, social skills, and sensory processing. By integrating technologies such as deep learning, computer vision, and natural language processing, the system utilizes eye-tracking, facial expression analysis, and real-time video monitoring to identify developmental challenges. Additionally, culturally adaptive parental questionnaires and interactive learning activities enhance the accuracy of ASD assessments and provide personalized intervention recommendations. The proposed approach bridges gaps in early ASD detection by offering a scalable, accessible, and contextually relevant solution for Sri Lanka. Experimental results show high accuracy in behavioral (90%), cognitive (92%), social (92%), and sensory (94%) models. This scalable, accessible solution bridges gaps in early ASD detection, offering a culturally relevant tool for families and healthcare providers in Sri Lanka. The system empowers caregivers with real-time insights and tailored interventions, improving the quality of life for children with ASD and their families, and advancing inclusive support systems globally.
Unveiling EEG Emotional Patterns during Interactive Engagement Activities: A Performance Comparison of Machine Learning Models
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Rathnayake, T; Subasinghe, S; Nawarathne, M; Sumathipala, P
This paper presents a comparative evaluation of machine learning and deep learning models for emotion recognition from electroencephalography (EEG) signals recorded during interactive engagement activities. EEG data were collected using a Muse headband as participants performed engagement activities designed to elicit five emotions: Afraid, Happy, Calm, Neutral, and Sensitive. After preprocessing and feature extraction, eight machine learning and deep learning models were trained. Based on the experiment, Random Forest emerged as the best-performing classifier, achieving ~95.6% accuracy with balanced precision-recall, while gradient boosting and SVM also shown comparative results. The models demonstrated potential for real-time streaming despite slightly lower accuracy, highlighting scalability with larger datasets. These findings demonstrate the feasibility of consumer-grade EEG devices, enhanced with multimodal features, for robust emotion recognition in realistic engagement activities. The results underscore the effectiveness of multimodal fusion and engagement-based design, confirming that lightweight EEG systems can support practical, real-time affective computing applications in education, adaptive interfaces, and mental well-being monitoring.
Leveraging Multi Modal AI Capabilities to Enhance Vehicle Insurance Claiming Process
(Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Wijesundara, A; Udumulla, C; Perera, H; Rathnayake, N; Tissera, W; Dassanayake, T; Vidhanaarachchi, S
The cracks of the existing manual systems are evident due to the increased demand for vehicle insurance services related to vehicle accidents. The major shortcomings of previous studies have hindered the ability of industrial deployment. This paper reports a Multimodal AI approach to automate the vehicle insurance claiming process by following deep learning approaches to detect damaged parts of a vehicle, identify the damage type and severity of the damaged parts, and perform a robust and transparent claim estimate with deeper insights for stakeholders. Significant advancements were achieved in external damage detection by identifying multiple damaged parts, types and severities independently. This, combined with the multimodal approach, achieved an accuracy of 95% on final claim estimates. This enables the provision of detailed claim estimates for insurance policy holders, with maximum transparency and reasoning.
Eye Health Monitoring and Eye Care System
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Herath H.M.S.C; Swarnajith T.H.M.P; Senanayaka W.S.H.M; Hettiarachchi H.W.R.A; Walgampaya, N; De Zoysa, R
This study presents a multi-modal AI-powered system designed for real-time eye health monitoring and early diagnosis. The proposed system integrates computer vision, machine learning, and image processing techniques to deliver non-invasive and accessible diagnostic solutions. The system comprises four key modules. The first one is a real-time eye exercise module that utilizes webcam-based eye tracking and adaptive machine learning to provide personalized routines for reducing digital eye fatigue. The second module is an AI-driven cataract detection module that analyzes retinal images to enable early diagnosis, particularly in resource-limited settings. The third module is a glaucoma detection system that tracks pupil dynamics, such as size and light reactivity, to identify early symptoms; and the fourth and final module is a color blindness and eye fatigue detection module that employs multi-modal AI techniques to assess color vision deficiencies and monitor signs of visual fatigue in real-time Collectively, these components form a comprehensive array of tools focused on improving eye health monitoring and treatment. Through the combination of advanced AI technologies with user-friendly interfaces, the proposed systems aim to democratize access to eye care, reduce the global burden of preventable vision loss, and enhance the quality of life for individuals everywhere.
Federated Edge Intelligence for Adaptive Health Prediction in Elderly Care
(Institute of Electrical and Electronics Engineers Inc., 2025-12-17) Silva G.D.T; Sineth H.M.G; Constantine S.A.D.N.C.H; Dissanayake M.E; Pandithage, D; Lokuliyana, S
This research presents the development and evaluation of an IoT-based nursing home monitoring system designed to enhance elderly healthcare through real-time detection, predictive alerts, and caregiver support. The system integrates multimodal sensing with edge and server-level machine learning to monitor three critical areas: sleep quality, fall detection, and emotion recognition in dementia patients. Hardware components, including a Raspberry Pi and non-intrusive sensors, were combined with lightweight neural networks to ensure low-latency processing. Detailed model optimizations, including 8-bit quantized TensorFlow Lite MobileNetV2 and CNN-LSTM pipelines, enabled on-device inference within 80-120 ms. A caregiver dashboard and mobile application provided intuitive visualization of real-time alerts and long-term health reports. Benchmarking against threshold-based and classical ML baselines demonstrated 9-18% performance improvements and 56% lower alert latency. Experimental evaluation demonstrated promising outcomes, with fall detection achieving 95% accuracy, emotion detection 91%, and sleep monitoring 90% reliability. System uptime reached 98%, and alerts were delivered within two seconds in 96% of cases. These results indicate that the proposed solution can effectively reduce caregiver burden, improve resident safety, and establish a scalable framework for intelligent elderly healthcare monitoring.
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The collection comprises the research output of SLIIT staff and postgraduate research students, including research publications, conference and symposium papers, books, book chapters, theses, and other scholarly materials. Access to full texts may be restricted depending on the access and licensing terms.