Faculty of Computing

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

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Now showing 1 - 4 of 4
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    ItemOpen Access
    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.
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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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    WORDEX: Early Dyslexia Detection and Support
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ganegoda, S.H; Dissanayake, O; Samarakoon, S; Jayawardana, N; Thelijjagoda, S; Gunathilake, P
    Dyslexia is a prevalent and complex learning disability that affects approximately 5% of primary school students worldwide. It often manifests as persistent difficulties in reading, writing, spelling, and overall academic performance, which can lead to long-term educational and psychological impacts if not addressed early. To facilitate the early identification and support of dyslexic learners aged 7 to 10, this paper introduces Wordex, an innovative and adaptive educational platform. Wordex is designed to screen for multiple dyslexia subtypes and provide targeted interventions through engaging, interactive, and personalized learning activities. The platform features an integrated machine learning-based screening system that analyzes user interactions and performance metrics to assess the risk of dyslexia. Upon identification, the platform delivers tailored remedial exercises that align with national school curricula, aiming to strengthen specific cognitive and linguistic skills. Wordex is developed using a modern technology stack including Spring Boot, Flutter, Python libraries, Firebase, and MongoDB, and incorporates capabilities such as image processing, supervised learning algorithms, real-time progress tracking, and cloud-based data management. A user-centered design approach and iterative testing cycles were employed to ensure the platform is accessible, intuitive, and pedagogically effective. Wordex contributes significantly to the field of educational technology by offering a scalable, research-informed intervention tool. Future enhancements include multilingual support, broader age group coverage, and integration with classroom learning environments.
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    "articulearn": An Integrative, AI-Driven Speech Therapy System for Children With Speech Disorders
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ranasinghe, K; Zoysa, S.P.D; Annasiwatta, S; Fernando, P; Thelijjagoda, S; Weerathunga, I
    "ArticuLearn", a personalized speech therapy system for children with speech sound disorders that integrates advanced machine learning techniques and interactive digital tools to provide targeted intervention across four key domains: phonological disorder detection, fluency disorder identification and intervention, therapy for childhood apraxia of speech, and personalized speech activity filtering for articulation disorders. By leveraging dedicated LSTM-based classifiers and feature extraction techniques such as Mel-frequency cepstral coefficients (MFCCs), this approach automatically identifies specific error types, including phoneme substitutions, omissions, and vowel mispronunciations. In addition, a hierarchical deep learning framework employing attention mechanisms and dynamic time warping is applied to quantify motor planning deficits associated with childhood apraxia of speech, while a reinforcement learning agent adapts therapy prompts based on individual performance. Data were collected from eight children per disorder category along with a normative sample of twenty typically developing children, providing a basis for personalized intervention and progress monitoring. ArticuLearn is designed to complement traditional therapy methods by offering an accessible, scalable solution that supports remote intervention and enhances clinical decision-making. Pilot evaluations suggest that the system can facilitate targeted speech exercises, improve self-monitoring, and foster adaptive learning in young users. This research underscores the potential of combining AI-driven analysis with interactive therapy to transform speech rehabilitation, particularly in resource-limited settings where access to specialized care is challenging.