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Author: search.filters.author.Weerasinghe, L

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    A Mobile Application to Enhance Skills in Children with Nonverbal Learning Disability
    (Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Harshana W.C; Hettiarachchi H.K.Y.K; Mendis T.S.P; Madanayake P.C.S.; Weerasinghe, L; Nawarathne, M
    Nonverbal Learning Disability (NVLD) is a neurodevelopmental disorder affecting children, characterized by strong verbal skills but significant challenges in visual-spatial processing, motor skills, and communication. This paper introduces a mobile application designed to address these challenges through interactive and personalized activities. The app leverages machine learning and artificial intelligence to improve visual-spatial abilities, communication skills, and cognitive development. By engaging children in pattern recognition, word recognition, and touch screen integration, the app aims to enhance critical thinking, decision-making, and relationship identification in children aged 10-13. The algorithms used in this research are the Dynamic Difficulty Adjustment Algorithm, Random Forest Classifier, Real-Time Object Detection Algorithm, and Deep Q-Network (DQN). This paper explores the development, features, and impact of the app in supporting children with NVLD.
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    Early Detection of Student Mental Health and Academic Burnout Using Multimodal AI-Based Behavioral, Physiological, and Emotional Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Indrapala W.V.H.; Kumarasinghe K.D.K.Y.; De Silva A.H.H.; Ranathunga A.K.M.; Weerasinghe, L; Weerathunga, I
    Mental health issues such as stress, anxiety, depression, and academic burnout are increasingly common among university students and have a significant impact on academic performance and long-term well-being. Existing assessment approaches rely mainly on self-reported questionnaires and periodic evaluations, which are reactive, subjective, and ineffective for early intervention. This paper presents a multimodal artificial intelligence-based system for early identification of student mental health conditions by analyzing behavioral, physiological, emotional, and academic data. The proposed framework integrates facial emotion recognition, wearable sensor data analysis, natural language processing of reflective text to continuously monitor student well-being in a privacy-aware manner. Machine learning and deep learning models are employed to detect stress, anxiety, and burnout indicators and to predict future mental health risks. Experimental results obtained from real and synthetic datasets demonstrate that multimodal analysis provides more reliable and accurate predictions than single-source methods. The proposed system enables early risk identification and supports timely intervention in academic environments.
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    Multimodal AI Framework for Personalized and Context-Aware Skin Disease Diagnosis, Monitoring, and Treatment Support
    (Institute of Electrical and Electronics Engineers, 2026-05-22) Wijesinghe H.W.M.O.P.; Laksopan R; Mihisandali W.K.M.; Devindi K.P.T.; Weerasinghe, L; De Silva, A
    Dermatoscopic assessment of skin diseases based on visual morphology may not provide sufficient discrimination due to differences in cutaneous appearance, the severity of disease symptoms and individual biological or environmental factors. Current artificial intelligence (AI)-based dermatological systems mainly integrate unimodal image-based data which is constrained by comparative diagnostic performance in visually ambiguous conditions and across different skin complexions. Image-only approaches also do not utilize patient-reported symptoms needed to tailor treatment plans. In this study, we explore the potential of a unified multimodal AI framework towards robust, context-aware and patient- centric skin disease diagnosis, monitoring and treatment support. The framework combines deep learning-based image analysis with symptom-aware inputs extracted from voice recordings and structured text, which allows for improved diagnostic reliability. The proposed framework also introduces an explainable severity assessment module which evaluates disease progression via interpretable features and rule-based score. Domain adaptation methods further employed lead to better generalization for out-of-distribution data originating from different populations and reduce model bias. A knowledge-driven recommendation module generates context-aware personalized treatment recommendations based on predicted disease categories and patient-related information. Experimental results demonstrate that the proposed multimodal framework improves contextual understanding and robustness in visually ambiguous cases while enhancing interpretability, improved generalization, and practical applicability in teledermatology environments, while adding interpretability, fairness and real-world applicability of teledermatology systems.