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Author: search.filters.author.Nanayakkara, NHas files: Yes

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    Grade 6 Coding Education through Interactive Learning in Sri Lanka
    (Institute of Electrical and Electronics Engineers, 2025-12-08) Nallaperuma, S; Perera, A; Nanayakkara, N; Hansaja, D; Krishara, J; Wijendra, D
    This study proposes an AI-powered Adaptive Learning Management System (LMS) to enhance coding education for school students. The system integrates six AI/ML models and FaceNet-based facial authentication to deliver adaptive learning pathways, real-time feedback, and personalized assessments. It begins with an initial benchmarking assessment across seven categories, after which the Exam Difficulty and Question Count Model and a fine-tuned GPT-2 generator collaboratively create customized multiple-choice questions based on each learner's capability. Learning is supported through skill prediction, dynamic difficulty monitoring, and emotional state detection to ensure both cognitive and emotional engagement. Students scoring above 60% advance directly to coding curricula, while others receive personalized scaffolding through multimodal learning-style-based modules. Teachers benefit from AI-powered analytics tools that offer continuous feedback, weak-topic detection, and pedagogical guidance to enhance teaching effectiveness. By combining authentication, adaptive assessment, emotional intelligence, and teacher support, this study presents a comprehensive framework to democratize coding education and foster resilience, motivation, and mastery among students aged 10-12.
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    An integrated data-driven approach for Chronic Kidney Disease of Unknown Etiology (CKDu) risk profiling and prediction in Sri Lanka
    (SPIE, 2025) Rajapaksha, N; Rajawasan, H; Ubeysinghe, R; Perera,S; Swarnakantha, N.H.P.R.S; Gamage, M; Nanayakkara, N; Wijayakulasooriya, J; Herath, D; Lakmali, M
    Chronic kidney disease of unknown etiology is a significant public health issue in Sri Lanka, especially in rural farming communities. The exact causes remain unclear, with potential links to environmental and socio-economic factors. This research employs Biological Data and Geographic Information Systems to analyze risk factors such as water quality, agricultural practices, climatic conditions, Demographic Factors, Socio-economic Factors. This study uses data from government health records, the Centre for Research-National Hospital Kandy, and field surveys. By identifying patterns and correlations, the study aims to inform public health interventions and reduce the impact of CKDu, ultimately improving health outcomes for affected populations. This will greatly contribute to preventing the disease, reducing the risk, and identifying patients at an early stage.