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Author: search.filters.author.Krishara, JSubject: search.filters.subject.Artificial Intelligence

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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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    Enhancing the Performance of Supply Chain using Artificial Intelligence
    (Institute of Electrical and Electronics Engineers Inc., 2025) Wijedasa, S; Gnanathilake, K; Alahakoon, T; Warunika, R; Krishara, J; Tissera, W
    Optimizing warehouse operations is essential to meet dynamic customer demands while maintaining efficiency in the rapidly changing supply chain landscape. Using four key components, this research presents a comprehensive AI-based approach to improve supply chain management performance. The first component uses Long Short-Term Memory (LSTM) networks to predict demand and returns, allowing for accurate forecasting of product demand and returns based on historical sales data. The second component uses Q-learning, a Reinforcement Learning (RL) technique that optimizes the scheduling of product replenishments by prioritizing critical stock shortages based on inventory levels and predicted demand. The third component analyzes customer purchasing patterns using FP Growth and clustering algorithms to analyze customer buying patterns, strategically placing items in aisles to reduce selection time and improve picking efficiency. The final component involves customer churn prediction using machine learning techniques to identify at-risk customers and facilitate proactive retention strategies. To bridge the gap between complex AI models and practical warehouse operations, a web-based application named 'OptiFlow AI' has been developed. This platform provides warehouse workers with user-friendly interfaces to access demand forecasts, replenishment priorities, optimized product placements, and customer retention insights. The proposed system significantly enhances operational efficiency, reduces time delays, and improves customer satisfaction, contributing to a more resilient and intelligent supply chain ecosystem.