Browsing by Author "Wijedasa, S"

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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.
Open Access
Knowledge Related to Ovulation among Female Undergraduate Students of a Selected Private University in Sri Lanka
(School of Nursing, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Rasiq, D; Muthucumarana, M; Samarasinghe, C; Katukurunda, S; Sathsarani, K; Wijedasa, S
The understanding of the menstrual cycle and the awareness of the fertile window enable individuals to make informed reproductive decisions. It helps promote reproductive health and prevent unintended pregnancies. However, the level of knowledge and awareness about ovulation and fertile window among young adults, particularly among university undergraduates in Sri Lankan context, was not clearly explicit. Hence, this study aimed to assess the knowledge of ovulation among female undergraduates at a non-state higher education institution in Sri Lanka. A descriptive cross-sectional study was done from December 2024 to March 2025. The study targeted female undergraduates across all faculties, excluding students of the Nursing program. A convenient sample of 111 participants was selected, and a self-administered, anonymized Google forms distributed via the official email account of the participants for the data collection. This study revealed a basic level of knowledge on ovulation among female undergraduates.