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Hasan, S., Islam, A., Islam, T. and Ma, H. (2025) Analysis of Machine Learning Models for Stroke Prediction with Emphasis on Hyperparameter Tuning Techniques. In: Xin, B., Ma, H., She, J. and Cao, W., Eds., Computational Intelligence and Industrial Applications, Springer, 1-9.
https://doi.org/10.1007/978-981-96-4756-9_1

has been cited by the following article:

• TITLE: Optimizing Medical Supply Chain Resilience for Future Pandemics: A Data-Driven Framework Integrating Public Health Risk, Logistics Efficiency, and Predictive Analytics

  AUTHORS: Mst. Hasna Akter, Soumitra Palit, Kazi Md. Shahadat Hossain, Md. Ekramul Hoque, Tahera Shabnam

  KEYWORDS: COVID-19, Medical Supply, Health Risk, Supply Chain

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.17 No.3, July 31, 2025

  ABSTRACT: The COVID-19 pandemic exposed critical vulnerabilities in global medical supply chains, resulting in widespread shortages of essential healthcare products. This study aims to optimize the resilience of medical supply chains against future pandemics by evaluating key mitigation strategies such as inventory buffering, multi-sourcing, and local manufacturing. Using an agent-based simulation model that incorporates realistic demand surges, supplier disruptions, and transportation delays, we quantify the impact of these strategies on supply chain performance metrics including fulfillment rate, lead time, and recovery speed. Our results demonstrate that integrating multiple resilience strategies significantly improves service levels and reduces recovery times compared to traditional supply chain configurations. The findings provide actionable insights for policymakers and healthcare organizations to strengthen preparedness and ensure reliable access to critical medical supplies in times of crisis.