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Identifying Risk Factors and Predicting Food Security Status using Supervised Machine Learning Techniques., , , and . ICT4DA, page 12-17. IEEE, (2021)Machine learning integrated patient flow simulation: why and how?, , , and . J. Simulation, 17 (5): 580-593 (September 2023)Data through the Computational Lens., , , , , , , and . J. Comput. Sci., (2017)Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory., , , , , and . ICCS, volume 108 of Procedia Computer Science, page 7-17. Elsevier, (2017)Investigating Coordination of Hospital Departments in Delivering Healthcare for Acute Coronary Syndrome Patients using Data-Driven Network Analysis., , , , and . CoRR, (2020)Why Machine Learning Integrated Patient Flow Simulation?, , , and . CoRR, (2021)Predicting perinatal mortality based on maternal health status and health insurance service using homogeneous ensemble machine learning methods., , and . BMC Medical Informatics Decis. Mak., 22 (1): 341 (2022)Investigate Risk Factors and Predict Neonatal and Infant Mortality Based on Maternal Determinants using Homogenous Ensemble Methods., , , and . ICT4DA, page 18-23. IEEE, (2021)Constructing Holistic Patient Flow Simulation Using System Approach., , , and . ICCS (4), volume 12140 of Lecture Notes in Computer Science, page 418-429. Springer, (2020)Investigating Coordination of Hospital Departments in Delivering Healthcare for Acute Coronary Syndrome Patients Using Data-Driven Network Analysis., , , , and . ICCS (4), volume 12140 of Lecture Notes in Computer Science, page 430-440. Springer, (2020)