Proshenjit Sarker is pursuing his Master of Science degree in Electronics and Communication Engineering at Khulna University, Bangladesh, where he also completed his Bachelor’s degree. His research focuses on machine learning, metaheuristic optimization algorithms, and explainable AI with applications in medical diagnosis and human activity recognition.
His recent work includes developing XGBoost-based classifiers optimized with novel metaheuristic algorithms such as Golden Jackal Optimization (GJO), War Strategy Optimization (WARSO), and Sand Cat Swarm Optimization (SCSO) for critical healthcare applications including dengue fever detection, breast cancer prediction, gallstone classification, and human activity recognition. He employs explainable AI techniques (SHAP and DiCE) to ensure model interpretability and transparency.
His publications have appeared in high-impact journals including Sensors, Bioengineering, and Information (MDPI), demonstrating strong contributions to the intersection of machine learning, optimization, and healthcare informatics.