Project
StrokeGuard: RF–XGBoost Ensemble Learning with SMOTE & SHAP
Built an RF–XGBoost ensemble with SMOTE for class imbalance and SHAP for explainability. Validated on Kaggle Stroke and CDC BRFSS datasets, achieving best AUC of 78.45% and 79.80%.
Kamrul The Dream Maker
SOFTWARE ENGINEER | AI / ML | WEB DEVELOPMENT
American International University-Bangladesh
Project
Built an RF–XGBoost ensemble with SMOTE for class imbalance and SHAP for explainability. Validated on Kaggle Stroke and CDC BRFSS datasets, achieving best AUC of 78.45% and 79.80%.
Project
Backend system with product/category management, services, controllers, modules, DTOs, validation, and RESTful APIs.
Project
Implemented array, stack/queue, sorting, searching, matrix operations, and file handling.
Project
Collected and validated information on 100 medicinal plants using WHO, PubMed, Google Scholar, and peer-reviewed sources. Surveyed 50 participants to examine reliability and perceptions of AI-generated herbal medicine information.