Papers I've authored, with DOIs for the published versions. Filter by year or type, and copy a citation in IEEE style.
IEEE WIECON-ECE 2025 — First author
doi:10.1109/WIECON-ECE69386.2025.11526163
A hybrid ensemble machine learning framework for early-stage, real-time brain stroke risk prediction, with clinical decision support features that give healthcare practitioners data-driven predictive insights.
IEEE RAAICON 2025 — First author
doi:10.1109/RAAICON69033.2025.11502538
An IoT-enabled smart helmet that integrates real-time telemetry with road-condition monitoring, adding proactive accident prevention and alerts to improve rider safety and situational awareness.
2026 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON) — Third Author
Contributed to the development of DHT-Stack, a Deep-Hybrid Transformer Stacking framework designed for early and reliable dengue screening using routine hematological and clinical blood parameters. The research addresses critical challenges in traditional machine learning-based dengue screening, including class imbalance, false negative predictions, model interpretability, and prediction uncertainty. Key Research Contributions: Developed a hybrid stacking ensemble architecture integrating TabPFN, CatBoost, LightGBM, and XGBoost as Level-0 base learners. Applied SMOTEENN for handling class imbalance and improving the quality of training data. Utilized Boruta feature selection to identify 17 significant hematological biomarkers from 18 clinical parameters. Implemented cost-sensitive learning with a false-negative penalty of 5.0 to prioritize patient safety and reduce missed dengue-positive cases. Designed a Level-1 Ridge Logistic Regression meta-learner using out-of-fold predictions and stratified 10-fold cross-validation. Incorporated model disagreement-based uncertainty quantification to identify predictions requiring additional clinical review. Applied SHAP for global feature importance analysis and LIME for local prediction explanations. Evaluated model performance against standalone machine learning models using accuracy, precision, recall, and AUC-ROC metrics.