Academic Research Experience
Academic research experience in machine learning, deep learning, and explainable AI, covering healthcare-related classification, plant health screening, and agricultural forecasting.
My academic research explores practical applications of machine learning, deep learning, and explainable artificial intelligence. Research topics include ECG arrhythmia classification, depression-related social media text classification, money plant leaf anomaly screening, and district-level crop production forecasting in Bangladesh.
My research contributions include papers accepted at BECITHCON 2026 and ICEFronT 2026, alongside submissions to ICEEICT 2027. These studies involve different approaches to classification, anomaly screening, forecasting, and model interpretation.
I also completed an online presentation of Paper ID 530 at IEEE BECITHCON 2026. This experience helped me develop my ability to explain research methods, discuss findings, and communicate technical work to an academic audience.