Journal of Artificial Intelligence and Digital Health

Safiul Haque Chowdhury is a graduate researcher and M.Sc. student in the Department of Computer Science and Engineering at Jahangirnagar University, Bangladesh. He completed his B.Sc. in Computer Science and Engineering from Bangladesh University with a strong academic record and a thesis focused on explainable AI for liver disease diagnosis. He has authored numerous journal and conference publications in machine learning, deep learning, explainable AI, biomedical applications, and privacy-preserving data science. His research contributions span liver disease detection, diabetes risk prediction, brain tumor analysis, maternal health risk assessment, and more. Professionally, he served as a Machine Learning Team Lead at Orion Informatics Ltd., contributing to AI model development, optimization, and team leadership. He also has significant teaching experience as an ICT lecturer for higher secondary students.

• Machine Learning
• Deep Learning
• Data Science

1. Peer reviewer for multiple prestigious journals (Elsevier, MDPI, SAGE, Frontiers, etc.)

2. Presented research at numerous IEEE international conferences

3. Best Paper Award (Runner-up) at QPAIN 2025

4. Reviewer experience across ML, deep learning, biomedical AI, and quantum computing domains

1. ILF: A Quantum Semi-Supervised Learning Approach for Binary Classification

2. Explainability Enhanced Liver Disease Diagnosis Technique Using Tree Selection and Stacking Ensemble-Based Random Forest Model

3. An Ensemble Approach for Artificial Neural Network-Based Liver Disease Identification from Optimal Features through Hybrid Modeling Integrated with Advanced Explainable AI

4. A Secure Bank Loan Prediction System by Bridging Differential Privacy and Explainable Machine Learning

5. A Transparent House Price Prediction Framework Using Ensemble Learning, Genetic Algorithm-Based Tuning, and ANOVA-Based Feature Analysis

6. NeuroBlend-3: Hybrid Deep and Machine Learning Framework with Explainable AI for Multi-Class Brain Tumor Detection Using MRI Scans

7. Identification of Maternal Health Risk from Optimal Features Using Explainable Machine Learning