Md. Sadiq Iqbal
Professor & Head
Md. Sadiq Iqbal is a computer science and engineering academic with extensive experience in higher education, academic leadership, information technology, software development, networking, database systems, and web technologies. He is currently serving as Professor & Head of the Department of Computer Science & Engineering at Bangladesh University. He holds a Ph.D. from Dhaka University of Engineering & Technology (DUET), currently in progress, along with a Master of Computer Science and a Bachelor of Computer Engineering from the National Technical University of Ukraine, Kiev, Ukraine. His academic and professional background combines advanced computer science education with extensive teaching, administrative, and technological experience.
Academic Experience
Md. Sadiq Iqbal has been serving as Associate Professor and Head of the Department of Computer Science & Engineering at Bangladesh University since August 2014. Prior to this appointment, he served as Assistant Professor and Head of the department from August 2008 to July 2014 and as a Lecturer from May 2005 to July 2008. Earlier in his academic career, he worked as a Teaching Assistant at the National Technical University of Ukraine, Kiev, from September 1999 to February 2001.
Professional Practice
Alongside his academic responsibilities, Md. Sadiq Iqbal has undertaken significant professional and institutional leadership roles. Since August 2009, he has been serving as CEO of the Center for Excellence in Research, Entrepreneurship & Teaching (CERET), Bangladesh University, contributing to research, entrepreneurship, professional development, and academic innovation. His professional expertise encompasses a broad range of computing technologies, including server platforms, networking and system administration, programming, database technologies, internet and web development, and graphic design tools.
Professional Development
Md. Sadiq Iqbal has completed professional certifications from Microsoft Corporation, including Accessing Data with Microsoft .NET Framework 4 (November 2010), Web Applications Development with Microsoft .NET Framework 4 (October 2010), and Windows Application Development with Microsoft .NET Framework 4 (October 2010). These certifications complement his extensive technical expertise in software development and information technology.
Area of Interest
1. Telemedicine
2. Machine Learning
3. Network Security
4. WAN Technologies
5. Cloud Computing
6. Web Programming
7. High Performance Computing
Machine Learning-Enhanced SPR-Based Optical Biosensor for Cancer Cell Detection. Journal: Optics and Laser Technology. Publisher: Elsevier. DOI: https://www.doi.org/10.1016/j.optlastec.2025.113796
Performance analysis of classical and quantum support vector machines for diagnosis of chronic kidney disease. Journal: Informatics and Health. Publisher: Elsevier. DOI: https://doi.org/10.1016/j.infoh.2025.08.003
A Machine Learning Framework for Discriminating between ChatGPT and Web Search Results. Journal: Statistics, Optimization & Information Computing. Publisher: International Academic Press. DOI: https://doi.org/10.19139/soic-2310-5070-2338
A Machine Learning Framework for Identifying Sources of AI-Generated Text. Journal: Statistics, Optimization & Information Computing. Publisher: International Academic Press. DOI: https://doi.org/10.19139/soic-2310-5070-2225
An Ensemble Approach for Artificial Neural Network-Based Liver Disease Identification. Journal: Medinformatics. Publisher: Bon View Publishing. DOI: https://doi.org/10.47852/bonviewMEDIN52024744
Explainability enhanced liver disease diagnosis technique using tree selection and stacking ensemble-based random forest model. Journal: Informatics and Health. Publisher: Elsevier. DOI: https://doi.org/10.1016/j.infoh.2025.01.001
Blood pressure abnormality detection and Interpretation utilizing Explainable Artificial Intelligence. Published in: Intelligent Medicine (Elsevier). DOI: http://dx.doi.org/10.1016/j.imed.2024.09.005
Analysis of the Performance of Feature Optimization Techniques for the Diagnosis of Machine Learning-based Chronic Kidney Disease. Journal: Machine Learning with Applications. Publisher: Elsevier. DOI: http://dx.doi.org/10.1016/j.mlwa.2022.100330
Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain. Published in: IEEE Transactions on Artificial Intelligence. DOI: http://dx.doi.org/10.1109/TAI.2024.3394799
An Explainable AI Driven Machine Learning Approach for Maternal Health Risk Analysis. Publisher: IEEE. Available: ResearchGate Link (https://www.researchgate.net/publication/391536838_An_Explainable_AI_Driven_Machine_Learning_Approach_for_Maternal_Health_Risk_Analysis)
A Noble Cost Effective Distributed Electronic Health System for Patient Data Handling. Journal: IJCSIS. Available: Zenodo Link (https://zenodo.org/records/4925706)
Publication in Progress:
Lagribot: The Development of Agricultural Robot Using Iot with Deep Learning Paradigm to Identify Locust. Preprint available: http://dx.doi.org/10.2139/ssrn.4406704
A Secure Telemedicine Scheme Based on Distributed Database, Machine Learning and Iot for Diagnosis of Diabetes Disease. Preprint available: http://dx.doi.org/10.2139/ssrn.5205532
A Comparative Machine Learning Based Obfuscated Malware Detection through Feature Scaling and Feature Optimization. Journal: Multimedia Tools and Applications. Publisher: Springer. Status: Under review.
