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Mohammad Sayem Chowdhury

M.Sc. in Computer Science

Research Assistant

Contact:

Advanced Machine Intelligence Research Lab
(AMIRL), Block-B, Banani. Dhaka-1213, Bangladesh

E-mail:
m.sayem.c@gmail.com
Telephone:

Office:
Cell:

Publication Profile:
Google Scholar Research Gate
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Master of Science in Computer Science

American International University-Bangladesh (AIUB), Dhaka, Bangladesh
(January 2023 – December 2023)

  • Grade: 3.85 out of 4.00
  • Thesis: Leveraging Deep Neural Networks to Uncover Unprecedented Levels of Precision in the Diagnosis of Hair and Scalp Disorders

 

Bachelor of Science in Computer Science and Engineering

American International University-Bangladesh (AIUB), Dhaka, Bangladesh
(May 2018 – April 2022)

  • Grade: 3.77 out of 4.00
  • Thesis: Optimized Approaches for Real-Life Sentiment Analysis in E-Commerce

I hold a Master’s degree in Computer Science with academic and research experience in machine learning, deep learning, and natural language processing. My work includes developing AI models, analyzing large datasets, and contributing to publications in peer-reviewed journals. I am particularly interested in applying artificial intelligence techniques to solve complex problems and aim to continue exploring innovative research directions in this field.

  • Chowdhury, M. S., et al. (2024). Leveraging Deep Neural Networks to Uncover Unprecedented Levels of Precision in the Diagnosis of Hair and Scalp Disorders. Skin Research and Technology, Wiley. DOI: https://doi.org/10.1111/srt.13660.

  • Jahan, N., Chowdhury, M. S., et al. (2024). SafeguardNet: Enhancing Corporate Safety via Tailored Deep Transfer Learning for Threat Recognition. IEEE Access. DOI: https://ieeexplore.ieee.org/document/10637407.

  • Sultan, T., Chowdhury, M. S., et al. (2024). Deep Learning-Based Multistage Fire Detection System and Emerging Direction. Fire, MDPI. DOI: https://doi.org/10.3390/fire7120451.

  • Sultan, T., Chowdhury, M.S., et al. (2025). LeafDNet: Transforming Leaf Disease Diagnosis Through Deep Transfer Learning. Plant Direct, Wiley. DOI: https://doi.org/10.1002/pld3.70047.

  • Chowdhury, M. S., et al. (2024). Unveiling the Unique Dermatological Signatures of Human Pox Diseases through Deep Transfer Learning Model Based on DenseNet and Validation with Explainable AI. Data-Driven Clinical Decision-Making Using Deep Learning in Imaging, Springer Nature. DOI: https://doi.org/10.1007/978-981-97-3966-0_7.

  • Chowdhury, M. S., et al. Optimized Approaches for Real-Life Sentiment Analysis in E-Commerce. Optimizing Solutions for Real-Life Problems (Book Series: Springer Tracts in Nature-Inspired Computing). Springer. (Accepted Chapter, yet to be published). Book Link: https://link.springer.com/book/9789819632152.

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