Editor Profile

editor profile image is not found.

Dr. Muhammad Usama Islam, Ph.D.

Researcher

Present

University of Louisiana

Lafayette - Louisiana

United States

editor profile QR Code is not found.

Muhammad Usama Islam is a Ph.D. Candidate (ABD) in Computer Science at the University of Louisiana at Lafayette, where he also earned his M.S. in Computer Science. With over six years of research experience and two years of faculty-level teaching experience, his work focuses on Human-Computer Interaction (HCI), healthcare informatics, and the adoption of AI-driven technologies. Usama has published 25 peer-reviewed articles and book chapters in high-ranking journals and conferences such as DIS, HCII, IEEE Access, and PLoS One. His interdisciplinary approach leverages machine learning, UX design, and explainable AI (XAI) to develop inclusive, low-cost, and accessible technological solutions. He is also an active reviewer for several Q1 and Q2 journals and has served on multiple technical program committees.

  • Human-Computer Interaction (HCI)
  • Explainable Artificial Intelligence (XAI)
  • Healthcare Informatics
  • Assistive Technology and Gerontechnology
  • User Experience (UX) Design
  • Machine Learning and Deep Learning
  • Voice Assistant Adoption and Evaluation
  • Low-Cost Design for Low-Income and Low-Literacy Populations
  • Privacy-Preserving ML/DL
  • Technology Adoption and Usability Studies
  • Uddin, J., Platts, J., Rajan, G., Fung, W. K., Islam, S. Z., & Islam, M. U. (2024). Resonance Effects in Periodic and Aperiodic Lattice Structures. IEEE Microwave Magazine, 25(7), 63-78.
  • Rahman, M., Islam, A., Pasha, S., Islam, M.U., & Alam, M. (2024). IDF23-0558 A Federated Learning Approach for Type-2 diabetes detection using a naive Bayes classifier. Diabetes Research and Clinical Practice, 209, 111538.
  • Ashraf, F. B., Akter, S., Mumu, S. H., Islam, M. U., & Uddin, J. (2023). Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches. Plos one, 18(9), e0288053.
  • Hasan, M. M., Islam, M. U., & Uddin, J. (2023). Advanced persistent threat identification with boosting and explainable AI. SN Computer Science, 4(3), 271.
  • Ashraf, F. B., Islam, M. U., Kabir, M. R., & Uddin, J. (2023). Yonet: A neural network for yoga pose classification. SN Computer Science, 4(2), 198.
  • Hasan, M. M., Islam, M. U., Sadeq, M. J., Fung, W. K., & Uddin, J. (2023). Review on the evaluation and development of artificial intelligence for COVID-19 containment. Sensors, 23(1), 527.
  • Islam, M. U., & Chaudhry, B. M. (2022). A framework to enhance user experience of older adults with speech-based intelligent personal assistants. IEEE Access, 11, 16683-16699.
  • Hasan, M. M., Murtaz, S. B., Islam, M. U., Sadeq, M. J., & Uddin, J. (2022). Robust and efficient COVID-19 detection techniques: A machine learning approach. PLoS One, 17(9), e0274538.
  • Islam, M. U., Hossain, M. M., & Kashem, M. A. (2021). COVFake: A word embedding coupled with LSTM approach for COVID related fake news detection. International Journal of Computer Applications, 174(10), 1-5.
  • Hossain, M. M., Prottoy, M. I., Morshed, M. S., Kashem, M. A., & Islam, M. U. (2024). IoT-Blockchain in Remote Pregnancy Care Coordination. In Advancing Healthcare through Data-driven Innovations (pp. 140-153). CRC Press.
  • Islam, M. U., Mozaharul Mottalib, M., Hassan, M., Alam, Z. I., Zobaed, S. M., & Fazle Rabby, M. (2022). The past, present, and prospective future of xai: A comprehensive review. Explainable Artificial Intelligence for Cyber Security: Next Generation Artificial Intelligence, (pp. 1-29). Springer.
  • Zobaed, S. M., Hassan, M., Islam, M. U., & Haque, M. E. (2021). Deep learning in IOT-based healthcare applications. In Deep Learning for Internet of Things Infrastructure (pp. 183-200). CRC Press.
  • Hasan, M. M., Islam, M. U., & Sadeq, M. J. (2022). Towards the technological adaptation of advanced farming through artificial intelligence, the internet of things, and robotics: A comprehensive overview. Artificial Intelligence and Smart Agriculture Technology, (pp. 21-42). Taylor and Francis.