University of Science and Technology Chittagong

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Moshin Sarkar

Md. Mohsin Sarker Raihan

Assistant Professor
Electrical and Electronic Engineering
Email: mohsin@ustc.ac.bd

Profile

Md. Mohsin Sarker Raihan is an Assistant Professor in the Department of Electrical and Electronic Engineering at the University of Science and Technology Chittagong (USTC). His teaching and research focus on biomedical signal processing, artificial intelligence, and wearable health systems. He specializes in developing machine learning and deep learning models for intelligent healthcare diagnostics, physiological signal analysis (ECG, EOG, EGG), and human–computer interaction. His research contributions include motion artifact reduction in wearable ECG biosensors, Wi-Fi-based human activity and interaction recognition, and AI-driven models for disease prediction and telehealth applications. Beyond research, he is actively involved in mentoring students, supervising undergraduate theses, and guiding the FSET Robotics Society at USTC. He has contributed to curriculum design, outcome-based education (OBE) alignment, and BAETE accreditation activities within the department. Driven by the vision of accessible and ethical healthcare innovation, his current work aims to bridge technology and medicine through AI-powered biomedical systems that are low-cost, real-time, and privacy-preserving. His long-term goal is to advance interdisciplinary research that integrates signal intelligence, edge-AI, and mobile health systems to improve human wellbeing.

Google Scholar : https://scholar.google.com/citations?user=bqOg-G4AAAAJ&hl=en
Personal Website: https://sites.google.com/view/mohsinsarkerraihan/home

Education

  • M.Sc. in Biomedical Engineering, Khulna University of Engineering & Technology
  • B.Sc. in Electrical and Electronic Engineering, Bangladesh University of Business and Technology”

Program Affiliated

  • B.Sc. in Electrical and Electronic Engineering

Areas of Interest

  • Biomedical Engineering
  • Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Public Health Care

Courses Taught

  • Programing Language (Theory and Lab)
  • Intelligent System
  • Electrical Circuit II (Theory and Lab)
  • Microprocessor and Microcontroller System (Theory and Lab)
  • Control System Design (Theory and Lab)
  • Electrical and Electronics Simulation Lab

Journals & Articles

Journals:

  • Ullah, H., Heyat, M. B. B., Biswas, T., Neha, N. I., Md. Mohsin Sarker Raihan, & Lai, D. (2025). An end-to-end motion artifacts reduction method with 2d convolutional de-noising auto-encoders on ecg signals of wearable flexible biosensors. Digital Signal Processing, 1G0, 105053. doi:https://doi.org/10.1016/j.dsp.2025.105053
  • Khan, M. M. U., Shams, A. B., & Raihan, Mohsin Sarker. (2024). A prospective approach for human-to-human interaction recognition from wi-fi channel data using attention bidirectional gated recurrent neural network with gui application implementation. Multimedia Tools and Applications, 1–44.
  • Tasmi, S. T., Raihan, Md. Mohsin Sarker, & Shams, A. B. (2022b). Obstructive sleep apnea (osa) and covid-19: Mortality prediction of covid-19-infected patients with osa using machine learning approaches. COVID, 2(7), 877–894. doi:10.3390/covid2070064
  • Ghosh, M., Raihan, M., Raihan, M., Akter, L., Bairagi, A. K., Alshamrani, S. S., & Masud, M. (2021). A comparative analysis of machine learning algorithms to predict liver disease. INTELLIGENT AUTOMATION AND SOFT COMPUTING, /0(3), 917–928.
  • Monjur, O., Preo, R. B., Shams, A. B., Raihan, M., Sarker, M., & Fairoz, F. (2021). Covid-19 prognosis and mortality risk predictions from symptoms: A cloud-based smartphone application. BioMed, 1(2), 114–125.
  • Shams, A. B., Hoque Apu, E., Rahman, A., Sarker Raihan, M., Siddika, N., Preo, R. B., . . . Kabir, R. et al. (2021). Web search engine misinformation notifier extension (seminext): A machine learning based approach during covid-19 pandemic. y(2), 156.

Book Chapters:

  • Raihan, Md Mohsin Sarker, Khan, M. M. U., Akter, L., & Shams, A. B. (2023). Development of a risk-free covid-19 screening algorithm from routine blood tests using ensemble machine learning. In Applied intelligence for industry 4.0 (pp. 132–144). Chapman and Hall/CRC.
  • Rahman, S., Raihan, M., Talukder, K. H., Mithila, S. K., Hassan, M. M., Akter, L., & Mohsin, Md. (2022, January). Efficient machine learning approaches to detect fake news of covid-19. (pp. 513–525). doi:10.1007/978-981-19-2347-0_40
  • Akter, L., Raihan, M., M and Raihan, Sarker,Mohsin, Ghosh, M., Alvi, N. et al. (2022). Breast cancer risk prediction using different clustering techniques.
  • Tasmi, S. T., Raihan, Md Mohsin Sarker, Nasif, A. I., & Shams, A. B. (2022a). Pactdet-an artificially intelligent approach to detect pulmonary illnesses: Pneumonia, asthma, covid-1y, and tuberculosis.
  • Raihan, M. M. S., Islam, M. M., Fairoz, F., & Shams, A. B. (2021). Identification of the resting position based on egg, ecg, respiration rate and spo 2 using stacked ensemble learning.

Conference Proceedings:

  • Chakma and M. M. S. Raihan, “”HaatKotha: A Real-Time Deep Learning Approach for Detecting Bengali Sign Language (BdSL),”” 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN), Rangpur, Bangladesh, 2025, pp. 1-6, doi: 10.1109/QPAIN66474.2025.11171983.
  • Rana, M. F., Islam Niloy, T., & Sarker Raihan, Md. Mohsin. (2025). Mobile health (mhealth) solutions for breast cancer detection: A deep learning approach. In 2025 international conference on electrical, computer and communication engineering (ecce) (pp. 1–6). doi:10.1109/ECCE64574.2025.11013093
  • Barua, A., Raihan, M. M. S., & Akter, L. (2024). A bayesian optimization-based framework for building stacked ensemble models in liver disease prediction. In 2024 27th international conference on computer and information technology (iccit) (pp. 3086–3091). doi:10.1109/ICCIT64611.2024.11022610
  • Sakib, W. S., Shams, A. B., Ridi, R. T., Raihan, M. M. S., & Shapnil, R. J. (2024). Highly accurate two channel single-cycle eog classification for smart wearable technologies. In 2024 ieee international conference on biomedical engineering, computer and information technology for health (becithcon) (pp. 224–229). doi:10.1109/BECITHCON64160.2024.10962712
  • Shawon, S. M., Neha, N. I., Jui, A. N., Dey, N., & Raihan, Md. Mohsin Sarker. (2024). Agriai: Machine learning frameworks for tailored crop recommendations using soil nutrient parameters. In 2024 international conference on advances in computing, communication, electrical, and smart systems (icaccess) (pp. 1–6). doi:10.1109/iCACCESS61735.2024.10499482
  • Shawon, S. M., Barua Ema, F., Mahi, A. K., & Mohsin Sarker Raihan, M. (2023). Crop yield prediction: Robust machine learning approaches for precision agriculture. In 202/ 2Gth international conference on computer and information technology (iccit) (pp. 1–6). doi:10.1109/ICCIT60459.2023.10441634
  • Tasmi, S. T., Ahmed, S., & Sarker Raihan, Md. Mohsin. (2023). Performance analysis of machine learning algorithms for autism spectrum disorder level detection using behavioural symptoms. In 202/ 2Gth international conference on computer and information technology (iccit) (pp. 1–6). doi:10.1109/ICCIT60459.2023.10441249
  • Ahmad, M. U., Akib, A. R., Raihan, M. M. S., & Shams, A. B. (2022). Abo3 perovskites’ formability prediction and crystal structure classification using machine learning. In 2022 international conference on innovations in science, engineering and technology (iciset) (pp. 480–485). doi:10.1109/ICISET54810.2022.9775906
  • Adib, Q. A. R., Tasmi, S. T., Bhuiyan, S. I., Raihan, M. S., & Shams, A. B. (2021). Prediction model for mortality analysis of pregnant women affected with covid-19. In 2021 24th international conference on computer and information technology (iccit) (pp. 1–6). doi:10.1109/ICCIT54785.2021.9689824
  • Alam, M. M., Raihan, Md. Mohsin Sarker, Chowdhury, M. R., & Shams, A. B. (2021). High precision eye tracking based on electrooculography (eog) signal using artificial neural network (ann) for smart technology application. In 2021 24th international conference on computer and information technology (iccit) (pp. 1–6). doi:10.1109/ICCIT54785.2021.9689821
  • Raihan, M. M. S., Raihan, M., & Akter, L. (2021). A comparative study to predict the diabetes risk using different kernels of support vector machine. In 2021 2nd international conference on robotics, electrical and signal processing techniques (icrest) (pp. 547–551). IEEE.
  • Raihan, M. M. S., Ahmed, E., Karim, A., Azam, S., Raihan, M., Akter, L., & Hassan, M. M. (2021). Chronic renal disease prediction using clinical data and different machine learning techniques. In 2021 2nd international informatics and software engineering conference (iisec) (pp. 1–5). doi:10.1109/IISEC54230.2021.9672365
  • Zaman, S. M., Qureshi, W. M., Raihan, Md. Mohsin Sarker, Shams, A. B., & Sultana, S. (2021). Survival prediction of heart failure patients using stacked ensemble machine learning algorithm. In 2021 ieee international women in engineering (wie) conference on electrical and computer engineering (wiecon-ece) (pp. 117–120). doi:10.1109/WIECON-ECE54711.2021.9829577
  • Raihan, M. M. S., & Islam, M. M. (2020). Determination of the best resting position using electrogastrography after having a light meal. In 2020 ieee region 10 symposium (tensymp) (pp. 1684–1687). IEEE.
  • Raihan, M. M. S., Shams, A. B., & Preo, R. B. (2020). Multi-class electrogastrogram (egg) signal classification using machine learning algorithms. In 2020 2/rd international conference on computer and information technology (iccit) (pp. 1–6). IEEE.