staff-members
Mohamed Abdirahman Elmi
Senior Lecturer
Phone: +252 61 5244370
Mohamed Abdirahman Elmi is a seasoned academic and digital systems expert with over 14 years of experience in higher education and technology-driven solutions. He currently serves as a Senior Lecturer at SIMAD University and Head of Digital Services at Dhoobaale Library.
He holds an MBA from Open University Malaysia and a BSc. of Information Technology from SIMAD University. Mohamed is also a Certified Associate in Project Management (CAPM)® and an active member of the Project Management Institute (PMI).
His research interests include Business Information Systems, Machine Learning, Electronic Health Records (EHR), Electronic Medical Records (EMR), and Health Informatics. He is passionate about applying emerging technologies to improve healthcare systems and digital infrastructure in developing regions.
Mohamed has taught a wide range of courses, including Management Information Systems (MIS), Database Systems (SQL Server), Information Technology Project Management, Computer Application and Technology, and Fundamentals of Computing. His teaching approach emphasizes practical application and innovation, preparing students for the evolving demands of the digital economy.
- Business Information systems, Electronic Health Records (EHR) and Electronic Medical Records (EMR), Health Informatics and Machine Learning.
- Management Information Systems (MIS), Database Systems (SQL Server), Information Technology Project Management, Computer Application and Technology, Fundamentals of Computing.
- Master of Business Administration (MBA). – [Open University Malaysia], [2015]
- BSc in information Technology – [SIMAD University], [2011]
- Elmi, M. A., Hashi, A. O., Dahir, U. M., Abdirahman, A. A., & Rodriguez, O. E. R. (2024). Electronics in healthcare: Adaptation and challenges of digital records in Somali hospitals. International Journal of Electronics and Communication Engineering, 11(9), 1–10.
- Abdirahman, A. A., Hashi, A. O., Dahir, U. M., Elmi, M. A., & Hashim, S. Z. M. (2025). Enhanced vehicle tracking: A GPS-GSM-IoT approach. International Journal of Computing and Digital Systems, 17(1), 1–11.
- Elmi, M. A., Hashi, A. O., Abdirahman, A. A., Dahir, U. M., & Rodriguez, O. E. R. (2024). Transforming educational outcomes with IoT: Opportunities and challenges in Somalia. International Journal of Electrical and Electronics Engineering, 11(9), 186–195.
- Dahir, U. M., Hashi, A. O., Abdirahman, A. A., Elmi, M. A., & Rodriguez, O. E. R. (2024). Using IoT and machine learning for enhanced home energy management in Somalia. International Journal of Electrical and Electronics Engineering, 11(6), 108–116.
- Abdirahman, A. A., Hashi, A. O., Dahir, U. M., Elmi, M. A., & Rodriguez, O. E. R. (2024). Enhancing security in mobile wallet payments: Machine learning-based fraud detection across prominent wallet platforms. International Journal of Electronics and Communication Engineering, 11(3), 96–105.
- Abdirahman, A., Hashi, A., Dahir, U., Elmi, M., & Rodriguez, O. (2023). Enhancing vehicle tracking through SMS: A cost-effective approach integrating GPS and GSM. SSRG International Journal of Electronics and Communication Engineering, 10(9).
- Hashi, A. O., Abdirahman, A. A., & Elmi, M. A. (2023). Deep learning models for crime intention detection using object detection. International Journal of Advanced Computer Science and Applications.
- Hashi, A. O., Hussein, I. H., Rodriguez, O. E. R., Abdirahman, A. A., & Elmi, M. A. (2021). Ship detection approach using machine learning algorithms. International Conference of Reliable Information and Communication Technology (pp. 16–25). Springer International Publishing.
- Hashi, A. O., Abdirahman, A. A., Elmi, M. A., Hashi, S. Z. M., & Rodriguez, O. E. R. (2021). A real-time flood detection system based on machine learning algorithms with emphasis on deep learning. International Journal of Engineering Trends and Technology, 69(5), 249–256.
- Hashi, A. O., Elmi, M. A., Abdirahman, A. A., & Hashim, S. Z. M. (2020). A real time flood detection system based on machine learning algorithms. in International Conference of Reliable Information and Communication Technology (pp. 364–373). Springer International Publishing.
- Project title: A Real-Time Flood Detection System Based on Machine Learning: Case Study, Beledweyne–Somalia
- Funding source: Internal research support, SIMAD University
- Role in the project: Co-Researcher – Responsible for device setup, coding of water level sensors, data collection and mining, system evaluation, and technical documentation.
- Timeline or year: 02 July 2019 – 15 April 2020
- Project Management Institute (PMI).
