Role
Dr Salisu Wada Yahaya is a Senior Lecturer with the Department of Computer Science at Nottingham Trent University, Course Leader for the MSc Artificial Intelligence, and a member of the Computational Intelligence and Applications (CIA) research group.
Career overview
Dr Yahaya received his B.Sc. in Computer Science from Nasarawa State University Keffi, his MSc in Cybernetics and Communication from Nottingham Trent University, and his PhD in Computer Science from Nottingham Trent University. His doctoral research developed data-driven adaptive anomaly detection systems for human activity recognition.
Before his current role he worked as a system administrator, sessional tutor at Nottingham Trent International College, and fixed-term lecturer at NTU. He teaches modules in Artificial Intelligence, including Foundations of Artificial Intelligence and Internet Application Programming, and supervises undergraduate, postgraduate and doctoral research projects. He is also a Fellow of the Higher Education Academy (Advance HE).
Research areas
Salisu's research interest is in the application of computational intelligence for human activity recognition, behaviour modelling and abnormality detection.
External activity
Member of the IEEE Computational Intelligence Society (2019 - Present)
Publications
- Abdussalam, S., Naser, A., Lotfi, A. and Yahaya, S., 2026. The blanket barrier to reliable sleep posture classification: a customized convolutional neural network and thermal imaging approach. IEEE Access, 14: 46830-46842.
- Shahid, M.M., Machado, P., Bird, J.J., Yahaya, S.W., Inoue, S., Lotfi, A. and Ihianle, I.K., 2026. Position-aware indoor human activity recognition and fall detection. IEEE Sensors Journal.
- Tolosa, E., Ihianle, I.K., Machado, P., Yahaya, S.W. and Lotfi, A., 2026. From patterns to deviations: detecting behavioural drift for mental health monitoring using smartphone and wearable data. Electronics, 15 (4): 885.
- Abdussalam, S., Naser, A., Yahaya, S. and Lotfi, A., 2026. A robust cascaded posture classification network under different sleep conditions. In: 22nd International Conference on Intelligent Environments (IE), 1-8.
- Irishna, M.S., Machado, P., Otuka, R.I., Yahaya, S.W., Neves dos Santos, F. and Ihianle, I.K., 2025. Plant leaf disease detection using deep learning: a multi-dataset approach. J, 8 (1): 4.
- Abdussalam, S., Naser, A., Yahaya, S. and Lotfi, A., 2025. Convolutional neural network for sleep posture classification using thermal sensor array. In: International Conference on Image and Video Processing (ICIVP), 104-109.
- Fontes, L., Machado, P., Vinkemeier, D., Yahaya, S., Bird, J.J. and Ihianle, I.K., 2024. Enhancing stress detection: a comprehensive approach through rPPG analysis and deep learning techniques. Sensors, 24 (4): 1096.
- Swann, M., Machado, P., Ihianle, I.K., Yahaya, S. and Zorriassatine, F., 2024. SmartAntenna: enhancing wireless range with autonomous orientation. In: Annual Conference Towards Autonomous Robotic Systems, 339-351.
- Boralessa, K., Ihianle, I.K., Machado, P., Yahaya, S.W. and Lotfi, A., 2024. Input-adaptation approach for human activity recognition. In: Proceedings of the 17th International Conference on Pervasive Technologies Related to Assistive Environments.
- Yahaya, S.W., Lotfi, A. and Mahmud, M., 2021. Towards a data-driven adaptive anomaly detection system for human activity. Pattern Recognition Letters, 145: 200-207.
- Yahaya, S.W., Lotfi, A. and Mahmud, M., 2021. Detecting anomaly and its sources in activities of daily living. SN Computer Science, 2 (1): 14.
- Yahaya, S.W., Lotfi, A. and Mahmud, M., 2019. A consensus novelty detection ensemble approach for anomaly detection in activities of daily living. Applied Soft Computing, 83: 105613.
- Lotfi, A., Langensiepen, C. and Yahaya, S.W., 2018. Socially assistive robotics: robot exercise trainer for older adults. Technologies, 6 (1): 32.
More on his publications can be found here Google Scholar and ResearchGate.