Personal profile
Biography
Dr Umer Saeed is an academic researcher and lecturer specialising in artificial intelligence, machine learning and intelligent healthcare systems. His work focuses on developing data-driven solutions for real-time monitoring, anomaly detection and wireless sensor technologies. He has contributed to multiple interdisciplinary research projects, including sensor fault diagnosis, non-invasive respiratory monitoring and AI-enabled healthcare applications. Dr Saeed is committed to high-quality teaching, student engagement and creating research that delivers genuine societal impact.
Research interests
- Artificial Intelligence in Healthcare
- Machine Learning for Anomaly and Fault Detection
- Sensor Fault Diagnosis in Wireless Networks
- Human Activity Recognition
- Non-invasive Respiratory Monitoring
- Intelligent Wearable and IoT-based Healthcare Systems
- Data-driven Modelling and Predictive Analytics
Collaboration
- University of Glasgow, UK
- Coventry University, UK
- University of Ulsan, South Korea
Teaching Expertise
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Wireless Sensor Networks
- Internet of Things (IoT)
- Big Data & Data Analytics
- Data Science
- Applied Computing for Healthcare
Education/Academic qualification
PhD, Intelligent Healthcare, Coventry University
Master, Electrical Engineering, University of Ulsan
External positions
Guest Editor, AI-Driven Wireless Sensing and Intelligent Health Monitoring Systems, Springer Nature Collection
Guest Editor, AI-enabled Smart Healthcare Systems, Technologies (MDPI) Journal
Keywords
- T Technology
- Artificial Intelligence
- Machine Learning
- Intelligent Healthcare
- Wireless Sensor Networks
- Fault Detection
- Human Activity Recognition
- IoT
- Deep Learning
- Data Analytics
- Respiratory Monitoring
- Healthcare Technology
- Predictive Modelling
Fingerprint
- 1 Similar Profiles
Collaborations and top research areas from the last five years
-
AI-enabled human activity recognition: bridging contact-based and RF-based contactless sensing paradigms — a review
Parveen, T., Khan, R., Saeed, U. & Koo, I., 12 Mar 2026, In: IEEE Sensors Journal.Research output: Contribution to journal › Review article › peer-review
Open AccessFile1 Downloads (Pure) -
A conditional GAN and dual-channel hybrid deep feature framework for robust sensor fault detection in WSNs
Khan, R., Saeed, U. & Koo, I., 18 Feb 2025, 2025 International Conference on Electronics, Information, and Communication, ICEIC 2025. Institute of Electrical and Electronics Engineers Inc., (2025 International Conference on Electronics, Information, and Communication, ICEIC 2025).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Open AccessFile3 Citations (Scopus)1 Downloads (Pure) -
Generative adversarial networks-enabled anomaly detection systems: a survey
Saeed, U., Jan, S. U., Ahmad, J., Shah, S. A., Alshehri, M. S., Ghadi, Y. Y., Pitropakis, N. & Buchanan, W. J., 10 Jul 2025, In: Expert Systems with Applications. 296, 128978.Research output: Contribution to journal › Review article › peer-review
Open AccessFile9 Citations (Scopus)1 Downloads (Pure) -
Robust sensor fault detection in wireless sensor networks using a hybrid conditional generative adversarial networks and convolutional autoencoder
Khan, R., Saeed, U. & Koo, I., 10 Mar 2025, In: IEEE Sensors Journal. 25, 8, p. 13912-13926 15 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile15 Citations (Scopus) -
Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning
Khan, R., Khan, S. U., Saeed, U. & Koo, I. S., Jun 2024, In: Bioengineering. 11, 6, 586.Research output: Contribution to journal › Article › peer-review
Open Access26 Citations (Scopus)1 Downloads (Pure)