Lecturer in Computer Science

Dr Syed Atif Moqurrab is a Lecturer in the School of Computer Science and Technology at the University of Bedfordshire. He holds a PhD in Computer Science from COMSATS Institute of Information Technology, Pakistan, and a master’s degree from the National University of Computer and Emerging Sciences, Pakistan. His research focuses on privacy, security, and advanced data analytics, with significant contributions to Artificial Intelligence and privacy-preserving techniques.

Prior to joining Bedfordshire, Dr. Moqurrab served as a Research Fellow at the University of Southampton and as a Research Assistant Professor at Gachon University, South Korea. He also held an Assistant Professorship at Air University, Pakistan, where he taught subjects ranging from machine learning to data science and led various academic committees.

Dr. Moqurrab’s extensive publication record showcases innovative approaches in deep learning, data privacy, and cybersecurity.

Qualifications

• Ph.D. from COMSATS University in 2022

Teaching Expertise

• Machine Learning, Data Science, and Big Data Analytics
• Information Security and Data Privacy
• Programming Fundamentals and Object-Oriented Programming
• Data Structures and Algorithms
• Operating Systems and Database Systems
• Information security and Cryptography

Research Interests

• Artificial intelligence
• Data science
• Data privacy
• Bioinformatics

External Activities

• Member of the Program Committee, International Web Information Systems Engineering (WISE) Conference 2024.
• Review Editor for High Performance Big Data Systems, Frontiers.

Publications

• Moqurrab, S., Naeem, T., Malik, M., Fayyaz, A., Jamal, A., & Srivastava, G. (2023). UtilityAware: A framework for data privacy protection in e-health. Information Sciences, 643, 119247.
  
• Saqib, N., Malik, S., Anjum, A., Syed, M., Moqurrab, S., Srivastava, G., & Lin, J.W. (2023). Preserving Privacy in the Internet of Vehicles (IoV): A Novel Group Leader-based Shadowing Scheme using Blockchain. IEEE Internet of Things Journal.
• Xiong, Y., Moqurrab, S., & Ahmad, A. (2023). Enhancing Human Motion Prediction through Joint-based Analysis and AVI Video Conversion. Mobile Networks and Applications, 1–14.
• Wang, C., Moqurrab, S., & Yoo, J. (2023). Face recognition of remote teaching video image based on improved frame difference method. Mobile Networks and Applications, 1–12.
• Rehman, Z., Tariq, N., Moqurrab, S., Yoo, J., & Srivastava, G. (2024). Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues. Expert Systems, 41(1), e13467.
• Iqbal, M., Naqvi, R., Alizadehsani, R., Hussain, S., Moqurrab, S., & Lee, S.W. (2024). An adaptive ensemble deep learning framework for reliable detection of pandemic patients. Computers in Biology and Medicine, 168, 107836.
• Rai, H., Yoo, J., Moqurrab, S., & Dashkevych, S. (2023). Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets. Measurement, 114059.
• Gilani, S., Anjum, A., Khan, A., Syed, M., Moqurrab, S., & Srivastava, G. (2024). A robust Internet of Drones security surveillance communication network based on IOTA. Internet of Things, 25, 101066.
• Malik, M., Jawad, S., Moqurrab, S., & Srivastava, G. (2024). DeepMedFeature: An Accurate Feature Extraction and Drug-Drug Interaction Model for Clinical Text in Medical Informatics. ACM Transactions on Asian and Low-Resource Language Information Processing.
• Rahman, H., Shah, U., Riaz, S., Kifayat, K., Moqurrab, S., & Yoo, J. (2024). Digital twin framework for smart greenhouse management using next-gen mobile networks and machine learning. Future Generation Computer Systems, 156, 285–300.
• Chen, H., & Moqurrab, S. (2024). A Non-Rigid Three-Dimensional Image Reconstruction Algorithm Based on Deformable Shape Reliability. IEEE Access.
• Wang, D., Moqurrab, S., & Yoo, J. (2024). An improved mobile reinforcement learning for wrong actions detection in aerobics training videos. Mobile Networks and Applications, 1–13.
• Hyder, S., Tariq, N., Moqurrab, S., Ashraf, M., Yoo, J., & Srivastava, G. (2024). BERT-Based Deceptive Review Detection in Social Media: Introducing DeceptiveBERT. IEEE Transactions on Computational Social Systems.
• Moqurrab, S., Rai, H., & Yoo, J. (2024). HRIDM: Hybrid Residual/Inception-Based Deeper Model for Arrhythmia Detection from Large Sets of 12-Lead ECG Recordings. Algorithms, 17(8), 364.
  
• Zulfiqar, Z., Malik, S., Moqurrab, S., Zulfiqar, Z., Yaseen, U., & Srivastava, G. (2024). DeepDetect: An innovative hybrid deep learning framework for anomaly detection in IoT networks. Journal of Computational Science, 83, 102426.

Contact Details

E: Syedatif.moqurrab@beds.ac.uk