Dr Muhammad Khalid

Dr Muhammad Khalid

Lecturer/Assistant Professor

Faculty and Department

  • Faculty of Science and Engineering
  • School of Computer Science

Qualifications

  • PhD / DPhil (Northumbria University)
  • FHEA (University of Hull)

Summary

I am actively looking for potential PhD students, please get in touch m.khalid@hull.ac.uk

Muhammad Khalid is currently working as a Lecturer(Assistant Professor) in computer science at the school of computer science, University of Hull. Khalid is leading various modules (Digital Disruption and Innovation, Python programming, Fundamentals of Robotics), designing content, setting up assessments. Before this, he worked Post-Doctoral Research Fellow at the University of Lincoln, Lincoln. He was responsible for assuring robot autonomy toward safety aspects in the project called MeSAPro. In this project, he ensured robotic safety through human-robot interaction for the robots used in the strawberry collection. Khalid completed my PhD from Northumbria University in 2020 on Autonomous parking in intelligent smart cities, where he was awarded a fully funded PhD studentship in 2017 under research development framework. He has also worked with Northumbria University as an Associate Lecturer/Lab Demonstrator for 3 years, where he delivered different courses at the undergraduate and postgraduate levels.

Recent outputs

View more outputs

Journal Article

Deep reinforcement learning based Evasion Generative Adversarial Network for botnet detection

Randhawa, R. H., Aslam, N., Alauthman, M., Khalid, M., & Rafiq, H. (2024). Deep reinforcement learning based Evasion Generative Adversarial Network for botnet detection. Future generations computer systems : FGCS, 150, 294-302. https://doi.org/10.1016/j.future.2023.09.011

Autonomous valet parking optimization with two-step reservation and pricing strategy

Hu, Z., Cao, Y., Li, X., Zhu, Y., Khalid, M., & Ahmad, N. (2023). Autonomous valet parking optimization with two-step reservation and pricing strategy. Journal of Network and Computer Applications, 219, Article 103727. https://doi.org/10.1016/j.jnca.2023.103727

Engineering the advances of the artificial neural networks (ANNs) for the security requirements of Internet of Things: a systematic review

Ali, Y., Ullah Khan, H., & Khalid, M. (2023). Engineering the advances of the artificial neural networks (ANNs) for the security requirements of Internet of Things: a systematic review. Journal Of Big Data, 10(1), Article 128. https://doi.org/10.1186/s40537-023-00805-5

Symmetry-based decomposition for optimised parallelisation in 3D printing processes

Hatton, H., Khalid, M., Manzoor, U., & Murray, J. (2023). Symmetry-based decomposition for optimised parallelisation in 3D printing processes. International Journal of Advanced Manufacturing Technology, 127, 2935–2954. https://doi.org/10.1007/s00170-023-11205-7

Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging

Ahmad, A., Khalid, M., Ullah, Z., Ahmad, N., Aljaidi, M., Malik, F. A., & Manzoor, U. (2022). Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging. Energies, 15(24), Article 9471. https://doi.org/10.3390/en15249471

Research interests

Autonomous/Electric vehicles

Autonomous Parking in Future Smart Cities

Applied AI in Autonomy

IoT in Future Smart Cities

AI-based Decision Making in IoT devices

Autonomous Transportation for Emergency Medical Services

Machine Learning in Wireless Networks

Machine Learning in Digital Health

Wireless communication

Robotics

Postgraduate supervision

PhD Students:

1. Ahmeed Farhan, Tumor detection using deep learning.

2. Haylay Hatton, Efficient 3D printing processes.

3. Franky George, GANs using DRL for improved autonomous transportation.

4. Waqas Ahmad RL for emergency autonomous transportation system.

5. Muneeb Anjum, Long term robot autonomy in adverse conditions.

6. Yingxiu Chang, Drone Navigation in GPS denied areas.

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