Dr Mahdi Maktabdar Oghaz

Mahdi is a Lecturer in our School of Computing and Information Science. His primary teaching focus is computer vision, artificial intelligence, and machine learning, Mahdi is also involved with modules on software engineering, computer networks, and electronics. Mahdi's research interests include deep learning and convolutional neural networks, machine learning, crowd analysis, and medical image processing.

mahdi.maktabdar@aru.ac.uk

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View Mahdi's profile on ResearchGate

Background

Following his B.E in Software Engineering at AZAD University in Iran, Mahdi moved to Malaysia to pursue postgraduate studies in the field of computer science. Mahdi obtained an MSc and then PhD in computer science from University Technology Malaysia in 2016. Mahdi's research at University Technology Malaysia was mainly focused on computer vision and machine learning. As a result of this research, several articles in various international journals and conferences have been published.

After Mahdi completed his PhD in 2016, he worked at University Technology Malaysia as a postdoctoral researcher, involved in a couple of projects sponsored by Cyber Security Malaysia, mainly aimed to promote safety and security in cyberspace using artificial intelligence.

Following his postdoctoral position at University Technology Malaysia, Mahdi joined Kingston University London as a researcher to work on H2020 MONICA project, aimed to promote the crowd safety and security in large scale outdoor events using video analytics, artificial intelligence, and computer vision techniques. Mahdi joined ARU in 2019, as a Lecturer in the School of Computing and Information Science.

Research interests

• Computer vision
• Data mining and machine learning
• Deep learning

Areas of research supervision

• Computer vision
• Data mining and machine learning

Teaching

• Programming (Python, C++, Java)
• Computer vision
• Software engineering
• Statistics, compilers
• Digital logic
• Computer networks
• Deep learning
• Data mining
• Automata theory languages, and computation

Selected recent publications

Maktabdar Oghaz, M., Razaak, M., Kerdegari, H., Argyriou, V., Remagnino, P., 2019. Scene and Environment Monitoring Using Aerial Imagery and Deep Learning. In: Distributed Computing in Sensor Systems (DCOSS), 29-31 May 2019 (IEEE Xplore).

Maktabdar Oghaz, M., Argyriou, V., Monekosso, D., Remagnino, P., 2019. Skin Identification Using Deep Convolutional Neural Network. In: Bebis, G (Ed.), 2019. International Symposium on Visual Computing (Cham: Springer).

Kim, C. E., Maktabdar Oghaz, M., Argyriou, V., Remagnino, P., 2019. A Comparison of Embedded Deep Learning Methods for Person Detection. In: Trémeau, A., Farinella, G. M., Braz, J. (Eds.), 2019. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019, Volume 5: VISAPP, Prague, Czech Republic, February 25-27, 2019 (SciTePress).

Maktabar Oghaz, M., Zainal, A., Maarof, M. A., Kassim, M. N., 2018. Content Based Fraudulent Website Detection Using Supervised Machine Learning Techniques. Hybrid Intelligent Systems, 11, pp. 294–304.

Maktabdar Oghaz, M., Maarof, M. A., Rohani, M. F., Zainal, A., Shaid, S. Z. M., 2017. An optimized skin texture model using gray-level co-occurrence matrix. Neural Computing and Applications, 31(6), pp. 1835–1853.

Maktabdar Oghaz, M., Maarof, M. A., Zainal, A., Rohani, M. F., Yaghoubyan, S. H., 2015. A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique. PLOS ONE, 10(8), e0134828.

Oghaz, M. M., Maarof, M. A., Rohani, M. F., Zainal, A., Shaid, S. Z. M., 2017. A Hybrid Color Space for Skin Recognition for Real-Time Applications. Journal of Computational and Theoretical Nanoscience, 14(4), pp. 1852–1861.

Yaghoubyan, S. H., Maarof, M. A., Zainal, A., Kiani, M. J., Rad, F., Maktabdar Oghaz, M., 2016. A Robust Keypoint Descriptor Based on Tomographic Image Reconstruction Using Heuristic Genetic Algorithm and Principal Component Analysis Techniques. Journal of Computational and Theoretical Nanoscience, 13(8), pp. 5554–5568.