Dr Mahdi Maktabdar Oghaz

Mahdi is a Senior Lecturer in our School of Computing and Information Science. He leads the MSc Applied Data Science conversion course and teaches modules on Artificial Intelligence, Artificial Neural Networks, and Python programming. Additionally, he takes on the role of school outreach lead, overseeing the school's engagement with partner colleges. In terms of research, Mahdi's interests revolve around the application of Artificial Intelligence in sustainability and healthcare research, remote sensing, aerial image analysis, and computer vision applications.

[email protected]

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View Mahdi's profile on ResearchGate

Background

Following his B.E. in Software Engineering at AZAD University in Iran, Mahdi obtained an M.Sc and later a PhD in Computer Science from the University Technology Malaysia (UTM) in 2016. His primary research focus during his postgraduate studies at UTM was computer vision and machine learning, specifically accurate skin detection for medical and security applications.

In 2016, Mahdi embarked on his career as a postdoctoral researcher at UTM, where he joined a research project sponsored by Cyber Security Malaysia and the Ministry of Higher Education Malaysia. The project aimed to enhance safety and security in cyberspace through the application of Artificial Intelligence and machine learning techniques.

Subsequently, he joined the ROVIT research team at Kingston University London, working on the H2020 MONICA project. This project aimed to improve crowd safety and security in large-scale outdoor events by utilizing video analytics, Artificial Intelligence, and computer vision techniques.

In 2019, Mahdi joined the School of Computing and Information Science at Anglia Ruskin University to pursue his career as a Lecturer. Throughout his research career, he has successfully published over 30 scientific articles in various international journals and conferences. His primary research areas encompass artificial intelligence applications in sustainability and healthcare research, remote sensing, aerial image analysis, and computer vision applications.

Mahdi is a member of the Computing, Informatics and Applications Research Group.

Research interests

• Artificial Neural networks (Deep Learning)
• Generative AI
• Aerial Image processing and remote sensing
• AI applications in sustainability and healthcare
• Internet of Things (IoT)
• AI for Climate Change, NetZero

Areas of research supervision

• Computer vision
• Data mining and machine learning
• AI applications in sustainability and healthcare
• Internet of Things (IoT)
• Aerial Image processing and remote sensing

Teaching

• Programming (Python, C++, Java)
• Computer vision
• Deep learning (Artificial Neural Networks)
• Natural Language Processing
• Data Mining and Machine Learning

Selected recent publications

Ness, E., Fatima, A., & Maktabdar Oghaz, M. 2023. Data-Driven Model to Investigate Political Bias In Mainstream Media. IEEE Access.

Maktabdar Oghaz, M., Razak, M., Remagnino, P. 2022. Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-resolution, Feature Fusion and Deconvolution. MDPI Sensors journal.

Saheer, L.B., Bhasy, A., Maktabdar Oghaz, M. and Zarrin, J., 2022. Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas. Frontiers in Big Data journal.

Burrows, H., Zarrin, J., Babu-Saheer, L. and Maktabdar Oghaz, M., 2021. Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks. MDPI Electronics journal, 11(1), p.118.

Maktabdar Oghaz, M., Maarof, M.A., Rohani, M.F., Zainal, A. and Shaid, S.Z.M., 2019. 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., Rohani, M.F., Zainal, A. and 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. and 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.

Maktabdar Oghaz, M., Maarof, M.A., Zainal, A., Rohani, M.F. and Yaghoubyan, S.H., 2015. Hybrid color space for skin detection using genetic algorithm heuristic search and principal component analysis technique. PloS One, 10(8).

Waters, E., Maktabdar Oghaz, M. and Saheer, L.B., 2021. Urban Tree Species Classification Using Aerial Imagery. The International Conference on Machine Learning 2021.

Wamambo, T., Luca, C., Fatima, A. and Maktabdar Oghaz, M., 2021, September. Use Case Prediction Using Deep Learning. SAI Intelligent Systems Conference (pp. 309-317). Springer.

Maktabdar Oghaz, M., Lakshmi Saheer, L., and Zarrin, J., 2022. Urban Tree Detection and Species Classification Using Aerial Imagery, Computing Conference 2022.

Rakhymzhan, T., Zarrin, J., Maktabdar Oghaz, M., Babu-Saheer, L. Deep Convolutional Neural Networks for COVID-19 Detection from Chest X-Ray Images using ResNetV2, Computing Conference 2022.

Maktabdar Oghaz, M., Argyriou, V., Monekosso, D. and Remagnino, P., 2019, October. Skin identification using deep convolutional neural network. In International Symposium on Visual Computing (pp. 181-193). Springer.

Khadka, A.R., Maktabdar Oghaz, M., Matta, W., Cosentino, M., Remagnino, P. and Argyriou, V., 2019, July. Learning how to analyse crowd behaviour using synthetic data. In Proceedings of the 32nd International Conference on Computer Animation and Social Agents (pp. 11-14).

Maktabdar Oghaz, M., Khadka, A.R., Argyriou, V. and Remagnino, P., 2019. Content-aware density map for crowd counting and density estimation. CASA2019.

Maktabdar Oghaz, M., Razaak, M., Kerdegari, H., Argyriou, V. and Remagnino, P., 2019, May. Scene and environment monitoring using aerial imagery and deep learning. In 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 362-369). IEEE.

Kim, C.E., Maktabdar Oghaz, M., Fajtl, J., Argyriou, V. and Remagnino, P., 2018. A comparison of embedded deep learning methods for person detection. The International Conference on Computer Vision Theory and Applications2019.

Maktabdar Oghaz, M., Zainal, A., Maarof, M.A. and Kassim, M.N., 2017, December. Content based fraudulent website detection using supervised machine learning techniques. In International Conference on Hybrid Intelligent Systems (pp. 294-304). Springer.

Fernandez Montenegro, J.M., Maktab Dar Oghaz, M., Gkelias, A., Tzimiropoulos, G. and Argyriou, V., 2018. Features extraction based on an origami representation of 3D landmarks. The International Conference on Computer Vision Theory and Applications2019.