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  • Dr Syed Afaq Ali Shah

    Dr. Syed Afaq Shah is a Senior Lecturer (Computer Science) in the School of Science and core member of Centre for AI and Machine Learning, ECU.

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Dr Syed Afaq Ali Shah

Senior Lecturer

Staff Member Details
Telephone: +61 8 6304 2379
Email: afaq.shah@ecu.edu.au
Campus: Joondalup  
Room: JO18.418C  
ORCID iD: https://orcid.org/0000-0003-2181-8445

Dr. Syed Afaq Shah is a Senior Lecturer (Computer Science) in the School of Science and core member of Centre for AI and Machine Learning, ECU.

Current Teaching

  • CSI6208 - Programming Principles (Postgraduate)
  • CSP2151 - Programming Fundamentals

Background

Dr. Afaq Shah is a Senior Lecturer at Edith Cowan University (ECU) and Adjunct Senior Lecturer (Department of Computer Science and Software Engineering), the University of Western Australia (UWA), Perth. He leads the Robotics and Artificial Intelligence Research (RAIR) group. Prior to joining ECU, he worked as a Lecturer at Murdoch University (2018 to 2021) and Lecturer ICT at Central Queensland University (2018). He received his PhD degree from UWA and later worked as a Research Fellow for 2.5 years at UWA. Prior to his enrolment at the University of Western Australia, he worked for seven years as Project Manager and later Deputy Chief Engineer (Information Technology) in the aviation industry.

Afaq’s main field of research and interest is ‘Artificial Intelligence’. He develops deep learning techniques for image/video/data analysis, scene understanding, health (e.g., prediction of cardiovascular and Alzheimer disease), agricultural monitoring, medical/bio-medical applications, remote sensing, security, surveillance and monitoring. He has significantly contributed to machine learning, 3D feature descriptors, 3D object recognition and reconstruction, image segmentation, biometrics, 2D-3D scene understanding, and classification, human computer interaction, 2D-3D action and gesture recognition, image captioning, and health analytics. He has published over 50 research papers in high impact factor journals including IJCV, IEEE TNNLS, Pattern Recognition and reputable conferences including NeurIPS and ECCV. He has also co-authored a book, “A Guide to Convolutional Neural Networks for Computer Vision”. He has been awarded over $350,000 in different competitive research funding schemes. He is Australian Computer Society Certified Professional and Fellow Higher Education Academy (FHEA) UK.

Professional Memberships

  • 2019-Present – Australian Computer Society Member
  • 2020-Present – Fellow Higher Education Academy (FHEA), UK
  • Life Member of Pakistan Engineering Council (Member of Washington Accord)

Awards and Recognition

National and International Awards

  • 2020 – DST Best Contribution to Science Award, Digital Image Computing: Techniques and Applications (DICTA) 2020
  • 2020 - ACU Early Career Conference Award
  • 2019 - Australian Computer Society Certified Professional
  • 2018 - NeurIPS Conference Travel Award
  • 2017 - UWA Research Collaboration Award
  • 2016 - UWA Start Something Award for Research Impact through Enterprise

University and National Teaching Awards

  • 2020 - Fellow Higher Education Academy (FHEA), UK
  • 2019 – Associate Fellow, Higher Education Academy (FHEA), UK
  • 2016 - Nominated for Teaching and Learning Excellence Award (UWA)

National and International Research Positions

  • 2021-Present – Associate Editor, Network: Computation in Neural Systems
  • 2021 – Workshop and Special Session Chair, DICTA2021
  • 2020 – Guest Editor, Remote Sensing Journal
  • 2020-Present – Lead Reviewer Serbian Research Fund and reviewer, Mitacs Accelerate grant (Canada).
  • 2017-2018 – Program committee member Advanced Concepts for Intelligent Vision Systems (ACIVS).

Research Areas and Interests

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Pattern recognition
  • Computer Vision
  • Health Analytics
  • Facial Analysis
  • Robotics and Autonomous Systems
  • Object Detection, Recognition and Segmentation
  • Image/video Processing
  • Digital AgriTech

Qualifications

  • Doctor of Philosophy, The University of Western Australia, 2016.

Research Outputs

2024

Journal Articles

  • Bughio, K., Cook, D., Shah, A. (2024). Developing a Novel Ontology for Cybersecurity in Internet of Medical Things-Enabled Remote Patient Monitoring. Sensors, 24(9), Article number 2804. https://doi.org/https://doi.org/10.3390/s24092804.
  • Śliwiak, P., Shah, A. (2024). Text-to-Text Generative Approach for Enhanced Complex Word Identification. Neurocomputing, 610(Article in press), Article number 128501. https://doi.org/10.1016/j.neucom.2024.128501.
  • Li, H., Zhu, G., Zhang, L., Jiang, Y., Dang, Y., Hau, H., Shen, P., Zhao, X., Shah, A., Bennamoun, M. (2024). Scene Graph Generation: A comprehensive survey. Neurocomputing, 566(Article in press), Article number 127052. https://doi.org/https://doi.org/10.1016/j.neucom.2023.127052.
  • Lei, Z., Zhu, W., Liu, J., Hua, C., Li, J., Shah, A., Zhang, L., Bennamoun, M., Mao, C. (2024). RLAD: A Reliable Hippo-guided Multi-task Model for Alzheimer's Disease Diagnosis. IEEE Journal of Biomedical and Health Informatics, 2024(Article in press), 1-12. https://doi.org/10.1109/JBHI.2024.3412926.
  • Xion, G., Wang, N., Li, H., Zhu, G., Shah, A., Zhang, L., Bennamoun, M. (2024). Enhancing Object recognition: The Role of Object Knowledge Decomposition and Component-Labeled Datasets. Neurocomputing, 617(Article in press), Article number 128969. https://doi.org/10.1016/j.neucom.2024.128969.
  • Bughio, S., Cook, D., Shah, A. (2024). Investigating the intersections of vulnerability detection and Internet of Medical Things (IoMT) in healthcare, a scoping review protocol for Remote Patient Monitoring. Research, Society and Development, 13(6), Article number e11313646080. https://doi.org/10.33448/rsd-v13i6.46080.

Conference Publications

  • Bughio, K., Cook, D., Shah, A. (2024). Novel Knowledge Graph-Based Modeling for Vulnerability Detection in the Internet of Medical Things. Recent Challenges in Intelligent Information and Database Systems, Communications in Computer and Information Science (314-325). SPRINGER. https://doi.org/https://doi.org/10.1007/978-981-97-5937-8_26.
  • Bughio, S., Cook, D., Shah, A. (2024). GenAI in Rule-based Systems for IoMT Security: Testing and Evaluation. Procedia Computer Science (5330 - 5339). Elsevier B.V.. https://doi.org/https://doi.org/10.1016/j.procs.2024.09.652.
  • Mirnateghi, N., Islam, S., Suter, D., Shah, A. (2024). Towards Explainability of Affordance Learning in Robot Vision. IEEE Digital Image Computing: Techniques & Applications (6). IEEE.
  • Tahir, S., Mirnateghi, N., Shah, A., Sohel, F. (2024). DEER: Deep Emotion-sets for fine-grained Emotion Recognition. IEEE Digital Image Computing: Techniques & Applications (6). IEEE.
  • Wang, N., Zhu, G., Li, H., Zhang, L., Shah, A., Bennamoun, M. (2024). Language Model Guided Interpretable Video Action Reasoning. CVPR 2024 conference (10 pages). IEEE. https://doi.org/10.1109/CVPR52733.2024.01786.

2023

Journal Articles

  • Ye, G., Song, J., Feng, M., Zhu, G., Shen, P., Zhang, L., Shah, A., Bennamoun, M. (2023). Position and structure-aware graph learning. Neurocomputing, 556(November), Article number 126581. https://doi.org/https://doi.org/10.1016/j.neucom.2023.126581.
  • Li, J., Zhu, G., Hua, C., Feng, M., Bennamoun, B., Li, P., Lu, X., Song, J., Shen, P., Xu, X., Mei, L., Zhang, L., Shah, A., Bennamoun, M. (2023). A Systematic Collection of Medical Image Datasets for Deep Learning. ACM Computing Surveys, 56(5), Article number 116. https://doi.org/https://doi.org/10.1145/3615862.
  • Shah, A., Deng, W., Cheema, M., Bais, A. (2023). CommuNety: deep learning-based face recognition system for the prediction of cohesive communities. Multimedia Tools and Applications, 82(7), 10641-10659. https://doi.org/10.1007/s11042-022-13741-y.

Conference Publications

  • Mahmood, H., Iqbal, A., Islam, S., Shah, A. (2023). 3D brain registration with intensity shift robustness. Proc of the 30th IEEE International Conference on Image Processing (ICIP 2023) (2805-2809). IEEE. https://doi.org/10.1109/ICIP49359.2023.10222341.
  • Khalifa, Z., Shah, A. (2023). A Large Scale Multi-view RGBD Visual Affordance Learning Dataset. Proc of the 30th IEEE International Conference on Image Processing (ICIP 2023) (5). IEEE. https://doi.org/10.48550/arXiv.2203.14092.
  • Shah, A., Khalifa, Z. (2023). Hierarchical Transformer for Visual Affordance Understanding using a Large Scale Dataset. IEEE/RSJ International Conference on Intelligent Robots and Systems (11371-11376). IEEE. https://doi.org/10.1109/IROS55552.2023.10341976.
  • Mirnateghi, N., Shah, A., Bennamoun, M. (2023). Deep Bayesian Image Set Classification Approach for Defence against Adversarial Attacks. The International Conference on Digital Image Computing: Techniques and Applications (9). IEEE. https://ro.ecu.edu.au/ecuworks2022-2026/3725.
  • Gu, J., Jiang, M., Li, H., Lu, X., Zhu, G., Shah, A., Zhang, L., Bennamoun, M. (2023). UE4-NeRF: Neural Radiance Field for Real-Time Rendering of Large-Scale Scene. The Thirty-seventh Annual Conference on Neural Information Processing Systems (13 pages). NeurIPS. https://ro.ecu.edu.au/ecuworks2022-2026/3728.
  • Mirnateghi, N., Shah, A., Bennamoun, M. (2023). Deep Bayesian Image Set Classification Approach for Defense against Adversarial Attacks. 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (501-508). IEEE. https://doi.org/10.1109/DICTA60407.2023.00075.
  • Islam, S., Shah, A., Nguyen, CD. (2023). Deep Learning Approach for Automatic Segmentation of Dirt on Cattle Skin using Image Data. International Conference Image and Vision Computing New Zealand (6 pages). IEEE Computer Society. https://doi.org/10.1109/IVCNZ61134.2023.10344224.

2022

Journal Articles

  • Imtiaz, M., Shah, A., Ur Rehman, Z. (2022). A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges. Neuroscience Informatics, 2(4), Article number 100079. https://doi.org/10.1016/j.neuri.2022.100079.
  • Zhang, L., Li, J., Lu, G., Shen, P., Bennamoun, M., Shah, A., Miao, Q., Zhu, G., Li, P., Lu, X. (2022). Analysis and Variants of Broad Learning System. IEEE Transactions on Systems, Man and Cybernetics: Systems, 52(1), 334 - 344. https://doi.org/10.1109/TSMC.2020.2995205.
  • Shah, A., Pickupana, P., Luo, H., Ekeze, A., Sohel, F., Laga, H., Li, C., Paynter, B., Wang, P. (2022). Automatic and Fast Classification of Barley Grains from Images: A Deep Learning Approach. Smart Agricultural Technology, 2(December 2022), article number 100036. https://doi.org/10.1016/j.atech.2022.100036.
  • Zeng, Z., Wang, T., Ma, F., Zhang, L., Shen, P., Shah, A., Bennamoun, M. (2022). Probability-based Framework to Fuse Temporal Consistency and Semantic Information for Background Segmentation. IEEE Transactions on Multimedia, 24(2022), 740-754. https://doi.org/10.1109/TMM.2021.3058770.
  • Ayris, D., Imtiaz, M., Horbury, K., Williams, B., Blackney, M., See, C., Shah, A. (2022). Novel Deep Learning Approach to Model and Predict the spread of COVID-19. Intelligent Systems with Applications, 14(May 2022), article number 200068. https://doi.org/10.1016/j.iswa.2022.200068.

2021

Journal Articles

  • Lubna, ., Mufti, N., Shah, A. (2021). Automatic number plate recognition:A detailed survey of relevant algorithms. Sensors, 21(9), Article number 3028. https://doi.org/10.3390/s21093028.
  • Xue, Z., Li, P., Zhang, L., Lu, X., Zhu, G., Shen, P., Shah, A., Bennamoun, M. (2021). Multi-Modal Co-Learning for Liver Lesion Segmentation on PET-CT Images. IEEE Transactions on Medical Imaging, 40(12), 3531-3542. https://doi.org/10.1109/TMI.2021.3089702.
  • Fan, Z., Li, J., Zhang, L., Zhu, G., Li, P., Lu, X., Shen, P., Shah, A., Bennamoun, M., Hua, T., Wei, W. (2021). U-net based analysis of MRI for Alzheimer’s disease diagnosis. Neural Computing and Applications, 33(20), 13587-13599. https://doi.org/10.1007/s00521-021-05983-y.
  • Nadeem, U., Shah, A., Bennamoun, M., Togneri, R., Sohel, F. (2021). Real time surveillance for low resolution and limited data scenarios: An image set classification approach. Information Sciences, 580(November 2021), 578-597. https://doi.org/10.1016/j.ins.2021.08.093.
  • Li, P., Kong, X., Li, J., Zhu, G., Lu, X., Shen, P., Shah, A., Bennamoun, M., Hua, T. (2021). A Dataset of Pulmonary Lesions With Multiple-Level Attributes and Fine Contours. Academy of Nutrition and Dietetics, 2(February 2021), Article number 609349. https://doi.org/10.3389/fdgth.2020.609349.

Conference Publications

  • Sharif, N., Bennamoun, M., Liu, W., Shah, A. (2021). SubICap: Towards Subword-informed Image Captioning. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (Article number 20696987). IEEE. https://doi.org/10.1109/WACV48630.2021.00358.

2020

Book Chapters

  • Sharif, N., Nadeem, U., Shah, A., Bennamoun, M., Liu, W. (2020). Vision to Language: Methods, Metrics and Datasets. Machine Learning Paradigms. Advances in Deep Learning-based Technological Applications (9-62). Springer. https://doi.org/10.1007/978-3-030-49724-8_2.

Journal Articles

  • Zhang, L., Zhang, J., Shen, P., Zhu, G., Li, P., Lu, X., Zhang, H., Shah, A., Bennamoun, M. (2020). Block Level Skip Connections across Cascaded V-Net for Multi-Organ Segmentation. IEEE Transactions on Medical Imaging, 39(9), 2782-2793. https://doi.org/10.1109/TMI.2020.2975347.
  • Li, H., Zhang, L., Zhang, X., Zhang, M., Zhu, G., Shen, P., Li, P., Bennamoun, M., Shah, A. (2020). Color vision deficiency datasets & recoloring evaluation using GANs. Multimedia Tools and Applications, 79(37-38), 27583-27614. https://doi.org/10.1007/s11042-020-09299-2.
  • Zhu, G., Zhang, L., Li, H., Shen, P., Shah, A., Bennamoun, M. (2020). Topology-learnable graph convolution for skeleton-based action recognition. Pattern Recognition Letters, 135(Jul 2020), 286-292. https://doi.org/10.1016/j.patrec.2020.05.005.
  • Chen, Y., Sohel, F., Shah, A., Ding, S. (2020). Deep Boltzmann machine for corrosion classification using eddy current pulsed thermography. Optik, 219(Oct 2021), Article number 164828. https://doi.org/10.1016/j.ijleo.2020.164828.
  • Zhu, G., Zhang, L., Yang, L., Mei, L., Shah, A., Bennamoun, M., Shen, P. (2020). Redundancy and Attention in Convolutional LSTM for Gesture Recognition. IEEE Transactions on Neural Networks and Learning Systems, 31(4), 1323-1335. https://doi.org/10.1109/TNNLS.2019.2919764.

Conference Publications

  • Shah, A. (2020). Spatial Hierarchical Analysis Deep Neural Network for RGB-D Object Recognition. Image and Video Technology PSIVT 2019 International Workshops Sydney, NSW, Australia, November 18–22, 2019 Revised Selected Papers (183-193). Springer. https://doi.org/10.1007/978-3-030-39770-8_15.
  • Zhang, L., Wang, X., Li, H., Zhu, G., Shen, P., Li, P., Lu, X., Shah, A., Bennamoun, M. (2020). Structure-Feature based Graph Self-adaptive Pooling. The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020 (3098-3104). Association for Computing Machinery, Inc. https://doi.org/10.1145/3366423.3380083.
  • Sharif, N., White, L., Bennamoun, M., Liu, W., Shah, A. (2020). WEmbSim: A Simple yet Effective Metric for Image Captioning. Proceedings of the 2020 Digital Image Computing: Techniques and Applications (DICTA) Conference (Article number 9363392). Institute of Electrical and Electronics Engineers Inc. (IEEE). https://doi.org/10.1109/DICTA51227.2020.9363392.
  • Sharif, N., Jalwana, M., Bennamoun, M., Liu, W., Shah, A. (2020). Leveraging Linguistically-aware Object Relations and NASNet for Image Captioning. Proceedings of 35th International Conference on Image and Vision Computing New Zealand (Article number 9290719). IEEE. https://doi.org/10.1109/IVCNZ51579.2020.9290719.
  • Zhang, L., Liu, Y., Xiao, H., Yang, L., Zhu, G., Shah, A., Bennamoun, M., Shen, P. (2020). Efficient Scene Text Detection with Textual Attention Tower. Proceedings of International Conference on Acoustics, Speech and Signal Processing (4272-4276). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9054213.
  • Shah, A., Bougre, M., Akhtar, N., Bennamoun, M., Zhang, L. (2020). Efficient Detection of Pixel-Level Adversarial Attacks. Proceedings of International Conference on Image Processing, ICIP (718-722). IEEE Computer Society. https://doi.org/10.1109/ICIP40778.2020.9191084.

2019

Book Chapters

Journal Articles

  • Shah, A., Bennamoun, M., Molton, M. (2019). Machine learning approaches for prediction of facial rejuvenation using real and synthetic data. IEEE Access, 7(2019), 23779-23787. https://doi.org/10.1109/ACCESS.2019.2899379.
  • Sharif, N., White, L., Bennamoun, M., Liu, W., Shah, A. (2019). LCEval: Learned Composite Metric for Caption Evaluation. International Journal of Computer Vision, 127(10), 1586-1610. https://doi.org/10.1007/s11263-019-01206-z.
  • Zhu, G., Zhang, L., Shen, P., Song, J., Shah, A., Bennamoun, M. (2019). Continuous gesture segmentation and recognition using 3dcnn and convolutional lstm. IEEE Transactions on Multimedia, 21(4), 1011-1021. https://doi.org/10.1109/TMM.2018.2869278.

Conference Publications

  • Zhang, L., Zhang, S., Shen, P., Zhu, G., Shah, A., Bennamoun, M. (2019). Relationship detection based on object semantic inference and attention mechanisms. ICMR 2019 - Proceedings of the 2019 ACM International Conference on Multimedia Retrieval (68-72). Association for Computing Machinery, Inc. https://doi.org/10.1145/3323873.3325025.
  • Shah, A., Bennamoun, M., Molton, M. (2019). A Training-Free Mesh Upsampling and Morphing Technique for 3D Face Rejuvenation. 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ 2018) (Article number 8634685). IEEE. https://doi.org/10.1109/IVCNZ.2018.8634685.
  • Shah, A., Bennamoun, M., Molton, M. (2019). A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation. 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ 2018) (Article number 8634657). IEEE. https://doi.org/10.1109/IVCNZ.2018.8634657.

2018

Journal Articles

  • Zhang, L., Xu, Q., Zhu, G., Song, J., Zhang, X., Shen, P., Wei, W., Shah, A., Bennamoun, M. (2018). Improved colour-to-grey method using image segmentation and colour difference model for colour vision deficiency. IET Image Processing, 12(3), 314-319. https://doi.org/10.1049/iet-ipr.2017.0482.
  • Zhang, L., Feng, Y., Shen, P., Zhu, G., Wei, W., Song, J., Shah, A., Bennamoun, M. (2018). Efficient finer-grained incremental processing with MapReduce for big data. Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications, 80(March 2018), 102-111. https://doi.org/10.1016/j.future.2017.09.079.
  • Shah, A., Bennamoun, M., Boussaid, F., While, L. (2018). Evolutionary Feature Learning for 3-D Object Recognition. IEEE Access, 6(2018), 2434-2444. https://doi.org/10.1109/ACCESS.2017.2783331.
  • Zhang, L., Li, H., Shen, P., Zhu, G., Song, J., Shah, A., Bennamoun, M., Zhang, L. (2018). Improving Semantic Image Segmentation with a Probabilistic Superpixel-Based Dense Conditional Random Field. IEEE Access, 6(2018), 15297-15310. https://doi.org/10.1109/ACCESS.2018.2814568.
  • Zhang, L., Wang, L., Zhang, X., Shen, P., Bennamoun, M., Zhu, G., Shah, A., Song, J. (2018). Semantic scene completion with dense CRF from a single depth image. Neurocomputing, 318(27-Nov-18), 182-195. https://doi.org/10.1016/j.neucom.2018.08.052.

Conference Publications

  • Zhang, L., Kong, X., Shen, P., Zhu, G., Song, J., Shah, A., Bennamoun, M. (2018). Reflective Field for Pixel-Level Tasks. Proceedings - International Conference on Pattern Recognition (529-534). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2018.8545817.
  • Sharif, N., White, L., Bennamoun, M., Shah, A. (2018). Learning-based composite metrics for improved caption evaluation. Proceedings of ACL 2018, Student Research Workshop (14-20). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p18-3003.
  • Sharif, N., White, L., Bennamoun, M., Shah, A. (2018). NNEval: Neural network based evaluation metric for image captioning. Proceedings of Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (39-55). Springer Verlag. https://doi.org/10.1007/978-3-030-01237-3_3.
  • Zhang, L., Zhu, G., Mei, L., Shen, P., Shah, A., Bennamoun, M. (2018). Attention in convolutional LSTM for gesture recognition. Proceedings of 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) (1953-1962). Neural information processing systems foundation.

2017

Journal Articles

Conference Publications

  • Zhang, L., Zhu, G., Shen, P., Song, J., Shah, A., Bennamoun, M. (2017). Learning spatiotemporal features using 3DCNN and convolutional LSTM for gesture recognition. Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017 (3120-3128). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCVW.2017.369.
  • Shah, A., Nadeem, U., Bennamoun, M., Sohel, F., Togneri, R. (2017). Efficient Image Set Classification Using Linear Regression Based Image Reconstruction. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (601-610). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2017.88.
  • Hu, H., Shah, A., Bennamoun, M., Molton, M. (2017). 2D and 3D face recognition using convolutional neural network. IEEE Region 10 Annual International Conference, Proceedings/TENCON (133-138). IEEE. https://doi.org/10.1109/TENCON.2017.8227850.

2016

Journal Articles

  • Shah, A., Bennamoun, M., Boussaid, F. (2016). Iterative deep learning for image set based face and object recognition. Neurocomputing, 174(22-Jan-16), 866-874. https://doi.org/10.1016/j.neucom.2015.10.004.
  • Molton, M., Shah, A., Bennamoun, M. (2016). Improving the Face of Cosmetic Medicine: An Automatic Three-dimensional Analysis System for Facial Rejuvenation. Journal of Aesthetic & Reconstructive Surgery, 2(2), 6p.. https://doi.org/10.4172/2472-1905.100021.
  • Shah, A., Bennamoun, M., Boussaid, F. (2016). A novel feature representation for automatic 3D object recognition in cluttered scenes. Neurocomputing, 205(12-Sep-16), 1-15. https://doi.org/10.1016/j.neucom.2015.11.019.

Conference Publications

  • Shah, A., Bennamoun, M., Boussaid, F. (2016). Automatic 3D face landmark localization based on 3D vector field analysis. Proceedings of International Conference Image and Vision Computing New Zealand (6p.). IEEE Computer Society. https://doi.org/10.1109/IVCNZ.2015.7761526.

Research Student Supervision

Current Supervisions

Principal Supervisor

  • Doctor of Philosophy, Towards AI Explainability: Unraveling Black-Box Models
  • Doctor of Philosophy, Deep learning techniques for classification and evaluaton of artificial empathy

Associate Supervisor

  • Doctor of Philosophy, Boxing Performance Analysis: The Role of Camera Systems, Match Conditions, and Automation
  • Doctor of Philosophy, Combining mass spectrometry and machine learning to discover bioactive peptides from crops

Completed Supervisions

Principal Supervisor

  • Masters by Research, Deep Bayesian Image Set Classification: A Novel Approach for Defence Against Adversarial Attacks on Deep Learning Systems
  • Masters by Research, EMOTENET: Deep Neural Network for Facial Emotion Recognition Using Image Set Classification

Associate Supervisor

  • Doctor of Philosophy, Domain shift robustness in deep learnng models
  • Masters by Research, 2D and 3D Face Recognition Using Convolutional Neural Network
  • Doctor of Philosophy, Natural Language Description of Images
  • Doctor of Philosophy, IoMT Security: A Semantic Framework for Vulnerability Detection in Remote Patient Monitoring
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