Halim Noor is an academic in the School of Computer Sciences at Universiti Sains Malaysia (USM). Prior to this, I served at the Universiti Teknologi MARA Pulau Pinang as a Senior Lecturer in Computer Engineering.

My research is in the fields of machine learning and deep learning for computer vision and pervasive computing. Currently, I am focusing on problems in human motion analysis such as signal and image segmentation, representation (feature) learning, and classification.

Looking forward, I am interested in

  • how to achieve an effective segmentation of input data
  • unsupervised feature learning using deep learning i.e. to learn a more salient feature representation
  • data augmentation/generation using deep generative models

My research has been published in Journal of Ambient Intelligence and Humanized Computing, Neural Computing and Applications, Knowledge-based Systems, Pervasive and Mobile Computing and in the proceeding of several conferences.

Publication databases: Google Scholar, Scopus, Publons

Feel free to contact me to discuss any related topic or to propose a research topic.
Email: halimnoor@usm.my
Address: 610, School of Computer Sciences, Universiti Sains Malaysia

Selected Publications

*[IF: XXX], impact factor of the year published

[20] M. H. M. Noor, S. Y. Tan, M. N. A. Wahab, “Deep Temporal Conv-LSTM for Activity Recognition“, Neural Processing Letters, vol. , pp. , Mar. 2022. [IF: 2.908] doi, pdf, read

[19] A. Baraka, M. H. M. Noor, “Weakly-supervised Temporal Action Localization: A Survey”, Neural Computing and Applications, vol. , pp. , Mar. 2022. [IF: 5.606] doi, pdfread

[18] M. N. A. Wahab, A. T. Z. Ren, A. Nazir, M. H. M. Noor, M. F. Akbar and A. S. A. Mohamed, “EfficientNet-Lite and Hybrid CNN-KNN Implementation for Facial Expression Recognition on Raspberry Pi”, IEEE Access, vol. , pp. , Sep. 2021. [IF: 3.367] doi

[17] A. O. Jimale, M. H. M. Noor, “Subject Variability in Sensor-based Activity Recognition”, Journal of Ambient Intelligence and Humanized Computing, , , Sep. 2021. [IF: 7.104] doi,pdf,read

[16] M. H. M. Noor, A. Nazir, M. N. A. Wahab, J. Y. L, Ooi, “Detection of Freezing of Gait using Unsupervised Convolutional Denoising Autoencoder”, IEEE Access, vol. 9, pp. 115700-115709, Aug. 2021. [IF: 3.367] doi

[15] H. Abdu, M. H. M. Noor, R. Abdullah, “An Efficient Multi-sensor Positions Human Activity Recognition: Elderly Peoples in Rural Areas in Focus”, International Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020), 2020. Lecture Notes in Networks and Systems, vol. 254. doi

[14] M. Aminu, N. A. Ahmad, M. H. M. Noor “Covid-19 Detection via Deep Neural Network and Occlusion Sensitivity Maps”, Alexandria Engineering Journal, vol. 60, pp. 4829-4855, Oct. 2021. [IF: 2.460] doi,pdf

[13] N. Nordin, Z. Zainol, M. H. M. Noor, L. F. Chan, “A Comparative Study of Machine Learning Techniques for Suicide Attempts Predictive Model”, Health Informatics Journal, vol. 27, Issue 1, pp. 1-16, 2021. [IF: 2.932] doi,pdf

[12] M. H. M. Noor, “Feature Learning using Convolutional Denoising Autoencoder for Activity Recognition”, Neural Computing and Applications, vol. 33, pp. 10909–10922, Sep. 2021. [IF: 4.774] doi,pdf,read,github

[11] M. H. Chan, M. H. M. Noor, “A Unified Generative Model using Generative Adversarial Network for Activity Recognition”, Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 8119–8128, Jul. 2021 . [IF: 4.594] doi,pdf,read

[10] M. H. M. Noor, M. A. Ahmadon, M. K. Osman, “Activity Recognition using Deep Denoising Autoencoder,” 2019 9th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2019, pp. 188 – 192. doi,pdf

[9] N. A. M. Yusof, A. Ibrahim, M. H. M. Noor, N. M. Tahir, N. M. Yusof, N. Z. Abidin M. K. Osman, “Deep Convolution Neural Network for Crack Detection on Asphalt Pavement,” Journal of Physics: Conference Series, Vol. 1349. No. 1. IOP Publishing, 2019. doi,pdf

[8] N. A. M. Yusof, M. K. Osman, Z. Hussain, M. H. M. Noor, A. Ibrahim, N. M. Tahir, N. Z. Abidin, “Automated Asphalt Pavement Crack Detection and Classification using Deep Convolution Neural Network,” 2019 9th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2019, pp. 215 – 220. doi

[7] N. A. M. Yusof ; M. K. Osman ; M. H. M. Noor ; A. Ibrahim ; N. M. Tahir ; N. M. Yusof , “Crack Detection and Classification in Asphalt Pavement Images using Deep Convolution Neural Network,” 2018 8th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2018, pp. 227 – 232. doi

[6] M. H. M. Noor, Z. Salcic, and K. I.-K. Wang, “Ontology-based Sensor Fusion Activity Recognition”, Journal of Ambient Intelligence and Humanized Computing, vol. 11, Issue 8, pp. 3073–3087, Jan. 2018. [IF: 2.505] doi,pdf

[5] M. H. M. Noor, Z. Salcic, and K. I.-K. Wang, “Adaptive Sliding Window Segmentation for Physical Activity Recognition using a Single Tri-axial Accelerometer”, Pervasive and Mobile Computing, vol. 38, Part 1, pp. 41–59, Jul. 2017. [IF: 2.349] doi,pdf

[4] M. H. M. Noor, Z. Salcic, and K. I.-K. Wang, “Enhancing Ontological Reasoning with Uncertainty Handling for Activity Recognition”, Knowledge-Based Systems, vol. 114, pp. 47–60, Dec. 2016. [IF: 4.529] doi,pdf

[3] M. H. M. Noor, Z. Salcic, and K. I.-K. Wang, “Dynamic Sliding Window Method for Physical Activity Recognition using a Single Tri-axial Accelerometer”, in 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), 2015, pp. 102–107. doi

[2] M. H. M. Noor, Z. Hussain, K. A. Ahmad, and A. R. Ainihayati, “Gel Electrophoresis Image Segmentation with Otsu Method based on Particle Swarm Optimization”, in 2011 IEEE 7th International Colloquium on Signal Processing and its Applications (CSPA), 2011, pp. 426–429. doi

[1] M. H. M. Noor, A. R. Ahmad, Z. Hussain, K. A. Ahmad, and A. R. Ainihayati, “Multilevel Thresholding of Gel Electrophoresis Images using Firefly Algorithm”, in 2011 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2011, pp. 18–21. doi

Supervision

Ph.D.
1. Nor Aizam Muhamed Yusof, Pavement Distress Analysis using Deep Learning, 2017-2021, Co-supervisor
2. Noratikah Nordin, Prediction by Machine Learning of Suicide Attempts among Adolescents in Malaysia, 2018-Present, Co-supervisor
3. Haruna Abdu, Wearable Sensor-based Activity Recognition. 2019-Present, Co-supervisor
4. Raid S. A. Basheer, Multiscale Brain Modeling and Analysis, 2019-Present, Main Supervisor
5. Ali Olow Jimale, Deep Learning Approach for Activity Recognition, 2020-Present, Main Supervisor
6. Abdulrahman M A Baraka, Weakly-Supervised Temporal Action Localization, 2020-Present, Main Supervisor
7. Fathe Said Emhemed Shaninah, Defining the Best Personalized Learning Method using Machine Learning, 2020-Present, Main Supervisor
8. Ige Ayokunle Olalekan, Activity Recognition using Hybrid Unsupervised Deep Learning Techniques in Healthcare, 2020-Present, Main Supervisor
9. Hadeel Sameer Mohd Al Tahainah, Developing and Analyzing Artificial Intelligence-Based Algorithms for Obtaining Super Resolution Satellite Images, 2020-Present, Main Supervisor
10. Al Tabrawee Hussein Allawi Hasan, Medical Assistant System Based On Machine Learning Using Heterogynous Medical Data Sources, 2020-Present, Main Supervisor
11. Alqablan Tamara Amjad Abdelkarim, A Hybrid Model of Hopfield Neural Network and Ant Colony Optimization Algorithm For Effective Network Intrusion Detection System, 2020-Present, Main Supervisor
12. Sani Tijjani, Semi-Supervised Learning for Human Activity Recognition, 2020-Present, Main Supervisor
13. Al Kadhmawee Ahmed Adil Abdulwahid, Automated Diagnosis System of Skin Cancer Diseases Based on Machine Learning, 2021-Present, Main Supervisor

M.Sc.
1. Chan Mang Hong, Data Generation using Generative Adversarial Network for Human Activity Recognition, 2019, Main Supervisor
2. Jodene Ooi Yen Ling, Predicting Freezing of Gait in Parkinson’s Disease with Autoencoder-based Representation Learning, 2019, Main Supervisor
3. Loh Jing Zhi, MobileNet-SVM: A Hybrid, Light-weight Deep Learning Architecture for Human Activity Recognition, 2019, Main Supervisor
4. Yap Kah Liong, Signal Segmentation using You Only Look Once Network for Human Activity Recognition, 2019, Main Supervisor
5. Lim Chin Tiong, Comparative Study of Deep Learning-based Object Detection Algorithms on Real-time Embedded System, 2019, Main Supervisor
6. Tan Sen Yan, Hybrid Deep Learning Architecture for Activity Recognition, 2020, Main Supervisor
7. Liau Wei Jie Brigitte, Semiconductor OCR Using Deep Learning, 2021-Present, Main Supervisor
8. Hazqeel Afyq Athaillah Kamarul Aryffin, The use of Artificial Intelligence to Improve Accuracy in Triage System of Emergency Department, 2022-Present, Main Supervisor

Research Grants

1. Real-time Activity Recognition using Wearable Inertial Sensors, Short-term Research Grant, USM, RM34,488.40 – Principal Investigator.
2. Shaping Pro-Environment Behaviours: Awareness Apps, Long-term Research Grant Scheme, Ministry of Education, RM186,400 – Principal Investigator.
3. Dimensionality Reduction for Wearable Health Devices, Fundamental Research Grant Scheme, Ministry of Education, RM74,700.00 – Principal Investigator.
4. Image Data Analytics for Industry 4.0, CREST, RM206,500.00 – Co-Investigator.

Research Visits

Japan Advanced Institute of Science and Technology: 27/10/2018 – 10/11/2018