About

Halim Noor is an academic at the School of Computer Sciences, Universiti Sains Malaysia (USM). Previously, I was a lecturer at the Faculty of Electrical Engineering, UiTM Pulau Pinang.

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 video and signal 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 using deep generative models

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

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

Research Works

A key factor in signal segmentation is to select the suitable window size for activity classification. Window size is important because it needs to capture necessary characteristics of a signal in order to achieve correct detection/classification. Short windows could slice an activity signal into multiple separate windows. Thus a truncated signal lacks the full information to describe the activity. On the other hand, larger window size could contain multiple activity signals which could also lead to misinterpretation of physical activities. The most effective window size depends on the type of signals being evaluated because different activities have different periods of motion. In this research, we proposed a novel signal segmentation approach which can adaptively change the initial fixed window size to deal with transitional activity signals of varying lengths. [pdf]
Despite the advantages of ontology-based technique, there are still limitations that must be tackled which is dealing with uncertainty. In this research, we proposed a novel reasoning algorithm by integrating OWL ontological reasoning mechanism with Dempster-Shafer theory of evidence to provide support for handling uncertainty in activity recognition. [pdf]
There are two major approaches for sensor-based activity recognition. The first approach makes use of dedicated wearable sensors and the second one makes use of sensors attached to objects that are a part of the environment. In this research, best of both human sensing approaches are harnessed to achieve a robust and comprehensive activity recognition. [pdf]

Selected Publications

[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, pp. 1–15, Jan. 2018. [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. [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. [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. Mohd 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. Mohd 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.
Nor Aizam Muhamed Yusof, Pavement Distress Analysis using Deep Learning, 2017-Present, Co-supervisor
Noratikah Nordin, Prediction by Machine Learning of Suicide Attempts among Adolescents in Malaysia, 2018-Present, Co-supervisor
Haruna Abdu, Wearable Sensor-based Activity Recognition. 2019-Present, Co-supervisor
Raid S. A. Basheer, Lightweight AI-based Approach for IoT DDos Detection, 2019-Present, Main Supervisor
Ali Olow Jimale, Deep Learning Approach for Activity Recognition, 2020-Present, Main Supervisor
Bello Ibrahim Kangiwa, Exploring and Determining the Characteristics, Contributory Factors, Effects and Challenges of e-Learning Adoption and Implementation in Nigeria Higher Education Institutions (HEIs), 2020-Present, Main Supervisor
Abdulrahman M A Baraka, Weakly-Supervised Temporal Action Localization, 2020-Present, Co-supervisor
Fathi Said Emhemed Shaninah, Defining the Best Personalized Learning Method using Machine Learning, 2020-Present, Co-supervisor
Ige Ayokunle Olalekan, Activity Recognition using Hybrid Unsupervised Deep Learning Techniques in Healthcare, 2020-Present, Main Supervisor
Hadeel Sameer Mohd Al Tahainah, Developing and Analyzing Artificial Intelligence-Based Algorithms for Obtaining Super Resolution Satellite Images, 2020-Present, Main Supervisor

Master (Mix Mode)
Chan Mang Hong, Data Generation using Generative Adversarial Network for Human Activity Recognition, 2019, Main Supervisor
Jodene Ooi Yen Ling, Predicting Freezing of Gait in Parkinson’s Disease with Autoencoder-based Representation Learning, 2019, Main Supervisor
Loh Jing Zhi, MobileNet-SVM: A Hybrid, Light-weight Deep Learning Architecture for Human Activity Recognition, 2019, Main Supervisor
Yap Kah Liong, Signal Segmentation using You Only Look Once Network for Human Activity Recognition, 2019, Main Supervisor
Lim Chin Tiong, Comparative Study of Deep Learning-based Object Detection Algorithms on Real-time Embedded System, 2019, Main Supervisor

Research Grants

Real-time Activity Recognition using Wearable Inertial Sensors, Short-term Research Grant, USM, RM34,488.40, 2018-2020 – Principal Investigator.
Shaping Pro-Environment Behaviours: Awareness Apps, Long-term Research Grant Scheme, Ministry of Education, RM186,400, 2019-2022 – Co-Investigator.
Dimensionality Reduction for Wearable Health Devices, Fundamental Research Grant Scheme, Ministry of Education, RM74,700.00, 2019-2021 – Principal Investigator.

Research Visits

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

Databases