Mohd Halim Mohd Noor, Associate Professor Dr.

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  Associate Professor

  Email : This email address is being protected from spambots. You need JavaScript enabled to view it.

  Tel : +604 653 4757
  Fax : +604 653 3335
  Room : 610
  
  School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Pulau Pinang

  Research Cluster

  Data To Knowledge

Qualifications

PhD in Computer Systems Engineering (Univ. of Auckland, New Zealand)
MSc in Electrical and Electronic Engineering (USM)
BEng in Computer and Information Engineering (UIAM)

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Research Interest

My research interests lie in the area of artificial intelligence in healthcare. My research focuses on designing and developing intelligent systems to support pervasive healthcare and medical decision-making, leveraging machine learning, human motion analysis, wearable sensing and computer vision.

Specialization

Machine Learning and Deep Learning

Research Profile

ORCID: 0000-0002-3300-3270
Scopus: 36656106400
Web of Science ResearcherID: J-9700-2013
Google Scholar: OSxzfFkAAAAJ
USM Expert: halimnoor

For more information, please visit https://halimnoor.com.

Projects

Multimodal Deep Learning-based Patient Selection for Deep Inspiration Breath-Hold Breast Radiotherapy Treatment, Transdisciplinary Research University Grant Scheme, Universiti Sains Malaysia - Principal Investigator (Dec 2024 - Nov 2027)

Deep Learning Image Segmentation for Medical Image Analysis, Fundamental Research Grant Scheme, KEMENTERIAN PENGAJIAN TINGGI (FRGS) - Principal Investigator (Oct 2023 - Sep 2026)

Machine Learning Model Development Including Execution for Computer Vision System, Dyson Malaysia - Principal Investigator (May 2023 - Dec 2023)

Use of Artificial Intelligence to Improve Accuracy in Triage System of Emergency Department, USM Research University Grant - Co-Investigator, (May 2021 - Oct 2024)

Image Data Analytics for Industry 4.0, COLLABORATIVE RESEARCH IN ENGINEERING, SCIENCE AND TECHNOLOGY CENTER (CREST) - Co-Investigator (Jan 2020 - Dec 2023)

Enhancing Piecewise Aggregate Approximation for Data Dimensionality Reduction in Wearable Health Devices, KEMENTERIAN PENGAJIAN TINGGI (FRGS) - Principal Investigator (Sep 2019 - May 2022)

Shaping Pro-Environment Behaviours: Awareness Apps, KEMENTERIAN PENGAJIAN TINGGI (LRGS) - Principal Investigator (Mar 2019 - Jun 2023)

Real-time Physical Activity Recognition using Wearable Inertial Sensors, USM (SHORT TERM) - Principal Investigator (Aug 2018 - Nov 2020)

USM's Machine Learning for Academic Governance Group - Member (Oct 2020 - Present)

USM's Data Analytic Group for Analysing the Potential of Institutes and Centers of Excellence - Member (Nov 2020 - Jan 2021)

SELECTED Publications

Please visit https://halimnoor.com/publications/ for a complete list of publications.

Noor M.H.M., Ige A.O. A Survey on Deep Learning and State-of-the-art Applications (2025) Engineering Applications of Artificial Intelligence. [read]

Haojun Z., Noor M.H.M. AdvancedScoreCam: Enhancing Visual Explainability through Hierarchical Upsampling (2025) Applied Soft Computing. DOI: 10.1016/j.asoc.2025.113265. [read] [d/l]

Altabrawee H., Noor M.H.M. Repeat and learn: Self-supervised visual representations learning by Repeated Scene Localization (2024) Pattern Recognition. DOI: j.patcog.2024.110804. [read] [d/l]

Ige A.O., Noor M.H.M. A Deep Local-Temporal Architecture with Attention for Lightweight Human Activity Recognition (2023) Applied Soft Computing. DOI: 10.1016/j.asoc.2023.110954. [read] [d/l]

Al-Qablan T. A., Noor M. H. M., Al-Betar M. A. and Khader A. T., A Survey on Sentiment Analysis and Its Applications (2023) Neural Computing and Applications, , pp. . DOI: 10.1007/s00521-023-08941-y [read]

Baraka A., Noor M.H.M. Similarity Segmentation Approach for Sensor-based Activity Recognition (2023) IEEE Sensors Journal. DOI: 10.1109/JSEN.2023.3295778 [read]

Ige A. O., Tomar N. K., Aranuwa F. O., Oriola O., Akingbesote A. O., Noor M. H. M., Mazzara M., Aribisala B. ConvSegNet: Automated Polyp Segmentation from Colonoscopy using Context Feature Refinement with Multiple Convolutional Kernel Sizes (2023) IEEE Access, , pp. . DOI: 10.1109/ACCESS.2023.3244789. [read]

Nordin N., Zainol Z., Noor M.H.M. Lai Fong C. An Explainable Predictive Model for Suicide Attempt Risk using An Ensemble Learning and Shapley Additive Explanations (SHAP) Approach (2023) Asian Journal of Psychiatry, 79, pp. 103316. DOI: 10.1016/j.ajp.2022.103316. [read]

Abdu H., Noor M.H.M. A Survey on Waste Detection and Classification using Deep Learning (2022) IEEE Access. DOI: 10.1109/ACCESS.2022.3226682. [read]

Jimale A.O., Noor M.H.M., Fully Connected Generative Adversarial Network For Human Activity Recognition (2022) IEEE Access. DOI: 10.1109/ACCESS.2022.3206952. [read]

Ige A.O., Noor M.H.M. A Survey on Unsupervised Learning for Wearable Sensor-based Activity Recognition (2022) Applied Soft Computing. DOI: 10.1016/j.asoc.2022.109363. [read]

Noor M.H.M., Tan S.Y., Wahab M.N.A. Deep Temporal Conv-LSTM for Activity Recognition (2022) Neural Processing Letter. DOI: 10.1007/s11063-022-10799-5. [read]

Jimale A.O, Noor M.H.M. Subject Variability in Sensor-based Activity Recognition (2021) Journal of Ambient Intelligence and Humanized Computing. DOI: 10.1007/s12652-021-03465-6. [read]

Noor, M.H.M. Feature Learning using Convolutional Denoising Autoencoder for Activity Recognition (2021) Neural Computing and Applications, 33, pp. 10909–10922. DOI: 10.1007/s00521-020-05638-4 [read]

Chan, M.H., Noor, M.H.M. A unified generative model using generative adversarial network for activity recognition (2020) Journal of Ambient Intelligence and Humanized Computing, 12, pp. 8119–8128. DOI: 10.1007/s12652-020-02548-0 [read]

Supervision

Graduated
Ph.D.
Noratikah Nordin, A Fusion-based Framework for Explainable Suicide Attempt Prediction
Abdulrahman M A Baraka, Similarity Segmentation Approach for Sensor-based Human Activity Recognition
Ali Olow Jimale, Enhanced Conditional Generative Adversarial Network for Handling Subject Variability In Human Activity Recognition
Ige Ayokunle Olalekan, Deep Local-Temporal Architecture Towards Lightweight Deep Learning Activity Recognition
Nor Aizam Muhamed Yusof, Pavement Distress Analysis using Deep Learning

M.Sc.
Chan Mang Hong, Data Generation using Generative Adversarial Network for Human Activity Recognition
Jodene Ooi Yen Ling, Predicting Freezing of Gait in Parkinson's Disease with Autoencoder-based Representation Learning
Loh Jing Zhi, MobileNet-SVM: A Hybrid, Light-weight Deep Learning Architecture for Human Activity Recognition
Yap Kah Liong, Signal Segmentation using You Only Look Once Network for Human Activity Recognition
Lim Chin Tiong, Comparative Study of Deep Learning-based Object Detection Algorithms on Real-time Embedded System
Tan Sen Yan, Deep Temporal Conv-LSTM for Human Activity Recognition
Xu Ziyue, Data Augmentation using Improved Generative Adversarial Network for Lung Image Classification, Main Supervisor
Jwaber Safa Alaa Hussein, Improved Rotation Pretext with Sorting Operation for Self-Supervised Visual Feature Learning
Ammar Nayeef Makki Al-Khafaji, Adaptation of Attention Separable Convolution Residual in the U-Net Architecture for Lung Nodule Segmentation
Al-Battat Asaad Qasim Mahdi, Improved ResNet-50 Model with Multiscale Feature Representation for Cancer Classification in Histopathological Image

On-going
Ph.D.
Raid S. A. Basheer - Graph Neural Network, Brain Connectivity
Fathi Said Emhemed Shaninah - Deep Tabular Learning, Learning Analytics
Hadeel Sameer Mohd Al Tahainah - Generative Deep Learning, Computer Vision
Al Tabrawee Hussein Allawi Hasan - Self-Supervised Deep Learning, Computer Vision
Sani Tijjani - Feature Selection, Metaheuristic Optimization
Tamara Amjad Abdelkarim Alqablan - Feature Selection, Metaheuristic Optimization
Al Kadhmawee Ahmed Adil Abdulwahid - Deep Learning, Time-series Analysis
Itriq Mariam Abed Alfattah Ali - Deep Learning, Hate Speech Detection
Loh Swee Kuan - Transfer Learning, Time-series Analysis
Zhao Haojun - Explainable Deep Learning
Maqsood Iqra - Medical Image Analysis, Deep Learning
Huang Keyang - Deep Learning, Model Compression
Hachami Hadeel Jameel Nassr - Deep Tabular Learning
Zhang Junrui - Deep Learning, Model Compression
Ahmad Elham Fahmi Mousa - Deep Learning, Human Activity Recognition
Mohammed Mohammed Jasim Mohammed
Xiao Zhanpeng

M.Sc.
Liau Wei Jie Brigitte - Deep Learning, Text Spotting
Hazqeel Afyq Athaillah Kamarul Aryffin - Deep Learning, Emergency Triage Classification

Teaching

(1) CDS503 Machine Learning, (2) CDS501 Principles & Practices of Data Science & Analytics, (3) CPC251 Machine Learning and Computational Intelligence, (4) CST432 Microprocessors and Embedded Systems

Recognition

Peer Review
Pervasive and Mobile Computing
Knowledge-based Systems
IEEE Access
Journal of Ambient Intelligence and Humanized Computing
Plos One
Medical and Biological Engineering & Computing
International Journal of Imaging Systems and Technology
Health Informatics Journal

Training & Consultancy
Coordinator & Trainer, Professional Certification in Data Science  - Kulim Advanced Technologies Sdn. Bhd. (2020-current)
Trainer, Data Science Module - Tropical Data Science School with Universidad Politechnica de Madrid, Spain (2021)
Trainer, Workshop on Introduction to Machine Learning by Python - USM (2021)
Trainer, Introduction to Machine Learning in Python - Sophic Automation (2020)
Trainer, TVET Workshop on Embedded System and 3D Printing Design - SIRIM (2020)
Trainer, Introduction to Python - PPKT, Universiti Sains Malaysia (2020)
Trainer, MDEC Intel AI Academy (2019)