Halim Noor

Selected Publications

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

[30] A. O. Ige, N. K. Tomar, F. O. Aranuwa, O. Oriola, A. O. Akingbesote, M. H. M. Noor, M. Mazzara, B. Aribisala, “ConvSegNet: Automated Polyp Segmentation from Colonoscopy using Context Feature Refinement with Multiple Convolutional Kernel Sizes”, IEEE Access, vol. , pp. , Feb. 2023. [IF: 3.476] doi

[29] A. O. Ige, M. H. M. Noor, “A Lightweight Deep Learning with Feature Weighting for Activity Recognition”, Computational Intelligence, vol. , pp. , Dec. 2022. [IF: 2.142] doi, pdf

[28] H. Abdu, M. H. M. Noor, “A Survey on Waste Detection and Classification using Deep Learning”, IEEE Access, vol. 10, pp. 128151-128165, Dec. 2022. [IF: 3.476] doi, pdf

[27] N. Nordin, Z. Zainol, M. H. M. Noor, L. F. Chan, “An Explainable Predictive Model for Suicide Attempt Risk using An Ensemble Learning and Shapley Additive Explanations (SHAP) Approach”, Asian Journal of Psychiatry, vol. 79, pp. 103316, Nov. 2022. [IF: 13.89] doi, pdf

[26] A. O. Ige, M. H. M. Noor, “Unsupervised Feature Learning in Activity Recognition using Convolutional Denoising Autoencoders with Squeeze and Excitation Networks”, 5th International Conference on Information and Communications Technology (ICOIACT), 2022, doi

[25] H. Abdu, M. H. M. Noor, “Domestic Trash Classification with Transfer Learning Using VGG16”, IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE2022), 2022, doi, pdf

[24] N. Nordin, Z. Zainol, M. H. M. Noor, L. F. Chan, “Explainable Machine Learning Models for Suicidal Behavior Prediction”, 6th International Conference on Medical and Health Informatics (ICMHI2022), 2022, pp. 118 – 123. doi, pdf

[23] A. O. Jimale, M. H. M. Noor, “Fully Connected Generative Adversarial Network For Human Activity Recognition”, IEEE Access, vol. 10, pp. 100257-100266, Sep. 2022. [IF: 3.476] doi, pdf

[22] N. Nordin, Z. Zainol, M. H. M. Noor, L. F. Chan, “Suicidal Behaviour Prediction Models using Machine Learning Techniques: A Systematic Review”, Artificial Intelligence in Medicine, vol. 132, pp. 102395, Sep. 2022. [IF: 7.011] doi, pdf

[21] A. O. Ige, M. H. M. Noor, “A Survey on Unsupervised Learning for Wearable Sensor-based Activity Recognition”, Applied Soft Computing, vol. 127, pp. 109363, Jul. 2022. [IF: 8.263] doi, pdf, read

[20] M. H. M. Noor, S. Y. Tan, M. N. A. Wahab, “Deep Temporal Conv-LSTM for Activity Recognition”, Neural Processing Letters, vol. 54, pp. 4027–4049, Mar. 2022. [IF: 2.908] doi, pdf, read, code

[19] A. Baraka, M. H. M. Noor, “Weakly-supervised Temporal Action Localization: A Survey”, Neural Computing and Applications, vol. 34, pp. 8479–8499, Mar. 2022. [IF: 5.606] doi, pdf, read

[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. 9, pp. 134065-134080, 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, vol. , pp. , 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, code

[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 [Graduated]
2. Noratikah Nordin, Prediction by Machine Learning of Suicide Attempts among Adolescents in Malaysia, 2018-2023, Co-supervisor
3. Haruna Abdu, Waste Detection and Classification using Deep Learning. 2019-Present, Co-supervisor
4. Raid S. A. Basheer, Brain Connectivity Networks analysis with Graph Theory and Graph Neural Network for Brain Disorder Detection, 2019-Present, Main Supervisor
5. Ali Olow Jimale, Enhancing Conditional Generative Adversarial Network for Handling Subject Variability In Human Activity Recognition, 2020-2023, Main Supervisor
6. Abdulrahman M A Baraka, Similarity Segmentation Approach for Sensor-based Human Activity Recognition, 2020-2023, Main Supervisor
7. Fathe Said Emhemed Shaninah, Predicting Student Academic Performance using Machine Learning, 2020-Present, Main Supervisor
8. Ige Ayokunle Olalekan, Improved Feature Learning Architecture for Lightweight Deep Learning Activity Recognition, 2020-2023, 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, Self-supervised Learning for Human Action Recognition, 2020-Present, Main Supervisor
11. Alqablan Tamara Amjad Abdelkarim, Improved Filter-Wrapper Feature Selection Method Using Enhanced Grey Wolf Algorithm For Sentiment Analysis, 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
14. Itriq Mariam Abed Alfattah Ali, The Machine Translation in Natural Language Processing. 2022-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
9. Xu Ziyue, Data Augmentation Using Improved Generative Adversarial Network for Lung Image Classification, 2022, 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