Dr. May ThuNovember 2, 2023 2023-11-02 15:07
Dr. May Thu
Dr. May Thu
Research Associate and Lecturer
Dr. May Thu received the B.E degree in Information Technology from Hmawbi Technological University, Hmawbi, Yangon, Myanmar and later achieved the M.E degree in Information Technology from Yangon Technological University, Yangon, Myanmar. Her academic journey culminated with the successful completion of her Ph.D. degree in Computer Engineering from Prince of Songkla University in Hat Yai, Thailand. Throughout her academic journey, she served as a dedicated researcher at the Intelligence Automation Research Center at Prince of Songkla University, Hat Yai, Thailand. Additionally, she contributed her expertise as a Lecturer in the Department of Information Technology at the School of Informatics, Walailak University (WU) in Nakhom Si Thammarat, Thailand. During her time at WU, she was a member of the Informatics Innovation Center of Excellence (IICE), contributing to its mission and goals. In addition to her academic achievements, she has made significant contributions to her field by authoring numerous research articles published in ISI and Scopus peer-reviewed journals. Her current research interests include machine vision and image processing, complex data classification, human behavior analysis, cognitive science, machine learning, deep learning, and artificial intelligence.
Teaching and Supervision
Machine Learning and Deep Learning
Artificial Intelligence and Data Analytics for Health
- Artificial Intelligence and Data Analytics for Business
- Medical Information System Development Studio
Application Development Studio
Medical Database Development
- Medical Image Analytics
- Electronics Office Software
Ph.D. Major in Computer Engineering at Prince of Songkla University (PSU), Thailand.
Master of Engineering, Major in Information Technology at Yangon Technological University (YTU), Myanmar.
Bachelor of Technology, Major in Information Technology at Hmawbi Technological University (HTU), Myanmar.
-  Thu, M., and Suvonvorn, N., “TCNN Architecture for Partial Occlusion Handling in Pedestrian Classification,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 37, no. 10, 2350025, DOI: 10.1142/S0218001423500258, 2023.
-  Thu, M and Suvonvorn, N., and Kittiphattanabawon, N., “Pedestrian classification on transfer learning based deep convolutional neural network for partial occlusion handling,” International Journal of Electrical and Computer Engineering, vol.12, pp. 2812-2826, DOI: 10.11591/ijece.v13i3, pp. 2812-2826, 2023.
-  Thu, M., and Suvonvorn, N., “Pyramidal Part-Based Model for Partial, Occlusion Handling in Pedestrian Classification,” Advances in Multimedia Journal, vol. 2020, DOI:10.1155/2020/6153580, 2020.
-  Thu, M., Suvonvorn, N., and Karnjanadecha, M., “A new dataset benchmark for pedestrian detection,” ACM International Conference Proceeding Series, pp. 17-22, ICBIP, Korea, 2018.
-  Thu, M., Suvonvorn, N., and Karnjanadecha, M., “Pedestrian Detection using Linear SVM Classifier with HOG Features,” Information Processing Society of Japan (IPSJ) Journal Series, pp. 49-51, APRIS, Phuket, Thailand, 2018.
- Email: firstname.lastname@example.org
- LinkedIn: https://www.linkedin.com/in/may-thu-474834158/
- Tel: +855 15 559 050
1. Park, S. M., Yu, X., Chum, P., Lee, W. Y., & Sim, K. B. (2017). Symmetrical feature for interpreting motor imagery EEG signals in the brain-computer interface. Optik, 129, 163–171. https://doi.org/10.1016/j.ijleo.2016.10.047
2. Yu, X., Chum, P., & Sim, K. B. (2014). Analysis the effect of PCA for feature reduction in non-stationary EEG based motor imagery of BCI system. Optik, 125(3), 1498–1502. https://doi.org/10.1016/j.ijleo.2013.09.013
3. Pharino, C. (2015). A Comparison Study for an Optimal Common Spatial Pattern Algorithm for EEG Signal Classification applicable to BCI Systems. 8TH AUN/SEED-NET INT’L CONFERENCE ON EEE 2014, 1–14.
4. Yu, X., Park, S. M., Ko, K.-E., Chum, P., & Sim, K. (2013). A study on STFT Feature Extraction of Motor Imagery Brain-Computer Interface. Proceedings of KIIS Spring Conference, 23(1), 8726.
5. Pharino Chum, Park, S.-M., & Kwang-Eun Ko and Kwee-Bo Sim. (2013). VCSP Method for EEG Feature Extraction of Motor Imagery Brain-Computer Interface. Proceedings of KIIS Spring Conference, 23(1), 115–116.
6. Yu, X., Park, S., Ko, K., Pharino, C., & Sim, K. (2012). Discriminative Power Band Feature Selection using PCA for Motor Imagery Classification in EEG-based BCI System. Proceedings of KIIS Fall Conference, 37–38.
7. Chum, P., Park, S.-M., Ko, K.-E., & Sim, K.-B. (2012). Particle swarm optimization based optimal spatial-spectral-temporal component search in motor imagery brain-computer interface. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 7425 LNCS. https://doi.org/10.1007/978-3-642-32645-5_59
8. Chum, P., Park, S. M., Ko, K. E., & Sim, K. B. (2012). Particle swarm optimization based optimal spatial-spectral-temporal component search in motor imagery brain-computer interface. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7425 LNCS, 469–476. https://doi.org/10.1007/978-3-642-32645-5_59
9. Chum, P., Park, S., Ko, K., & Sim, K. (2012). Improved Method for Extracting Optimal Time-Frequency EEG Signal Feature. Proceedings of KIIS Spring Conference 2012, 22(1), 143–144.
10. Chum, P., Park, S., Ko, K., & Sim, K. (2012). Orthonormal Polynomial based Optimal EEG Feature Extraction for Motor Imagery Brain-Computer Interface. Journal of Korean Institute of Intelligent Systems, 22(6), 793–798.
11. Chum, P., Park, S., Ko, K., & Sim, K. (2012). Orthonormal Polynomial based Optimal EEG Feature Extraction for Motor Imagery Brain-Computer Interface. Proceedings of KIIS Fall Conference 2012, 22(2), 19–20.
12. Chum, P., Park, S. M., Ko, K. E., & Sim, K. B. (2012). Optimal EEG feature selection by genetic algorithm for classification of imagination of hand movement. IECON Proceedings (Industrial Electronics Conference), 1561–1566. https://doi.org/10.110/IECON.2012.6388508
13. Chum, P., Kim, J., Park, S., Ko, K., & Sim, K. (2012). Particle Swarm Optimization based Automatic Feature Selection for Spatio-Spectral- Temporal Filtering in Brain Computer Interface. International Conference on Smart Convergence Technologies and Applications 2012, 2(3), 2012.
14. Chum, P., Park, S.-M. M., & Sim, K. B. (2013). Parallel model feature extraction to improve performance of a BCI system. Journal of Institute of Control, Robotics and Systems, 19(2012), 1022–1028. https://doi.org/10.5302/J.ICROS.2013.13.1930.
Service and Leadership:
• Cambodia Robocon Committee
• Email: email@example.com
• Tel: (855) 89-910-904
• Linkedin: www.linkedin.com/in/pharino-chum-446b5a85