Personal profile
Research interests
Thuseethan received the BSc degree from the University of Jaffna, Sri Lanka and the PhD degree from Deakin University, Australia. He is currently a Lecturer in Information Technology at Charles Darwin University, Australia. He was a Postdoctoral Research Fellow with the School of Information Technology, Deakin University, Australia. His research interests include deep learning, emotion recognition and applications of machine learning.
Dr. Thuseethan's research focuses on machine learning, deep learning, and computer vision, with applications including emotion recognition, medical imaging, and precision agriculture.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 2 Zero Hunger -
SDG 3 Good Health and Well-being -
SDG 7 Affordable and Clean Energy -
SDG 8 Decent Work and Economic Growth -
SDG 11 Sustainable Cities and Communities -
SDG 12 Responsible Consumption and Production
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Collaborations and top research areas from the last five years
Projects
- 2 Active
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Biosecurity Risk Area Tool for Northern Australia
Sebastian, Y. (Principal Investigator/Chief Investigator A), Yeo, C. (Co Investigator/Chief Investigator B) & Selvarajah, T. (Chief Investigator C)
15/05/25 → 15/11/26
Project: Research
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Optimizing Precision Agriculture Artificial Intelligence (AI) Models for Improved Performance and Broader Applications
Selvarajah, T. (Principal Investigator/Chief Investigator A)
12/06/25 → 1/06/26
Project: Research
Research output
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Cyber attack detection in smart grids: A survey of methods, challenges and future directions
Vigneshwaran, P., Thuseethan, S., Shanmugam, B. & Thennadil, S., Jan 2026, In: Computer Science Review. 60, p. 1-27 27 p., 100915.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
GRU+KAN: A Hybrid Deep Learning Framework for Subject-Independent EEG-Based Emotion Recognition
Leenas, T., Nimishan, S., Thuseethan, S., Vasanthapriyan, S. & Ragel, R. G., Apr 2026, 2025 IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2025. IEEE, Institute of Electrical and Electronics Engineers, p. 77-82 6 p. (2025 IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2025).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review
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Privacy Preserved and Explainable Deep Medical Image Analysis: A Survey
Chowdhury, L., Thuseethan, S., Sebastian, Y., Rajasegarar, S., Alazab, M. & Yearwood, J., Apr 2026, (E-pub ahead of print) In: IEEE Transactions on Emerging Topics in Computational Intelligence. p. 1-21 21 p.Research output: Contribution to journal › Article › peer-review
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A GRU+KAN Hybrid Deep Learning Framework for EEG-Based Emotion Recognition
Leenas, T., Nimishan, S., Thuseethan, S., Vasanthapriyan, S. & Ragel, R. G., 2025, 2025 8th International Conference on Signal Processing and Information Security (ICSPIS). IEEE, Institute of Electrical and Electronics Engineers, p. 1-6 6 p. (2025 8th International Conference on Signal Processing and Information Security, ICSPIS 2025).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review
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An Innovative Coverage Path Planning Approach for UAVs to Boost Precision Agriculture and Rescue Operations
Fahad, N. M., Thuseethan, S., Azid, S. I. & Azam, S., Jul 2025, In: International Journal of Intelligent Systems. 2025, 1, p. 1-24 24 p., 4700518.Research output: Contribution to journal › Article › peer-review
Open AccessFile1 Link opens in a new tab Citation (Scopus)10 Downloads (Pure) -
An interpretable approach for schizophrenia classification using fMRI and sMRI features
Chakraborty, A., Chowdhury, L., Thuseethan, S. & Sebastian, Y., Dec 2025, In: Health Information Science and Systems. 14, 1, 14.Research output: Contribution to journal › Article › peer-review
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Background-Masked Lightweight Approach for Pear Leaf Disease Recognition
George, R., Thuseethan, S., Ragel, R. G., Rajasegarar, S., Alazab, M., Campbell, H. & Yearwood, J., 1 Aug 2025, In: IEEE Access. 13, p. 133668-133680 13 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile35 Downloads (Pure)
Press/Media
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Studies from Charles Darwin University in the Area of Computer Science Described (Siamese Network-Based Lightweight Framework for Tomato Leaf Disease Recognition)
25/12/24
1 item of Media coverage
Press/Media: Research
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Researchers from Deakin University Provide Details of New Studies and Findings in the Area of Networks (Deep3dcann: a Deep 3dcnn-ann Framework for Spontaneous Micro-expression Recognition)
7/06/23
1 item of Media coverage
Press/Media: Research
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New Findings from Deakin University in the Area of Plant Diseases and Conditions Described (P2op-plant Pathology On Palms: a Deep Learning-based Mobile Solution for In-field Plant Disease Detection)
22/11/22
1 item of Media coverage
Press/Media: Research
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Researchers from Deakin University Describe Findings in Networks (Deep Continual Learning for Emerging Emotion Recognition)
8/12/22
1 item of Media coverage
Press/Media: Research
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Data from Sabaragamuwa University of Sri Lanka Advance Knowledge in Remote Sensing (Change Detection for Forest Ecosystems Using Remote Sensing Images with Siamese Attention U-Net)
Shanmugam, B. & Selvarajah, T.
27/09/24
1 item of Media coverage
Press/Media: Research