We are glad to share with you that our recent conference paper “GumAgent: Towards an Accessible Gum Disease Detection Tool Leveraging Vision Language Model” has been accepted in 2025 International Conference on Information Communication and Signal Processing (ICICSP): C. Cheng, H. S.-H. Tsang, R. T.-C. Hsung, Y.-L. Chan, W.-L. Lo,
We are pleased to announce the publication of a journal article titled “Meteorological Visibility Estimation Using Landmark Object Extraction and the ANN Method” in Sensors, Volume 25, Issue 3, Article 951 (February 2025). The paper was led by Professor Wai-Lun Lo of our institution as the first author, in collaboration
We are pleased to announce that a groundbreaking conference paper titled “Robust Graph Contrastive Learning with Information Restoration” has been accepted for publication in the August 2025 issue of the IEEE Transactions on Information Forensics and Security. The paper was completed through collaboration between our faculty member, Dr. Yulin Zhu
We are pleased to announce that a groundbreaking conference paper titled “Simple yet Effective Gradient-Free Graph Convolutional Networks” will be presented at the International Joint Conference on Neural Networks in September 2025. This work is led by Dr. Yulin Zhu from our institute as the first author, in collaboration with
Congratulations to our MSAAI graduate, Haiyu He, whose master’s thesis has been accepted by the international journal Artificial Intelligence and Robotics Research (https://www.hanspub.org/journal/AIRR.html)! Haiyu He, Youhai Peng, Yulin Zhu(通訊作者), Wai-Lun Lo. (May. 2025). Research on the Application of Machine Learning Algorithms for Steel Defect Classification in the Perspective of Smart
Congratulations to our MSAAI graduate, Li-Sha Zhou, on having her master’s capstone project accepted at the International Conference on Machine Intelligence and Nature-Inspired Computing 2025 (https://www.ic-mind.org/)! Li-Sha Zhou, Richard Tai-Chiu Hsung, Harris Sik-Ho Tsang, Tony Yu-Lin Zhu, Wai-Lun Lo, “A Robust Drone based Parking Space Detection System Based on Deep
