Spatio-temporal graph learning has been widely applied to urban spatio-temporal forecasting tasks, analyzing the relationships between data to achieve accurate predictions. However, in recent years, model structures have become increasingly complex in pursuit of higher performance, resulting in greater computational co...
Wen-Chao Weng, Han-Yu Jiang, Mei Wu et al.· IEEE Transactions on Knowled...· 0 citations
Experiments on multimodal AD and PD datasets demonstrate consistent improvements over state-of-the-art baselines in multi-stage classification tasks, highlighting the interpretability and scientific utility of the proposed framework for neurodegenerative disease analysis.
Jing Ren, Kefan Yang, Le Linh Dan Nguyen et al.· Proceedings of the 32nd ACM...· 1 citation
Passenger demand prediction is essential for intelligent transportation systems (ITSs) and the Internet of Vehicles (IoV), supporting efficient transit management, network bandwidth allocation, onboard content distribution, and edge computing scheduling. With buses increasingly equipped with wireless connectivity, pass...
Xiangjie Kong, Hao Tan, Zhehui Shen et al.· IEEE Internet of Things Jour...· 0 citations
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