2 citations · 2 across the 2 of their papers we have counts for
4 papers · 1 filter
Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology
Bin Wang, Shuo Lian, Yuanyuan Hou +5
Depression screening from large-scale behavioral data is challenged by fragmented circadian indicators, limited interpretability, and the lack of intervention-oriented analysis. Ex…
Unlocking air traffic flow prediction through microscopic aircraft-state modeling
Bin Wang, Anqi Liu, Jiangtao Zhao +8
Short-term air traffic flow prediction in terminal airspace is essential for proactive air traffic management. Existing approaches predominantly model traffic flow as aggregated ti…
From Time Series to State: Situation-Aware Modeling for Air Traffic Flow Prediction
Anqi Liu, Jiangtao Zhao, Guiyuan Jiang +3
Accurate air traffic prediction in the terminal airspace (TA) is pivotal for proactive air traffic management (ATM). However, existing data-driven approaches predominantly rely on…
Spatiotemporal-aware Trend-Seasonality Decomposition Network for Traffic Flow Forecasting
Lingxiao Cao, Bin Wang, Guiyuan Jiang +2
Traffic prediction is critical for optimizing travel scheduling and enhancing public safety, yet the complex spatial and temporal dynamics within traffic data present significant c…