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cs.LG2026
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…
cs.LG2026
Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models
Attia Qammar, Qazi Haseeb Yousaf, Ali Azam +3
Climate change, primarily driven by carbon dioxide (CO2) emissions, requires accurate forecasting tools to support effective mitigation policies and sustainable development strateg…