What is it about?
Predicting Human Mobility During COVID-19 Using Graph Neural Networks
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Why is it important?
This research is unique and timely because it addresses the changes in human mobility due to COVID-19, which are crucial for infrastructure redesign and emergency management. Our model, using Cross- and Context-Aware Attention based Spatial-Temporal Graph Convolutional Networks (CCAAT-GCN), accurately predicts mobility patterns by capturing the relationship between COVID-19 cases and human movement. This can significantly aid planners and policymakers in future pandemic preparedness and response.
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This page is a summary of: AI-Based Affective Music Generation Systems: A Review of Methods and Challenges, ACM Computing Surveys, July 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3673227.
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