معرفی
Manuel Curado is a researcher with a focus on graph theory, network analysis, and machine learning applications. His work spans spatio-temporal forecasting, traffic accident prediction, medical imaging, and urban mobility studies. He collaborates extensively with researchers like José-Francisco Vicent, Francisco Escolano, and Leandro Tortosa.
- 2025: Self-explainable graph convolutional recurrent networks for traffic forecasting
- 2024: GeoCC-ConvLSTM for air quality and CTextureFusion for lung CT super-resolution
- 2023: Return Random Walk Gravity Centrality for node influence metrics
- 2022: Human mobility analysis using Twitter data and directed graph centrality models
His research emphasizes graph-based algorithms, deep learning, and data science for real-world problems in transportation and healthcare. He has contributed to journals like Information Sciences, Applied Soft Computing, and Neural Computing & Applications.
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