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Spatiotemporal Fracture Data Inference in Sparse Mobile Crowdsensing: A Graph-and Attention-Based Approach Journal article
Guo, Xianwei, Huang, Fangwan, Yang, Dingqi, Tu, Chunyu, Yu, Zhiyong, Guo, Wenzhong. Spatiotemporal Fracture Data Inference in Sparse Mobile Crowdsensing: A Graph-and Attention-Based Approach[J]. IEEE-ACM TRANSACTIONS ON NETWORKING, 2024, 32(2), 1631-1644.
Authors:  Guo, Xianwei;  Huang, Fangwan;  Yang, Dingqi;  Tu, Chunyu;  Yu, Zhiyong; et al.
Favorite | TC[WOS]:2 TC[Scopus]:2  IF:3.0/3.6 | Submit date：2024/02/22
Mobile Crowdsensing  Spatiotemporal Fracture Data Inference  Graph Attention Networks  Transformer  
Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements Journal article
Zhou, Kai, Sun, Yaoting, Li, Lu, Zang, Zelin, Wang, Jing, Li, Jun, Liang, Junbo, Zhang, Fangfei, Zhang, Qiushi, Ge, Weigang, Chen, Hao, Sun, Xindong, Yue, Liang, Wu, Xiaomai, Shen, Bo, Xu, Jiaqin, Zhu, Hongguo, Chen, Shiyong, Yang, Hai, Huang, Shigao, Peng, Minfei, Lv, Dongqing, Zhang, Chao, Zhao, Haihong, Hong, Luxiao, Zhou, Zhehan, Chen, Haixiao, Dong, Xuejun, Tu, Chunyu, Li, Minghui, Zhu, Yi, Chen, Baofu, Li, Stan Z., Guo, Tiannan. Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements[J]. Computational and Structural Biotechnology Journal, 2021, 19, 3640-3649.
Authors:  Zhou, Kai;  Sun, Yaoting;  Li, Lu;  Zang, Zelin;  Wang, Jing; et al.
Favorite | TC[WOS]:26 TC[Scopus]:29  IF:4.4/5.0 | Submit date：2021/12/07
Covid-19  Sars-cov-2  Severity Prediction  Machine Learning  Routine Clinical Test  Longitudinal Dynamics