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10.1016/j.neucom.2020.03.080 http://scihub22266oqcxt.onion/10.1016/j.neucom.2020.03.080 C7252178!7252178!32501365     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Neurocomputing 2020 ; 403 (ä): 153-66 Nephropedia Template TP {{tp|p=32501365|t=2020. A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction |pdf=|usr=}}{{32501365}} gab.com Text A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction JO: Neurocomputing http://www.kidney.de/pget.php?&tw=1&pmid=32501365 #FOAvip ?A hierarchical temporal attention based model is proposed to support short-term and long-term human mobility sequence prediction.?The proposed hierarchical temporal attention incorporates individual mobility patterns into the model architecture.?The model is compared with four baseline methods on individual trajectory datasets with varying degree of traveling uncertainty.?Experiments demonstrate the outperformance of the proposed method using three evaluation metrics.?The proposed model uncovers individual frequential and periodical mobility patterns in an interpretable manner. Twit Text FOAVip A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction ????Neurocomputing http://www.kidney.de/pget.php?&tw=1&pmid=32501365 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32501365 #FOAvip English Wikipedia <ref>{{Cite pmid|32501365}}</ref> A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction #MMPMID32501365 Li F; Gui Z; Zhang Z; Peng D; Tian S; Yuan K; Sun Y; Wu H; Gong J; Lei Y Neurocomputing 2020[Aug]; 403 (ä): 153-66 PMID32501365show ga ?A hierarchical temporal attention based model is proposed to support short-term and long-term human mobility sequence prediction.?The proposed hierarchical temporal attention incorporates individual mobility patterns into the model architecture.?The model is compared with four baseline methods on individual trajectory datasets with varying degree of traveling uncertainty.?Experiments demonstrate the outperformance of the proposed method using three evaluation metrics.?The proposed model uncovers individual frequential and periodical mobility patterns in an interpretable manner. äA hierarchical temporal attention-based LSTM encoder-decoder model for(epmc).pdf DeepDyve Pubget Overpricing