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10.1016/j.dib.2020.106175 http://scihub22266oqcxt.onion/10.1016/j.dib.2020.106175 32839733!7437445!32839733     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Data+Brief 2020 ; 32 (ä): 106175 Nephropedia Template TP {{tp|p=32839733|t=2020. Generated time-series prediction data of COVID-19 s daily infections in Brazil by using recurrent neural networks |pdf=|usr=}}{{32839733}} gab.com Text Generated time-series prediction data of COVID-19 s daily infections in Brazil by using recurrent neural networks JO: Data Brief http://www.kidney.de/pget.php?&tw=1&pmid=32839733 #FOAvip In light of the COVID-19 pandemic that has struck the world since the end of 2019, many endeavors have been carried out to overcome this crisis. Taking into consideration the uncertainty as a feature of forecasting, this data article introduces long-term time-series predictions for the virus's daily infections in Brazil by training forecasting models on limited raw data (30 time-steps and 40 time-steps alternatives). The primary reuse potential of this forecasting data is to enable decision-makers to develop action plans against the pandemic, and to help researchers working in infection prevention and control to: (1) explore limited data usage in predicting infections. (2) develop a reinforcement learning model on top of this data-lake, which can perform an online game between the trained models to generate a new capable model for predicting future true data. The prediction data was generated by training 4200 recurrent neural networks (54 to 84 days validation periods) on raw data from Johns Hopkins University's online repository, to pave the way for generating reliable extended long-term predictions. Twit Text FOAVip Generated time-series prediction data of COVID-19 s daily infections in Brazil by using recurrent neural networks ????Data Brief http://www.kidney.de/pget.php?&tw=1&pmid=32839733 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32839733 #FOAvip English Wikipedia <ref>{{Cite pmid|32839733}}</ref> Generated time-series prediction data of COVID-19 s daily infections in Brazil by using recurrent neural networks #MMPMID32839733 Hawas M Data Brief 2020[Oct]; 32 (ä): 106175 PMID32839733show ga In light of the COVID-19 pandemic that has struck the world since the end of 2019, many endeavors have been carried out to overcome this crisis. Taking into consideration the uncertainty as a feature of forecasting, this data article introduces long-term time-series predictions for the virus's daily infections in Brazil by training forecasting models on limited raw data (30 time-steps and 40 time-steps alternatives). The primary reuse potential of this forecasting data is to enable decision-makers to develop action plans against the pandemic, and to help researchers working in infection prevention and control to: (1) explore limited data usage in predicting infections. (2) develop a reinforcement learning model on top of this data-lake, which can perform an online game between the trained models to generate a new capable model for predicting future true data. The prediction data was generated by training 4200 recurrent neural networks (54 to 84 days validation periods) on raw data from Johns Hopkins University's online repository, to pave the way for generating reliable extended long-term predictions. äGenerated time-series prediction data of COVID-19 s daily infections i(epmc).pdf DeepDyve Pubget Overpricing