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10.1007/s00521-020-05626-8 http://scihub22266oqcxt.onion/10.1007/s00521-020-05626-8 33564213!7861008!33564213     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\33564213.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Neural+Comput+Appl 2021 ; � (�): 1-11 Nephropedia Template TP {{tp|p=33564213|t=2021. A review on COVID-19 forecasting models |pdf=|usr=}}{{33564213}} gab.com Text A review on COVID-19 forecasting models JO: Neural Comput Appl http://www.kidney.de/pget.php?&tw=1&pmid=33564213 #FOAvip The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study. Twit Text FOAVip A review on COVID-19 forecasting models ????Neural Comput Appl http://www.kidney.de/pget.php?&tw=1&pmid=33564213 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33564213 #FOAvip English Wikipedia <ref>{{Cite pmid|33564213}}</ref> A review on COVID-19 forecasting models #MMPMID33564213 Rahimi I; Chen F; Gandomi AH Neural Comput Appl 2021[Feb]; � (�): 1-11 PMID33564213show ga The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study. �A review on COVID-19 forecasting models (epmc).pdf DeepDyve Pubget Overpricing