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10.1016/j.chaos.2020.110203 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.110203 32834663!7428770!32834663     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       Chaos+Solitons+Fractals 2020 ; 140 (ä): 110203 Nephropedia Template TP {{tp|p=32834663|t=2020. Neural network powered COVID-19 spread forecasting model |pdf=|usr=}}{{32834663}} gab.com Text Neural network powered COVID-19 spread forecasting model JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834663 #FOAvip Virus spread prediction is very important to actively plan actions. Viruses are unfortunately not easy to control, since speed and reach of spread depends on many factors from environmental to social ones. In this article we present research results on developing Neural Network model for COVID-19 spread prediction. Our predictor is based on classic approach with deep architecture which learns by using NAdam training model. For the training we have used official data from governmental and open repositories. Results of prediction are done for countries but also regions to provide possibly wide spectrum of values about predicted COVID-19 spread. Results of the proposed model show high accuracy, which in some cases reaches above 99%. Twit Text FOAVip Neural network powered COVID-19 spread forecasting model ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834663 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32834663 #FOAvip English Wikipedia <ref>{{Cite pmid|32834663}}</ref> Neural network powered COVID-19 spread forecasting model #MMPMID32834663 Wieczorek M; Silka J; Wozniak M Chaos Solitons Fractals 2020[Nov]; 140 (ä): 110203 PMID32834663show ga Virus spread prediction is very important to actively plan actions. Viruses are unfortunately not easy to control, since speed and reach of spread depends on many factors from environmental to social ones. In this article we present research results on developing Neural Network model for COVID-19 spread prediction. Our predictor is based on classic approach with deep architecture which learns by using NAdam training model. For the training we have used official data from governmental and open repositories. Results of prediction are done for countries but also regions to provide possibly wide spectrum of values about predicted COVID-19 spread. Results of the proposed model show high accuracy, which in some cases reaches above 99%. äNeural network powered COVID-19 spread forecasting model (epmc).pdf DeepDyve Pubget Overpricing