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10.1016/j.procs.2021.01.250 http://scihub22266oqcxt.onion/10.1016/j.procs.2021.01.250 33936325!8076817!33936325     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       Procedia+Comput+Sci 2021 ; 181 (ä): 940-947 Nephropedia Template TP {{tp|p=33936325|t=2021. A COVID-19 time series forecasting model based on MLP ANN |pdf=|usr=}}{{33936325}} gab.com Text A COVID-19 time series forecasting model based on MLP ANN JO: Procedia Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33936325 #FOAvip With the accelerated spread of COVID-19 worldwide and its potentially fatal effects on human health, the development of a tool that effectively describes and predicts the number of infected cases and deaths over time becomes relevant. This makes it possible for administrative sectors and the population itself to become aware and act more precisely. In this work, a machine learning model based on the multilayer Perceptron artificial neural network structure was used, which effectively predicts the behavior of the series mentioned in up to six days. The model, which is trained with data from 30 countries together in a 20-day context, is assessed using global and local MSE and MAE measures. For the construction of training and test sets, four time series (number of: accumulated infected cases, new cases, accumulated deaths and new deaths) from each country are used, which are started on the day of the first confirmed infection case. In order to soften the sudden transitions between samples, a moving average filter with a window size 3 and a normalization by maximum value were used. It is intended to make the model's predictions available online, collaborating with the fight against the pandemic. Twit Text FOAVip A COVID-19 time series forecasting model based on MLP ANN ????Procedia Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33936325 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33936325 #FOAvip English Wikipedia <ref>{{Cite pmid|33936325}}</ref> A COVID-19 time series forecasting model based on MLP ANN #MMPMID33936325 Borghi PH; Zakordonets O; Teixeira JP Procedia Comput Sci 2021[]; 181 (ä): 940-947 PMID33936325show ga With the accelerated spread of COVID-19 worldwide and its potentially fatal effects on human health, the development of a tool that effectively describes and predicts the number of infected cases and deaths over time becomes relevant. This makes it possible for administrative sectors and the population itself to become aware and act more precisely. In this work, a machine learning model based on the multilayer Perceptron artificial neural network structure was used, which effectively predicts the behavior of the series mentioned in up to six days. The model, which is trained with data from 30 countries together in a 20-day context, is assessed using global and local MSE and MAE measures. For the construction of training and test sets, four time series (number of: accumulated infected cases, new cases, accumulated deaths and new deaths) from each country are used, which are started on the day of the first confirmed infection case. In order to soften the sudden transitions between samples, a moving average filter with a window size 3 and a normalization by maximum value were used. It is intended to make the model's predictions available online, collaborating with the fight against the pandemic. äA COVID-19 time series forecasting model based on MLP ANN (epmc).pdf DeepDyve Pubget Overpricing