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10.3389/fpubh.2020.00441 http://scihub22266oqcxt.onion/10.3389/fpubh.2020.00441 32984242!7485390!32984242     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32984242&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\32984242.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Front+Public+Health 2020 ; 8 (ä): 441 Nephropedia Template TP {{tp|p=32984242|t=2020. COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models |pdf=|usr=}}{{32984242}} gab.com Text COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models JO: Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32984242 #FOAvip The primary aim of this study is to investigate suitable Statistical Neural Network (SNN) models and their hybrid version for COVID-19 mortality prediction in Indian populations and is to estimate the future COVID-19 death cases for India. SNN models such as Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Network (GRNN) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For this purpose, we have used two datasets as D1 and D2. The performances of these models are evaluated using Root Mean Square Error (RMSE) and "R," a correlation value between actual and predicted value. To improve prediction accuracy, the new hybrid models have been constructed by combining SNN models and the Non-linear Autoregressive Neural Network (NAR-NN). This is to predict the future error of the SNN models, which adds to the predicted value of these models for getting better MRP value. The results showed that the PNN and RBFNN-based MRP model performed better than the other models for COVID-19 datasets D2 and D1, respectively. Twit Text FOAVip COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models ????Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32984242 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32984242 #FOAvip English Wikipedia <ref>{{Cite pmid|32984242}}</ref> COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models #MMPMID32984242 Dhamodharavadhani S; Rathipriya R; Chatterjee JM Front Public Health 2020[]; 8 (ä): 441 PMID32984242show ga The primary aim of this study is to investigate suitable Statistical Neural Network (SNN) models and their hybrid version for COVID-19 mortality prediction in Indian populations and is to estimate the future COVID-19 death cases for India. SNN models such as Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Network (GRNN) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For this purpose, we have used two datasets as D1 and D2. The performances of these models are evaluated using Root Mean Square Error (RMSE) and "R," a correlation value between actual and predicted value. To improve prediction accuracy, the new hybrid models have been constructed by combining SNN models and the Non-linear Autoregressive Neural Network (NAR-NN). This is to predict the future error of the SNN models, which adds to the predicted value of these models for getting better MRP value. The results showed that the PNN and RBFNN-based MRP model performed better than the other models for COVID-19 datasets D2 and D1, respectively. |*COVID-19[MESH] |Forecasting[MESH] |Humans[MESH] |India/epidemiology[MESH] |Neural Networks, Computer[MESH]COVID-19 Mortality Rate Prediction for India Using Statistical Neural (epmc).pdf DeepDyve Pubget Overpricing