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10.1016/j.matpr.2021.02.244 http://scihub22266oqcxt.onion/10.1016/j.matpr.2021.02.244 33643854!7901277!33643854     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 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Mater+Today+Proc 2021 ; 46 (ä): 11098-11102 Nephropedia Template TP {{tp|p=33643854|t=2021. Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification |pdf=|usr=}}{{33643854}} gab.com Text Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification JO: Mater Today Proc http://www.kidney.de/pget.php?&tw=1&pmid=33643854 #FOAvip COVID-19 is the present-day pandemic around the globe. WHO has estimated that approx 15% of the world's population may have been infected with coronavirus with a large number of population on the verge of being infected. It is quite difficult to break the virus chain since asymptomatic patients can result in the spreading of the infection apart from the seriously infected patients. COVID-19 has many similar symptoms to SARS-D however, the symptoms can worsen depending on the immunity power of the patients. It is necessary to be able to find the infected patients even with no symptoms to be able to break the spread of the chain. In this paper, the comparison table describes the accuracy of deep learning architectures by the implementation of different optimizers with different learning rates. In order to remove the overfitting issue, different learning rate has been experimented. Further in this paper, we have proposed the classification of the COVID-19 images using the ensemble of 2 layered Convolutional Neural Network with the Transfer learning method which consumed lesser time for classification and attained an accuracy of nearly 90.45%. Twit Text FOAVip Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification ????Mater Today Proc http://www.kidney.de/pget.php?&tw=1&pmid=33643854 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33643854 #FOAvip English Wikipedia <ref>{{Cite pmid|33643854}}</ref> Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification #MMPMID33643854 Verma P; Tripathi V; Pant B Mater Today Proc 2021[]; 46 (ä): 11098-11102 PMID33643854show ga COVID-19 is the present-day pandemic around the globe. WHO has estimated that approx 15% of the world's population may have been infected with coronavirus with a large number of population on the verge of being infected. It is quite difficult to break the virus chain since asymptomatic patients can result in the spreading of the infection apart from the seriously infected patients. COVID-19 has many similar symptoms to SARS-D however, the symptoms can worsen depending on the immunity power of the patients. It is necessary to be able to find the infected patients even with no symptoms to be able to break the spread of the chain. In this paper, the comparison table describes the accuracy of deep learning architectures by the implementation of different optimizers with different learning rates. In order to remove the overfitting issue, different learning rate has been experimented. Further in this paper, we have proposed the classification of the COVID-19 images using the ensemble of 2 layered Convolutional Neural Network with the Transfer learning method which consumed lesser time for classification and attained an accuracy of nearly 90.45%. äComparison of different optimizers implemented on the deep learning ar(epmc).pdf DeepDyve Pubget Overpricing