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10.1007/s42979-021-00701-w http://scihub22266oqcxt.onion/10.1007/s42979-021-00701-w 34075356!8160081!34075356     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       SN+Comput+Sci 2021 ; 2 (4): 304 Nephropedia Template TP {{tp|p=34075356|t=2021. A Bi-fold Approach to Detect and Classify COVID-19 X-Ray Images and Symptom Auditor |pdf=|usr=}}{{34075356}} gab.com Text A Bi-fold Approach to Detect and Classify COVID-19 X-Ray Images and Symptom Auditor JO: SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=34075356 #FOAvip In this paper, we propose an ensemble-based transfer learning method to predict the X-ray image of a COVID-19 affected person. We have used a weighted Euclidean distance average as the parameter to ensemble the transfer learning model viz. ResNet50, VGG16, VGG19, Xception, and InceptionV3. Image augmentations have been carried out using generative adversarial network modelling. We took 784 training images, and 278 test images to validate our model accuracy, and the accuracy of our proposed model was around 98.67% for the training data set and 95.52% for the test data set. Along with that, we also propose a genetic algorithm optimized classification algorithm, to analyze the symptoms of COVID-19 for low, medium, and high-risk patients. The accuracy for the optimized set overshadowed the accuracy of un-optimized classification, and the optimized accuracy is as high as 88.96% for the optimized model. The novelty of this paper lies in the bi-sided model of the paper, i.e., we propose two major models, and one is the genetic algorithm optimized model to analyze the symptoms for a patient of varied risk and the other is to classify the X-ray image using an ensemble-based transfer learning model. Twit Text FOAVip A Bi-fold Approach to Detect and Classify COVID-19 X-Ray Images and Symptom Auditor ????SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=34075356 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34075356 #FOAvip English Wikipedia <ref>{{Cite pmid|34075356}}</ref> A Bi-fold Approach to Detect and Classify COVID-19 X-Ray Images and Symptom Auditor #MMPMID34075356 Chatterjee A; Roy S; Das S SN Comput Sci 2021[]; 2 (4): 304 PMID34075356show ga In this paper, we propose an ensemble-based transfer learning method to predict the X-ray image of a COVID-19 affected person. We have used a weighted Euclidean distance average as the parameter to ensemble the transfer learning model viz. ResNet50, VGG16, VGG19, Xception, and InceptionV3. Image augmentations have been carried out using generative adversarial network modelling. We took 784 training images, and 278 test images to validate our model accuracy, and the accuracy of our proposed model was around 98.67% for the training data set and 95.52% for the test data set. Along with that, we also propose a genetic algorithm optimized classification algorithm, to analyze the symptoms of COVID-19 for low, medium, and high-risk patients. The accuracy for the optimized set overshadowed the accuracy of un-optimized classification, and the optimized accuracy is as high as 88.96% for the optimized model. The novelty of this paper lies in the bi-sided model of the paper, i.e., we propose two major models, and one is the genetic algorithm optimized model to analyze the symptoms for a patient of varied risk and the other is to classify the X-ray image using an ensemble-based transfer learning model. äA Bi-fold Approach to Detect and Classify COVID-19 X-Ray Images and Sy(epmc).pdf DeepDyve Pubget Overpricing