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10.1155/2021/9996737 http://scihub22266oqcxt.onion/10.1155/2021/9996737 34394338!8357494!34394338     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Intell+Neurosci 2021 ; 2021 (ä): 9996737 Nephropedia Template TP {{tp|p=34394338|t=2021. Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19 |pdf=|usr=}}{{34394338}} gab.com Text Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19 JO: Comput Intell Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=34394338 #FOAvip The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world's healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak's spread, and restore full functionality to the world's healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID-19 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep- and transfer-learning techniques to develop accurate, general, and robust models for detecting COVID-19. The developed models utilize either convolutional neural networks or transfer-learning models or hybridize them with powerful machine-learning techniques to exploit their full potential. For experimentation, we applied the proposed models to two data sets: the COVID-19 Radiography Database from Kaggle and a local data set from Asir Hospital, Abha, Saudi Arabia. The proposed models achieved promising results in detecting COVID-19 cases and discriminating them from normal and other viral pneumonia with excellent accuracy. The hybrid models extracted features from the flatten layer or the first hidden layer of the neural network and then fed these features into a classification algorithm. This approach enhanced the results further to full accuracy for binary COVID-19 classification and 97.8% for multiclass classification. Twit Text FOAVip Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19 ????Comput Intell Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=34394338 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34394338 #FOAvip English Wikipedia <ref>{{Cite pmid|34394338}}</ref> Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19 #MMPMID34394338 Qaid TS; Mazaar H; Al-Shamri MYH; Alqahtani MS; Raweh AA; Alakwaa W Comput Intell Neurosci 2021[]; 2021 (ä): 9996737 PMID34394338show ga The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world's healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak's spread, and restore full functionality to the world's healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID-19 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep- and transfer-learning techniques to develop accurate, general, and robust models for detecting COVID-19. The developed models utilize either convolutional neural networks or transfer-learning models or hybridize them with powerful machine-learning techniques to exploit their full potential. For experimentation, we applied the proposed models to two data sets: the COVID-19 Radiography Database from Kaggle and a local data set from Asir Hospital, Abha, Saudi Arabia. The proposed models achieved promising results in detecting COVID-19 cases and discriminating them from normal and other viral pneumonia with excellent accuracy. The hybrid models extracted features from the flatten layer or the first hidden layer of the neural network and then fed these features into a classification algorithm. This approach enhanced the results further to full accuracy for binary COVID-19 classification and 97.8% for multiclass classification. |*COVID-19[MESH] |*Deep Learning[MESH] |Artificial Intelligence[MESH] |Humans[MESH] |Machine Learning[MESH] |Pandemics[MESH]Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-(epmc).pdf DeepDyve Pubget Overpricing