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10.1016/j.procs.2021.03.070 http://scihub22266oqcxt.onion/10.1016/j.procs.2021.03.070 34025823!8128670!34025823     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       Procedia+Comput+Sci 2021 ; 184 (ä): 558-564 Nephropedia Template TP {{tp|p=34025823|t=2021. Using Deep Learning Model for Adapting and Managing COVID-19 Pandemic Crisis |pdf=|usr=}}{{34025823}} gab.com Text Using Deep Learning Model for Adapting and Managing COVID-19 Pandemic Crisis JO: Procedia Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=34025823 #FOAvip The purpose of current paper is to create a smart and effective tool for telemedicine to early detect and diagnose COVID-19 disease and therefore help to manage Pandemic Crisis (MCPC) in Sultanate of Oman, as a tool for future pandemic containment. In this paper, we used tools to create robust models in real-time to support Telemedicine, it is Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks using Tensorflow (CNN-TF), and CNN Deployment. These models will assist telemedicine, 1) developing Automated Medical Immediate Diagnosis service (AMID). 2) Analysis of Chest X-rays image (CXRs). 3) Simplifying Classification of confirmed cases according to its severity. 4) Overcoming the lack of experience, by improving the performance of medical diagnostics and providing recommendations to the medical staff. The results show that the best Regression among the five Regression models is Random Forest Regression. while the best classification among the eight classification models and Recurrent Neural Network using Tensorflow (RNNTF) is Random Forest classification, and the best Clustering model among two Clustering models is K-Means++. Furthermore, CNN-TF model was able to discriminate between those with positive cases Covid-19 and those with negative cases. Twit Text FOAVip Using Deep Learning Model for Adapting and Managing COVID-19 Pandemic Crisis ????Procedia Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=34025823 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34025823 #FOAvip English Wikipedia <ref>{{Cite pmid|34025823}}</ref> Using Deep Learning Model for Adapting and Managing COVID-19 Pandemic Crisis #MMPMID34025823 Alodat M Procedia Comput Sci 2021[]; 184 (ä): 558-564 PMID34025823show ga The purpose of current paper is to create a smart and effective tool for telemedicine to early detect and diagnose COVID-19 disease and therefore help to manage Pandemic Crisis (MCPC) in Sultanate of Oman, as a tool for future pandemic containment. In this paper, we used tools to create robust models in real-time to support Telemedicine, it is Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks using Tensorflow (CNN-TF), and CNN Deployment. These models will assist telemedicine, 1) developing Automated Medical Immediate Diagnosis service (AMID). 2) Analysis of Chest X-rays image (CXRs). 3) Simplifying Classification of confirmed cases according to its severity. 4) Overcoming the lack of experience, by improving the performance of medical diagnostics and providing recommendations to the medical staff. The results show that the best Regression among the five Regression models is Random Forest Regression. while the best classification among the eight classification models and Recurrent Neural Network using Tensorflow (RNNTF) is Random Forest classification, and the best Clustering model among two Clustering models is K-Means++. Furthermore, CNN-TF model was able to discriminate between those with positive cases Covid-19 and those with negative cases. äUsing Deep Learning Model for Adapting and Managing COVID-19 Pandemic (epmc).pdf DeepDyve Pubget Overpricing