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10.1016/j.bspc.2021.102605 http://scihub22266oqcxt.onion/10.1016/j.bspc.2021.102605 33824682!8015425!33824682     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Signal+Process+Control 2021 ; 68 (ä): 102605 Nephropedia Template TP {{tp|p=33824682|t=2021. Framework for Real-Time Detection and Identification of possible patients of COVID-19 at public places |pdf=|usr=}}{{33824682}} gab.com Text Framework for Real-Time Detection and Identification of possible patients of COVID-19 at public places JO: Biomed Signal Process Control http://www.kidney.de/pget.php?&tw=1&pmid=33824682 #FOAvip The novel Corona Virus (COVID-19) has become the reason for the world to declare it as a global pandemic, which has already taken many lives from all around the world. This pandemic has become a disaster since the spreading rate from person to person is incredibly high and many techniques have come forth to aid in stopping the infection. Although various types of methods have been put into implementation, the search and suggestions of new approaches to reduce the increasing rate of infection will never come to an end until a vaccine terminates this pandemic. This study focuses on proposing a new framework that is based on Deep Learning algorithms for recognizing the COVID-19 cases, mostly in public places. The algorithms include Background Subtraction for extracting the foreground of thermal images from thermal videos generated by Thermal Cameras through the Thermal Imaging process and the Convolutional Neural Network for detecting people infected with the virus. This automated prototype works in a real-time scenario that helps identify people with the disease and will try to trace it while separating them from having any other contact. This proposal intends to achieve a satisfying growth in determining the real cases of COVID-19 and minimize the spreading rate of this virus to the max, ultimately avoiding more deaths. Twit Text FOAVip Framework for Real-Time Detection and Identification of possible patients of COVID-19 at public places ????Biomed Signal Process Control http://www.kidney.de/pget.php?&tw=1&pmid=33824682 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33824682 #FOAvip English Wikipedia <ref>{{Cite pmid|33824682}}</ref> Framework for Real-Time Detection and Identification of possible patients of COVID-19 at public places #MMPMID33824682 Peddinti B; Shaikh A; K R B; K C NK Biomed Signal Process Control 2021[Jul]; 68 (ä): 102605 PMID33824682show ga The novel Corona Virus (COVID-19) has become the reason for the world to declare it as a global pandemic, which has already taken many lives from all around the world. This pandemic has become a disaster since the spreading rate from person to person is incredibly high and many techniques have come forth to aid in stopping the infection. Although various types of methods have been put into implementation, the search and suggestions of new approaches to reduce the increasing rate of infection will never come to an end until a vaccine terminates this pandemic. This study focuses on proposing a new framework that is based on Deep Learning algorithms for recognizing the COVID-19 cases, mostly in public places. The algorithms include Background Subtraction for extracting the foreground of thermal images from thermal videos generated by Thermal Cameras through the Thermal Imaging process and the Convolutional Neural Network for detecting people infected with the virus. This automated prototype works in a real-time scenario that helps identify people with the disease and will try to trace it while separating them from having any other contact. This proposal intends to achieve a satisfying growth in determining the real cases of COVID-19 and minimize the spreading rate of this virus to the max, ultimately avoiding more deaths. äFramework for Real-Time Detection and Identification of possible patie(epmc).pdf DeepDyve Pubget Overpricing