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10.3390/diagnostics10060396 http://scihub22266oqcxt.onion/10.3390/diagnostics10060396 32545224!7345105!32545224     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       Diagnostics+(Basel) 2020 ; 10 (6): ä Nephropedia Template TP {{tp|p=32545224|t=2020. Group Testing-Based Robust Algorithm for Diagnosis of COVID-19 |pdf=|usr=}}{{32545224}} gab.com Text Group Testing-Based Robust Algorithm for Diagnosis of COVID-19 JO: Diagnostics (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=32545224 #FOAvip At the time of writing, the COVID-19 infection is spreading rapidly. Currently, there is no vaccine or treatment, and researchers around the world are attempting to fight the infection. In this paper, we consider a diagnosis method for COVID-19, which is characterized by a very rapid rate of infection and is widespread. A possible method for avoiding severe infections is to stop the spread of the infection in advance by the prompt and accurate diagnosis of COVID-19. To this end, we exploit a group testing (GT) scheme, which is used to find a small set of confirmed cases out of a large population. For the accurate detection of false positives and negatives, we propose a robust algorithm (RA) based on the maximum a posteriori probability (MAP). The key idea of the proposed RA is to exploit iterative detection to propagate beliefs to neighbor nodes by exchanging marginal probabilities between input and output nodes. As a result, we show that our proposed RA provides the benefit of being robust against noise in the GT schemes. In addition, we demonstrate the performance of our proposal with a number of tests and successfully find a set of infected samples in both noiseless and noisy GT schemes with different COVID-19 incidence rates. Twit Text FOAVip Group Testing-Based Robust Algorithm for Diagnosis of COVID-19 ????Diagnostics (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=32545224 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32545224 #FOAvip English Wikipedia <ref>{{Cite pmid|32545224}}</ref> Group Testing-Based Robust Algorithm for Diagnosis of COVID-19 #MMPMID32545224 Seong JT Diagnostics (Basel) 2020[Jun]; 10 (6): ä PMID32545224show ga At the time of writing, the COVID-19 infection is spreading rapidly. Currently, there is no vaccine or treatment, and researchers around the world are attempting to fight the infection. In this paper, we consider a diagnosis method for COVID-19, which is characterized by a very rapid rate of infection and is widespread. A possible method for avoiding severe infections is to stop the spread of the infection in advance by the prompt and accurate diagnosis of COVID-19. To this end, we exploit a group testing (GT) scheme, which is used to find a small set of confirmed cases out of a large population. For the accurate detection of false positives and negatives, we propose a robust algorithm (RA) based on the maximum a posteriori probability (MAP). The key idea of the proposed RA is to exploit iterative detection to propagate beliefs to neighbor nodes by exchanging marginal probabilities between input and output nodes. As a result, we show that our proposed RA provides the benefit of being robust against noise in the GT schemes. In addition, we demonstrate the performance of our proposal with a number of tests and successfully find a set of infected samples in both noiseless and noisy GT schemes with different COVID-19 incidence rates. äGroup Testing-Based Robust Algorithm for Diagnosis of COVID-19 (epmc).pdf DeepDyve Pubget Overpricing