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10.1016/j.asoc.2020.106906 http://scihub22266oqcxt.onion/10.1016/j.asoc.2020.106906 33204229!7659585!33204229     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       Appl+Soft+Comput 2021 ; 99 (ä): 106906 Nephropedia Template TP {{tp|p=33204229|t=2021. Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network |pdf=|usr=}}{{33204229}} gab.com Text Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network JO: Appl Soft Comput http://www.kidney.de/pget.php?&tw=1&pmid=33204229 #FOAvip COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly between people and approaches millions of people worldwide in few months. It is very much essential to quickly and accurately identify the infected people so that prevention of spread can be taken. Although several medical tests have been used to detect certain injuries, the hopefully detection efficiency has not been accomplished yet. In this paper, a new Hybrid Diagnose Strategy (HDS) has been introduced. HDS relies on a novel technique for ranking selected features by projecting them into a proposed Patient Space (PS). A Feature Connectivity Graph (FCG) is constructed which indicates both the weight of each feature as well as the binding degree to other features. The rank of a feature is determined based on two factors; the first is the feature weight, while the second is its binding degree to its neighbors in PS. Then, the ranked features are used to derive the classification model that can classify new persons to decide whether they are infected or not. The classification model is a hybrid model that consists of two classifiers; fuzzy inference engine and Deep Neural Network (DNN). The proposed HDS has been compared against recent techniques. Experimental results have shown that the proposed HDS outperforms the other competitors in terms of the average value of accuracy, precision, recall, and F-measure in which it provides about of 97.658%, 96.756%, 96.55%, and 96.615% respectively. Additionally, HDS provides the lowest error value of 2.342%. Further, the results were validated statistically using Wilcoxon Signed Rank Test and Friedman Test. Twit Text FOAVip Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network ????Appl Soft Comput http://www.kidney.de/pget.php?&tw=1&pmid=33204229 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33204229 #FOAvip English Wikipedia <ref>{{Cite pmid|33204229}}</ref> Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network #MMPMID33204229 Shaban WM; Rabie AH; Saleh AI; Abo-Elsoud MA Appl Soft Comput 2021[Feb]; 99 (ä): 106906 PMID33204229show ga COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly between people and approaches millions of people worldwide in few months. It is very much essential to quickly and accurately identify the infected people so that prevention of spread can be taken. Although several medical tests have been used to detect certain injuries, the hopefully detection efficiency has not been accomplished yet. In this paper, a new Hybrid Diagnose Strategy (HDS) has been introduced. HDS relies on a novel technique for ranking selected features by projecting them into a proposed Patient Space (PS). A Feature Connectivity Graph (FCG) is constructed which indicates both the weight of each feature as well as the binding degree to other features. The rank of a feature is determined based on two factors; the first is the feature weight, while the second is its binding degree to its neighbors in PS. Then, the ranked features are used to derive the classification model that can classify new persons to decide whether they are infected or not. The classification model is a hybrid model that consists of two classifiers; fuzzy inference engine and Deep Neural Network (DNN). The proposed HDS has been compared against recent techniques. Experimental results have shown that the proposed HDS outperforms the other competitors in terms of the average value of accuracy, precision, recall, and F-measure in which it provides about of 97.658%, 96.756%, 96.55%, and 96.615% respectively. Additionally, HDS provides the lowest error value of 2.342%. Further, the results were validated statistically using Wilcoxon Signed Rank Test and Friedman Test. äDetecting COVID-19 patients based on fuzzy inference engine and Deep N(epmc).pdf DeepDyve Pubget Overpricing