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10.1007/s11227-020-03586-3 http://scihub22266oqcxt.onion/10.1007/s11227-020-03586-3 33424118!7781398!33424118     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Supercomput 2021 ; 77 (7): 7074-7088 Nephropedia Template TP {{tp|p=33424118|t=2021. Accurate computation: COVID-19 rRT-PCR positive test dataset using stages classification through textual big data mining with machine learning |pdf=|usr=}}{{33424118}} gab.com Text Accurate computation: COVID-19 rRT-PCR positive test dataset using stages classification through textual big data mining with machine learning JO: J Supercomput http://www.kidney.de/pget.php?&tw=1&pmid=33424118 #FOAvip In every field of life, advanced technology has become a rapid outcome, particularly in the medical field. The recent epidemic of the coronavirus disease 2019 (COVID-19) has promptly become outbreaks to identify early action from suspected cases at the primary stage over the risk prediction. It is overbearing to progress a control system that will locate the coronavirus. At present, the confirmation of COVID-19 infection by the ideal standard test of reverse transcription-polymerase chain reaction (rRT-PCR) by the extension of RNA viral, although it presents identified from deficiencies of long reversal time to generate results in 2-4 h of corona with a necessity of certified laboratories. In this proposed system, a machine learning (ML) algorithm is used to classify the textual clinical report into four classes by using the textual data mining method. The algorithm of the ensemble ML classifier has performed feature extraction using the advanced techniques of term frequency-inverse document frequency (TF/IDF) which is an effective information retrieval technique from the corona dataset. Humans get infected by coronaviruses in three ways: first, mild respiratory disease which is globally pandemic, and human coronaviruses are caused by HCoV-NL63, HCoV-OC43, HCoV-HKU1, and HCoV-229E; second, the zoonotic Middle East respiratory syndrome coronavirus (MERS-CoV); and finally, higher case casualty rate defined as severe acute respiratory syndrome coronavirus (SARS-CoV). By using the machine learning techniques, the three-way COVID-19 stages are classified by the extraction of the feature using the data retrieval process. The TF/IDF is used to measure and evaluate statistically the text data mining of COVID-19 patient's record list for classification and prediction of the coronavirus. This study established the feasibility of techniques to analyze blood tests and machine learning as an alternative to rRT-PCR for detecting the category of COVID-19-positive patients. Twit Text FOAVip Accurate computation: COVID-19 rRT-PCR positive test dataset using stages classification through textual big data mining with machine learning ????J Supercomput http://www.kidney.de/pget.php?&tw=1&pmid=33424118 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33424118 #FOAvip English Wikipedia <ref>{{Cite pmid|33424118}}</ref> Accurate computation: COVID-19 rRT-PCR positive test dataset using stages classification through textual big data mining with machine learning #MMPMID33424118 Ramanathan S; Ramasundaram M J Supercomput 2021[]; 77 (7): 7074-7088 PMID33424118show ga In every field of life, advanced technology has become a rapid outcome, particularly in the medical field. The recent epidemic of the coronavirus disease 2019 (COVID-19) has promptly become outbreaks to identify early action from suspected cases at the primary stage over the risk prediction. It is overbearing to progress a control system that will locate the coronavirus. At present, the confirmation of COVID-19 infection by the ideal standard test of reverse transcription-polymerase chain reaction (rRT-PCR) by the extension of RNA viral, although it presents identified from deficiencies of long reversal time to generate results in 2-4 h of corona with a necessity of certified laboratories. In this proposed system, a machine learning (ML) algorithm is used to classify the textual clinical report into four classes by using the textual data mining method. The algorithm of the ensemble ML classifier has performed feature extraction using the advanced techniques of term frequency-inverse document frequency (TF/IDF) which is an effective information retrieval technique from the corona dataset. Humans get infected by coronaviruses in three ways: first, mild respiratory disease which is globally pandemic, and human coronaviruses are caused by HCoV-NL63, HCoV-OC43, HCoV-HKU1, and HCoV-229E; second, the zoonotic Middle East respiratory syndrome coronavirus (MERS-CoV); and finally, higher case casualty rate defined as severe acute respiratory syndrome coronavirus (SARS-CoV). By using the machine learning techniques, the three-way COVID-19 stages are classified by the extraction of the feature using the data retrieval process. The TF/IDF is used to measure and evaluate statistically the text data mining of COVID-19 patient's record list for classification and prediction of the coronavirus. This study established the feasibility of techniques to analyze blood tests and machine learning as an alternative to rRT-PCR for detecting the category of COVID-19-positive patients. äAccurate computation: COVID-19 rRT-PCR positive test dataset using sta(epmc).pdf DeepDyve Pubget Overpricing