Forging An Optimized Bayesian Network Model With Selected Parameters For Detection of The Coronavirus In Delta State of Nigeria

Journal of Applied Science, Engineering, Technology, and Education

View Publication Info
 
 
Field Value
 
Title Forging An Optimized Bayesian Network Model With Selected Parameters For Detection of The Coronavirus In Delta State of Nigeria
 
Creator Ojugo, Arnold
Otakore, Oghenevwede Debby
 
Subject coronavirus
Nigeria
machine learning
malware
Bayesian Network
epidemiology
pandemic
COVID-19
 
Description Machine learning algorithm have become veritable tools for effective decision support towards the construction of systems that assist experts (individuals) in their field of exploits and endeavor with regards to problematic tasks.. They are best suited for tasks where data is explored and exploited; and cases where the dataset contains noise, partial truth, ambiguities and in cases where there is shortage of datasets. For this study, we employ the Bayesian network to construct a model trained towards a target system that can help predict best parameters used for classification of the novel coronavirus (covid-19). Data was collected from Federal Medical Center Epidemiology laboratory (a centralized databank for all cases of the covid-19 in Delta State). Data was split into training and investigation (test) dataset for the target system. Results show high predictive capability.
 
Publisher Yayasan Ahmar Cendekia Indonesia
 
Date 2020-06-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://jurnal.ahmar.id/index.php/asci/article/view/115
10.35877/454RI.asci2163
 
Source Journal of Applied Science, Engineering, Technology, and Education; Vol. 3 No. 1 (2021); 37-45
2685-0591
 
Language eng
 
Relation http://jurnal.ahmar.id/index.php/asci/article/view/115/102
 
Rights Copyright (c) 2021 Arnold Ojugo, Oghenevwede Debby Otakore (Author)
https://creativecommons.org/licenses/by-nc-sa/4.0
 

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