Detecting adverse events in an active theater of war using advanced computational intelligence techniques

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dc.contributor.authorZurada, Jozef
dc.contributor.authorShi, Donghui
dc.contributor.authorKarwowski, Waldemar
dc.contributor.authorGuan, Jian
dc.contributor.authorÇakıt, Erman
dc.contributor.editorAliev, RA
dc.contributor.editorKacprzyk, J
dc.contributor.editorPedrycz, W
dc.contributor.editorJamshidi, M
dc.contributor.editorSadikoglu, FM
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-16T09:16:44Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-16T09:16:44Z
dc.date.issued2019
dc.departmentMühendislik Fakültesi
dc.description13th International Conference on Application of Fuzzy Systems and Soft Computing (ICAFS) -- AUG 27-28, 2018 -- Warsaw, POLAND
dc.descriptionWOS:000461058100121
dc.description.abstractThis study investigates the effectiveness of advanced computational intelligence techniques in detecting adverse events in Afghanistan. The study first applies feature reduction techniques to identify significant variables. Then it uses five cost-sensitive classification methods. Finally, the study reports the resulting classification accuracy rates and areas under the receiver operating characteristics charts for adverse events for each method for the entire country and its seven regions. It appears that when analysis is performed for the entire country, there is little correlation between adverse events and project types and the number of projects. However, the same type of analysis performed for each of its seven regions shows a connection between adverse events and the infrastructure budget and the number of projects allocated for the specific regions and times. Among the five classifiers, the C4.5 decision tree and k-nearest neighbor seem to be the best in terms of global performance.
dc.description.sponsorshipAzerbaijan Assoc Zadehs Legacy & Artificial Intelligence, Azerbaijan State Oil & Ind Univ, Berkeley Initiat Soft Comp, Georgia State Univ, Near E Univ, TOBB Econ & Technol Univ, Univ Alberta, Univ Siegen, Univ Texas, Univ Toronto
dc.description.sponsorshipOffice of Naval Research [10523339]
dc.description.sponsorshipThis study was supported in part by Grant no. 10523339, Complex Systems Engineering for Rapid Computational Socio-Cultural Network Analysis, from the Office of Naval Research awarded to Dr. W. Karwowski at the University of Central Florida.
dc.identifier.doi10.1007/978-3-030-04164-9_121
dc.identifier.endpage921en_US
dc.identifier.isbn978-3-030-04164-9
dc.identifier.issn2194-5357
dc.identifier.issn2194-5365
dc.identifier.scopusqualityN/A
dc.identifier.startpage914en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-04164-9_121
dc.identifier.urihttps://hdl.handle.net/20.500.12451/4645
dc.identifier.volume896en_US
dc.identifier.wosWOS:000461058100121
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSprınger Internatıonal Publıshıng Ag
dc.relation.ispartof13th Internatıonal Conference on Theory and Applıcatıon of Fuzzy Systems and Soft Computıng - Icafs-2018
dc.relation.ispartofseriesAdvances in Intelligent Systems and Computing
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDetecting Adverse Events
dc.subjectActive War Theater
dc.subjectComputational Intelligence
dc.subjectSoft Computing
dc.titleDetecting adverse events in an active theater of war using advanced computational intelligence techniques
dc.typeConference Object

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