Publication:
A Bayesian Network Application in Occupational Health and Safety

dc.authorscopusid57205585628
dc.authorscopusid57205571054
dc.authorscopusid57205573849
dc.authorscopusid15833929800
dc.authorscopusid22953804000
dc.contributor.authorPekel, E.
dc.contributor.authorAkschir, Z.D.
dc.contributor.authorMeto, B.
dc.contributor.authorAkleylek, S.
dc.contributor.authorKilic, E.
dc.date.accessioned2020-06-21T13:12:02Z
dc.date.available2020-06-21T13:12:02Z
dc.date.issued2018
dc.departmentOndokuz Mayıs Üniversitesien_US
dc.department-temp[Pekel] Ebru, Department of Computer Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Akschir] Z. Duygu, Department of Computer Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Meto] Bilal, Rönesans Holding, Ankara, Turkey; [Akleylek] Sedat, Department of Computer Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Kilic] Erdal, Department of Computer Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkeyen_US
dc.description.abstractWith the development of technology, there has been an increase in production and the number of accidents has increased. Progressive technology, the development of the industry and the lack of protective precaution, and the responsibility for the uneducated employees are the main causes of work accidents. In this study, the types of injuries in work accidents and the effects of the injuries on the body are analyzed via Bayesian Networks (BNs). The BNs reflect the conditional dependency relations between variables and, the fact that they are not dependent on a single independent variable. BNs are constructed on a dataset from an international construction company. The accuracy rate and other performance measures of the constructed Bayesian network are analyzed and the effectiveness of the constructed model is analyzed. According to the experimental results, it's explicit that some cases of job accidents can be predicted beforehand with high accuracies by using machine learning techniques. © 2018 IEEE.en_US
dc.identifier.doi10.1109/UBMK.2018.8566568
dc.identifier.endpage243en_US
dc.identifier.isbn9781538678930
dc.identifier.scopus2-s2.0-85060635209
dc.identifier.startpage239en_US
dc.identifier.urihttps://doi.org/10.1109/UBMK.2018.8566568
dc.identifier.wosWOS:000459847400045
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof-- 3rd International Conference on Computer Science and Engineering, UBMK 2018 -- 2018-09-20 through 2018-09-23 -- Sarajevo -- 143560en_US
dc.relation.journal2018 3Rd International Conference on Computer Science and Engineering (Ubmk)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAccidenten_US
dc.subjectBayesian Networken_US
dc.subjectClassificationen_US
dc.subjectMachine Learningen_US
dc.titleA Bayesian Network Application in Occupational Health and Safetyen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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