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dc.contributor.authorPekel, Ebru
dc.contributor.authorAksehir, Z. Duygu
dc.contributor.authorMeto, Bilal
dc.contributor.authorAkleylek, Sedat
dc.contributor.authorKilic, Erdal
dc.date.accessioned2020-06-21T13:12:02Z
dc.date.available2020-06-21T13:12:02Z
dc.date.issued2018
dc.identifier.isbn978-1-5386-7893-0
dc.identifier.urihttps://hdl.handle.net/20.500.12712/11806
dc.description3rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEGen_US
dc.descriptionWOS: 000459847400045en_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.en_US
dc.description.sponsorshipBMBB, Istanbul Teknik Univ, Gazi Univ, ATILIM Univ, Int Univ Sarajevo, Kocaeli Univ, TURKiYE BiLiSiM VAKFIen_US
dc.description.sponsorshipRonesans Holdingen_US
dc.description.sponsorshipThis study was partially supported by Ronesans Holding.en_US
dc.language.isoengen_US
dc.publisherIeeeen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBayesian networken_US
dc.subjectclassificationen_US
dc.subjectmachine learningen_US
dc.subjectaccidenten_US
dc.titleA Bayesian Network Application in Occupational Health and Safetyen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentOMÜen_US
dc.identifier.startpage239en_US
dc.identifier.endpage243en_US
dc.relation.journal2018 3Rd International Conference on Computer Science and Engineering (Ubmk)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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