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dc.contributor.authorDelgado, Alexi
dc.contributor.authorCondori, Ruth
dc.contributor.authorHernández, Miluska
dc.contributor.authorLee Huamani, Enrique
dc.contributor.authorAndrade-Arenas, Laberiano
dc.date.accessioned2023-09-20T22:10:33Z
dc.date.available2023-09-20T22:10:33Z
dc.date.issued2023-03-03
dc.identifier.urihttps://hdl.handle.net/20.500.13053/9401
dc.description.abstractIndustrial hygiene is a preventive technique that tries to avoid professional illnesses and damage to health caused by several possible toxic agents. The purpose of this study is to simultaneously analyze different risk factors (body vibration, lighting, heat stress and noise), to obtain an overall risk assessment of these factors and to classify them on a scale of levels of Unacceptable, Not recommended or Acceptable. In this work, an artificial intelligence model based on the grey clustering method was applied to evaluate the quality of industrial hygiene. The grey clustering method was selected, as it enables the integration of objective factors related to hazards present in the workplace with subjective employee evaluations. A case study, in the three warehouses of a beer industry in Peru, was developed. The results obtained showed that the warehouses have an acceptable level of quality. These results could help industries to make decisions about conducting evaluations of the different occupational agents and determine whether the quality of hygiene represents a risk, as well as give certain recommendations with respect to the factors presented.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherMDPIes_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es_PE
dc.subjectartificial intelligence; grey clustering; industrial hygienees_PE
dc.title“Artificial Intelligence Model Based on Grey Clustering to Access Quality of Industrial Hygiene: A Case Study in Peru“es_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.doi10.3390/computation11030051
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE
dc.publisher.countryUSAes_PE
dc.subject.ocde1.02.00 -- Informática y Ciencias de la Informaciónes_PE


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