{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T10:47:36Z","timestamp":1768819656265,"version":"3.49.0"},"reference-count":22,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T00:00:00Z","timestamp":1677801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Industrial 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.<\/jats:p>","DOI":"10.3390\/computation11030051","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T04:29:58Z","timestamp":1678076998000},"page":"51","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial Intelligence Model Based on Grey Clustering to Access Quality of Industrial Hygiene: A Case Study in Peru"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0470-8535","authenticated-orcid":false,"given":"Alexi","family":"Delgado","sequence":"first","affiliation":[{"name":"Mining Engineering Section, Pontificia Universidad Cat\u00f3lica del Per\u00fa, Lima 15023, Peru"}]},{"given":"Ruth","family":"Condori","sequence":"additional","affiliation":[{"name":"Faculty of Environmental Engineering, Universidad Nacional de Ingenier\u00eda, Lima 15012, Peru"}]},{"given":"Miluska","family":"Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Faculty of Environmental Engineering, Universidad Nacional de Ingenier\u00eda, Lima 15012, Peru"}]},{"given":"Enrique","family":"Lee Huamani","sequence":"additional","affiliation":[{"name":"Image Processing Research Laboratory, Universidad de Ciencias y Humanidades, Lima 15304, Peru"}]},{"given":"Laberiano","family":"Andrade-Arenas","sequence":"additional","affiliation":[{"name":"Systems Engineering Program, Universidad Privada Norbert Wiener, Lima 15046, Peru"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chumachenko, D., Meniailov, I., Bazilevych, K., Chumachenko, T., and Yakovlev, S. 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