Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/978404
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dc.contributor.authorMAIA, A. P. dept_BR
dc.contributor.authorOLIVEIRA, S. R. de M.pt_BR
dc.contributor.authorMOURA, D. J. dept_BR
dc.contributor.authorSARUBBI, J.pt_BR
dc.contributor.authorVERCELLINO, R. do A.pt_BR
dc.contributor.authorMEDEIROS, B. B. L.pt_BR
dc.contributor.authorGRISKA, P. R.pt_BR
dc.date.accessioned2014-02-04T11:11:11Zpt_BR
dc.date.available2014-02-04T11:11:11Zpt_BR
dc.date.created2014-02-04pt_BR
dc.date.issued2013pt_BR
dc.identifier.citationScientia Agricola, Piracicaba, v. 70, n. 6, p. 377-383, Nov./Dec. 2013.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/978404pt_BR
dc.descriptionABSTRACT: Thermal comfort is of great importance in preserving body temperature homeostasis during thermal stress conditions. Although the thermal comfort of horses has been widely studied, there is no report of its relationship with surface temperature (TS). This study aimed to assess the potential of data mining techniques as a tool to associate surface temperature with thermal comfort of horses. TS was obtained using infrared thermography image processing. Physiological and environmental variables were used to defi ne the predicted class, which classifi ed thermal comfort as ?comfort? and ?discomfort?. The variables of armpit, croup, breast and groin TS of horses and the predicted classes were then subjected to a machine learning process. All variables in the dataset were considered relevant for the classifi cation problem and the decision-tree model yielded an accuracy rate of 74 %. The feature selection methods used to reduce computational cost and simplify predictive learning decreased model accuracy to 70 %; however, the model became simpler with easily interpretable rules. For both these selection methods and for the classifi cation using all attributes, armpit and breast TS had a higher power rating for predicting thermal comfort. Data mining techniques show promise in the discovery of new variables associated with the thermal comfort of horses.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectMineração de dadospt_BR
dc.subjectConforto térmico de cavalospt_BR
dc.subjectTermografiapt_BR
dc.titleA decision-tree-based model for evaluating the thermal comfort of horses.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2014-02-06T11:11:11Zpt_BR
dc.subject.thesagroTermorregulaçãopt_BR
dc.subject.nalthesaurusSurface temperaturept_BR
dc.subject.nalthesaurusThermoregulationpt_BR
riaa.ainfo.id978404pt_BR
riaa.ainfo.lastupdate2014-02-06pt_BR
dc.contributor.institutionANA PAULA DE ASSIS MAIA, Unicamp, Feagri; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; DANIELLA JORGE DE MOURA, Unicamp, Feagri; JULIANA SARUBBI, Universidade Federal de Santa Maria, RS; RIMENA DO AMARAL VERCELLINO, Unicamp, Feagri; BRENDA BATISTA LEMOS MEDEIROS, Unicamp, Feagri; PAULO ROBERTO GRISKA, Faculdade de Jaguariúna, SP.pt_BR
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