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| Registros recuperados : 527 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
04/02/2014 |
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Data da última atualização: |
06/02/2014 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
MAIA, A. P. de; OLIVEIRA, S. R. de M.; MOURA, D. J. de; SARUBBI, J.; VERCELLINO, R. do A.; MEDEIROS, B. B. L.; GRISKA, P. R. |
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Afiliação: |
ANA 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. |
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Título: |
A decision-tree-based model for evaluating the thermal comfort of horses. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Scientia Agricola, Piracicaba, v. 70, n. 6, p. 377-383, Nov./Dec. 2013. |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT: 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. |
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Palavras-Chave: |
Conforto térmico de cavalos; Mineração de dados; Termografia. |
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Thesagro: |
Termorregulação. |
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Thesaurus NAL: |
Surface temperature; Thermoregulation. |
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Categoria do assunto: |
S Ciências Biológicas |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/96318/1/2013-Scientia-data-mining-horses.pdf
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Marc: |
LEADER 02181naa a2200265 a 4500
001 1978404
005 2014-02-06
008 2013 bl uuuu u00u1 u #d
100 1 $aMAIA, A. P. de
245 $aA decision-tree-based model for evaluating the thermal comfort of horses.$h[electronic resource]
260 $c2013
520 $aABSTRACT: 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.
650 $aSurface temperature
650 $aThermoregulation
650 $aTermorregulação
653 $aConforto térmico de cavalos
653 $aMineração de dados
653 $aTermografia
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aMOURA, D. J. de
700 1 $aSARUBBI, J.
700 1 $aVERCELLINO, R. do A.
700 1 $aMEDEIROS, B. B. L.
700 1 $aGRISKA, P. R.
773 $tScientia Agricola, Piracicaba$gv. 70, n. 6, p. 377-383, Nov./Dec. 2013.
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Embrapa Agricultura Digital (CNPTIA) |
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