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| 15. | | CORDEIRO, A. F. da S.; NÄÄS, I. de A.; OLIVEIRA, S. R. de M.; VIOLARO, F.; ALMEIDA, A. C. M. de Almeida. Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization. Engenharia Agrícola, Jaboticabal, v. 32, n. 2, p. 208-216, Mar./Apr. 2012.| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 17 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
30/08/2012 |
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Data da última atualização: |
23/01/2013 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
CORDEIRO, A. F. da S.; NÄÄS, I. de A.; OLIVEIRA, S. R. de M.; VIOLARO, F.; ALMEIDA, A. C. M. de Almeida. |
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Afiliação: |
ALEXANDRA F. DA S. CORDEIRO, Feagri/Unicamp; IRENILZA DE A. NÄÄS, Feagri/Unicamp; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; FABIO VIOLARO, Faculdade de Engenharia Elétrica/Unicamp; ANDRÉIA C. M. DE ALMEIDA, Feagri/Unicamp. |
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Título: |
Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Engenharia Agrícola, Jaboticabal, v. 32, n. 2, p. 208-216, Mar./Apr. 2012. |
|
Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT: Among the challenges of pig farming in today's competitive market, there is factor of the product traceability that ensures, among many points, animal welfare. Vocalization is a valuable tool to identify situations of stress in pigs, and it can be used in welfare records for traceability. The objective of this work was to identify stress in piglets using vocalization, calling this stress on three levels: no stress, moderate stress, and acute stress. An experiment was conducted on a commercial farm in the municipality of Holambra, São Paulo State , where vocalizations of twenty piglets were recorded during the castration procedure, and separated into two groups: without anesthesia and local anesthesia with lidocaine base. For the recording of acoustic signals, a unidirectional microphone was connected to a digital recorder, in which signals were digitized at a frequency of 44,100 Hz. For evaluation of sound signals, Praat software was used, and different data mining algorithms were applied using Weka software. The selection of attributes improved model accuracy, and the best attribute selection was used by applying Wrapper method, while the best classification algorithms were the k-NN and Naive Bayes. According to the results, it was possible to classify the level of stress in pigs through their vocalization. |
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Palavras-Chave: |
Data mining; Expressão vocal; Mineração de dados; Pig farming; Suinos. |
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Thesagro: |
Suinocultura. |
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Thesaurus NAL: |
Vocalization. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/65330/1/Efficiency1v32n2.pdf
|
|
Marc: |
LEADER 02131naa a2200253 a 4500
001 1932708
005 2013-01-23
008 2012 bl uuuu u00u1 u #d
100 1 $aCORDEIRO, A. F. da S.
245 $aEfficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization.$h[electronic resource]
260 $c2012
520 $aABSTRACT: Among the challenges of pig farming in today's competitive market, there is factor of the product traceability that ensures, among many points, animal welfare. Vocalization is a valuable tool to identify situations of stress in pigs, and it can be used in welfare records for traceability. The objective of this work was to identify stress in piglets using vocalization, calling this stress on three levels: no stress, moderate stress, and acute stress. An experiment was conducted on a commercial farm in the municipality of Holambra, São Paulo State , where vocalizations of twenty piglets were recorded during the castration procedure, and separated into two groups: without anesthesia and local anesthesia with lidocaine base. For the recording of acoustic signals, a unidirectional microphone was connected to a digital recorder, in which signals were digitized at a frequency of 44,100 Hz. For evaluation of sound signals, Praat software was used, and different data mining algorithms were applied using Weka software. The selection of attributes improved model accuracy, and the best attribute selection was used by applying Wrapper method, while the best classification algorithms were the k-NN and Naive Bayes. According to the results, it was possible to classify the level of stress in pigs through their vocalization.
650 $aVocalization
650 $aSuinocultura
653 $aData mining
653 $aExpressão vocal
653 $aMineração de dados
653 $aPig farming
653 $aSuinos
700 1 $aNÄÄS, I. de A.
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aVIOLARO, F.
700 1 $aALMEIDA, A. C. M. de Almeida
773 $tEngenharia Agrícola, Jaboticabal$gv. 32, n. 2, p. 208-216, Mar./Apr. 2012.
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