Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/932708
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dc.contributor.authorCORDEIRO, A. F. da S.pt_BR
dc.contributor.authorNÄÄS, I. de A.pt_BR
dc.contributor.authorOLIVEIRA, S. R. de M.pt_BR
dc.contributor.authorVIOLARO, F.pt_BR
dc.contributor.authorALMEIDA, A. C. M. de Almeidapt_BR
dc.date.accessioned2012-08-30T11:11:11Zpt_BR
dc.date.available2012-08-30T11:11:11Zpt_BR
dc.date.created2012-08-30pt_BR
dc.date.issued2012pt_BR
dc.identifier.citationEngenharia Agrícola, Jaboticabal, v. 32, n. 2, p. 208-216, Mar./Apr. 2012.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/932708pt_BR
dc.descriptionABSTRACT: 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.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectExpressão vocalpt_BR
dc.subjectSuinospt_BR
dc.subjectMineração de dadospt_BR
dc.subjectData miningpt_BR
dc.subjectPig farmingpt_BR
dc.titleEfficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2013-01-23T11:11:11Zpt_BR
dc.subject.thesagroSuinoculturapt_BR
dc.subject.nalthesaurusVocalizationpt_BR
riaa.ainfo.id932708pt_BR
riaa.ainfo.lastupdate2013-01-23pt_BR
dc.contributor.institutionALEXANDRA 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.pt_BR
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