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dc.provenanceCONICET-
dc.creatorZárate, Marcos Daniel-
dc.creatorLewis, Mirtha Noemi-
dc.date2018-01-31T19:31:15Z-
dc.date2018-01-31T19:31:15Z-
dc.date2016-09-
dc.date2017-12-12T20:04:43Z-
dc.date.accessioned2019-04-29T15:39:18Z-
dc.date.available2019-04-29T15:39:18Z-
dc.date.issued2016-09-
dc.identifierZárate, Marcos Daniel; Lewis, Mirtha Noemi; Estimate of the Anesthesia Stage in Southern Elephant Seals using WEKA Data Mining Tool; Foundation of Computer Science; International Journal of Applied Information Systems; 11; 4; 9-2016; 48-52-
dc.identifier973-93-80892-65-1-
dc.identifier2249-0868-
dc.identifierhttp://hdl.handle.net/11336/35255-
dc.identifierCONICET Digital-
dc.identifierCONICET-
dc.identifier.urihttp://rodna.bn.gov.ar:8080/jspui/handle/bnmm/298671-
dc.descriptionPrediction syst ems are techniques that build and study new forecasts through a branch of the artificial intelligence called Machine Lea rning. In this work it is estimate the time that remains anesthetized a southern elephant seal to which you have applied a combination of d rugs (Zoletil®), the fundamental objective of anesthesia is to avoid risky situations to researchers studying this species. To know these times, data mining techniques and classification algorithms are used, particularly algorithms it were compared J48, SM O, Random Tree, NB Tree y Naïve Bayes with data mining tool WEKA and a data set containing the records of 96 individuals undergoing anesthesia procedure. It is concluded that after tests (Random Tree) was the classification algorithm that best responded, with an accuracy of 98.79%.-
dc.descriptionFil: Zárate, Marcos Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina-
dc.descriptionFil: Lewis, Mirtha Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina-
dc.formatapplication/pdf-
dc.formatapplication/pdf-
dc.formatapplication/pdf-
dc.languageeng-
dc.publisherFoundation of Computer Science-
dc.relationinfo:eu-repo/semantics/altIdentifier/url/http://www.ijais.org/archives/volume11/number4/zarate-2016-ijais-451603.pdf-
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.5120/ijais2016451603-
dc.rightsinfo:eu-repo/semantics/restrictedAccess-
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/-
dc.sourcereponame:CONICET Digital (CONICET)-
dc.sourceinstname:Consejo Nacional de Investigaciones Científicas y Técnicas-
dc.sourceinstacron:CONICET-
dc.source.urihttp://hdl.handle.net/11336/35255-
dc.subjectMACHINE LEARNING-
dc.subjectWEKA-
dc.subjectANESTHESIA-
dc.subjectMIROUNGA LEONINA-
dc.subjectClassification-
dc.subjectCiencias de la Computación-
dc.subjectCiencias de la Computación e Información-
dc.subjectCIENCIAS NATURALES Y EXACTAS-
dc.titleEstimate of the Anesthesia Stage in Southern Elephant Seals using WEKA Data Mining Tool-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.typeinfo:ar-repo/semantics/articulo-
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