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dc.contributor.authorMendoza Palechor, Fabiospa
dc.contributor.authorVicario, Enricospa
dc.contributor.authorPATARA, FULVIOspa
dc.contributor.authorDe la Hoz Manotas, Alexis Kevinspa
dc.contributor.authorMolina Estren, Diegospa
dc.date.accessioned2022-09-19T13:57:25Z
dc.date.available2022-09-19T13:57:25Z
dc.date.issued2022
dc.identifier.citationPalechor, F.M., Vicario, E., Patara, F., De la Hoz Manotas, A., Estren, D.M. (2022). Semi-supervised Adaptive Method for Human Activities Recognition (HAR). In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2022. Lecture Notes in Computer Science, vol 13293. Springer, Cham. https://doi.org/10.1007/978-3-031-10539-5_1spa
dc.identifier.isbn978-3-031-10538-8spa
dc.identifier.urihttps://hdl.handle.net/11323/9522spa
dc.description.abstractUsing sensors and mobile devices integrated with hardware and software tools for Human Recognition Activities (HAR), is a growing scientific field, the analysis based on this information have promising benefits to detect regular and irregular behaviors in individuals during their daily activities. In this study, the Van Kasteren dataset was used for the experimental stage, and it all data was processed using the data mining classification methods: Decision Trees (DT), Support Vector Machines (SVM) and Naïve Bayes (NB). These methods were applied during the training and validation processes with the proposed methodology, and the results obtained showed that all these three methods were successful to identify the cluster associated to the activities contained in the Van Kasteren dataset. The Support Vector Machines (SVM) method showed the best results with the evaluation metrics: True Positive Rate (TPR) 99.2%, False Positive Rate (FPR) 0.6%, precision (99.2%), coverage (99.2%) and F-Measure (98.8%).eng
dc.format.extent1 páginaspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherSpringer, Chamspa
dc.relation.ispartofseriesComputer Information Systems and Industrial Management. CISIM 2022.;spa
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)spa
dc.rights© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AGspa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.titleSemi-supervised adaptive method for human activities recognition (HAR)eng
dc.typeCapítulo - Parte de Librospa
dc.identifier.urlhttps://doi.org/10.1007/978-3-031-10539-5_1spa
dc.source.urlhttps://link.springer.com/chapter/10.1007/978-3-031-10539-5_1spa
dc.rights.accessrightsinfo:eu-repo/semantics/embargoedAccessspa
dc.identifier.doi10.1007/978-3-031-10539-5_1spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.publisher.placeSwitzerlandspa
dc.relation.ispartofbookLecture Notes in Computer Sciencespa
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dc.subject.proposalHAReng
dc.subject.proposalData miningeng
dc.subject.proposalClustereng
dc.subject.proposalEvaluation metriceng
dc.subject.proposalDataseteng
dc.subject.proposalVan Karesteneng
dc.type.coarhttp://purl.org/coar/resource_type/c_3248spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/bookPartspa
dc.type.redcolhttp://purl.org/redcol/resource_type/CAP_LIBspa
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dc.rights.coarhttp://purl.org/coar/access_right/c_f1cfspa
dc.identifier.eisbn978-3-031-10539-5spa


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