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dc.contributor.authorAriza-Colpas, Paola
dc.contributor.authorOñate-Bowen, Alvaro Agustín
dc.contributor.authorSuarez-Brieva, Eydy del Carmen
dc.contributor.authorOviedo-Carrascal, Ana
dc.contributor.authorUrina Triana, Miguel
dc.contributor.authorPiñeres-Melo, Marlon
dc.contributor.authorButt, Shariq Aziz,
dc.contributor.authorCollazos Morales, Carlos Andrés
dc.contributor.authorRamayo González, Ramón Enrique
dc.date.accessioned2021-10-01T21:49:43Z
dc.date.available2021-10-01T21:49:43Z
dc.date.issued2021
dc.identifier.issn18770509
dc.identifier.urihttps://hdl.handle.net/20.500.12442/8605
dc.description.abstractReminders are a core component of many assistive technology systems and are aimed specifically at helping people with dementia function more independently by compensating for cognitive deficits. These technologies are often utilized for prospective reminding, reminiscence, or within coaching-based systems. Traditionally, reminders have taken the form of nontechnology based aids, such as diaries, notebooks, cue cards and white boards. This article is based on the use of machine learning algorithms for the detection of Alzheimer’s disease. In the experimentation, the LWL, SimpleLogistic, Logistic, MultiLayerPercepton and HiperPipes algorithms were used. The result showed that the LWL algorithm produced the following results: Accuracy 98.81%, Precission 100%, Recall 97.62% and F- measure 98.80%eng
dc.format.mimetypepdfeng
dc.language.isoengeng
dc.publisherElsevierspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceProcedia Computer Scienceeng
dc.sourceVol. 191, (2021)
dc.subjectMachine learningeng
dc.subjectHAReng
dc.subjectADLeng
dc.subjectHuman Activity Recognitioneng
dc.subjectActivity Daily Livingeng
dc.subjectVanKasteren Dataseteng
dc.titleMachine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataseteng
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versioninfo:eu-repo/semantics/publishedVersionspa
dc.type.driverinfo:eu-repo/semantics/articleeng
dc.identifier.doihttps://doi.org/10.1016/j.procs.2021.07.070
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S1877050921014733?via%3Dihub
dc.type.spaArtículo científicospa


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