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dc.contributor.authorAuccahuasi, Wilver
dc.contributor.authorPeláez, Brayan
dc.contributor.authorFlores, Pedro
dc.contributor.authorRurbano, Kitty
dc.contributor.authorBernardo, Grisi
dc.contributor.authorBernardo, Madelaine
dc.contributor.authorSernaque, Fernando
dc.contributor.authorBenites, Nicanor
dc.date.accessioned2021-06-03T15:14:25Z
dc.date.available2021-06-03T15:14:25Z
dc.date.issued2020-11-30
dc.identifier.citationAuccahuasi, W. ...[et al]. (2020). Computational model, based on machine learning, to predict the level of success in legal cases. PalArch’s Journal of Archaeology of Egypt / Egyptology, 17(6), 1775-1781. https://archives.palarch.nl/index.php/jae/article/view/1061es_PE
dc.identifier.issn1567-214X
dc.identifier.urihttps://hdl.handle.net/11537/26692
dc.description.abstractABSTRACT With the development of information and communication technologies, new opportunities and applications of many technologies are emerging that before could not be thought to be used, in this sense artificial intelligence is the technology that has gained greater strength, accompanied by the development of hardware that makes its execution possible and of software tools that make its implementation possible. The neural network is one of the most used techniques in the field of artificial intelligence. This work is based on analyzing possible cases of labor judicial problems, when workers who have suffered an abuse by employers are faced with. The success of the case according to the model presented, is based on being able to have the majority of documentation that evidences both the employment relationship, responsibilities of the employees, documents that support the payment of remuneration, documents that evidence any fault committed by the employee between others, a computational model was developed with a graphical user interface to make its application more practical. The model presents an effectiveness level of 93%, analyzed with 400 cases between positive and negative. For the training process, 100 cases corresponding to positive cases and 100 cases corresponding to negative cases were used. The model is practical in its use and can be scalable to different areas in the legal field.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherPalArch Foundationes_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 Estados Unidos de América*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.sourceUniversidad Privada del Nortees_PE
dc.sourceRepositorio Institucional - UPNes_PE
dc.subjectSimulación por computadoraes_PE
dc.subjectInteligencia artificiales_PE
dc.subjectProblemas laboraleses_PE
dc.subjectTrabajoes_PE
dc.titleComputational model, based on machine learning, to predict the level of success in legal caseses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.publisher.countryNLes_PE
dc.identifier.journalPalArch’s Journal of Archaeology of Egypt / Egyptologyes_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04es_PE


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