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dc.contributor.authorAuccahuasi, W.
dc.contributor.authorHerrera, L.
dc.contributor.authorRojas, K.
dc.contributor.authorUrbano, K.
dc.contributor.authorPeláez, B.
dc.contributor.authorPeña, P.F.
dc.contributor.authorOsorio, Y.M.
dc.contributor.authorBernardo, G.
dc.contributor.authorBernardo, M.
dc.contributor.authorMeza, S.
dc.contributor.authorOvalle, C.
dc.contributor.authorHilario, F.
dc.contributor.authorLiendo, M.
dc.contributor.authorSernaque, F.
dc.date.accessioned2025-10-31T18:32:21Z
dc.date.available2025-10-31T18:32:21Z
dc.date.issued2022
dc.identifier.urihttps://ceur-ws.org/Vol-3146/PAPER_06.pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12805/4600
dc.description.abstractICTs have allowed the applications of artificial intelligence to grow exponentially, where different applications are being presented, based on the application of neural networks as prediction mechanisms for different processes and applications, in the present work the use of the Neural networks for the legal case prediction process, in which the analysis of approximately 200 cases was used between cases that had "positive and negative" final results, the expected results after implementing the solution in the MATLAB tool, they presented us effectiveness results in a value of 93%, as a conclusion we can indicate that the model provided allows us to be applied in other conditions as well as to be scaled, taking into account the historical data that may be available for the training process.en_US
dc.formatapplication/pdf
dc.language.isoeng
dc.publisherCEUR-WS
dc.relation.ispartofurn:issn:1613-0073
dc.rightshttps://purl.org/coar/access_right/c_abf2
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectImage Segmentation, Deep Neural Networks and Object Detectionen_US
dc.subjectArtificial Intelligence and Legal Rights in Public Administrationen_US
dc.titleMethodology for the Use of Machine Learning, Applied in Predicting the Level of Success in Legal Casesen_US
dc.typehttps://purl.org/coar/resource_type/c_5794
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.01
dc.type.otherConference Paper
dc.identifier.journalCEUR Workshop Proceedings
dc.type.versionhttps://purl.org/coar/version/c_970fb48d4fbd8a85


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