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dc.contributor.authorAuccahuasi, W.
dc.contributor.authorHerrera, L.
dc.contributor.authorRojas, K.
dc.contributor.authorMeza, S.
dc.contributor.authorOvalle, C.
dc.contributor.authorPlasencia, I.
dc.contributor.authorLoza, A.B.
dc.contributor.authorRevilla, J.F.
dc.contributor.authorPeña, P.F.
dc.contributor.authorOsorio, Y.M.
dc.contributor.authorFuentes, A.
dc.contributor.authorUrbano, K.
dc.date.accessioned2025-10-31T18:32:21Z
dc.date.available2025-10-31T18:32:21Z
dc.date.issued2022
dc.identifier.urihttps://ceur-ws.org/Vol-3269/PAPER_10.pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12805/4602
dc.description.abstractTimes change, for many reasons, due to technological development, new ways of doing things and in some cases forced by a global condition, is the case of the present case, where we analyze the teacher selection processes, although many of the Academic activities are developed at a distance, the selection processes also accompany this model, in this process factors that must be presented according to the profile required by the institution are analyzed, in this work a technique is proposed to be able to classify the best candidates in a Teacher selection process, the methodology consists of analyzing three groups of characteristics that the candidates must present, such as the writing exercises, the group interview and finally a demonstration class, in each of them particular criteria are evaluated, a demonstrative example It is presented as a demonstration, where it can be conditioned according to the criteria of each ins As a result, we have a computational model based on neural networks, where the best candidates can be pre-selected or classified in a teacher selection process, the prototype can be scaled and used in different sectors.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.subjectNetwork Security and Intrusion Detectionen_US
dc.subjectTraffic Classification and Network Security Innovationsen_US
dc.titleSmart system model for the recruitment of teachersen_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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