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dc.contributor.authorRodríguez Ibáñez, Margarita-
dc.contributor.authorSoguero-Ruiz, Cristina-
dc.contributor.authorGimeno Blanes, Francisco Javier-
dc.contributor.authorRojo-Álvarez, José Luis-
dc.contributor.otherDepartamentos de la UMH::Ingeniería de Comunicacioneses_ES
dc.date.accessioned2024-01-23T08:23:32Z-
dc.date.available2024-01-23T08:23:32Z-
dc.date.created2023-06-
dc.identifier.citationApplied Sciences Volume 13 Issue 13 (2023)es_ES
dc.identifier.issn2076-3417-
dc.identifier.urihttps://hdl.handle.net/11000/30562-
dc.description.abstractMachine learning techniques, more commonly known today as artificial intelligence, are playing an increasingly important role in all aspects of our lives. Their applications extend to all areas of society where similar techniques can be accommodated to provide efficient and interesting solutions to a wide range of problems. In this Special Issue entitled Machine Learning for Society [1], we present some examples of the applications of this type of technique. From the valuation of unlisted companies to the characterization of clients, through the detection of financial crises or the prediction of the behavior of the exchange rate, this group of works presented here has in common the search for efficient solutions based on a set of historical data, and the application of artificial intelligence techniques. The techniques and datasets used, as well as the relevant findings developed in the different articles of this Special Issue, are summarized below.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent3es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectmachine learninges_ES
dc.subjectARIMAes_ES
dc.subjectrandom series modellinges_ES
dc.subjectstock exchangees_ES
dc.subjectcustomer managementes_ES
dc.subjectmultiple correspondence analysises_ES
dc.subjectdomain descriptiones_ES
dc.subjecthospitalityes_ES
dc.subjectvaluationes_ES
dc.subject.otherCDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnologíaes_ES
dc.titleOpening the 21st Century Technologies to Industries: On the Special Issue Machine Learning for Societyes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.3390/app13137371es_ES
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