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DC Field | Value | Language |
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dc.contributor.author | Rodríguez Ibáñez, Margarita | - |
dc.contributor.author | Soguero-Ruiz, Cristina | - |
dc.contributor.author | Gimeno Blanes, Francisco Javier | - |
dc.contributor.author | Rojo-Álvarez, José Luis | - |
dc.contributor.other | Departamentos de la UMH::Ingeniería de Comunicaciones | es_ES |
dc.date.accessioned | 2024-01-23T08:23:32Z | - |
dc.date.available | 2024-01-23T08:23:32Z | - |
dc.date.created | 2023-06 | - |
dc.identifier.citation | Applied Sciences Volume 13 Issue 13 (2023) | es_ES |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://hdl.handle.net/11000/30562 | - |
dc.description.abstract | Machine 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.format | application/pdf | es_ES |
dc.format.extent | 3 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | machine learning | es_ES |
dc.subject | ARIMA | es_ES |
dc.subject | random series modelling | es_ES |
dc.subject | stock exchange | es_ES |
dc.subject | customer management | es_ES |
dc.subject | multiple correspondence analysis | es_ES |
dc.subject | domain description | es_ES |
dc.subject | hospitality | es_ES |
dc.subject | valuation | es_ES |
dc.subject.other | CDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnología | es_ES |
dc.title | Opening the 21st Century Technologies to Industries: On the Special Issue Machine Learning for Society | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/app13137371 | es_ES |
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