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dc.contributor.authorHerrero Cosío, Álvaro
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.contributor.authorSáiz-Bárcena, Lourdes
dc.contributor.authorManzanedo, Miguel A.
dc.identifier.citation10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing . Volumen 368, pp. 65-75.
dc.identifier.isbn978-3-319-19718-0(Print) / 978-3-319-19719-7(Online)
dc.identifier.issn2194-5357(Print) / 2194-5365(Online)
dc.description.abstractIt is required for an organization, before successfully applying a Knowledge Management (KM) methodology, to develop and implement a knowledge infrastructure, consisting of people, organizational and technological systems. Up to now, few approaches have been proposed for such technological systems supporting KM in organizations. Present paper advances previous work by proposing neural projection models for the analysis of the KM status of companies from two different industrial sectors. Exploratory methods are applied to real-life case studies to know and understand the structure of KM data. Subsequently, the application of such models generates meaningful conclusions that allow experts to diagnose KM from two different points of view: companies on the one hand and industrial sectors on the other hand.
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.subjectComputer Science
dc.titleAnalysis of Knowledge Management in Industrial Sectors by Means of Neural Models

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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported