An Evaluative Model to Assess the Organizational Efficiency in Training Corporations

dc.contributor.authorFernandes, Ana
dc.contributor.authorVicente, Henrique
dc.contributor.authorFigueiredo, Margarida
dc.contributor.authorNeves, Mariana
dc.contributor.authorNeves, José
dc.contributor.editorDang, Tran Khanh
dc.contributor.editorWagner, Roland
dc.contributor.editorKüng, Josef
dc.contributor.editorThoai, Nam
dc.contributor.editorTakizawa, Makoto
dc.contributor.editorNeuhold, Erich
dc.date.accessioned2016-11-15T17:28:09Z
dc.date.available2016-11-15T17:28:09Z
dc.date.issued2016
dc.description.abstractIn an organisation any optimization process of its issues faces increasing challenges and requires new approaches to the organizational phenomenon. Indeed, in this work it is addressed the problematic of efficiency dynamics through intangible variables that may support a different view of the corporations. It focuses on the challenges that information management and the incorporation of context brings to competitiveness. Thus, in this work it is presented the analysis and development of an intelligent decision support system in terms of a formal agenda built on a Logic Programming based methodology to problem solving, complemented with an attitude to computing grounded on Artificial Neural Networks. The proposed model is in itself fairly precise, with an overall accuracy, sensitivity and specificity with values higher than 90 %. The proposed solution is indeed unique, catering for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in a quantitative or qualitative arrangement.por
dc.identifier.authoremailanavilafernandes@gmail.com
dc.identifier.authoremailhvicente@uevora.pt
dc.identifier.authoremailmtf@uevora.pt
dc.identifier.authoremailmaneves@deloitte.co.uk
dc.identifier.authoremailjneves@di.uminho.pt
dc.identifier.citationFernandes, Ana; Vicente, Henrique; Figueiredo, Margarida; Neves, Mariana; Neves, José.. An Evaluative Model to assess the Organizational Efficiency in Training Corporations. In Future Data and Security Engineering, Lecture Notes in Computer Science, Vol. 10018, ed. T. K. Dang, R. Wagner, J. Küng, N. Thoai, M. Takizawa & E. Neuhold, 415 - 428. Cham: Springer International Publishing. 2016por
dc.identifier.doi10.1007/978-3-319-48057-2_29por
dc.identifier.edicao1
dc.identifier.isbn978-3-319-48056-5
dc.identifier.issn0302-9743
dc.identifier.locationCham
dc.identifier.numpag14
dc.identifier.sharewithCIEP - Publicações - Capítulos de livrospor
dc.identifier.urihttp://www.springer.com/gp/book/9783319480565
dc.identifier.urihttp://hdl.handle.net/10174/19084
dc.identifier.volume10018
dc.language.isoengpor
dc.publisherSpringer International Publishingpor
dc.rightsopenAccesspor
dc.subjectOptimizationpor
dc.subjectEfficiencypor
dc.subjectLogic programmingpor
dc.subjectKnowledge representationpor
dc.subjectArtificial neural networkspor
dc.titleAn Evaluative Model to Assess the Organizational Efficiency in Training Corporationspor
dc.typebookPartpor

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