Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/19084

Title: An Evaluative Model to Assess the Organizational Efficiency in Training Corporations
Authors: Fernandes, Ana
Vicente, Henrique
Figueiredo, Margarida
Neves, Mariana
Neves, José
Editors: Dang, Tran Khanh
Wagner, Roland
Küng, Josef
Thoai, Nam
Takizawa, Makoto
Neuhold, Erich
Keywords: Optimization
Efficiency
Logic programming
Knowledge representation
Artificial neural networks
Issue Date: 2016
Publisher: Springer International Publishing
Citation: Fernandes, 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. 2016
Abstract: In 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.
URI: http://www.springer.com/gp/book/9783319480565
http://hdl.handle.net/10174/19084
ISBN: 978-3-319-48056-5
ISSN: 0302-9743
Type: bookPart
Appears in Collections:CIEP - Publicações - Capítulos de Livros
QUI - Publicações - Capítulos de Livros

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