Computational intelligence for measuring macro-knowledge competitiveness

Al-Shami, AQ, 2013. Computational intelligence for measuring macro-knowledge competitiveness. PhD, Nottingham Trent University.

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Abstract

The aim of this research is to investigate the utilisation of Computational Intelligence methods for constructing Synthetic Composite Indicators (SCI). In particular for delivering a Unified Macro-Knowledge Competitiveness Indicator (UKCI) to enable consistent and transparent assessments and forecasting of the progress and competitiveness of Knowledge Based Economy (KBE). SCI are assessment tools usually constructed to evaluate and contrast entities performance by aggregating intangible measures in many areas such as economy, education, technology and innovation. SCI key value is inhibited in its capacity to aggregate complex and multi-dimensional variables into a single meaningful value. As a result, SCIs have been considered as one of the most important tools for macro-level and strategic decision making. Considering the shortcomings of the existing SCI, this study is proposing an alternative approach to develop Intelligent Synthetic Composite Indicators (iSCI). The suggested approach utilizes Fuzzy Proximity Knowledge Mining technique to build the qualitative taxonomy initially, and Fuzzy c-mean is employed to form the new composite indicators.

Item Type:	Thesis
Creators:	Al-Shami, A.Q.
Date:	2013
Divisions:	Schools > School of Science and Technology
Record created by:	EPrints Services
Date Added:	09 Oct 2015 09:34
Last Modified:	09 Oct 2015 09:34
URI:	https://irep.ntu.ac.uk/id/eprint/125

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