Titlebook: Applied Decision Support with Soft Computing; Xinghuo Yu,Janusz Kacprzyk Book 2003 Springer-Verlag Berlin Heidelberg 2003 Planning.Technol

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Evolving Connectionist-based Decision Support Systems Such decision support systems use evolving connectionist systems (ECOS) — adaptive learning neural network models, thus their name — evolving connectionist based decision systems ECBDS. Case studies of ECBDS are presented from the areas of finance, economics, and bioinformatics.

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Decision Support Systems in Healthcare: Emerging Trends and Success Factorsng across operational and clinical processes in healthcare organizations. This article summarizes the historical and state of the art decision support systems in healthcare, analyzes the success factors needed for widespread deployment, and postulates the future trends of the field in the context of a new decision management paradigm.

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A View of Public and Private Sectors for Taiwan’s BOT Project Financing Using Fuzzy Multi-Criteria Ming phases of projects are divided into six segments for discussion. They are “lifetime phase”, “development phase”, “contract specified phase”, “construction phase”, “operation phase” and “transfer phase”. To choose theoretical financial criteria of the BOT projects in uncertain situations, this chapter suggests a two-stage selection procedure:

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Application of Kemeny’s Median for Group Decision Supportelated to those methods are given. Heuristic algorithms for computing a median for both cases under consideration are proposed. Numerical examples illustrating the application of these algorithms are presented. A real-life example is also described.

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English for Writing Research Papersnge. The complex and changing environment in which decision support systems are developed makes specification of the final system requirements difficult. Decision support systems need to be designed using an adaptive process of learning and experimentation. The emphasis in DSS development is on supp