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Exploring Intelligent Decision Support Systems [electronic resource] : Current State and New Trends /

Contributor(s): Language: ENG Series: Studies in Computational Intelligence ; 764Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018Description: XXIV, 238 p. 76 illus., 62 illus. in color. | Binding - Card Paper |Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319740027
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.3 23
Online resources: In: Springer Nature eBookSummary: This book presents innovative and high-quality research regarding advanced decision support systems (DSSs). It describes the foundations, methods, methodologies, models, tools, and techniques for designing, developing, implementing and evaluating advanced DSSs in different fields, including finance, health, emergency management, industry and pollution control. Decision support systems employ artificial intelligence methods to heuristically address problems that are cannot be solved using formal techniques. In this context, technologies such as the Semantic Web, linked data, big data, and machine learning are being applied to provide integrated support for individuals and organizations to make more rational decisions. The book is organized into two parts. The first part covers decision support systems for industry, while the second part presents case studies related to clinical emergency management and pollution control.
List(s) this item appears in: Springer Nature eBooks
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This book presents innovative and high-quality research regarding advanced decision support systems (DSSs). It describes the foundations, methods, methodologies, models, tools, and techniques for designing, developing, implementing and evaluating advanced DSSs in different fields, including finance, health, emergency management, industry and pollution control. Decision support systems employ artificial intelligence methods to heuristically address problems that are cannot be solved using formal techniques. In this context, technologies such as the Semantic Web, linked data, big data, and machine learning are being applied to provide integrated support for individuals and organizations to make more rational decisions. The book is organized into two parts. The first part covers decision support systems for industry, while the second part presents case studies related to clinical emergency management and pollution control.

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