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E-book: Transactions on Computational Collective Intelligence XXV

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  • Format: PDF+DRM
  • Series: Lecture Notes in Computer Science 9990
  • Pub. Date: 27-Sep-2016
  • Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Language: eng
  • ISBN-13: 9783662535806
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Transactions on Computational Collective Intelligence XXVLarger Image
  • Format: PDF+DRM
  • Series: Lecture Notes in Computer Science 9990
  • Pub. Date: 27-Sep-2016
  • Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Language: eng
  • ISBN-13: 9783662535806
Other books in subject:

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These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the semantic Web, social networks, and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies, such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This twenty-fifth issue contains 8 carefully selected and revised contributions.

High-level model for the design of KPIs for Smart Cities systems,-
Implementation of business processes in Smart Cities technology.- Designing
aggregate KPIs as a method of implementing decision-making processes in the
management of Smart Cities.- Designing aggregate KPIs as a method of
implementing decision-making processes in the management of Smart Cities.-
Smart Cities system design method based on Case Based Reasoning.- Model of an
integration bus of data and ontologies of Smart Cities processes Ontology of
the design pattern language for Smart Cities systems.- Text Classification
Using Anti-Bayesian Quantile Statistics-based Classifiers.- Two Novel
Techniques to Improve MDL-based Semi-Supervised Classification of Time
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