Transactions on Computational Collective Intelligence XXV

Herausgegeben:Nguyen, Ngoc Thanh; Kowalczyk, Ryszard; Ziólkowski, Artur

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Lecture Notes in Computer Science 9990
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-662-53579-0
• 1st ed. 2016
• Seitenzahl: 164
• Erscheinungstermin: 28. September 2016
• Englisch
• Abmessung: 235mm x 155mm x 10mm
• Gewicht: 260g
• ISBN-13: 9783662535790
• ISBN-10: 3662535793
• Artikelnr.: 45679728

Inhaltsangabe

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 Series.