International Joint Conference SOCO'18-CISIS'18-ICEUTE'18 (eBook, PDF)

San Sebastián, Spain, June 6-8, 2018 Proceedings
Redaktion: Graña, Manuel; Corchado, Emilio; Quintián, Héctor; Sáez, José Antonio; Herrero, Álvaro; Etxaniz, Oier; López-Guede, José Manuel

• Format: PDF

Produktbeschreibung

This book includes papers presented at SOCO 2018, CISIS 2018 and ICEUTE 2018, all held in the beautiful and historic city of San Sebastian (Spain), in June 2018.

Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze highly complex issues and phenomena.

After a rigorous peer-review process, the 13th SOCO 2018 International Program Committee selected 41 papers, with a special emphasis on optimization, modeling and control using soft computing techniques and soft computing applications in the field of industrial and environmental enterprises.

The aim of the 11th CISIS 2018 conference was to offer a meeting opportunity for academic and industry researchers from the vast areas of computational intelligence, information security, and data mining. The need for intelligent,flexible behaviour by large, complex systems, especially in mission-critical domains, was the catalyst for the overall event.

Eight of the papers included in the book were selected by the CISIS 2018 International Program Committee.

The International Program Committee of ICEUTE 2018 selected 11 papers for inclusion in these conference proceedings.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 613
• Erscheinungstermin: 6. Juni 2018
• Englisch
• ISBN-13: 9783319941202
• Artikelnr.: 52859040

Inhaltsangabe

An investment recommender multi-agent system in financial technology.- Using Genetic Algorithms to Optimize the Location of Electric Vehicle Charging Stations.- Steel Tube Cross Section Geometry Measurement by 3D Scanning.- Enhacing Confusion Entropy (CEN) as measure for evaluating classifiers.- Deep learning for big data time series forecasting applied to solar power.

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