Buch, Englisch, Band 393, 576 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1039 g
Recent Developments and the New Direction in Soft-Computing Foundations and Applications
1. Auflage 2021
ISBN: 978-3-030-47123-1
Verlag: Springer International Publishing
Selected Papers from the 7th World Conference on Soft Computing, May 29-31, 2018, Baku, Azerbaijan
Buch, Englisch, Band 393, 576 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1039 g
Reihe: Studies in Fuzziness and Soft Computing
ISBN: 978-3-030-47123-1
Verlag: Springer International Publishing
This book gathers authoritative contributions in the field of Soft Computing. Based on selected papers presented at the 7th World Conference on Soft Computing, which was held on May 29–31, 2018, in Baku, Azerbaijan, it describes new theoretical advances, as well as cutting-edge methods and applications. New theories and algorithms in fuzzy logic, cognitive modeling, graph theory and metaheuristics are discussed, and applications in data mining, social networks, control and robotics, geoscience, biomedicine and industrial management are described. This book offers a timely, broad snapshot of recent developments, including thought-provoking trends and challenges that are yielding new research directions in the diverse areas of Soft Computing.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
- Technische Wissenschaften Technik Allgemein Betriebswirtschaft für Ingenieure
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
Weitere Infos & Material
Chapter 1: Fuzziness in Information Extracted from Tweets’ Hashtag and Keywords.- Chapter 2: Why Triangular and Trapezoid Membership Functions: A Simple Explanation.- Chapter 3: Statistical Approach to Fuzzy Cognitive Maps.- Chapter 4: Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm.- Chapter 5: Semi–Supervised Learning to Rank with Nonlinear Preference Model.- Chapter 6: The Concept of Linguistic Variable Revisited.- Chapter 7: Z-number.- Chapter 8: Fuzzy normed linear spaces.
