Recent Developments and the New Direction in Soft-Computing Foundations and Applications

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 2931, 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.

Offers a timely snapshot on new theories and methods in fuzzy logic Provides readers with a broad perspective on the applications of fuzzy sets Describes solutions for problems in engineering, medicine, as well as physical and social sciences

Klappentext
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.

Inhalt
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: SemiSupervised 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.