Soft Computing Applications

This book constitutes the selected papers from the 9th World Conference on Soft Computing or WConSC 2024, held on September 2527, 2024, in Baku, Azerbaijan. The conference was organized by the University of California Berkeley, California, USA, and Azerbaijan State Economic University, Azerbaijan.

The conference papers included in these proceedings were grouped into the following research areas: Fuzzy simulation and data mining, intelligent and multi-agent systems, fuzzy recognition, artificial intelligence, fuzzy sets and quantum computing, fuzzy control systems, fuzzy methods and modern approaches, fuzzy methods and modern approaches, fuzzy control and fuzzy approach, fuzzy sets and decision on making, soft computing and fuzzy logic, fuzzy applications and fuzzy control, fuzzy logic and its applications, fuzzy neural networks, intelligent methods and fuzzy approach, fuzzy methods and applications in medicine, fuzzy applications and analysis system.

The topics of this book provide useful information to professors, researchers, and graduated students in the area of soft computing techniques and applications.

Presents recent research on Soft Computing Applications Selected Papers of the 9th World Conference on Soft Computing, September 25-27, 2024, Baku, Azerbaijan Written by experts in the field

Inhalt

Fuzzy sets and Quantum Computing.- What Fuzzy and Quantum Computing can Learn from the Success of Deep Learning.- How to Fairly Allocate Safety Benefits of Self-Driving Cars.- Paradox of Causality and Paradoxes of Set Theory.- Fuzzy Recognition System.- Continuous Growth of Information and the Forgetting Function in the Recognition of Sound Information.- Technology Building a Virtual Room for Recognition of Visual and Sound Information.- Basic Methods for Recognizing Visual and Audio Information and the Results of Experiments.- Intelligent Methods and Fuzzy Approach.- An Approach to Feature Engineering in a Data-Driven Production Management.- How to Make a Decision Under Interval Uncertainty if we do not Know the Utility Function.-Number Representation with Varying Number of Bits.- Fuzzy Neural Networks.- How to Make a Neural Network Learn from a Small Number of Examplesand Learn Fast: An Idea.