Levente Kovacs – författare

Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learning, evolutionary algorithms, Bayesian optimization, and swarm intelligence. The book is a comprehensive guide that provides readers with theoretical concepts and case studies for applying advanced intelligence methods to real-world problems. Authored by a team of renowned experts in the field, the book offers a holistic approach to understanding and applying intelligence methods across various domains.It explores the fundamental concepts of data science and optimization, providing a strong foundation for readers to build upon, and will be a welcomed resource for AI researchers, data scientists, engineers, and developers on key topics such as evolutionary optimization techniques, reinforcement learning, Natural Language Processing, Bayesian optimization, advanced analytics for large-scale data, fuzzy logic, quantum computing, graph theory, convex optimization, differential evolution, and more.Provides comprehensive coverage of advanced intelligence methodsIncludes real-world examples and case studies that illustrate the application of these methods across a wide range of fieldsBegins with an introduction to Deep Learning concepts and quickly moves to the most leading-edge topics in computational intelligence, all with an application to data science techniques

This book gathers contributions on fuzzy neural control, intelligent and non-linear control, dynamic systems and cyber-physical systems. It presents the latest theoretical and practical results, including numerous applications of computational intelligence in various disciplines such as engineering, medicine, technology and the environment. The book is dedicated to Imre J. Rudas on his seventieth birthday.

The book publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics, and life science, as well as the methodologies behind them.The book contains monographs, lecture notes, and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution—this permits a rapid and broad disseminational of research results.Soft computing, as a collection of techniques exploiting approximation and tolerance for imprecision and uncertainty in traditionally intractable problems, has become very effective and popular especially because of the synergy derived from its components. The integration of constituent technologies provides complementary methods that allow developing flexible computing tools and solving complex problems. A wide area of natural applications of soft computing techniques consists of the control of dynamic systems, including robots. Loosely speaking, control can be understood as driving a process to attain a desired goal. Intelligent control can be seen as an extension of this concept, to include autonomous human-like interactions of a machine with the environment.Intelligent robots can be characterized by the ability to operate in an uncertain, changing environment with the help of appropriate sensing. They have the power to autonomously plan and execute motion sequences to achieve a goal specified by a human user without detailed instructions. In this book leading specialists address various theoretical and practical aspects in soft computing, intelligent robotics, and control. The problems discussed are taken from fuzzy systems, neural networks, interactive evolutionary computing, intelligent mobile robotics, and intelligent control of linear and nonlinear dynamic systems.