"This book provides information regarding state-of-the-art research outcomes and cutting-edge technology on various aspects of the human movement"--Provided by publisher.

"This book provides information regarding state-of-the-art research outcomes and cutting-edge technology on various aspects of the human movement"--Provided by publisher.

As in many other fields, biomedical engineers benefit from the use of computational intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even greater if there were scientific literature that specifically focused on the biomedical applications of computational intelligence techniques. The first comprehensive field-

Uncertainty in Computational Intelligence-Based Decision-Making focuses on techniques for reasoning and decision-making under uncertainty that are used to solve issues in artificial intelligence (AI). It covers a wide range of subjects, including knowledge acquisition and automated model construction, pattern recognition, machine learning, natural language processing, decision analysis, and decision support systems, among others. The first chapter of this book provides a thorough introduction to the topics of causation in Bayesian belief networks, applications of uncertainty, automated model construction and learning, graphic models for inference and decision making, and qualitative reasoning. The following chapters examine the fundamental models of computational techniques, computational modeling of biological and natural intelligent systems, including swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems, and evolutionary computation. They also examine decision making and analysis, expert systems, and robotics in the context of artificial intelligence and computer science. - Provides readers a thorough understanding of the uncertainty that arises in artificial intelligence (AI), computational intelligence (CI) paradigms, and algorithms - Encourages readers to put concepts into practice and solve complex real-world problems using CI development frameworks like decision support systems and visual decision design - Provides a comprehensive overview of the techniques used in computational intelligence, uncertainty, and decision

This book presents recent research on computational intelligence (CI) algorithms in the field of sport. In the modern age, information technologies have greatly reduced the need for human effort in the carrying out of many daily tasks. These technologies have radically influenced the lives of humans, and the information society in general. Unfortunately, these advances have brought with them certain negative effects, including the encouragement of sedentary lifestyles and the attendant health problems such as obesity that these engender. Other modern maladies, chiefly cardiovascular disease, diabetes, and cancer, have also been on the increase. Today, sports are virtually the only activity that still connects modern humans to their original lifestyle, which was based on physical motion. This book tears familiarizing sports scientists with the foundations of computational intelligence, while at the same time presenting the problems that have arisen in the training domain to computer scientists. Lastly, the book proposes the use of an Artificial Sports Trainer designed to enhance the training of modern athletes who cannot afford the considerable expense of hiring a human personal trainer. This intelligent system can monitor performance and design and direct appropriate future training, thus promoting both healthy lifestyles and competitive success in athletes.

System designers are faced with a large set of data which has to be analysed and processed efficiently. Advanced computational intelligence paradigms present tremendous advantages by offering capabilities such as learning, generalisation and robustness. These capabilities help in designing complex systems which are intelligent and robust. The book includes a sample of research on the innovative applications of advanced computational intelligence paradigms. The characteristics of computational intelligence paradigms such as learning, generalization based on learned knowledge, knowledge extraction from imprecise and incomplete data are the extremely important for the implementation of intelligent machines. The chapters include architectures of computational intelligence paradigms, knowledge discovery, pattern classification, clusters, support vector machines and gene linkage analysis. We believe that the research on computational intelligence will simulate great interest among designers and researchers of complex systems. It is important to use the fusion of various constituents of computational intelligence to offset the demerits of one paradigm by the merits of another.