Applications of Computational Intelligence in Multi-Disciplinary Research provides the readers with a comprehensive handbook for applying the powerful principles, concepts, and algorithms of computational intelligence to a wide spectrum of research cases. The book covers the main approaches used in computational intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods, all of which can be collectively viewed as soft computing. Other key approaches included are swarm intelligence and artificial immune systems. These approaches provide researchers with powerful tools for analysis and problem-solving when data is incomplete and when the problem under consideration is too complex for standard mathematics and the crisp logic approach of Boolean computing. - Provides an overview of the key methods of computational intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods - Includes case studies and real-world examples of computational intelligence applied in a variety of research topics, including bioinformatics, biomedical engineering, big data analytics, information security, signal processing, machine learning, nanotechnology, and optimization techniques - Presents a thorough technical explanation on how computational intelligence is applied that is suitable for a wide range of multidisciplinary and interdisciplinary research

This book focuses on computational intelligence techniques and their applications — fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches./a

Computational Intelligence: Principles, Techniques and Applications presents both theories and applications of computational intelligence in a clear, precise and highly comprehensive style. The textbook addresses the fundamental aspects of fuzzy sets and logic, neural networks, evolutionary computing and belief networks. The application areas include fuzzy databases, fuzzy control, image understanding, expert systems, object recognition, criminal investigation, telecommunication networks, and intelligent robots. The book contains many numerical examples and homework problems with sufficient hints so that the students can solve them on their own.

Computational intelligence (CI) in concrete technology has not yet been fully explored worldwide because of some limitations in data sets. This book discusses the selection and separation of data sets, performance evaluation parameters for different types of concrete and related materials, and sensitivity analysis related to various CI techniques. Fundamental concepts and essential analysis for CI techniques such as artificial neural network, fuzzy system, support vector machine, and how they work together for resolving real-life problems, are explained. Features: It is the first book on this fast-growing research field. It discusses the use of various computation intelligence techniques in concrete technology applications. It explains the effectiveness of the methods used and the wide range of available techniques. It integrates a wide range of disciplines from civil engineering, construction technology, and concrete technology to computation intelligence, soft computing, data science, computer science, and so on. It brings together the experiences of contributors from around the world who are doing research in this field and explores the different aspects of their research. The technical content included is beneficial for researchers as well as practicing engineers in the concrete and construction industry.

Written as a result of a seven year research project using computational intelligence techniques for solving mineral processing problems at the U.S. Bureau of Mines, this book is about intelligent, adaptive process control. It brings together ideas from the field of computational intelligence , a part of the larger field of artificial intelligence, including fuzzy mathematics, genetic algorithms, and neural networks and uses these ideas to develop a generic architecture for accomplishing adaptive process control. In the development of this architecture, the requisite tools are described and then demonstrated on a number of problems. Moreover, most of the examples are of interest in industrial settings (although some simple examples are provided in the beginning so that the reader can focus on technique and not be overburdened with the complexity of the problems being solved.) The focus of Practical Applications of Computational Intelligence for Adaptive Control is on practical applications. It provides practicing engineers and scientists with the information they need to solve process control problems in industry and academia. If the reader is interested in solving difficult control problems or interested in the mechanics of basic computational intelligence techniques, then this book is an excellent place to start.

The field of computational intelligence has grown tremendously over that past five years, thanks to evolving soft computing and artificial intelligent methodologies, tools and techniques for envisaging the essence of intelligence embedded in real life observations. Consequently, scientists have been able to explain and understand real life processes and practices which previously often remain unexplored by virtue of their underlying imprecision, uncertainties and redundancies, and the unavailability of appropriate methods for describing the incompleteness and vagueness of information represented. With the advent of the field of computational intelligence, researchers are now able to explore and unearth the intelligence, otherwise insurmountable, embedded in the systems under consideration. Computational Intelligence is now not limited to only specific computational fields, it has made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design to name a few. Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications explores the use of this computational paradigm across a wide range of applied domains which handle meaningful information. Chapters investigate a broad spectrum of the applications of computational intelligence across different platforms and disciplines, expanding our knowledge base of various research initiatives in this direction. This volume aims to bring together researchers, engineers, developers and practitioners from academia and industry working in all major areas and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing. - Provides insights into the theory, algorithms, implementation, and application of computational intelligence techniques - Covers a wide range of applications of deep learning across various domains which are researching the applications of computational intelligence - Investigates novel techniques and reviews the state-of-the-art in the areas of machine learning, computer vision, soft computing techniques

Computational science, in collaboration with engineering, acts as a bridge between hypothesis and experimentation. It is essential to use computational methods and their applications in order to automate processes as many major industries rely on advanced modeling and simulation. Computational science is inherently interdisciplinary and can be used to identify and evaluate complicated systems, foresee their performance, and enhance procedures and strategies. Applications of Computational Science in Artificial Intelligence delivers technological solutions to improve smart technologies architecture, healthcare, and environmental sustainability. It also provides background on key aspects such as computational solutions, computation framework, smart prediction, and healthcare solutions. Covering a range of topics such as high-performance computing and software infrastructure, this reference work is ideal for software engineers, practitioners, researchers, scholars, academicians, instructors, and students.

Industrial engineering is a branch of engineering dealing with the optimization of complex processes or systems. It is concerned with the development, improvement, implementation and evaluation of production and service systems. Computational Intelligence Systems find a wide application area in industrial engineering: neural networks in forecasting, fuzzy sets in capital budgeting, ant colony optimization in scheduling, Simulated Annealing in optimization, etc. This book will include most of the application areas of industrial engineering through these computational intelligence systems. In the literature, there is no book including many real and practical applications of Computational Intelligence Systems from the point of view of Industrial Engineering. Every chapter will include explanatory and didactic applications. It is aimed that the book will be a main source for MSc and PhD students.

This book constitutes the thoroughly refereed proceedings of the Second IEEE Colombian Conference, ColCACI 2019, held in Barranquilla, Colombia, in June 2019. The 21 full papers presented were carefully reviewed and selected from 59 submissions. The papers cover such topics as video processing; biomedical systems; image processing, etc.

Computational Intelligence: Concepts to Implementations provides the most complete and practical coverage of computational intelligence tools and techniques to date. This book integrates various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook on the subject, supported with lots of practical examples. It asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence. This book lays emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective. The book moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific con. It explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. It details the metrics and analytical tools needed to assess the performance of computational intelligence tools. The book concludes with a series of case studies that illustrate a wide range of successful applications. This book will appeal to professional and academic researchers in computational intelligence applications, tool development, and systems. - Moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific concepts and paradigms that inform the authors' methodologies - Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation - Details the metrics and analytical tools needed to assess the performance of computational intelligence tools - Concludes with a series of case studies that illustrate a wide range of successful applications - Presents code examples in C and C++ - Provides, at the end of each chapter, review questions and exercises suitable for graduate students, as well as researchers and practitioners engaged in self-study