Books in the Studies in Computational Intelligence series

This book proposes a number of promising models and methods for adaptive segmentation, swarm partition, permissible segmentation, and transform properties, as well as techniques for spatio-temporal video segmentation and interpretation, online fuzzy clustering of data streams, and fuzzy systems for information retrieval.

This comprehensive book is primarily intended for researchers, engineers, mathematicians and computer security specialists who are interested in multimedia security, steganography, encryption, and related research fields.

This book builds on decades of research and provides contemporary theoretical foundations for practical applications to intelligent technologies and advances in artificial intelligence (AI). Reflecting the growing realization that computational models of human reasoning and interactions can be improved by integrating heterogeneous information resources and AI techniques, its ultimate goal is to promote integrated computational approaches to intelligent computerized systems. The book covers a range of interrelated topics, in particular, computational reasoning, language, syntax, semantics, memory, and context information. The respective chapters use and develop logically oriented methods and techniques, and the topics selected are from those areas of logic that contribute to AI and provide its mathematical foundations.The intended readership includes researchers working in the areas of traditional logical foundations, and onnew approaches to intelligent computational systems.

This book includes original research findings in the field of memetic algorithms for image processing applications.

With the proliferation of technology, science became a medium used to create and interpret heritage in a way that redefines human achievements.

This book discusses the challenges facing current research in knowledge discovery and data mining posed by the huge volumes of complex data now gathered in various real-world applications (e.g., business process monitoring, cybersecurity, medicine, language processing, and remote sensing).

This book provides step-by-step explanations of successful implementations and practical applications of machine learning. deep convolutional neural networks with performance enhancement techniques (including network design, learning rate optimization, data augmentation, transfer learning);

This book discusses heuristic methods - methods lacking a solid theoretical justification - which are ubiquitous in numerous application areas, and explains techniques that can make heuristic methods more reliable.

This book discusses various aspects of Industry 4.0 from the perspective of information system evolution. The interdisciplinary book addresses a number of topics related to modern information technologies, and presents innovative concepts, methods, models and tools for the development of information systems to support Industry 4.0.

Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models.

Mainly focusing on processing uncertainty, this book presents state-of-the-art techniques and demonstrates their use in applications to econometrics and other areas. Measurement uncertainty is usually described using probabilistic techniques, while uncertainty in expert estimates is often described using fuzzy techniques.

This book is useful to understand and write alongside non-human agents, examine the impact of algorithms and AI on writing, and accommodate relationships with autonomous agents. This book prepares researchers, students, practitioners, and citizens to work with AI writers, virtual humans, and social robots.

This book covers all core technologies like neural networks, fuzzy systems, and evolutionary computation and their applications in the systems. Computationally intelligent system is a new concept for advanced information processing. The computationally intelligent system highly relies on numerical information supplied by manufacturers unlike AI.

We are, therefore, putting an effort to write this edited book on the future applications of machine learning on robotics where several applications have been included in separate chapters. This book will provide the future vision on the unexplored areas of applications of Robotics using machine learning.

Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.The workshop organizers selected the best papers from those papers accepted for presentation at the workshop.

The spectrum of the presented contributions ranges from education and training, industrial applications in production and logistics to the development of new approaches in basic research, which will further expand the possibilities of future applications of AI in industrial settings.

We describe in this book, recent developments on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their application in areas such as, intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems.

This book is a comprehensive collection of extended contributions from the Workshops on Computational Optimization 2019.Our everyday life is unthinkable without optimization.

This book discusses human-computer interaction (HCI) which is a multidisciplinary field of study which aims at developing and implementing tools and techniques to attain an effective and efficient interaction between the humans (the users) and computers.

This book states that the major aim audience are people who have some familiarity with Internet of things (IoT) but interested to get a comprehensive interpretation of the role of deep Learning in maintaining the security and privacy of IoT. A reader should be friendly with Python and the basics of machine learning and deep learning.

This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences.

This edited book presents scientific results of the International Semi-Virtual Workshop on Data Science and Digital Transformation in the Fourth Industrial Revolution (DSDT 2020) which was held on October 15, 2020, at Soongsil University, Seoul, Korea.