Böcker i Studies in Computational Intelligence-serien

The aim of this publication is to identify and apply suitable methods for analysing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general.

Nowadays, medical data is available in many different forms like MRI images, CT scan images, EHR data, test reports, histopathological data and doctor patient conversation data.

This book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.

This book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) - Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework of Granular Computing.

The book contains the newest advances related to research and development of complex intellectual systems of various nature, acting under conditions of uncertainty and multifactor risks, intelligent systems for decision-making, high performance computing, state-of-the-art information technologies for needs of science, industry, economy, and environment. The most important problems of sustainable development and global threats estimation, forecast and foresight in tasks of planning and strategic decision-making are investigated. This monograph will be useful to researchers, post-graduates, and advanced students specializing in system analysis, decision-making, strategic planning or engineering design, fundamentals of computational Intelligence, artificial Intelligence systems based on hybrid neural networks, big data, and data mining.

This book covers theoretical work, applications, approaches, and techniques for computational models of information and its presentation by language (artificial, human, or natural in other ways).

This book bridges principles and real-world applications, while also providing thorough theory and technology for the development of artificial intelligence and robots. A lack of cross-pollination between AI and robotics research has led to a lack of progress in both fields. Now that both technologies have made significant strides, there is increased interest in combining the two domains in order to create a new integrated AI and robotics trend. In order to achieve wiser urbanization and more sustainable development, AI in smart cities will play a significant part in equipping the cities with advanced features that will allow residents to safely move about, stroll, shop, and enjoy a more comfortable way of life.If you are a student, researcher, engineer, or professional working in this field, or if you are just curious in the newest advancements in robotics and artificial intelligence for cybersecurity, this book is for you!

This book presents different techniques and methodologies that used to help improve the decision-making process and increase the likelihood of success in sector as follows: agriculture, financial services, logistics, energy services, health and others.

Introduction.- Preliminaries.- Semantical Domains.- Concurrency Traces.

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.

The book focuses on original approaches intended to support the development of biologically inspired cognitive architectures. It bridges together different disciplines, including artificial intelligence, linguistics, neuro- and social sciences, psychology and philosophy of mind, among others. The chapters are based on contributions presented at the 12th Annual Meeting of the BICA Society (BICA 2021), which consisted of two parallel virtual events:  Information in Biologically Inspired Cognitive Architectures based Systems, held during the 2021 Summit of the International Society for the Study of Information, on September 12-19, 2021, from Vienna, Austria, and the 2021 International Workshop on Biologically Inspired Cognitive Architectures, held during the 21st ACM International Conference on Intelligent Virtual Agents, on September 14-17, 2021, from the Fukuchiyama City, Kyoto, Japan. The book discusses emerging methods, theories and ideas towards the realization of general-purpose humanlike artificial intelligence or fostering a better understanding of the ways the human mind works. It provides engineers, mathematicians, psychologists, computer scientists and other experts with a timely snapshot of recent research and a source of inspiration for future developments in the broadly intended areas of artificial intelligence and biological inspiration.

This book confronts this old issue head on, with a historical look, incorporating recent advances and new perspectives, thus leading to promising new methods and approaches.

The topics covered include representation of fuzzy spatiotemporal XML data, topological relationship determination for fuzzy spatiotemporal XML data, mapping between the fuzzy spatiotemporal relational database model and fuzzy spatiotemporal XML data model, and consistencies in fuzzy spatiotemporal XML data updating.

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems.

This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "e;n-D glasses,"e; where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level.  The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.

This book collects 43 regular papers received from 18 countries that present innovative advances in intelligent and distributed computing, encompassing both architectural and algorithmic results related to these fields. Significant attention is given to new models, techniques, and applications for distributed intelligent architectures and high-performance architectures, machine learning techniques, Internet of Things, blockchain, intelligent transport systems, data analytics, trust and reputation systems, and many others. The book includes the peer-reviewed proceedings of the 14th International Symposium on Intelligent Distributed Computing (IDC 2021), which was held in online mode due to the COVSARS2 pandemic situation, during September 16-18, 2021. The IDC 2021 event included sessions on Internet of Things, data analytics, machine learning, multi-agent systems, algorithms, future intelligent transport solutions, blockchain, intelligent distributed computing for cyber-physical security, and security and trust and reputation in intelligent environments.

With rise of smart medical sensors, cloud computing and the health care technologies, "e;connected health"e; is getting remarkable consideration everywhere. Recently, the Internet of Things (IoT) has brought the vision of a smarter world into reality. Cloud computing fits well in this scenario as it can provide high quality of clinical experience. Thus an IoT-cloud convergence can play a vital role in healthcare by offering better insight of heterogeneous healthcare content supporting quality care. It can also support powerful processing and storage facilities of huge data to provide automated decision making. This book aims to report quality research on recent advances towards IoT-Cloud convergence for smart healthcare, more specifically to the state-of-the-art approaches, design, development and innovative use of those convergence methods for providing insights into healthcare service demands. Students, researchers, and medical experts in the field of information technology, medicine, cloud computing, soft computing technologies, IoT and the related fields can benefit from this handbook in handling real-time challenges in healthcare. Current books are limited to focus either on soft computing algorithms or smart healthcare. Integration of smart and cloud computing models in healthcare resulting in connected health is explored in detail in this book.

This book takes a forward-looking approach by bringing in research and contributions that facilitate in mapping the impact of AI and big data on businesses, the nature of work along with providing practical solutions for preparing the work, workplace, and the workforce of the future.Organizations globally have been experiencing immense transformation due to the reinvention and redefining of the business models due to the dynamic nature of the business environment. Looking at an organizational context, undeniably, the definition of 'work' and 'organizations' is genuinely changing. Artificial intelligence, big data, automation, and robotics are a few of those keywords that are seemingly entering the workplace and reshaping the way work is being done. Moreover, the transition that is being addressed herein not only focuses upon aspects that are operative within an organization like the organizational culture, team building, networking, recruitments, and so on but also aims to address the external aspects like supply chain management, value chain analysis, investment management, etc. Broadly, every single step that is now taken is intensely experiencing this impact upon its functioning. This book serves as a guide not just to the academia but also to the industry to adopt suitable strategies that offer insights into global best practices as well as the innovations in the domain.

This book collects different methodologies that permit metaheuristics and machine learning to solve real-world problems. This book has exciting chapters that employ evolutionary and swarm optimization tools combined with machine learning techniques. The fields of applications are from distribution systems until medical diagnosis, and they are also included different surveys and literature reviews that will enrich the reader. Besides, cutting-edge methods such as neuroevolutionary and IoT implementations are presented in some chapters. In this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and can be used in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the material can be helpful for research from the evolutionary computation, artificial intelligence communities. 

Manifold optimization is an emerging field of contemporary optimization that constructs efficient and robust algorithms by exploiting the specific geometrical structure of the search space. In our case the search space takes the form of a manifold. Manifold optimization methods mainly focus on adapting existing optimization methods from the usual "e;easy-to-deal-with"e; Euclidean search spaces to manifolds whose local geometry can be defined e.g. by a Riemannian structure. In this way the form of the adapted algorithms can stay unchanged. However, to accommodate the adaptation process, assumptions on the search space manifold often have to be made. In addition, the computations and estimations are confined by the local geometry.This book presents a framework for population-based optimization on Riemannian manifolds that overcomes both the constraints of locality and additional assumptions. Multi-modal, black-box manifold optimization problems on Riemannian manifolds can be tackled using zero-order stochastic optimization methods from a geometrical perspective, utilizing both the statistical geometry of the decision space and Riemannian geometry of the search space.This monograph presents in a self-contained manner both theoretical and empirical aspects of stochastic population-based optimization on abstract Riemannian manifolds.