Medical images are at the base of many routine clinical decisions and their influence continues to increase in many fields of medicine. Since the last decade, computers have become an invaluable tool for supporting medical image acquisition, processing, organization and analysis. Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques provides a panorama of the current boundary between biomedical complexity coming from the medical image context and the multiple techniques which have been used for solving many of these problems. This innovative publication serves as a leading industry reference as well as a source of creative ideas for applications of medical issues.

This book explains the most prominent and some promising new, general techniques that combine metaheuristics with other optimization methods. A first introductory chapter reviews the basic principles of local search, prominent metaheuristics, and tree search, dynamic programming, mixed integer linear programming, and constraint programming for combinatorial optimization purposes. The chapters that follow present five generally applicable hybridization strategies, with exemplary case studies on selected problems: incomplete solution representations and decoders; problem instance reduction; large neighborhood search; parallel non-independent construction of solutions within metaheuristics; and hybridization based on complete solution archives. The authors are among the leading researchers in the hybridization of metaheuristics with other techniques for optimization, and their work reflects the broad shift to problem-oriented rather than algorithm-oriented approaches, enabling faster and more effective implementation in real-life applications. This hybridization is not restricted to different variants of metaheuristics but includes, for example, the combination of mathematical programming, dynamic programming, or constraint programming with metaheuristics, reflecting cross-fertilization in fields such as optimization, algorithmics, mathematical modeling, operations research, statistics, and simulation. The book is a valuable introduction and reference for researchers and graduate students in these domains.

The goal of this book is to crystallize the emerging mobile computing technologies and trends into positive efforts to focus on the most promising solutions in services computing. Many toys built today are increasingly using these technologies together and it is important to understand the various research and practical issues. The book will provide clear proof that mobile technologies are playing an ever increasing important and critical role in supporting toy computing, which is a new research discipline in computer science. It is also expected that the book will further research new best practices and directions in toy computing. The goal of this book is to bring together academics and practitioners to describe the use and synergy between the above-mentioned technologies. This book is mainly intended for researchers and students working in computer science and engineering, and for toy industry technology providers, having particular interests in mobile services. The wide range of authors of this book will help the various communities understand both specific and common problems. This book facilities software developers and researchers to become more aware of this challenging research opportunity. As well, the book is soliciting shall provide valuable strategic outlook on the emerging toy industry.

This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.

This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who deal with stream data, e.g. in telecommunication, banking, and sensor networks.

This book offers a timely overview of fuzzy and rough set theories and methods. Based on selected contributions presented at the International Symposium on Fuzzy and Rough Sets, ISFUROS 2017, held in Varadero, Cuba, on October 24-26, 2017, the book also covers related approaches, such as hybrid rough-fuzzy sets and hybrid fuzzy-rough sets and granular computing, as well as a number of applications, from big data analytics, to business intelligence, security, robotics, logistics, wireless sensor networks and many more. It is intended as a source of inspiration for PhD students and researchers in the field, fostering not only new ideas but also collaboration between young researchers and institutions and established ones.

This book focuses on the characteristics of cooperative control problems for general linear multi-agent systems, including formation control, air traffic control, rendezvous, foraging, role assignment, and cooperative search. On this basis and combined with linear system theory, it introduces readers to the cooperative tracking problem for identical continuous-time multi-agent systems under state-coupled dynamics; the cooperative output regulation for heterogeneous multi-agent systems; and the optimal output regulation for model-free multi-agent systems. In closing, the results are extended to multiple leaders, and cooperative containment control for uncertain multi-agent systems is addressed. Given its scope, the book offers an essential reference guide for researchers and designers of multi-agent systems, as well as a valuable resource for upper-level undergraduate and graduate students.

DeLancey shows that our best philosophical and scientific understanding of the emotions provides essential insights on key questions in the philosophy of mind and artificial intelligence. This is the first such book to offer this perspective. Passionate Engines also provides a bold new approach to the study of the mind based on the latest scientific research and along the way, provides an accessible overview of the science of emotion, with minimal jargon and explanation of the technical issues that arise. It is accessible to a wide range of readers, including philosophers, psychologists, computer scientists, and others in disciplines touching on cognitive science.

There is a deep desire in men, in order to reproduce intelligence and place it in a machine. Neural Networks are an attempt to reproduce the synaptic connections of our brain in a computer. Duplicating the way we use our neurons to think in a machine, it is expected to have a device that could be able to do intelligent tasks, the ones reserved just to humans some time ago. Neural Network is a reality now, not a fantasy, and they have been made in order to recognize patterns (a face, a photograph or a song, are patterns) and forecast trends. I have seen many books about this subject in my life. All of them are hard to read, and tedious to learn, so I decided to make my own one.

For beginner readers, I have tried to use a simple language, in order to be understood by anyone who wants to know about nets. An easy to read, practical and concise work. If you are interested in the brain functions and how can we simulate it in a computer, youll get here a differenty to penetrate into their secrets. For advanced readers who want to make their own nets, I have included a methodology for building neural networks and complete sample computer source-code with tricks that will save you a lot of time while designing it.

This book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas - as Webster's 1923 "English Composition and Literature" puts it: "A sentence is a group of words expressing a complete thought". Thus the representation of sentences and the words that make them up is vital in advancing artificial intelligence and other "smart" systems currently being developed. Providing an overview of the research in the area, from Bengio et al.'s seminal work on a "Neural Probabilistic Language Model" in 2003, to the latest techniques, this book enables readers to gain an understanding of how the techniques are related and what is best for their purposes. As well as a introduction to neural networks in general and recurrent neural networks in particular, this book details the methods used for representing words, senses of words, and larger structures such as sentences or documents. The book highlights practical implementations and discusses many aspects that are often overlooked or misunderstood. The book includes thorough instruction on challenging areas such as hierarchical softmax and negative sampling, to ensure the reader fully and easily understands the details of how the algorithms function. Combining practical aspects with a more traditional review of the literature, it is directly applicable to a broad readership. It is an invaluable introduction for early graduate students working in natural language processing; a trustworthy guide for industry developers wishing to make use of recent innovations; and a sturdy bridge for researchers already familiar with linguistics or machine learning wishing to understand the other.

This book provides an overview of intelligent decision-making techniques and discusses their application in production and retail operations. Manufacturing and retail enterprises have stringent standards for using advanced and reliable techniques to improve decision-making processes, since these processes have significant effects on the performance of relevant operations and the entire supply chain. In recent years, researchers have been increasingly focusing attention on using intelligent techniques to solve various decision-making problems. The opening chapters provide an introduction to several commonly used intelligent techniques, such as genetic algorithm, harmony search, neural network and extreme learning machine. The book then explores the use of these techniques for handling various production and retail decision-making problems, such as production planning and scheduling, assembly line balancing, and sales forecasting.

This book addresses information technologies recently applied in the field of construction safety. Combining case studies, literature reviews and interviews to study the issue, it presents cutting-edge applications of various information technologies (ITs) in construction in different parts of the world, together with a wealth of figures, tables and examples. Though primarily intended for researchers and experts in the field, the book will also benefit graduate students.

This book provides readers with a timely and comprehensive yet concise view on the field of fuzzy logic and its real-world applications. The chapters, written by authoritative scholars in the field, report on promising new models for data analysis, decision making, and systems modeling, with a special emphasis on their applications in management science. The book is a token of appreciation from the fuzzy research community to Professor Christer Carlsson for his long time research and organizational commitment, which have among other things resulted in the foundation and success of the Institute for Advanced Management Systems Research (IAMSR) at Abo Akademi University, in Abo (Turku), Finland. The book serves as timely guide for the fuzzy logic and operations research communities alike.

This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods. Gerard Biau is a professor at Universite Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).

This provides a comprehensive overview of the key principles of security concerns surrounding the upcoming Internet of Things (IoT), and introduces readers to the protocols adopted in the IoT. It also analyses the vulnerabilities, attacks and defense mechanisms, highlighting the security issues in the context of big data. Lastly, trust management approaches and ubiquitous learning applications are examined in detail. As such, the book sets the stage for developing and securing IoT applications both today and in the future.

This book provides a comprehensive overview of computational intelligence methods for semantic knowledge management. Contrary to popular belief, the methods for semantic management of information were created several decades ago, long before the birth of the Internet. In fact, it was back in 1945 when Vannevar Bush introduced the idea for the first protohypertext: the MEMEX (MEMory + indEX) machine. In the years that followed, Bush's idea influenced the development of early hypertext systems until, in the 1980s, Tim Berners Lee developed the idea of the World Wide Web (WWW) as it is known today. From then on, there was an exponential growth in research and industrial activities related to the semantic management of the information and its exploitation in different application domains, such as healthcare, e-learning and energy management. However, semantics methods are not yet able to address some of the problems that naturally characterize knowledge management, such as the vagueness and uncertainty of information. This book reveals how computational intelligence methodologies, due to their natural inclination to deal with imprecision and partial truth, are opening new positive scenarios for designing innovative semantic knowledge management architectures.

This book clarifies the common misconception that there are no systematic instruments to support ideation, heuristics and creativity. Using a collection of articles from professionals practicing the Theory of Inventive Problem Solving (TRIZ), this book presents an overview of current trends and enhancements within TRIZ in an international context, and shows its different roles in enhancing creativity for innovation in research and practice. Since its first introduction by Genrikh Saulovich Altshuller in 1956 in the USSR, the TRIZ method has been widely used by inventors, design engineers and has become a standard element of innovation support tools in many Fortune 500 companies. However, TRIZ has only recently entered the domain of scientific publications and discussion. This collection of articles is meant as a record of scientific discussion on TRIZ that reflects the most interesting talking points, research interests, results and expectations. Topics such as Creative and Inventive Design, Patent Mining, and Knowledge Harvesting are also covered in this book.

This book offers an essential guide to IoT Security, Smart Cities, IoT Applications, etc. In addition, it presents a structured introduction to the subject of destination marketing and an exhaustive review on the challenges of information security in smart and intelligent applications, especially for IoT and big data contexts. Highlighting the latest research on security in smart cities, it addresses essential models, applications, and challenges. Written in plain and straightforward language, the book offers a self-contained resource for readers with no prior background in the field. Primarily intended for students in Information Security and IoT applications (including smart cities systems and data heterogeneity), it will also greatly benefit academic researchers, IT professionals, policymakers and legislators. It is well suited as a reference book for both undergraduate and graduate courses on information security approaches, the Internet of Things, and real-world intelligent applications.

This book presents current innovative, alternative and creative approaches that challenge traditional mechanisms in and across disciplines and industries targeting societal impact. A common thread throughout the book is human-centered, uni and multi-modal strategies across the range of human technologies, including sensing and stimuli; virtual and augmented worlds; games for serious applications; accessibility; digital-ethics and more. Focusing on engaging, meaningful, and motivating activities that at the same time offer systemic information on human condition, performance and progress, the book is of interest to anyone seeking to gain insights into the field, be they students, teachers, practicing professionals, consultants, or family representatives. By offering a wider perspective, it addresses the need for a core text that evokes and provokes, engages and demands and stimulates and satisfies.

This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.

This book presents an exhaustive and timely review of key research work on fuzzy XML data management, and provides readers with a comprehensive resource on the state-of-the art tools and theories in this fast growing area. Topics covered in the book include: representation of fuzzy XML, query of fuzzy XML, fuzzy database models, extraction of fuzzy XML from fuzzy database models, reengineering of fuzzy XML into fuzzy database models, and reasoning of fuzzy XML. The book is intended as a reference guide for researchers, practitioners and graduate students working and/or studying in the field of Web Intelligence, as well as for data and knowledge engineering professionals seeking new approaches to replace traditional methods, which may be unnecessarily complex or even unproductive.

Highlights key research currently being undertaken within the field of telepresence, providing the most detailed account of the field to date, advancing our understanding of a fundamental property of all media - the illusion of presence; the sense of "being there" inside a virtual environment, with actual or virtual others. This collection has been put together by leading international scholars from America, Europe, and Asia. Together, they describe the state-of-the-art in presence theory, research and technology design for an advanced academic audience. Immersed in Media provides research that can help designers optimize presence for users of advanced media technologies such as virtual and augmented reality, collaborative social media, robotics, and artificial intelligence and lead us to better understand human cognition, emotion and behaviour.

This contributed volume explores the achievements gained and the remaining puzzling questions by applying dynamical systems theory to the linguistic inquiry. In particular, the book is divided into three parts, each one addressing one of the following topics: 1) Facing complexity in the right way: mathematics and complexity 2) Complexity and theory of language 3) From empirical observation to formal models: investigation of specific linguistic phenomena, like enunciation, deixis, or the meaning of the metaphorical phrases The application of complexity theory to describe cognitive phenomena is a recent and very promising trend in cognitive science. At the time when dynamical approaches triggered a paradigm shift in cognitive science some decade ago, the major topic of research were the challenges imposed by classical computational approaches dealing with the explanation of cognitive phenomena like consciousness, decision making and language. The target audience primarily comprises researchers and experts in the field but the book may also be beneficial for graduate and post-graduate students who want to enter the field.

This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book * introduces novel machine-learning-based temporal normalization techniques * bridges research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition * provides detailed discussions of key research challenges and open research issues in gait biometrics recognition* compares biometrics systems trained and tested with the same footwear against those trained and tested with different footwear