The present book includes a set of selected extended papers from the sixth International Joint Conference on Computational Intelligence (IJCCI 2014), held in Rome, Italy, from 22 to 24 October 2014. The conference was composed by three co-located conferences: The International Conference on Evolutionary Computation Theory and Applications (ECTA), the International Conference on Fuzzy Computation Theory and Applications (FCTA), and the International Conference on Neural Computation Theory and Applications (NCTA). Recent progresses in scientific developments and applications in these three areas are reported in this book. IJCCI received 210 submissions, from 51 countries, in all continents. After a double blind paper review performed by the Program Committee, 15% were accepted as full papers and thus selected for oral presentation. Additional papers were accepted as short papers and posters. A further selection was made after the Conference, based also on the assessment of presentation quality and audience interest, so that this book includes the extended and revised versions of the very best papers of IJCCI 2014. Commitment to high quality standards is a major concern of IJCCI that will be maintained in the next editions, considering not only the stringent paper acceptance ratios but also the quality of the program committee, keynote lectures, participation level and logistics.

The original concept for the Vision in Vehicle series of international conferences was born from discussions within the Applied Vision Association which led eventually to the first conference being held in 1985. Ten years of progress later and this volume presents the selected and edited proceedings of the Sixth International Conference on Vision in Vehicles (VIV6) which was held at the University of Derby, 13-16 September 1995. The meeting was organised in association with the Applied Vision Association and the Ergonomics Society.
The conference is unique in the eclectic mix of disciplines which are attracted to the meetings and this enables research inssues to be considered in very broad contexts.

This volume presents a process for developing expert systems. As the field of instructional technology matures it is becoming clear that technological process, not technological devices, is the single most important factor in designing effective instruction. Computers as devices are helpful, but their primary advantage may be the discipline placed on thinking and design processes by using them. The process used when examining a problem determines the quality of information entered into a program and the ultimate effectiveness of the solution. The process in this volume is intended for small-scale expert system solutions that contribute to the solution of instructional problems. Hardware independent, the volume focuses on narrowly defined examples intended for small personal computer systems. Particular attention is paid to problems associated with education and training.

"Building Expert Systems in Training and Education" has one primary function: to help instructional designers derive the components of a problem and enter it into an expert system shell. It is totally process-oriented and focuses on the front-end knowledge engineering process. It provides a repertoire of practical tools and processes that can be used to select, define, and structure problems. Three types of examples are used to illustrate three ways to use expert systems: for instructional support, for instructional decision making, and for an instructional job aid. Each chapter is followed by a list of learning activities to facilitate practice and consolidation. When appropriate, answers or examples to the learning activities is given. This is a practical guide for instructional technology educators and students, and business and industrial training professionals.

This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.

This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.

This book presents the scientific outcomes of the 6th International Conference on Applied Computing and Information Technology (ACIT 2018), which was held in Kunming, China on June 13-15, 2018. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. The book includes research findings on all aspects (theory, applications and tools) of computer and information science and discusses the practical challenges encountered and the solutions adopted to address them. The book features 13 of the conference's most promising papers.

A comprehensive analysis of current theory and research in the psychological, computational, and neural sciences elucidates the stuctures and processes of language and thought. Chapters discuss language comprehension and artificial intelligence, ARCS system for analogical retrieval, ACME model of analogical mapping, PAULINE, an artificial intelligence system for pragmatic language generation, a theory of understanding of spoken and written text, recent developments and effect of different modes of language representation on the efficiency of information processing. This book will be of interest to professionals and scholars in psychology, artificial intelligence, and cognitive science.

This monograph bridges the gap between the nonlinear predictor as a concept and as a practical tool, presenting a complete theory of the application of predictor feedback to time-invariant, uncertain systems with constant input delays and/or measurement delays. It supplies several methods for generating the necessary real-time solutions to the systems' nonlinear differential equations, which the authors refer to as approximate predictors. Predictor feedback for linear time-invariant (LTI) systems is presented in Part I to provide a solid foundation on the necessary concepts, as LTI systems pose fewer technical difficulties than nonlinear systems. Part II extends all of the concepts to nonlinear time-invariant systems. Finally, Part III explores extensions of predictor feedback to systems described by integral delay equations and to discrete-time systems. The book's core is the design of control and observer algorithms with which global stabilization, guaranteed in the previous literature with idealized (but non-implementable) predictors, is preserved with approximate predictors developed in the book. An applications-driven engineer will find a large number of explicit formulae, which are given throughout the book to assist in the application of the theory to a variety of control problems. A mathematician will find sophisticated new proof techniques, which are developed for the purpose of providing global stability guarantees for the nonlinear infinite-dimensional delay system under feedback laws employing practically implementable approximate predictors. Researchers working on global stabilization problems for time-delay systems will find this monograph to be a helpful summary of the state of the art, while graduate students in the broad field of systems and control will advance their skills in nonlinear control design and the analysis of nonlinear delay systems.

This book covers the fundamental principles, new theories and methodologies, and potential applications of hybrid intelligent networks. Chapters focus on hybrid neural networks and networked multi-agent networks, including their communication, control and optimization synthesis. This text also provides a succinct but useful guideline for designing neural network-based hybrid artificial intelligence for brain-inspired computation systems and applications in the Internet of Things. Artificial Intelligence has developed into a deep research field targeting robots with more brain-inspired perception, learning, decision-making abilities, etc. This text devoted to a tutorial on hybrid intelligent networks that have been identified in nature and engineering, especially in the brain, modeled by hybrid dynamical systems and complex networks, and have shown potential application to brain-inspired intelligence. Included in this text are impulsive neural networks, neurodynamics, multiagent networks, hybrid dynamics analysis, collective dynamics, as well as hybrid communication, control and optimization methods. Graduate students who are interested in artificial intelligence and hybrid intelligence, as well as professors and graduate students who are interested in neural networks and multiagent networks will find this textbook a valuable resource. AI engineers and consultants who are working in wireless communications and networking will want to buy this book. Also, professional and academic institutions in universities and Mobile vehicle companies and engineers and managers who concern humans in the loop of IoT will also be interested in this book.

This book provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors. The work describes how the history of multimedia data processing can be viewed as a sequence of disruptive innovations. Across the chapters, the discussion covers the practical frameworks, libraries, and open source software that enable the development of ground-breaking research into practical applications. Features: reviews how innovations in mobile, social, cognitive, cloud and organic based computing impacts upon the development of multimedia data mining; provides practical details on implementing the technology for solving real-world problems; includes chapters devoted to privacy issues in multimedia social environments and large-scale biometric data processing; covers content and concept based multimedia search and advanced algorithms for multimedia data representation, processing and visualization.

This book presents recent research in the recognition of vulnerabilities of national systems and assets which gained special attention for the Critical Infrastructures in the last two decades. The book concentrates on R&D activities in the relation of Critical Infrastructures focusing on enhancing the performance of services as well as the level of security. The objectives of the book are based on a project entitled "Critical Infrastructure Protection Researches" (TAMOP-4.2.1.B-11/2/KMR-2011-0001) which concentrated on innovative UAV solutions, robotics, cybersecurity, surface engineering, and mechatornics and technologies providing safe operations of essential assets. This report is summarizing the methodologies and efforts taken to fulfill the goals defined. The project has been performed by the consortium of the Obuda University and the National University of Public Service.

This book aims to provide important information about adaptivity in computer-based and/or web-based educational systems. In order to make the student modeling process clear, a literature review concerning student modeling techniques and approaches during the past decade is presented in a special chapter. A novel student modeling approach including fuzzy logic techniques is presented. Fuzzy logic is used to automatically model the learning or forgetting process of a student. The presented novel student model is responsible for tracking cognitive state transitions of learners with respect to their progress or non-progress. It maximizes the effectiveness of learning and contributes, significantly, to the adaptation of the learning process to the learning pace of each individual learner. Therefore the book provides important information to researchers, educators and software developers of computer-based educational software ranging from e-learning and mobile learning systems to educational games including stand alone educational applications and intelligent tutoring systems.

This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learning. Includes advances on unsupervised learning using natural computing techniques Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning Features natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms

Artificial intelligence has been applied to many areas of science and technology, including the power and energy sector. Renewable energy in particular has experienced the tremendous positive impact of these developments. With the recent evolution of smart energy technologies, engineers and scientists working in this sector need an exhaustive source of current knowledge to effectively cater to the energy needs of citizens of developing countries. Computational Methodologies for Electrical and Electronics Engineers is a collection of innovative research that provides a complete insight and overview of the application of intelligent computational techniques in power and energy. Featuring research on a wide range of topics such as artificial neural networks, smart grids, and soft computing, this book is ideally designed for programmers, engineers, technicians, ecologists, entrepreneurs, researchers, academicians, and students.

This book examines the introduction of smart technologies into public administrations and the organizational issues caused by these implementations, and the potential of information and communication technologies (ICTs) to rationalize and improve government, transform governance and organizational issues, and address economic, social, and environmental challenges. Cities are increasingly using new technologies in the delivery of public sector services and in the improvement of government transparency, business-led urban development, and urban sustainability. The book will examine specific smart projects that cities are embracing to improve transparency, efficiency, sustainability, mobility, and whether all cities are prepared to implement smart technologies and the incentives for promoting implementation. This focus on the smart technologies applied to public sector entities will be of interest to academics, researchers, policy-makers, public managers, international organizations and technical experts involved in and responsible for the governance, development and design of Smart Cities.

There is widespread interest in the way that smart energy control systems, such as assessment and monitoring techniques for low carbon, nearly-zero energy and net positive buildings can contribute to a Sustainable future, for current and future generations. There is a turning point on the horizon for the supply of energy from finite resources such as natural gas and oil become less reliable in economic terms and extraction become more challenging, and more unacceptable socially, such as adverse public reaction to 'fracking'. Thus, in 2016 these challenges are having a major influence on the design, optimisation, performance measurements, operation and preservation of: buildings, neighbourhoods, cities, regions, countries and continents. The source and nature of energy, the security of supply and the equity of distribution, the environmental impact of its supply and utilization, are all crucial matters to be addressed by suppliers, consumers, governments, industry, academia, and financial institutions. This book entitled 'Smart Energy Control Systems for Sustainable Buildings' contains eleven chapters written by international experts based on enhanced conference papers presented at the Sustainability and Energy in Buildings International conference series. This book will be of interest to University staff and students; and also industry practioners.

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.

The first book of its kind devoted to this topic, this comprehensive text/reference presents state-of-the-art research and reviews current challenges in the application of computer vision to problems in sports. Opening with a detailed introduction to the use of computer vision across the entire life-cycle of a sports event, the text then progresses to examine cutting-edge techniques for tracking the ball, obtaining the whereabouts and pose of the players, and identifying the sport being played from video footage. The work concludes by investigating a selection of systems for the automatic analysis and classification of sports play. The insights provided by this pioneering collection will be of great interest to researchers and practitioners involved in computer vision, sports analysis and media production.

This book gathers a selection of the best papers presented during the 14th International Conference on Location Based Services, which was held in Zurich (Switzerland) between the 15th and 17th January 2018. It presents a general overview of recent research activities related to location based services. Such activities have grown in importance over the past several years, especially those concerning outdoor/indoor positioning, smart environments, spatial modeling, personalization and context-awareness, cartographic communication, novel user interfaces, crowdsourcing, social media, big data analysis, usability and privacy.

The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice.

Ontologies form an indispensable basis for modeling and engineering languages for business enterprise and information systems: fostering a need for the integration of structural and behavioral aspects in domain-oriented ontologies.

The book presents an integrative review of paleoneurology, the study of endocranial morphology in fossil species. The main focus is on showing how computed methods can be used to support advances in evolutionary neuroanatomy, paleoanthropology and archaeology and how they have contributed to creating a completely new perspective in cognitive neuroscience. Moreover, thanks to its multidisciplinary approach, the book addresses students and researchers approaching human paleoneurology from different angles and for different purposes, such as biologists, physicians, anthropologists, archaeologists and computer scientists. The individual chapters, written by international experts, represent authoritative reviews of the most important topics in the field. All the concepts are presented in an easy-to-understand style, making them accessible to university students, newcomers and also to anyone interested in understanding how methods like biomedical imaging, digital anatomy and computed and multivariate morphometrics can be used for analyzing ontogenetic and phylogenetic changes according to the principles of functional morphology, morphological integration and modularity.

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.

Including contributions from leading experts in the field, this book covers applications and developments of heuristic search methods for solving complex optimization problems. The book covers various local search strategies including genetic algorithms, simulated annealing, tabu search and hybrids thereof. These methods have proved extraordinarily successful by solving some of the most difficult, real-world problems. At the interface between Artificial Intelligence and Operational Research, research in this exciting area is progressing apace spurred on by the needs of industry and commerce. The introductory chapter provides a clear overview of the basic techniques and useful pointers to further reading and to current research. The second section of the book covers some of the most recent and exciting developments of the basic techniques, with suggestions not only for extending and improving these but also for hybridizing and incorporating automatic adaption. The third section contains a number of case studies, surveys and comparative studies which span a wide range of application areas ranging from the classic Steiner tree problem to more practical problems arising in telecommunications and data analysis. The coverage of the latest research and the illustrative case studies will ensure that the book is invaluable for researchers and professionals with an interest in heuristic search methods.