Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks. Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computation time of the training process is critical, a fast learning complex-valued neural network called as a fully complex-valued relaxation network along with its learning algorithm has been presented. The presence of orthogonal decision boundaries helps complex-valued neural networks to outperform real-valued networks in performing classification tasks. This aspect has been highlighted. The performances of various complex-valued neural networks are evaluated on a set of benchmark and real-world function approximation and real-valued classification problems.

This book describes five qualitative investment decision-making methods based on the hesitant fuzzy information. They are: (1) the investment decision-making method based on the asymmetric hesitant fuzzy sigmoid preference relations, (2) the investment decision-making method based on the hesitant fuzzy trade-off and portfolio selection, (3) the investment decision-making method based on the hesitant fuzzy preference envelopment analysis, (4) the investment decision-making method based on the hesitant fuzzy peer-evaluation and strategy fusion, and (5) the investment decision-making method based on the EHVaR measurement and tail analysis.

This volume contains a contemporary, integrated description of the processes of language. These range from fast scales (fractions of a second) to slow ones (over a million years). The contributors, all experts in their fields, address language in the brain, production of sentences and dialogues, language learning, transmission and evolutionary processes that happen over centuries or millenia, the relation between language and genes, the origins of language, self-organization, and language competition and death. The book as a whole will help to show how processes at different scales affect each other, thus presenting language as a dynamic, complex and profoundly human phenomenon.

Context-aware ranking is an important task with many applications. E.g. in recommender systems items (products, movies, ...) and for search engines webpages should be ranked. In all these applications, the ranking is not global (i.e. always the same) but depends on the context. Simple examples for context are the user for recommender systems and the query for search engines. More complicated context includes time, last actions, etc. The major problem is that typically the variable domains (e.g. customers, products) are categorical and huge, the observations are very sparse and only positive events are observed. In this book, a generic method for context-aware ranking as well as its application are presented. For modelling a new factorization based on pairwise interactions is proposed and compared to other tensor factorization approaches. For learning, the `Bayesian Context-aware Ranking' framework consisting of an optimization criterion and algorithm is developed. The second main part of the book applies this general theory to the three scenarios of item, tag and sequential-set recommendation. Furthermore extensions of time-variant factors and one-class problems are studied. This book generalizes and builds on work that has received the `WWW 2010 Best Paper Award', the `WSDM 2010 Best Student Paper Award' and the `ECML/PKDD 2009 Best Discovery Challenge Award'.

In Artificial Intelligence in Finance and Investing, authors Robert Trippi and Jae Lee explain this fascinating new technology in terms that portfolio managers, institutional investors, investment analysis, and information systems professionals can understand. Using real-life examples and a practical approach, this rare and readable volume discusses the entire field of artificial intelligence of relevance to investing, so that readers can realize the benefits and evaluate the features of existing or proposed systems, and ultimately construct their own systems. Topics include using Expert Systems for Asset Allocation, Timing Decisions, Pattern Recognition, and Risk Assessment; overview of Popular Knowledge-Based Systems; construction of Synergistic Rule Bases for Securities Selection; incorporating the Markowitz Portfolio Optimization Model into Knowledge-Based Systems; Bayesian Theory and Fuzzy Logic System Components; Machine Learning in Portfolio Selection and Investment Timing, including Pattern-Based Learning and Fenetic Algorithms; and Neural Network-Based Systems. To illustrate the concepts presented in the book, the authors conclude with a valuable practice session and analysis of a typical knowledge-based system for investment management, K-FOLIO. For those who want to stay on the cutting edge of the "application" revolution, Artificial Intelligence in Finance and Investing offers a pragmatic introduction to the use of knowledge-based systems in securities selection and portfolio management.

There isn't a facet of human life that has not been touched and influenced by robots and automation. What makes robots and machines versatile is their computational intelligence. While modern intelligent sensors and powerful hardware capabilities have given a huge fillip to the growth of intelligent machines, the progress in the development of algorithms for smart interaction, collaboration and pro-activeness will result in the next quantum jump.

This book deals with the recent advancements in design methodologies, algorithms and implementation techniques to incorporate intelligence in "robots and automation systems." Several articles deal with navigation, localization and mapping of mobile robots, a problem that engineers and researchers are grappling with all the time. Fuzzy logic, neural networks and neuro-fuzzy based techniques for real world applications have been detailed in a few articles.

This edited volume is targeted to present the latest state-of-the-art computational intelligence techniques in Robotics and Automation. It is a compilation of the extended versions of the very best papers selected from the many that were presented at the 5th International Conference on Automation, Robotics and Applications (ICARA 2011) which was held in Wellington, New Zealand from 6-8 December, 2011. Scientists and engineers who work with robots and automation systems will find this book very useful and stimulating.

This monograph comprises work on network-based Intrusion Detection (ID) that is grounded in visualisation and hybrid Artificial Intelligence (AI). It has led to the design of MOVICAB-IDS (MObile VIsualisation Connectionist Agent-Based IDS), a novel Intrusion Detection System (IDS), which is comprehensively described in this book. This novel IDS combines different AI paradigms to visualise network traffic for ID at packet level. It is based on a dynamic Multiagent System (MAS), which integrates an unsupervised neural projection model and the Case-Based Reasoning (CBR) paradigm through the use of deliberative agents that are capable of learning and evolving with the environment. The proposed novel hybrid IDS provides security personnel with a synthetic, intuitive snapshot of network traffic and protocol interactions. This visualisation interface supports the straightforward detection of anomalous situations and their subsequent identification. The performance of MOVICAB-IDS was tested through a novel mutation-based testing method in different real domains which entailed several attacks and anomalous situations.

This book contains the combined proceedings of the 4th International Conference on Ubiquitous Computing Application and Wireless Sensor Network (UCAWSN-15) and the 16th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT-15). The combined proceedings present peer-reviewed contributions from academic and industrial researchers in fields including ubiquitous and context-aware computing, context-awareness reasoning and representation, location awareness services, and architectures, protocols and algorithms, energy, management and control of wireless sensor networks. The book includes the latest research results, practical developments and applications in parallel/distributed architectures, wireless networks and mobile computing, formal methods and programming languages, network routing and communication algorithms, database applications and data mining, access control and authorization and privacy preserving computation.

This book examines modern artificial intelligence to display how it may be applied to computer games. It spans the divide that exists between the academic research community working with advanced artificial intelligence and the games programming community which must create and release new and interesting games.

Vast amounts of data are nowadays collected, stored and processed, in an effort to assist in making a variety of administrative and governmental decisions. These innovative steps considerably improve the speed, effectiveness and quality of decisions. Analyses are increasingly performed by data mining and profiling technologies that statistically and automatically determine patterns and trends. However, when such practices lead to unwanted or unjustified selections, they may result in unacceptable forms of discrimination. Processing vast amounts of data may lead to situations in which data controllers know many of the characteristics, behaviors and whereabouts of people. In some cases, analysts might know more about individuals than these individuals know about themselves. Judging people by their digital identities sheds a different light on our views of privacy and data protection. This book discusses discrimination and privacy issues related to data mining and profiling practices. It provides technological and regulatory solutions, to problems which arise in these innovative contexts. The book explains that common measures for mitigating privacy and discrimination, such as access controls and anonymity, fail to properly resolve privacy and discrimination concerns. Therefore, new solutions, focusing on technology design, transparency and accountability are called for and set forth.

Imagine yourself as a military officer in a conflict zone trying to identify locations of weapons caches supporting road-side bomb attacks on your country's troops. Or imagine yourself as a public health expert trying to identify the location of contaminated water that is causing diarrheal diseases in a local population. Geospatial abduction is a new technique introduced by the authors that allows such problems to be solved. Geospatial Abduction provides the mathematics underlying geospatial abduction and the algorithms to solve them in practice; it has wide applicability and can be used by practitioners and researchers in many different fields. Real-world applications of geospatial abduction to military problems are included. Compelling examples drawn from other domains as diverse as criminology, epidemiology and archaeology are covered as well. This book also includes access to a dedicated website on geospatial abduction hosted by University of Maryland. Geospatial Abduction targets practitioners working in general AI, game theory, linear programming, data mining, machine learning, and more. Those working in the fields of computer science, mathematics, geoinformation, geological and biological science will also find this book valuable.

Counting belongs to the most elementary and frequent mental activities of human beings. Its results are a basis for coming to a decision in a lot of situations and dimensions of our life. This book presents a novel approach to the advanced and sophisticated case, called intelligent counting, in which the objects of counting are imprecisely, fuzzily specified. Formally, this collapses to counting in fuzzy sets, interval-valued fuzzy sets or I-fuzzy sets (Atanassov's intuitionistic fuzzy sets). The monograph is the first one showing and emphasizing that the presented methods of intelligent counting are human-consistent: are reflections and formalizations of real, human counting procedures performed under imprecision and, possibly, incompleteness of information. Other applications of intelligent counting in various areas of intelligent systems and decision support will be discussed, too. The whole presentation is self-contained, systematic, and equipped with many examples, figures and tables. Computer and information scientists, researchers, engineers and practitioners, applied mathematicians, and postgraduate students interested in information imprecision are the target readers.

In this book, the editors explain how students enrolled in two digital forensic courses at their institution are exposed to experiential learning opportunities, where the students acquire the knowledge and skills of the subject-matter while also learning how to adapt to the ever-changing digital forensic landscape. Their findings (e.g., forensic examination of different IoT devices) are also presented in the book. Digital forensics is a topic of increasing importance as our society becomes "smarter" with more of the "things" around us been internet- and inter-connected (e.g., Internet of Things (IoT) and smart home devices); thus, the increasing likelihood that we will need to acquire data from these things in a forensically sound manner. This book is of interest to both digital forensic educators and digital forensic practitioners, as well as students seeking to learn about digital forensics.

This volume includes papers presented at IIH-MSP 2017, the 13th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, held from 12 to 15 August 2017 in Matsue, Shimane, Japan. The conference addresses topics ranging from information hiding and security, and multimedia signal processing and networking, to bio-inspired multimedia technologies and systems. This volume of Smart Innovation, Systems and Technologies focuses on subjects related to massive image/video compression and transmission for emerging networks, advances in speech and language processing, information hiding and signal processing for audio and speech signals, intelligent distribution systems and applications, recent advances in security and privacy for multimodal network environments, multimedia signal processing, and machine learning. Updated with the latest research outcomes and findings, the papers presented appeal to researchers and students who are interested in the corresponding fields.

This book explores cybersecurity research and development efforts, including ideas that deal with the growing challenge of how computing engineering can merge with neuroscience. The contributing authors, who are renowned leaders in this field, thoroughly examine new technologies that will automate security procedures and perform autonomous functions with decision making capabilities. To maximize reader insight into the range of professions dealing with increased cybersecurity issues, this book presents work performed by government, industry, and academic research institutions working at the frontier of cybersecurity and network sciences. Cybersecurity Systems for Human Cognition Augmentation is designed as a reference for practitioners or government employees working in cybersecurity. Advanced-level students or researchers focused on computer engineering or neuroscience will also find this book a useful resource.

This book presents powerful techniques for solving global optimization problems on manifolds by means of evolutionary algorithms, and shows in practice how these techniques can be applied to solve real-world problems. It describes recent findings and well-known key facts in general and differential topology, revisiting them all in the context of application to current optimization problems. Special emphasis is put on game theory problems. Here, these problems are reformulated as constrained global optimization tasks and solved with the help of Fuzzy ASA. In addition, more abstract examples, including minimizations of well-known functions, are also included. Although the Fuzzy ASA approach has been chosen as the main optimizing paradigm, the book suggests that other metaheuristic methods could be used as well. Some of them are introduced, together with their advantages and disadvantages. Readers should possess some knowledge of linear algebra, and of basic concepts of numerical analysis and probability theory. Many necessary definitions and fundamental results are provided, with the formal mathematical requirements limited to a minimum, while the focus is kept firmly on continuous problems. The book offers a valuable resource for students, researchers and practitioners. It is suitable for university courses on optimization and for self-study.

This book consists of 20 chapters in which the authors deal with different theoretical and practical aspects of new trends in Collective Computational Intelligence techniques. Computational Collective Intelligence methods and algorithms are one the current trending research topics from areas related to Artificial Intelligence, Soft Computing or Data Mining among others. Computational Collective Intelligence is a rapidly growing field that is most often understood as an AI sub-field dealing with soft computing methods which enable making group decisions and processing knowledge among autonomous units acting in distributed environments. Web-based Systems, Social Networks, and Multi-Agent Systems very often need these tools for working out consistent knowledge states, resolving conflicts and making decisions. The chapters included in this volume cover a selection of topics and new trends in several domains related to Collective Computational Intelligence: Language and Knowledge Processing, Data Mining Methods and Applications, Computer Vision, and Intelligent Computational Methods. This book will be useful for graduate and PhD students in computer science as well as for mature academics, researchers and practitioners interested in the methods and applications of collective computational intelligence in order to create new intelligent systems.

One of the most successful methodology that arose from the worldwide diffusion of Fuzzy Logic is Fuzzy Control. After the first attempts dated in the seventies, this methodology has been widely exploited for controlling many industrial components and systems. At the same time, and very independently from Fuzzy Logic or Fuzzy Control, the birth of the Web has impacted upon almost all aspects of computing discipline. Evolution of Web, Web2.0 and Web 3.0 has been making scenarios of ubiquitous computing much more feasible; consequently information technology has been thoroughly integrated into everyday objects and activities.

What happens when Fuzzy Logic meets Web technology? Interesting results might come out, as you will discover in this book. Fuzzy Mark-up Language is a son of this synergistic view, where some technological issues of Web are re-interpreted taking into account the transparent notion of Fuzzy Control, as discussed here. The concept of a Fuzzy Control that is conceived and modeled in terms of a native web wisdom represents another step towards the last picture of Pervasive Web Intelligence.

This book introduces the novel concept of a fuzzy network whose nodes are rule bases and the connections between the nodes are the interactions between the rule bases in the form of outputs fed as inputs. The concept is presented as a systematic study for improving the feasibility and transparency of fuzzy models by means of modular rule bases whereby the model accuracy and efficiency can be optimised in a flexible way. The study uses an effective approach for fuzzy rule based modelling of complex systems that are characterised by attributes such as nonlinearity, uncertainty, dimensionality and structure.The approach is illustrated by formal models for fuzzy networks, basic and advanced operations on network nodes, properties of operations, feedforward and feedback fuzzy networks as well as evaluation of fuzzy networks. The results are demonstrated by numerous examples, two case studies and software programmes within the Matlab environment that implement some of the theoretical methods from the book. The book shows the novel concept of a fuzzy network with networked rule bases as a bridge between the existing concepts of a standard fuzzy system with a single rule base and a hierarchical fuzzy system with multiple rule bases.

The book puts special stress on the contemporary techniques for reasoning-based image processing and analysis: learning based image representation and advanced video coding; intelligent image processing and analysis in medical vision systems; similarity learning models for image reconstruction; visual perception for mobile robot motion control, simulation of human brain activity in the analysis of video sequences; shape-based invariant features extraction; essential of paraconsistent neural networks, creativity and intelligent representation in computational systems. The book comprises 14 chapters. Each chapter is a small monograph, representing resent investigations of authors in the area. The topics of the chapters cover wide scientific and application areas and complement each-other very well. The chapters' content is based on fundamental theoretical presentations, followed by experimental results and comparison with similar techniques. The size of the chapters is well-ballanced which permits a thorough presentation of the investigated problems. The authors are from universities and R&D institutions all over the world; some of the chapters are prepared by international teams. The book will be of use for university and PhD students, researchers and software developers working in the area of digital image and video processing and analysis.

Biological and other natural processes have always been a source of inspiration for computer science and information technology. Many emerging problem solving techniques integrate advanced evolution and cooperation strategies, encompassing a range of spatio-temporal scales for visionary conceptualization of evolutionary computation.

The previous editions of NICSO were held in Granada, Spain (2006), Acireale, Italy (2007), Tenerife, Spain (2008), and again in Granada in 2010. NICSO evolved to be one of the most interesting and profiled workshops in nature inspired computing.

NICSO 2011 has offered an inspiring environment for debating the state of the art ideas and techniques in nature inspired cooperative strategies and a comprehensive image on recent applications of these ideas and techniques.

The topics covered by this volume include Swarm Intelligence (such as Ant and Bee Colony Optimization), Genetic Algorithms, Multiagent Systems, Coevolution and Cooperation strategies, Adversarial Models, Synergic Building Blocks, Complex Networks, Social Impact Models, Evolutionary Design, Self Organized Criticality, Evolving Systems, Cellular Automata, Hybrid Algorithms, and Membrane Computing (P-Systems).

This book primarily illustrates the rationale, design and technical realization/verification for the cooperative guidance and control systems (CGCSs) of missile autonomous formation (MAF). From the seven functions to the five major compositions of CGCS, the book systematically explains the theory and modeling, analysis, synthesis and design of CGCSs for MAF, including bionics-based theories. Further, the book addresses how to create corresponding digital simulation analysis systems, as well as hardware in the loop (HIL) simulation test systems and flight test systems, to evaluate the combat effectiveness of MAF. Lastly, it provides detailed information on digital simulation analysis for a large range of wind tunnel test data, as well as test results of HIL system simulations and embedded systems testing.

Ambient intelligence began as a vision for the future of technology and has now become a reality. The widespread use of modern technology has quickly expanded into the use of our everyday lives. On a daily basis, we are instantly connected to people, places, ideas, and information which have led to the acceleration of knowledge. As the continuing development of new technologies becomes available, those technologies will play an integral role in the future. Pervasive and Ubiquitous Technology Innovations for Ambient Intelligence Environments is a collection of research on the subject matter of human computer interaction, ubiquitous computing, embedded systems, and other areas of study which contribute to ambient intelligence. This comprehensive reference aims to broaden the overall knowledge on ambient intelligence as it relates to the aspects of modern life.