This book introduces computational proximity (CP) as an algorithmic approach to finding nonempty sets of points that are either close to each other or far apart. Typically in computational proximity, the book starts with some form of proximity space (topological space equipped with a proximity relation) that has an inherent geometry. In CP, two types of near sets are considered, namely, spatially near sets and descriptivelynear sets. It is shown that connectedness, boundedness, mesh nerves, convexity, shapes and shape theory are principal topics in the study of nearness and separation of physical aswell as abstract sets. CP has a hefty visual content. Applications of CP in computer vision, multimedia, brain activity, biology, social networks, and cosmology are included. The book has been derived from the lectures of the author in a graduate course on the topology of digital images taught over the past several years. Many of the students have provided important insights and valuable suggestions. The topics in this monograph introduce many forms of proximities with a computational flavour (especially, what has become known as the strong contact relation), many nuances of topological spaces, and point-free geometry.

This book provides a tool for generic readers and graduates who are interested or majoring in systems engineering, decision science, management science, and project management to sharpen their system thinking skills, equipping them with a multiangle perspective, and offering them broader view to understand the complex socioeconomic system in which we are embedded. It systematically investigates the root causes and mechanisms that generate errors through the use of fuzzy set theory, systems science, logic and set theory, and decision science - an area that has rarely been explored in literature. The topics covered include classic error set, fuzzy error set, multivariate error set, error function, identification of errors, error systems, error logic, error matrix, and practical application of error theory in a sewage project.

Computer Vision and Pattern Recognition (CVPR) together play an important role in the processes involved in environmental informatics due to their pervasive, non-destructive, effective, and efficient natures. As a result, CVPR has made significant contributions to the field of environmental informatics by enabling multi-modal data fusion and feature extraction, supporting fast and reliable object detection and classification, and mining the intrinsic relationship between different aspects of environmental data. Computer Vision and Pattern Recognition in Environmental Informatics describes a number of methods and tools for image interpretation and analysis, which enables observation, modelling, and understanding of environmental targets. In addition to case studies on monitoring and modeling plant, soil, insect, and aquatic animals, this publication includes discussions on innovative new ideas related to environmental monitoring, automatic fish segmentation and recognition, real-time motion tracking systems, sparse coding and decision fusion, and cell phone image-based classification and provides useful references for professionals, researchers, engineers, and students with various backgrounds within a multitude of communities.

Design of cognitive systems for assistance to people poses a major challenge to the fields of robotics and artificial intelligence. The Cognitive Systems for Cognitive Assistance (CoSy) project was organized to address the issues of i) theoretical progress on design of cognitive systems ii) methods for implementation of systems and iii) empirical studies to further understand the use and interaction with such systems. To study, design and deploy cognitive systems there is a need to considers aspects of systems design, embodiment, perception, planning and error recovery, spatial insertion, knowledge acquisition and machine learning, dialog design and human robot interaction and systems integration. The CoSy project addressed all of these aspects over a period of four years and across two different domains of application - exploration of space and task / knowledge acquisition for manipulation. The present volume documents the results of the CoSy project. The CoSy project was funded by the European Commission as part of the Cognitive Systems Program within the 6th Framework Program.

The purpose of the 12th Conference Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2011) held on July 6-8, 2011 in Sydney, Australia was to bring together scientists, engineers, computer users, and students to share their experiences and exchange new ideas and research results about all aspects (theory, applications and tools) of computer and information sciences, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.

The conference organizers selected 14 outstanding papers from SNPD 2011, all of which you will find in this volume of Springer's Studies in Computational Intelligence. "

"Interesting, innovative, and proposes an insightful approach towards the IVHS framework. This is a book every researher should go through for better ideas on communications, computers, or automotive technology." -- Telematics India

Evolution and complexity characterize both biological and artificial life by direct modeling of biological processes and the creation of populations of interacting entities from which complex behaviors can emerge and evolve.

This edited book includes invited chapters from leading scientists in the fields of artificial life, complex systems, and evolutionary computing. The contributions identify both fundamental theoretical issues and state-of-the-art real-world applications.

The book is intended for researchers and graduate students in the related domains."

The book written by Dr. Radu B. Rusu presents a detailed description of 3D Semantic Mapping in the context of mobile robot manipulation. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models that include the objects present in the world, together with their position, form, and other semantic aspects, as well as interpretations of these objects with respect to the robot tasks.

The book proposes novel 3D feature representations called Point Feature Histograms (PFH), as well as a frameworks for the acquisition and processing of Semantic 3D Object Maps with contributions to robust registration, fast segmentation into regions, and reliable object detection, categorization, and reconstruction. These contributions have been fully implemented and empirically evaluated on different robotic systems, and have been the original kernel to the widely successful open-source project the Point Cloud Library (PCL) -- see http: //pointclouds.org.

"Computer and Information Sciences" is a unique and comprehensive review of advanced technology and research in the field of Information Technology. It provides an up to date snapshot of research in Europe and the Far East (Hong Kong, Japan and China) in the most active areas of information technology, including Computer Vision, Data Engineering, Web Engineering, Internet Technologies, Bio-Informatics and System Performance Evaluation Methodologies.

Our everyday lives are practically unthinkable without optimization. We constantly try to minimize our effort and to maximize the reward or progress achieved. Many real-world and industrial problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume presents a comprehensive collection of extended contributions from the 2017 Workshop on Computational Optimization. Presenting recent advances in computational optimization, it addresses important concrete applications, e.g. the modeling of physical processes, wildfire modeling, modeling processes in chemical engineering, workforce planning, wireless access network topology, parameter settings for controlling various processes, berth allocation, identification of homogeneous domains, and quantum computing. The book shows how to develop algorithms for them based on new intelligent methods like evolutionary computations, ant colony optimization, constrain programming and others.

When comparing machine vision systems to the visual systems of humans and animals, there is much to be learned in terms of object segmentation, lighting invariance, and recognition of object categories. Studying the biological systems and applying the findings to the structure of computational vision models and artificial vision systems aims to be an essential approach of advancing the field of machine vision. Developing and Applying Biologically-Inspired Vision Systems: Interdisciplinary Concepts provides interdisciplinary research which evaluates the performance of machine visual models and systems in comparison to biological systems. Blending the ideas of current scientific knowledge and biological vision, this collection of new ideas intends to inspire approaches and cross-disciplinary research to applications in machine vision.

This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators' and learners' data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: * Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; * Using learning analytics to predict student performance; * Using learning analytics to create learning materials and educational courses; and * Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.

This book is the proceedings of the 3rd World Conference on Soft Computing (WCSC), which was held in San Antonio, TX, USA, on December 16-18, 2013. It presents start-of-the-art theory and applications of soft computing together with an in-depth discussion of current and future challenges in the field, providing readers with a 360 degree view on soft computing. Topics range from fuzzy sets, to fuzzy logic, fuzzy mathematics, neuro-fuzzy systems, fuzzy control, decision making in fuzzy environments, image processing and many more. The book is dedicated to Lotfi A. Zadeh, a renowned specialist in signal analysis and control systems research who proposed the idea of fuzzy sets, in which an element may have a partial membership, in the early 1960s, followed by the idea of fuzzy logic, in which a statement can be true only to a certain degree, with degrees described by numbers in the interval [0,1]. The performance of fuzzy systems can often be improved with the help of optimization techniques, e.g. evolutionary computation, and by endowing the corresponding system with the ability to learn, e.g. by combining fuzzy systems with neural networks. The resulting "consortium" of fuzzy, evolutionary, and neural techniques is known as soft computing and is the main focus of this book.

The present book includes a set of selected best papers from the 3rd International Conference on Recent Developments in Science, Engineering and Technology (REDSET 2016), held in Gurgaon, India, from 21 to 22 October 2016. The conference focused on the experimental, theoretical and application aspects of innovations in computational intelligence and provided a platform for the academicians and scientists. This book provides an insight into ongoing research and future directions in this novel, continuously evolving field. Many decades have been devoted to creating and refining methods and tools for computational intelligence such as Artificial Neural Networks, Evolutionary Computation, Fuzzy Logic, Computational Swarm Intelligence and Artificial Immune Systems. However, their applications have not yet been broadly disseminated. Computational intelligence can be used to provide solutions to many real-life problems, which could be translated into binary languages, allowing computers to process them. These problems, which involve various fields such as robotics, bioinformatics, computational biology, gene expression, cancer classification, protein function prediction, etc., could potentially be solved using computational intelligence techniques.

This book comprises four chapters that address some of the latest research in clouds robotics and sensor clouds. The first part of the book includes two chapters on cloud robotics. The first chapter introduces a novel resource allocation framework for cloud robotics and proposes a Stackelberg game model and the corresponding task oriented pricing mechanism for resource allocation. In the second chapter, the authors apply Cloud Computing for building a Cloud-Based 3D Point Cloud extractor for stereo images. Their objective is to have a dynamically scalable and applicable to near real-time scenarios.

The monograph written by John Mullane, Ba-Ngu Vo, Martin Adams and Ba-Tuong Vo is devoted to the field of autonomous robot systems, which have been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the problem of representing the environment and its uncertainty in terms of feature based maps. Random Finite Sets are adopted as the fundamental tool to represent a map, and a general framework is proposed for feature management, data association and state estimation. The approaches are tested in a number of experiments on both ground based and marine based facilities.

This book offers an introduction to artificial adaptive systems and a general model of the relationships between the data and algorithms used to analyze them. It subsequently describes artificial neural networks as a subclass of artificial adaptive systems, and reports on the backpropagation algorithm, while also identifying an important connection between supervised and unsupervised artificial neural networks. The book's primary focus is on the auto contractive map, an unsupervised artificial neural network employing a fixed point method versus traditional energy minimization. This is a powerful tool for understanding, associating and transforming data, as demonstrated in the numerous examples presented here. A supervised version of the auto contracting map is also introduced as an outstanding method for recognizing digits and defects. In closing, the book walks the readers through the theory and examples of how the auto contracting map can be used in conjunction with another artificial neural network, the "spin-net," as a dynamic form of auto-associative memory.

This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.

This monograph provides a comprehensive research review of intelligent techniques for personalisation of e-learning systems. Special emphasis is given to intelligent tutoring systems as a particular class of e-learning systems, which support and improve the learning and teaching of domain-specific knowledge. A new approach to perform effective personalization based on Semantic web technologies achieved in a tutoring system is presented. This approach incorporates a recommender system based on collaborative tagging techniques that adapts to the interests and level of students' knowledge. These innovations are important contributions of this monograph. Theoretical models and techniques are illustrated on a real personalised tutoring system for teaching Java programming language. The monograph is directed to, students and researchers interested in the e-learning and personalization techniques.

This book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included. This book carries out a study of the key related fields of distance metrics and alternative hypothesis. Chapters examine new and developing aspects of MIL such as data reduction for multi-instance problems and imbalanced MIL data. Class imbalance for multi-instance problems is defined at the bag level, a type of representation that utilizes ambiguity due to the fact that bag labels are available, but the labels of the individual instances are not defined. Additionally, multiple instance multiple label learning is explored. This learning framework introduces flexibility and ambiguity in the object representation providing a natural formulation for representing complicated objects. Thus, an object is represented by a bag of instances and is allowed to have associated multiple class labels simultaneously. This book is suitable for developers and engineers working to apply MIL techniques to solve a variety of real-world problems. It is also useful for researchers or students seeking a thorough overview of MIL literature, methods, and tools.

The focus of this book is on three influential cognitive motives: achievement, affiliation, and power motivation. Incentive-based theories of achievement, affiliation and power motivation are the basis for competence-seeking behaviour, relationship-building, leadership, and resource-controlling behaviour in humans. In this book we show how these motives can be modelled and embedded in artificial agents to achieve behavioural diversity. Theoretical issues are addressed for representing and embedding computational models of motivation in rule-based agents, learning agents, crowds and evolution of motivated agents. Practical issues are addressed for defining games, mini-games or in-game scenarios for virtual worlds in which computer-controlled, motivated agents can participate alongside human players. The book is structured into four parts: game playing in virtual worlds by humans and agents; comparing human and artificial motives; game scenarios for motivated agents; and evolution and the future of motivated game-playing agents. It will provide game programmers, and those with an interest in artificial intelligence, with the knowledge required to develop diverse, believable game-playing agents for virtual worlds.

This book focuses on the development of a theory of info-dynamics to support the theory of info-statics in the general theory of information. It establishes the rational foundations of information dynamics and how these foundations relate to the general socio-natural dynamics from the primary to the derived categories in the universal existence and from the potential to the actual in the ontological space. It also shows how these foundations relate to the general socio-natural dynamics from the potential to the possible to give rise to the possibility space with possibilistic thinking; from the possible to the probable to give rise to possibility space with probabilistic thinking; and from the probable to the actual to give rise to the space of knowledge with paradigms of thought in the epistemological space. The theory is developed to explain the general dynamics through various transformations in quality-quantity space in relation to the nature of information flows at each variety transformation. The theory explains the past-present-future connectivity of the evolving information structure in a manner that illuminates the transformation problem and its solution in the never-ending information production within matter-energy space under socio-natural technologies to connect the theory of info-statics, which in turn presents explanations to the transformation problem and its solution. The theoretical framework is developed with analytical tools based on the principle of opposites, systems of actual-potential polarities, negative-positive dualities under different time-structures with the use of category theory, fuzzy paradigm of thought and game theory in the fuzzy-stochastic cost-benefit space. The rational foundations are enhanced with categorial analytics. The value of the theory of info-dynamics is demonstrated in the explanatory and prescriptive structures of the transformations of varieties and categorial varieties at each point of time and over time from parent-offspring sequences. It constitutes a general explanation of dynamics of information-knowledge production through info-processes and info-processors induced by a socio-natural infinite set of technologies in the construction-destruction space.

Rapid growth of the mobile communication market has triggered extensive research on the bulk as well as surface acoustic wave devices in the last decade. Quite a few important results on the modeling and simulation of Film Bulk Acoustic Resonator (FBAR) and Layered SAW devices were reported recently. The other recent advance of acoustic waves in solids is the so-called phononic crystals or phononic band-gap materials. Analogous to the band-gap of light in photonic crystals, acoustic waves in periodic elastic structures also exhibit band-gap. Important applications of phononic band gap materials can potentially be found with creating a vibration free environment in microstructures, and design of advanced acoustic frequency filter, etc. In addition to the wave electronics and phononic crystals, to facilitate the emerging needs in the quantitative nondestructive evaluation of materials, waves in anisotropic solids and/or electro-, magneto- interaction problems also regained much attention recently. Topics treated include: Waves in piezoelectric crystals; Simulation of advanced BAW and SAW devices; Analysis of band gaps in phononic structures; Experimental investigation of phononic structures; Waves in multilayered media;Waves in anisotropic solids and/or electro-, magneto- interaction problems.

This monograph is dedicated to the systematic presentation of main trends, technologies and methods of computational intelligence (CI). The book pays big attention to novel important CI technology- fuzzy logic (FL) systems and fuzzy neural networks (FNN). Different FNN including new class of FNN- cascade neo-fuzzy neural networks are considered and their training algorithms are described and analyzed. The applications of FNN to the forecast in macroeconomics and at stock markets are examined. The book presents the problem of portfolio optimization under uncertainty, the novel theory of fuzzy portfolio optimization free of drawbacks of classical model of Markovitz as well as an application for portfolios optimization at Ukrainian, Russian and American stock exchanges. The book also presents the problem of corporations bankruptcy risk forecasting under incomplete and fuzzy information, as well as new methods based on fuzzy sets theory and fuzzy neural networks and results of their application for bankruptcy risk forecasting are presented and compared with Altman method. This monograph also focuses on an inductive modeling method of self-organization - the so-called Group Method of Data Handling (GMDH) which enables to construct the structure of forecasting models almost automatically. The results of experimental investigations of GMDH for forecasting at stock exchanges are presented. The final chapters are devoted to theory and applications of evolutionary modeling (EM) and genetic algorithms. The distinguishing feature of this monograph is a great number of practical examples of CI technologies and methods application for solution of real problems in technology, economy and financial sphere, in particular forecasting, classification, pattern recognition, portfolio optimization, bankruptcy risk prediction under uncertainty which were developed by authors and published in this book for the first time. All CI methods and algorithms are presented from the general system approach and analysis of their properties, advantages and drawbacks that enables practitioners to choose the most adequate method for their own problems solution.