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• Series Foreword; Preface; 1
• Introduction to Semi-Supervised Learning; 2
• A Taxonomy for Semi-Supervised Learning Methods; 3
• Semi-Supervised Text Classification Using EM; 4
• Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5
• Probabilistic Semi-Supervised Clustering with Constraints; 6
• Transductive Support Vector Machines; 7
• Semi-Supervised Learning Using Semi- Definite Programming; 8
• Gaussian Processes and the Null-Category Noise Model; 9
• Entropy Regularization; 10
• Data-Dependent Regularization.

• 11
• Label Propagation and Quadratic Criterion12
• The Geometric Basis of Semi-Supervised Learning; 13
• Discrete Regularization; 14
• Semi-Supervised Learning with Conditional Harmonic Mixing; 15
• Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17
• Modifying Distances; 18
• Large-Scale Algorithms; 19
• Semi-Supervised Protein Classification Using Cluster Kernels; 20
• Prediction of Protein Function from Networks; 21
• Analysis of Benchmarks; 22
• An Augmented PAC Model for Semi- Supervised Learning.

• 23
• Metric-Based Approaches for Semi- Supervised Regression and Classification24
• Transductive Inference and Semi-Supervised Learning; 25
• A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index.

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Scholkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tubingen. Scholkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by The MIT Press.
(source: Nielsen Book Data)
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.
(source: Nielsen Book Data)

• Related Work.- Periodic Patterns.- Statistically Significant Patterns.- Approximate Patterns.- Conclusion Remark.
• (source: Nielsen Book Data)

In many applications, e.g., bioinformatics, web access traces, system u- lization logs, etc., the data is naturally in the form of sequences. It has been of great interests to analyze the sequential data to find their inherent char- teristics. The sequential pattern is one of the most widely studied models to capture such characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. In this book, we focus on sequential pattern mining. To meet different needs of various applications, several models of sequential patterns have been proposed. We do not only study the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. The objective of this book is to provide computer scientists and domain - perts such as life scientists with a set of tools in analyzing and understanding the nature of various sequences by : (1) identifying the specific model(s) of - quential patterns that are most suitable, and (2) providing an efficient algorithm for mining these patterns. Chapter 1 INTRODUCTION Data Mining is the process of extracting implicit knowledge and discovery of interesting characteristics and patterns that are not explicitly represented in the databases. The techniques can play an important role in understanding data and in capturing intrinsic relationships among data instances. Data mining has been an active research area in the past decade and has been proved to be very useful.
(source: Nielsen Book Data)
The focus of this book is sequential pattern mining. In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences. This information has been of great interest for analyzing the sequential data to find its inherent characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces.
(source: Nielsen Book Data)

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification-that is, a scale parameter-rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
(source: Nielsen Book Data)

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification-that is, a scale parameter-rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
(source: Nielsen Book Data)

• I. Introduction
• 1. Nudge Nudge Wink Wink: Elements of Face-to-Face Conversation for Embodied Conversation Agents / Justine Cassell
• II. Systems
• 2. Human Conversation as a System Framework: Designing Embodied Conversational Agents / Justine Cassell, Tim Bickmore and Lee Campbell / [and others]
• 3. "May I Help You?": Designing Embodied Conversational Agent Allies / Elizabeth F. Churchill, Linda Cook and Peter Hodgson / [and others]
• 4. Task-Oriented Collaboration with Embodied Agents in Virtual Worlds / Jeff Rickel and W. Lewis Johnson
• 5. Deictic and Emotive Communication in Animated Pedagogical Agents / James C. Lester, Stuart G. Towns and Charles B. Callaway / [and others]
• 6. Performative Facial Expressions in Animated Faces / Isabella Poggi and Catherine Pelachaud
• 7. Emotion and Personality in a Conversational Agent / Gene Ball and Jack Breese
• 8. The Automated Design of Believable Dialogues for Animated Presentation Teams / Elisabeth Andre, Thomas Rist and Susanne van Mulken / [and others]
• 9. Parameterized Action Representation for Virtual Human Agents / Norman I. Badler, Rama Bindiganavale and Jan Allbeck / [and others]
• III. Evaluation
• 10. Developing and Evaluating Conversational Agents / Dominic W. Massaro, Michael M. Cohen and Jonas Beskow / [and others]
• 11. Designing and Evaluating Conversational Interfaces with Animated Characters / Sharon Oviatt and Bridget Adams
• 12. Measurement and Evaluation of Embodied Conversational Agents / Greogry A. Sanders and Jean Scholtz
• 13. Truth Is Beauty: Researching Embodied Conversational Agents / Clifford Nass, Katherine Isbister and Eun-Ju Lee.

This book describes research in all aspects of the design, implementation, and evaluation of embodied conversational agents as well as details of specific working systems. Embodied conversational agents are computer-generated cartoonlike characters that demonstrate many of the same properties as humans in face-to-face conversation, including the ability to produce and respond to verbal and nonverbal communication. They constitute a type of (a) multimodal interface where the modalities are those natural to human conversation: speech, facial displays, hand gestures, and body stance; (b) software agent, insofar as they represent the computer in an interaction with a human or represent their human users in a computational environment (as avatars, for example); and (c) dialogue system where both verbal and nonverbal devices advance and regulate the dialogue between the user and the computer. With an embodied conversational agent, the visual dimension of interacting with an animated character on a screen plays an intrinsic role. Not just pretty pictures, the graphics display visual features of conversation in the same way that the face and hands do in face-to-face conversation among humans. This book describes research in all aspects of the design, implementation, and evaluation of embodied conversational agents as well as details of specific working systems. Many of the chapters are written by multidisciplinary teams of psychologists, linguists, computer scientists, artists, and researchers in interface design. The authors include Elisabeth Andre, Norm Badler, Gene Ball, Justine Cassell, Elizabeth Churchill, James Lester, Dominic Massaro, Cliff Nass, Sharon Oviatt, Isabella Poggi, Jeff Rickel, and Greg Sanders.
(source: Nielsen Book Data)

Both specialists and laymen will enjoy reading this book. Using a lively, non-technical style and images from everyday life, the authors present the basic principles behind computing and computers. The focus is on those aspects of computation that concern networks of numerous small computational units, whether biological neural networks or artificial electronic devices.
(source: Nielsen Book Data)

• From the content: Heuristically Ordered Search in State Graphs.- Meta-Heuristics and Artificial Intelligence.- Automated Deduction.
• (source: Nielsen Book Data)

The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This second volume presents the main families of algorithms developed or used in AI to learn, to infer, to decide. Generic approaches to problem solving are presented: ordered heuristic search, as well as metaheuristics are considered. Algorithms for processing logic-based representations of various types (first-order formulae, propositional formulae, logic programs, etc.) and graphical models of various types (standard constraint networks, valued ones, Bayes nets, Markov random fields, etc.) are presented. The volume also focuses on algorithms which have been developed to simulate specific 'intelligent" processes such as planning, playing, learning, and extracting knowledge from data. Finally, an afterword draws a parallel between algorithmic problems in operation research and in AI.
(source: Nielsen Book Data)

• Chapter 1. Designing a method of data transfer using dual message queue brokers in an IoT environment (Hyun-Seong Lee).-
• Chapter 2. Bluetooth Low Energy Plate and PDR Hybrid for Indoor Navigation (Hee-Yong Kang).-
• Chapter 3. A Study on the Common Collaboration Platform Activation of Cloud-based Manufacturing Supply Management System (SCM) (Sanhae Kim).-
• Chapter 4. Service Management and Model Driven Management (Haeng-Kon Kim).-
• Chapter 5. Measuring the Effectiveness of E-Wallet in Malaysia (Faisal Nizam).-
• Chapter 6. Designing of Domain Modeling for Mobile Applications Development (Haeng-Kon Kim) etc.
• (source: Nielsen Book Data)

This book presents the outcomes of the 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2018), which was held on July 10-12, 2018 in Kanazawa. The aim of the conference was to bring together researchers and scientists, businesspeople and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science, to share their experiences, and to exchange new ideas and information in a meaningful way. All aspects (theory, applications and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here. The conference organizers selected the best papers from among those accepted for presentation. The papers were chosen on the basis of review scores submitted by members of the program committee and subsequently underwent further rigorous review. Following this second round of review, 13 of the conference's most promising papers were selected for this Springer (SCI) book. We eagerly await the important contributions that we know these authors will make to the field of computer and information science.
(source: Nielsen Book Data)

• Frontmatter
• Preface
• Contents
• List of Contributors
• 1. Feature selection in biomedical signal classification process and current software implementations / Jovic, Alan
• 2. An overview of skin lesion segmentation, features engineering, and classification / Sabri, My Abdelouahed / Filali, Youssef / Ennouni, Assia / Yahyaouy, Ali / Aarab, Abdellah
• 3. Brain tumor image segmentation and classification using SVM, CLAHE, and ARKFCM / Ishita, Banerjee / Madhumathy, P. / Kavitha, N.
• 4. Coronary Heart Disease prediction using genetic algorithm based decision tree / Sivaranjani, Reddi / Naresh, Vankamamidi S. / Murthy, Nistala V.E.S.
• 5. Intelligent approach for retinal disease identification / Rajyaguru, Vipul C. / Vithalani, Chandresh H. / Thanki, Rohit M.
• 6. Speech separation for interactive voice systems / Wiem, Belhedi / Anouar, Ben Messaoud Mohamed / Aïcha, Bouzid
• 7. Machine vision for human-machine interaction using hand gesture recognition / Verma, Varnita / Rajput, Anshuman / Chauhan, Piyush / Rathore, Harshit / Goyal, Piyush / Gupta, Mukul Kumar
• Index

Intelligent prediction and decision support systems are based on signal processing, computer vision (CV), machine learning (ML), software engineering (SE), knowledge based systems (KBS), data mining, artificial intelligence (AI) and include several systems developed from the study of expert systems (ES), genetic algorithms (GA), artificial neural networks (ANN) and fuzzy-logic systems The use of automatic decision support systems in design and manufacturing industry, healthcare and commercial software development systems has the following benifits: Cost savings in companies, due to employment of expert system technology. Fast decision making, completion of projects in time and development of new products. Improvement in decision making capability and quality. Usage of Knowledge database and Preservation of expertise of individuals Eases complex decision problems. Ex: Diagnosis in Healthcare To address the issues and challenges related to development, implementation and application of automatic and intelligent prediction and decision support systems in domains such as manufacturing, healthcare and software product design, development and optimization, this book aims to collect and publish wide ranges of quality articles such as original research contributions, methodological reviews, survey papers, case studies and/or reports covering intelligent systems, expert prediction systems, evaluation models, decision support systems and Computer Aided Diagnosis (CAD).
(source: Nielsen Book Data)

• A ROS-based framework for simulation and benchmarking of multi-robot patrolling algorithms.- ROS: Developing a new generation of operator interfaces.- Autonomous Exploration and Inspection Path Planning for Aerial Robots using the Robot Operating System.- A generic ROS-based System for Rapid Development and Testing of Algorithms for Autonomous Ground and Aerial Vehicles.- A generic ROS-based System for Rapid Development and Testing of Algorithms for Autonomous Ground and Aerial Vehicles.- ROS-based Approach for Unmanned Vehicles in Civil Applications.- A quadcopter and mobile robot cooperative task using Augmented Reality Tags.- An Extensible Optimization Toolset for Motion Planning and Control Prototyping and Benchmarking.- Online Trajectory Optimization and Navigation for Dynamic Environments in ROS.- A Backstepping Non-smooth Controller for ROS-based Differential-Drive Mobile Robots.- University Rover Challenge: Tutorials and Control System Survey.- SROS: Using and Developing Secure ROS Systems.- GPU and ROS - The use of general parallel processing architecture for robot perception.- Connecting ROS and FIWARE: concepts and tutorial.
• (source: Nielsen Book Data)

Building on the successful first and second volumes, this book is the third volume of the Springer book on the Robot Operating System (ROS): The Complete Reference. The Robot Operating System is evolving from year to year with a wealth of new contributed packages and enhanced capabilities. Further, the ROS is being integrated into various robots and systems and is becoming an embedded technology in emerging robotics platforms. The objective of this third volume is to provide readers with additional and comprehensive coverage of the ROS and an overview of the latest achievements, trends and packages developed with and for it. Combining tutorials, case studies, and research papers, the book consists of sixteen chapters and is divided into five parts. Part 1 presents multi-robot systems with the ROS. In Part 2, four chapters deal with the development of unmanned aerial systems and their applications. In turn, Part 3 highlights recent work related to navigation, motion planning and control. Part 4 discusses recently contributed ROS packages for security, ROS2, GPU usage, and real-time processing. Lastly, Part 5 deals with new interfaces allowing users to interact with robots. Taken together, the three volumes of this book offer a valuable reference guide for ROS users, researchers, learners and developers alike. Its breadth of coverage makes it a unique resource.
(source: Nielsen Book Data)

• Color Spaces Advantages and Disadvantages in Image Color Clustering Segmentation.- Multi-objective Whale Optimization Algorithm for Multi-level Thresholding Segmentation.- Evaluating Swarm Optimization Algorithms for Segmentation of Liver Images.- Thermal Image Segmentation Using Evolutionary Computation Techniques.- News Videos Segmentation Using Dominant Colors Representation.
• (source: Nielsen Book Data)

This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing. The material has been compiled from a scientific perspective, and the book is primarily intended for undergraduate and postgraduate science, engineering, and computational mathematics students. It can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence, and is a valuable resource for researchers in the evolutionary computation, artificial intelligence and image processing communities.
(source: Nielsen Book Data)

• On the use of matrix Bloom filters in data leak protection.- A hybrid approach for the automatic extraction of causal relations from text.- Ambient Intelligence by ATML Rules in BackHome.- Dense Semantic Graph and its Application in Single Document Summarisation.- Web Architecture of a Web Portal for Reliability Diagnosis of Bus Regularity.- An approach to Knowledge Formalization.
• (source: Nielsen Book Data)

This book addresses new challenges and emerging ideas in Distributed Information Filtering and Retrieval. It gathers extended papers presented at DART 2013 (the 7th International Workshop on Information Filtering and Retrieval), held on December 6, 2013 in Turin, Italy, and co-hosted with the XIII International Conference of the Italian Association for Artificial Intelligence. The main focus of DART was to discuss and compare suitable novel solutions based on intelligent techniques and applied to real-world contexts. The papers presented here offer a comprehensive review of related work and state-of-the-art techniques. The authors - a mix of respected practitioners and researchers - share their findings on a range of topics, including data leak protection on text comparison, natural language processing, ambient intelligence, information retrieval and web portals, and knowledge management. All contributions were carefully reviewed by experts in the respective area, who also provided useful suggestions to improve the book's overall quality.
(source: Nielsen Book Data)

• Optimal Group Decision Making Can Help Decrease Poverty, Inequality, and Discrimination.- An Overview and Re-Interpretation of Paradoxes of Responsiveness.- Veto in Yes-no and Yes-no-abstain Voting Systems.- Power Indices for Finance.- The binomial decomposition of the single parameter family of GB welfare functions.- The logic of information and processes in the system-of-systems applications.- Decision-making Process using Hyperstructures and Fuzzy Structures in the Social Sciences.- Social Preferences through Riesz Spaces: A First Approach.- Coherent conditional plausibility: a tool for handling fuzziness and uncertainty under partial information.- Intuitionistic fuzzy interpretations of some formulas for estimation of preference degree.- Fuzzified Likert Scales in Group Multiple-criteria Evaluation.- Maximal Entropy and Minimal Variability OWA Operator Weights: A Short Survey of Recent Developments.
• (source: Nielsen Book Data)

This book offers a concise introduction and comprehensive overview of the state of the art in the field of decision-making and consensus modeling, with a special emphasis on fuzzy methods. It consists of a collection of authoritative contributions reporting on the decision-making process from different perspectives: from psychology to social and political sciences, from decision sciences to data mining, and from computational sciences in general, to artificial and computational intelligence and systems. Written as a homage to Mario Fedrizzi for his scholarly achievements, creative ideas and long lasting services to different scientific communities, it introduces key theoretical concepts, describes new models and methods, and discusses a range of promising real-world applications in the field of decision-making science. It is a timely reference guide and a source of inspiration for advanced students and researchers.
(source: Nielsen Book Data)

• Agile Web Development Methodologies: A Survey and Evaluation.- Load Experiment of the vDACS Scheme in case of Increasing the simultaneous connection for the DACS SV.- Blind Channel Estimation Using Novel Independent Component Analysis with Pulse Shaping for Interference Cancellation.- Anticipated Test Design and its Application to Evaluate and Select Embedded Libraries.- Improving Web Application Reliablilty and Testing Using Accurate Usage Models.- C-PLAD-SM: Extending Component Requirements with Use Cases and State Machines.- A Structural Rule-Based Approach for Design Patterns Recovery.- DRSS: Distributed RDF SPARQL Streaming.- An Efficient Approach for Real-Time Processing of RDSZ-Based Compressed RDF Streams.- Energy Efficiency Cluster Head Election Using Fuzzy Logic Method for Wireless Sensor Networks.- Enabling GSD Task Allocation via Cloud-based Software Processes.- Composite Event Handling over a Distributed Event-Based System.
• (source: Nielsen Book Data)

This book gathers 12 of the most promising papers presented at the 15th International Conference on Software Engineering, Artificial Intelligence Research, Management and Applications (SERA 2017) held on June 7-9, 2017 at the University of Greenwich, London, UK. 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, to share their experiences and to exchange new ideas and information in a meaningful way. The book also presents research findings regarding all aspects (theory, applications and tools) of computer and information science, and discusses the practical challenges encountered along the way and the solutions adopted to solve them.
(source: Nielsen Book Data)

• BiTrust: A Comprehensive Trust Management Model for Multi-agent Systems.- Using Reference Points for Competitive Negotiations in Service Composition.- A Cooperative Framework for Mediated Group Decision Making.- A CPN-Based Protocol for Concurrent Multiple Negotiation
• A Dependency-based Mediation Mechanism for Complex Negotiations.- Using graph properties and clustering techniques to select division mechanisms for scalable negotiations.- Compromising Strategy considering Interdependencies of Issues for Multi-issue Closed Nonlinear Negotiations.
• (source: Nielsen Book Data)

This book addresses several important aspects of complex automated negotiations and introduces a number of modern approaches for facilitating agents to conduct complex negotiations. It demonstrates that autonomous negotiation is one of the most important areas in the field of autonomous agents and multi-agent systems. Further, it presents complex automated negotiation scenarios that involve negotiation encounters that may have, for instance, a large number of agents, a large number of issues with strong interdependencies and/or real-time constraints.
(source: Nielsen Book Data)

• Literature Review for Digital Implementations of Fuzzy Logic Type 1 and Type 2
• LABVIEW™ FPGA
• Real-Time Fuzzy-Logic Controllers
• Fuzzy Logic Type 1 and Type 2 LABVIEW™ FPGA Toolkit.

This book is a comprehensive introduction to LabVIEW FPGA"! a package allowing the programming of intelligent digital controllers in field programmable gate arrays (FPGAs) using graphical code. It shows how both potential difficulties with understanding and programming in VHDL and the consequent difficulty and slowness of implementation can be sidestepped. The text includes a clear theoretical explanation of fuzzy logic (type 1 and type 2) with case studies that implement the theory and systematically demonstrate the implementation process. It goes on to describe basic and advanced levels of programming LabVIEW FPGA and show how implementation of fuzzy-logic control in FPGAs improves system responses. A complete toolkit for implementing fuzzy controllers in LabVIEW FPGA has been developed with the book so that readers can generate new fuzzy controllers and deploy them immediately. Problems and their solutions allow readers to practice the techniques and to absorb the theoretical ideas as they arise. Fuzzy Logic Type 1 and Type 2 Based on LabVIEW FPGA"! helps students studying embedded control systems to design and program those controllers more efficiently and to understand the benefits of using fuzzy logic in doing so. Researchers working with FPGAs find the text useful as an introduction to LabVIEW and as a tool helping them design embedded systems

• Rate of Convergence of Basic Neural Network Operators to the Unit.- Rate of Convergence of Basic Multivariate Neural Network Operators.- Fractional Neural Network Operators Approximation.- Fractional Approximation Using Cardaliaguet-Euvrard Neural Networks.- Fractional Asymptotic Expansions for Quasi-interpolation neural Networks.- Voronovskaya Type Asymptotic Expansions for Multivariate Neural Networks.- Fractional Approximation by Bell and Squashing Neural Networks.- Fractional Asymptotic Expansions For Bell And Squashing Neural Networks.- Multivariate Asymptotic Expansions for Bell and Squashing Neural Networks.- Multivariate Fuzzy-Random Normalized Neural Network Approximation.- Fuzzy Fractional Approximations by Fuzzy Bell and Squashing Neural Networks.- Fuzzy Fractional Neural Network Approximation.- Multivariate Fuzzy Approximation Using Basic Neural Network Operators.- Multivariate Fuzzy Approximation Using Quasi-Interpolation Neural Networks.- Multivariate Fuzzy-Random Neural Networks Approximation.- Approximation by Kantorovich and Quadrature type neural Networks.- Univariate Error Function Based Neural Network Approximations.- Multivariate Error Function Based Neural Network Operators Approximation.- Asymptotic Expansions for Error Function Based Neural Networks.- Fuzzy Fractional Error Function Relied Neural Network Approximations.- Multivariate Fuzzy Approximation by Neural Networks.- Fuzzy-Random Error Function Relied Neural Network Approximations.- Approximation by Perturbed Neural Networks.- Approximations by Multivariate Perturbed Neural Networks.- Voronovskaya type Asymptotic Expansions for Perturbed Neural Networks.- Approximation using Fuzzy Perturbed Neural Networks.- Multivariate Fuzzy Perturbed Neural Network Approximations.- Multivariate Fuzzy-Random Perturbed Neural Network Approximations.
• (source: Nielsen Book Data)

This monograph is the continuation and completion of the monograph, "Intelligent Systems: Approximation by Artificial Neural Networks" written by the same author and published 2011 by Springer. The book you hold in hand presents the complete recent and original work of the author in approximation by neural networks. Chapters are written in a self-contained style and can be read independently. Advanced courses and seminars can be taught out of this brief book. All necessary background and motivations are given per chapter. A related list of references is given also per chapter. The book's results are expected to find applications in many areas of applied mathematics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also for all science and engineering libraries.
(source: Nielsen Book Data)

• State of the Art
• Non-Negative Fuzzy Optimal Solution of Fully Fuzzy Linear Programming Problems with Equality Constraints
• Fuzzy Optimal Solution of Fully Fuzzy Linear Programming Problems with Equality Constraints
• Fuzzy Optimal Solution of Fully Fuzzy Linear Programming Problems With Equality Constraints Having LR Flat Fuzzy Numbers
• Unique Fuzzy Optimal Value of Fully Fuzzy Linear Programming Problems With Equality Constraints Having LR Flat Fuzzy Numbers
• Future Scope.

The book presents a snapshot of the state of the art in the field of fully fuzzy linear programming. The main focus is on showing current methods for finding the fuzzy optimal solution of fully fuzzy linear programming problems in which all the parameters and decision variables are represented by non-negative fuzzy numbers. It presents new methods developed by the authors, as well as existing methods developed by others, and their application to real-world problems, including fuzzy transportation problems. Moreover, it compares the outcomes of the different methods and discusses their advantages/disadvantages. As the first work to collect at one place the most important methods for solving fuzzy linear programming problems, the book represents a useful reference guide for students and researchers, providing them with the necessary theoretical and practical knowledge to deal with linear programming problems under uncertainty.

• Part I Fuzzy Logic Theory
• Part II Neural Networks Theory
• Part III Neural Networks Applications
• Part IV Nature Inspired Optimization
• Part V Nature Inspired Optimization Applications
• Part VI Optimization: Theory and Applications
• Part VII Fuzzy Logic Applications
• Part VIII Fuzzy Logic and Metaheuristic.

This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents diverse applications of nature-inspired optimization algorithms. The sixth part contains papers describing new optimization algorithms. The seventh part contains papers describing applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. Finally, the eighth part contains papers that present enhancements to meta-heuristics based on fuzzy logic techniques.

• Motivations & Instructional Approach.- ROBOTIS' Robot Systems.- Hardware Characteristics.- Software Tools.- Foundational Concepts.- Actuator Position Control Basics.- Advanced Position Control.- Wireless Communications Programming.- Advanced Sensors.- Embedded C Options.- ROBOTIS-MINI System.
• (source: Nielsen Book Data)

This book presents foundational robotics concepts using the ROBOTIS BIOLOID and OpenCM-904 robotic systems, and is suitable as a curriculum for a first course in robotics for undergraduate students or a self-learner. It covers wheel-based robots, as well as walking robots. Although it uses the standard "Sense, Think, Act" approach, communications (bot-to-bot and PC-to-bot) programming concepts are treated in more depth (wired and wireless ZigBee/BlueTooth). Algorithms are developed and described via ROBOTIS' proprietary RoboPlus IDE, as well as the more open Arduino-based Embedded C environments. Additionally, web-based multimedia materials are used for illustrating robotics concepts, code implementations and videos of actual resulting robot behaviors. Advanced sensor interfacing for gyroscope, inertial measuring unit, foot pressure sensor and color camera are also demonstrated.
(source: Nielsen Book Data)

• Toward a Restriction-Centered Theory of Truth and Meaning (RCT)
• Solution of The Knowledge Level Control Problem: The Principles of Fuzzy Logic Rules and Linguistic Variables
• Learning Systems with FUZZY
• Fuzzy Modifiers at the Core of Interpretable Fuzzy Systems.- Human and Machine Intelligence? between Fuzzy Logic and Daoist Thought
• Developing Fuzzy State Models as Markov Chain Models with Fuzzy Encoding.- Incremental Granular Fuzzy Modeling using Imprecise Data Streams
• Fuzzy Measures and Integrals: Recent Developments
• Important New Terms and Classifications in Uncertainty And Fuzzy Logic
• Formalization and Visualization of Kansei Information Based on Fuzzy Set Approach
• Cognitive Informatics: A Proper Framework for the Use of Fuzzy Dynamic Programming for the Modeling of Regional Development?
• On Discord Between Expected and Actual Developments in Applications of Fuzzy Logic During its First Fifty Years
• Meta-Heuristic Optimization of a Fuzzy Character Recognizer
• Additive Fuzzy Systems as Generalized Probability Mixture Models
• Fuzzy Information Retrieval Systems: A Historical Perspective
• Fuzzy Information Retrieval Systems: A Historical Perspective Zadeh's Vision
• Handling Noise and Outliers in Fuzzy Clustering.- A Fuzzy-Based Approach to Survival Data Mining
• Knowledge Extraction from Support Vector Machines: A Fuzzy Logic Approach
• On Type-Reduction versus Direct Defuzzification for Type-2 Fuzzy Logic Systems
• On Fuzzy Theory for Econometrics.-m On Z-numbers and the Machine-mind for Natural Language Comprehension
• Evolutionary Reduction of Fuzzy Rule-Based Models
• Geospatial Uncertainty Representation: Fuzzy and Rough Set Approaches
• How to Efficiently Diagnose and Repair Fuzzy Database Queries that Fail
• Reformat, the Web, Similarity and Fuzziness
• The Genesis of Fuzzy Sets and Systems? Aspects in Science and Philosophy
• Fuzzy Logic in Speech Technology? Introductory and Overviewing Glimpses
• Towards the Scientific Domestication of Imprecision
• Type 1 and Full Type 2 Fuzzy System Models.- Complex Fuzzy Sets and Complex Fuzzy Logic an Overview of Theory and Applications.

This book presents a comprehensive report on the evolution of Fuzzy Logic since its formulation in Lotfi Zadehs seminal paper on fuzzy sets, published in 1965. In addition, it features a stimulating sampling from the broad field of research and development inspired by Zadehs paper. The chapters, written by pioneers and prominent scholars in the field, show how fuzzy sets have been successfully applied to artificial intelligence, control theory, inference, and reasoning. The book also reports on theoretical issues; features recent applications of Fuzzy Logic in the fields of neural networks, clustering, data mining, and software testing; and highlights an important paradigm shift caused by Fuzzy Logic in the area of uncertainty management. Conceived by the editors as an academic celebration of the fifty years anniversary of the 1965 paper, this work is a must-have for students and researchers willing to get an inspiring picture of the potentialities, limitations, achievements and accomplishments of Fuzzy Logic-based systems.

• On Reasoning With Words and Perceptions
• Language, Fuzzy Logic, Metalogic
• On What I Still Hope from Fuzzy Logic
• Fuzzy Logic and Modern Economics
• Linguistic Summaries of Time Series: A Powerful and Prospective Tool for Discovering Knowledge on Time Varying Processes And Systems.-Granular Geometry
• Inquiry About the Origin And Abundance of Vague Language: an Issue for the Future
• Fuzzy Natural Logic: Towards Mathematical Logic of Human Reasoning
• From Lattice Valued Theories to Lattice Valued Analysis
• Applying Fuzzy Mathematics to Empirical Work inPolitical Science
• Crisis' Origin's Causes. Contributions from the Fuzzy Logic in the Sustainability on the Socio-economic Systems
• Advanced Computing with Words: Status and Challenges
• Informal Meditation on Empiricism and Approximation in Fuzzy Logic and Set Theory: Descriptive Normativity, Formal Informality and Objective Subjectivity
• Formalizing the Informal, Precisiating the Imprecise: How Fuzzy Logic Can Help Mathematicians and Physicists by Formalizing their Intuitive Ideas
• Future is where Concepts, Theories and Applications Meet (Also in Fuzzy Logic)
• Graduated Conjectures
• Fuzzy Concepts and Fuzzy Logic in Historical and Genetic Epistemology.

This book provides readers with a snapshot of the state-of-the art in fuzzy logic. Throughout the chapters, key theories developed in the last fifty years as well as important applications to practical problems are presented and discussed from different perspectives, as the authors hail from different disciplines and therefore use fuzzy logic for different purposes. The book aims at showing how fuzzy logic has evolved since the first theory formulation by Lotfi A. Zadeh in his seminal paper on Fuzzy Sets in 1965. Fuzzy theories and implementation grew at an impressive speed and achieved significant results, especially on the applicative side. The study of fuzzy logic and its practice spread all over the world, from Europe to Asia, America and Oceania. The editors believe that, thanks to the drive of young researchers, fuzzy logic will be able to face the challenging goals posed by computing with words. New frontiers of knowledge are waiting to be explored. In order to motivate young people to engage in the future development of fuzzy logic, fuzzy methodologies, fuzzy applications, etc., the editors invited a team of internationally respected experts to write the present collection of papers, which shows the present and future potentials of fuzzy logic from different disciplinary perspectives and personal standpoints.

• Intro; Preface; Contents; Part I Theory; 1 Fuzzy Logic for Dynamic Parameter Tuning in ACO and Its Application in Optimal Fuzzy Logic Controller Design; Abstract; 1 Introduction; 2 Ant Colony Optimization (ACO); 3 Performance Analysis of ACO; 4 Fuzzy Logic Convergence Controller; 5 Simulation in TSP Problems; 6 Fuzzy Trajectory Controller for a Unicycle Mobile Robot; 7 ACO for Membership Functions Optimization; 8 ASRank + ConvCont for Membership Functions Optimization; 9 Simulation in Membership Functions Optimization Problem; 10 ASRank + ConvCont vs. S-ACO; 11 Conclusions; References

• 2 Fuzzy Classification System Design Using PSO with Dynamic Parameter Adaptation Through Fuzzy LogicAbstract; 1 Introduction; 2 Methodology for Parameter Adaptation; 3 Experimentation with the Fuzzy Systems and the Benchmark Mathematical Functions; 4 Statistical Comparison; 5 Fuzzy Classifier Design; 6 Methodology for Designing Fuzzy Classifiers; 7 Experimentation in the Design of Fuzzy Classifiers; 8 Statistical Comparison for Fuzzy Classifiers; 9 Conclusions; References; 3 Differential Evolution with Dynamic Adaptation of Parameters for the Optimization of Fuzzy Controllers; Abstract

• 1 Introduction2 Differential Evolution; 2.1 Population Structure; 2.2 Initialization; 2.3 Mutation; 2.4 Crossover; 2.5 Selection; 3 Proposed Method; 4 Benchmark Function; 5 Fuzzy System; 6 Experiments and Methodology; 7 Simulation Results; 8 Conclusions; References; 4 A New Bat Algorithm with Fuzzy Logic for Dynamical Parameter Adaptation and Its Applicability to Fuzzy Control Design; Abstract; 1 Introduction; 2 Bat Algorithm; 2.1 Rules of Bats; 2.2 Pseudocode for the Bat Algorithm; 2.3 Movements in the Bat Algorithm; 2.4 Loudness and Pulse Rates; 3 Genetic Algorithms; 3.1 Representation

• 3.2 Crossover Operations3.3 Mutation; 4 Benchmark Mathematical Functions; 5 Results Between GA and the Bat Algorithm; 6 Proposed Method; 7 Bat Algorithm Apply to the Inverted Pendulum; 8 Conclusions; Acknowledgments; References; 5 Optimization of Benchmark Mathematical Functions Using the Firefly Algorithm with Dynamic Parameters; Abstract; 1 Introduction; 2 Firefly Algorithm; 2.1 Attractiveness; 2.2 Distance; 2.3 Movement; 3 Methodology for Parameter Adaptation; 4 Experimentation with Benchmark Mathematical Functions; 5 Conclusion

• 6 Cuckoo Search via L00E9vy Flights and a Comparison with Genetic AlgorithmsAbstract; 1 Introduction; 2 Cuckoo Search Algorithm; 2.1 Variants; 2.2 L00E9vy Flights; 2.3 Pseudo Code for Cuckoo Search Algorithm; 2.4 Generate a New Solution; 3 Genetic Algorithms; 3.1 Representation
• the Chromosome; 3.2 Crossover Operations; 3.3 Mutation; 4 Benchmark Mathematical Functions; 5 Simulation Results; 5.1 Simulation Results with the Cuckoo Search Algorithm; 5.2 Simulation Results with The Genetic Algorithm (GA); 6 Conclusions; Acknowledgment; References

This book describes recent advances on fuzzy logic augmentation of nature-inspired optimization metaheuristics and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in two main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic augmentation of nature-inspired optimization metaheuristics, which basically consists of papers that propose new optimization algorithms enhanced using fuzzy systems. The second part contains papers with the main theme of application of optimization algorithms, which are basically papers using nature-inspired techniques to achieve optimization of complex optimization problems in diverse areas of application.

• Rethinking the Human-Agent Relationship: Which Social Cues Do Interactive Agents Really Need to Have? / Astrid Weiss, Manfred Tscheligi
• Believability Through Psychosocial Behaviour: Creating Bots That Are More Engaging and Entertaining / Christine Bailey, Jiaming You, Gavan Acton, Adam Rankin
• Actor Bots / Maria Arinbjarnar, Daniel Kudenko
• Embodied Conversational Agent Avatars in Virtual Worlds: Making Today's Immersive Environments More Responsive to Participants / Jacquelyn Ford Morie, Eric Chance, Kip Haynes, Dinesh Rajpurohit
• Human-Like Combat Behaviour via Multiobjective Neuroevolution / Jacob Schrum, Igor V. Karpov, Risto Miikkulainen
• Believable Bot Navigation via Playback of Human Traces / Igor V. Karpov, Jacob Schrum, Risto Miikkulainen
• A Machine Consciousness Approach to the Design of Human-Like Bots / Raúl Arrabales, Jorge Muñoz, Agapito Ledezma, German Gutierrez
• ConsScale FPS: Cognitive Integration for Improved Believability in Computer Game Bots / Raúl Arrabales, Agapito Ledezma, Araceli Sanchis
• Assessing Believability / Julian Togelius, Georgios N. Yannakakis, Sergey Karakovskiy, Noor Shaker
• Creating a Personality System for RTS Bots / Jacek Mańdziuk, Przemysław Szałaj
• Making Diplomacy Bots Individual / Markus Kemmerling, Niels Ackermann, Mike Preuss
• Towards Imitation of Human Driving Style in Car Racing Games / Jorge Muñoz, German Gutierrez, Araceli Sanchis.

We share our modern world with bots - chatbots to converse with, roombots to clean our houses, spambots to fill our e-mail inboxes, and medibots to assist our surgeons. This book is about computer game bots, virtual companions who accompany us in virtual worlds or sharpen our fighting skills. These bots must be believable, that is human players should believe they are interacting with entities operating at a human level - bots are more fun if they behave like we do. This book shows how to create believable bots that play computer games, and it discusses the implications of making them appear human. The chapters in this book present the state of the art in research on and development of game bots, and they also look beyond the design aspects to address deep questions: Is a bot that plays like a person intelligent? Does it have emotions? Is it conscious? The topic is inherently interdisciplinary, and the work draws from research and practice in many fields, such as design, creativity, entertainment, and graphics; learning, psychology, and sociology; artificial intelligence, embodiment, agents, machine learning, robotics, human-computer interaction, and artificial life; cognition and neuroscience; and evolutionary computing. The contributing authors are among the leading researchers and developers in this field, and most of the examples and case studies involve analysis of commercial products. The book will be of value to graduate students and academic researchers in artificial intelligence, and to engineers charged with the design of entertaining games.
(source: Nielsen Book Data)

• Organic Computing
• A Paradigm Shift for Complex Systems; Preface; Contents; Review Team; Projects; Contributors; Chapter 1: Theoretical Foundations; Chapter 1.1: Adaptivity and Self-organisation in Organic Computing Systems; Chapter 1.2: Quantitative Emergence; Chapter 1.3: Divergence Measures as a Generalised Approach to Quantitative Emergence; Chapter 1.4: Emergent Control; Chapter 1.5: Constraining Self-organisation Through Corridors of Correct Behaviour: The Restore Invariant Approach; Chapter 1.6: Ant Inspired Methods for Organic Computing.

• Chapter 1.7: Organic Computing: Metaphor or Model?
• Chapter 2: Methods and Tools; Chapter 2.1: Model-Driven Development of Self-organising Control Applications; Chapter 2.2: How to Design and Implement Self-organising Resource-Flow Systems; Chapter 2.3: Monitoring and Self-awareness for Heterogeneous, Adaptive Computing Systems; Chapter 2.4: Generic Emergent Computing in Chip Architectures; Chapter 2.5: Multi-objective Intrinsic Evolution of Embedded Systems; Chapter 2.6: Organisation-Oriented Chemical Programming; Chapter 2.7: Hovering Data Clouds for Organic Computing;
• Chapter 3: Learning.

• Chapter 3.1: Aspects of Learning in OC SystemsChapter 3.2: Combining Software and Hardware LCS for Lightweight On-chip Learning; Chapter 3.3: Collaborative Learning by Knowledge Exchange; Chapter 3.4: A Framework for Controlled Self-optimisation in Modular System Architectures; Chapter 3.5: Increasing Learning Speed by Imitation in Multi-robot Societies; Chapter 3.6: Learning to Look at Humans;
• Chapter 4: Architectures; Chapter 4.1: Observation and Control of Organic Systems; Chapter 4.2: Organic Computing Middleware for Ubiquitous Environments.

• Chapter 4.3: DodOrg-A Self-adaptive Organic Many-core ArchitectureChapter 4.4: The Artificial Hormone System-An Organic Middleware for Self-organising Real-Time Task Allocation; Chapter 4.5: ORCA: An Organic Robot Control Architecture; Chapter 4.6: The EPOC Architecture-Enabling Evolution Under Hard Constraints; Chapter 4.7: Autonomic System on Chip Platform;
• Chapter 5: Applications; Chapter 5.1: Organic Traffic Control; Chapter 5.2: Methods for Improving the Flow of Traffic; Chapter 5.3: Applying ASoC to Multi-core Applications for Workload Management.

• Chapter 5
• .4: Efficient Adaptive Communication from Resource-Restricted TransmittersChapter 5
• .5: OrganicBus: Organic Self-organising Bus-Based Communication Systems; Chapter 5
• .6: OC Principles in Wireless Sensor Networks; Chapter 5
• .7: Application of the Organic Robot Control Architecture ORCA to the Six-Legged Walking Robot OSCAR; Chapter 5
• .8: Energy-Awareness in Self-organising Robotic Exploration Teams; Chapter 5
• .9: A Fast Hierarchical Learning Approach for Autonomous Robots; Chapter 5
• .10: Emergent Computing with Marching Pixels for Real-Time Smart Camera Applications.

Organic Computing has emerged as a challenging vision for future information processing systems. Its basis is the insight that we will increasingly be surrounded by and depend on large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicating freely, and organising themselves in order to perform actions and services required by the users. These networks of intelligent systems surrounding us open fascinating application areas and at the same time bear the problem of their controllability. Hence, we have to construct such systems as robust, safe, flexible, and trustworthy as possible. In particular, a strong orientation towards human needs as opposed to a pure implementation of the technologically possible seems absolutely central. The technical systems, which can achieve these goals will have to exhibit life-like or "organic" properties. "Organic Computing Systems" adapt dynamically to their current environmental conditions. In order to cope with unexpected or undesired events they are self-organising, self-configuring, self-optimising, self-healing, self-protecting, self-explaining, and context-aware, while offering complementary interfaces for higher-level directives with respect to the desired behaviour. First steps towards adaptive and self-organising computer systems are being undertaken. Adaptivity, reconfigurability, emergence of new properties, and self-organisation are hot topics in a variety of research groups worldwide. This book summarises the results of a 6-year priority research program (SPP) of the German Research Foundation (DFG) addressing these fundamental challenges in the design of Organic Computing systems. It presents and discusses the theoretical foundations of Organic Computing, basic methods and tools, learning techniques used in this context, architectural patterns and many applications. The final outlook shows that in the mean-time Organic Computing ideas have spawned a variety of promising new projects

• Neural Networks.- Complex Functional Network Hebbian-Type Learning Algorithm and Convergence.- A New Intelligent Control Strategy of High-Voltage Power Supply for ECRH Based on CMAC Neural Network.- Self-configuration Using Artificial Neural Networks.- Evolutionary Learning and Genetic Algorithms.- An Improvement of AdaBoost for Face Detection with Random Forests.- Hybrid Good Point Set Evolutionary Strategy for Constrained Optimization.- Research of Modified Quantum Genetic Algorithm and It's Application in Collision Detection.- Granular Computing and Rough Sets.- A New Method of Attribute Reduction and Prediction in Fuzzy Decision System.- Particle Swarm Optimization and Niche Technology.- Development of Automatic Code Generation Tool for Condensation Algorithm.- Swarm Intelligence and Optimization.- A New Hybrid Multi-objective Pareto Archive PSO Algorithm for a Classic Job Shop Scheduling Problem with Ready Times.- Multi-objective Particle Swarm Optimization for Sequencing and Scheduling a Cellular Manufacturing System.- A Hybrid PSO Algorithm with Transposon for Multiobjective Optimization.- Independent Component Analysis and Blind Source Separation.- Joint Multichannel Blind Speech Separation and Dereverberation: A Real-Time Algorithmic Implementation.- An Efficient Pairwise Kurtosis Optimization Algorithm for Independent Component Analysis.- Combinatorial and Numerical Optimization.- The Mechanical Behavior of the Double Piece of Tape Spring.- Systems Biology and Computational Biology.- Cancer Immunoprevention: What Can We Learn from in Silico Models?.- Oscillatory Dynamics of Double Negative Feedback Loop Motif by MicroRNAs.- Neural Computing and Optimization.- Neural Network Approach for Greenery Warranty Systems.- Knowledge Discovery and Data Mining.- Comprehensive Evaluation of Effects of Naomaitong and Rhubarb Aglycone Combined with Bone Mesenchymal Stem Cells Transplantation on Brain in Rats with Cerebral Ischemia Based on Analytic Hierarchy Process.- Aggregating and Weighting Expert Knowledge in Group Decision Making.- A New Heuristic Feature Selection Algorithm Based on Rough Sets.- Developing a Procedure to Obtain Knowledge of Optimum Solutions in a Travelling Salesman Problem.- Trajectory Simplification and Classification for Moving Object with Road-Constraint.- Training a Pac-Man Player with Minimum Domain Knowledge and Basic Rationality.- Hybrid Self-Organizing Map and Neural Network Clustering Analysis for Technology Professionals Turnover Rate Forecasting.- Ensemble Methods.- A Study of Strength and Correlation in Random Forests.- Machine Learning Theory and Methods.- A Comparison Study of Conditional Random Fields Toolkits.- Robot Reinforcement Learning Based on Learning Classifier System.- Intelligent Computing in Bioinformatics.- Research of Marker Gene Selection for Tumor Classfication Based on Decision Forests.- Reverse Engineered Gene Networks Reveal Markers Predicting the Outcome of Breast Cancer.- New Tools for Expression Alternative Splicing Validation.- Intelligent Computing in Computational Biology and Drug Design.- Application for Artificial Bee Colony Algorithm in Migration of Mobile Agent.- Computational Genomics and Proteomics.- A Novel Tool for Assisted In-silico Cloning and Sequence Editing in Molecular Biology.- Intelligent Computing in Signal Processing.- A New Method Using Table to Sparse Representation in Pairs of Bases with Matching Pursuits.- Intelligent Computing in Pattern Recognition.- Palmprint Recognition Method Using WTA-ICA Based on 2DPCA.- Study of TCM Diagnosis of Syndromes of Acute Exacerbation of Chronic Obstructive Pulmonary Disease Based on Dynamic Fuzzy Kohonen Network.- Classification and Characteristics of TCM Syndromes of Chronic Respiratory Failure Based on Self-adaptive Fuzzy Inference System.- Implementation of the Pattern Matching System to Detect Flip Chip PCB Defects.- JPEG Steganalysis Using Estimated Image and Markov Model.- Applications of Two-Dimensional Heteroscedastic Discriminant Analysis in Face Recognition.- Intelligent Computing in Image Processing.- Multimodal Image Fusion Algorithm Using Dual-Tree Complex Wavelet Transform and Particle Swarm Optimization.- An Adaptive Method for Lane Marking Detection Based on HSI Color Model.- An Image Data Hiding Method Using Pixel-Based JND Model.- Intelligent Computing Applications Based on Eye Gaze: Their Role in Medical Image Interpretation.- Intelligent Computing in Communication and Computer Networks.- A Sequential Test Based Cooperative Spectrum Sensing Scheme Using Fuzzy Logic for Cognitive Radio Networks.- Solving Base Station Subsystem Assignment Problem in Mobile Communication Networks Using Hybridized Heuristic Algorithm.- A Sequential Cooperative Spectrum Sensing Scheme Based on Dempster Shafer Theory of Evidence.- High Resolution Direction Finding of Multiple Coherent Signals.- Extract and Maintain the Most Helpful Wavelet Coefficients for Continuous K-Nearest Neighbor Queries in Stream Processing.- A Neural Network-Based Cooperative Spectrum Sensing Scheme for Cognitive Radio Systems.- Intelligent Computing in Robotics.- Comparison of the Observability Indices for Robot Calibration considering Joint Stiffness Parameters.- Intelligent Computing in Computer Vision.- Human Computer Interaction Using Hand Gestures.- Smart Wheelchair Navigation Based on User's Gaze on Destination.- Entrance Detection of Building Component Based on Multiple Cues.- Intelligent Prediction and Time Series Analysis.- Developing an Evolutionary Neural Network Model for Stock Index Forecasting.- A Tower-Shadow Model for Wind Turbines Using a Wavelet-Prony Method.- Special Session on New Hand-Based Biometric Methods.- Contrast Enhancement and Metrics for Biometric Vein Pattern Recognition.- Feature Extraction Method for Contactless Palmprint Biometrics.- Special Session on Theories and Applications in Advanced Intelligent Computing.- Numerical Analysis for Stochastic Investment System with Poisson Jumps.- Quantum Collapsing Median Filter.- An Impulse C Application in the LDPC Decoding Algorithm.- Research on Algorithm of Parallel Garbage Collection Based on LISP 2 for Multi-core System.- Study on Anode Effect Prediction of Aluminium Reduction Applying Wavelet Packet Transform.- Rotating Machinery Fault Diagnosis Based on EMD-Approximate Entropy and LS-SVM.- Study of Applications Based on Measurement Technology in the Future Smart Grid.- Palm Line Extraction Using FRIT.- Fast ICA for Multi-speaker Recognition System.- Variable Universe Adaptive Fuzzy-PI Compound Control Applied in Maximum Power Point Tracking for Photovoltaic Energy Generation System.- Integrated and Automated Dielectric Measurement System at Millimeter Wavelengths.- Special Session on Search Based Software Engineering.- Software Security Testing of Web Applications Based on SSD.- Special Session on Bio-inspired Computing and Applications.- A Review of Bacterial Foraging Optimization Part I: Background and Development.- A Review of Bacterial Foraging Optimization Part II : Applications and Challenges.- Liquidity Risk Portfolio Optimization Using Swarm Intelligence.- Special Session on Advance in Dimensionality Reduction Methods and Its Applications.- Dimension Reduction with Semi-supervised Pairwise Covariance-Preserving Projection.- Special Session on Recent Advances in Medical Informatics.- Analysis of Impact Factors in Acupuncture for Patients with Migraine--Doubts on Prof. Andrew J Vickers' Conclusion.
• (source: Nielsen Book Data)

• Neural Networks.- Design of a Novel Six-Dimensional Force/Torque Sensor and Its Calibration Based on NN.- Incremental-Based Extreme Learning Machine Algorithms for Time-Variant Neural Networks.- Global Exponential Robust Stability of Hopfield Neural Networks with Reaction-Diffusion Terms.- Direct Inverse Model Control Based on a New Improved CMAC Neural Network.- Further Research on Extended Alternating Projection Neural Network.- Global Synchronization in an Array of Hybrid Coupling Neural Networks with Multiple Time-Delay Components.- Colour Image Segmentation Based on a Spiking Neural Network Model Inspired by the Visual System.- The White Noise Impact on the Optimal Performance of the Hopfield Neural Network.- The Study and Realization of Virtual Organization File System Based on DHT Technology.- Evolutionary Learning and Genetic Algorithms.- A Novel Quantum Genetic Algorithm for PID Controller.- Research on Hybrid Evolutionary Algorithms with Differential Evolution and GUO Tao Algorithm Based on Orthogonal Design.- An Improved Evolution Strategy for Constrained Circle Packing Problem.- Lecture Notes in Computer Science: Research on Multi-robot Avoidance Collision Planning Based on XCS.- Fuzzy Theory and Models.- An Integrated Method for the Construction of Compact Fuzzy Neural Models.- Scalarization of Type-1 Fuzzy Markov Chains.- Fuzzy Systems and Soft Computing.- Applying Fuzzy Differential Equations to the Performance Analysis of Service Composition.- Lattice Structures of Fuzzy Soft Sets.- A Predicate Formal System of Universal Logic with Projection Operator.- A Neuro-Evolutive Interval Type-2 TSK Fuzzy System for Volatile Weather Forecasting.- A Soft Computing Approach for Obtaining Transition Regions in Satellite Images.- Particle Swarm Optimization and Niche Technology.- Particle Swarm Optimization for Two-Stage Fuzzy Generalized Assignment Problem.- A Novel Cyclic Discrete Optimization Framework for Particle Swarm Optimization.- Economic Dispatch Considering Ancillary Service Based on Revised Particle Swarm Optimization Algorithm.- Particle Swarm Optimization-Based Extremum Seeking Control.- Image Contour Extraction Based on Ant Colony Algorithm and B-snake.- Supervised and Semi-supervised Learning.- An Improved Hidden Markov Model for Literature Metadata Extraction.- Discriminative Training of Subspace Gaussian Mixture Model for Pattern Classification.- Unsupervised and Reinforcement Learning.- A Stage by Stage Pruning Algorithm for Detecting the Number of Clusters in a Dataset.- Adaptive Independent Component Analysis by Modified Kernel Density Estimation.- Combinatorial and Numerical Optimization.- Cross System Bank Branch Evaluation Using Clustering and Data Envelopment Analysis.- He's Variational Iteration Method for Solving Convection Diffusion Equations.- GRASP for Low Autocorrelated Binary Sequences.- miRNA Target Prediction Method Based on the Combination of Multiple Algorithms.- Imperialistic Competitive Algorithm for Solving a Dynamic Cell Formation Problem with Production Planning.- Systems Biology and Computational Biology.- Genome-Wide DNA Methylation Profiling in 40 Breast Cancer Cell Lines.- GRIDUISS - A Grid Based Universal Immune System Simulator Framework.- Performance Comparison of Tumor Classification Based on Linear and Non-linear Dimensionality Reduction Methods.- Neural Computing and Optimization.- PH Optimal Control in the Clarifying Process of Sugar Cane Juice Based on DHP.- Nature Inspired Computing and Optimization.- Parameter-Free Deterministic Global Search with Simplified Central Force Optimization.- Comparison of Optimality and Robustness between SA, TS and GRASP Metaheuristics in FJSP Problem.- Hardware Emulation of Bacterial Quorum Sensing.- Knowledge Discovery and Data Mining.- Finding Research Community in Collaboration Network with Expertise Profiling.- The Ideal Data Representation for Feature Extraction of Traditional Malay Musical Instrument Sounds Classification.- Mining Reputation of Person/Product from Comment and Reply on UCC/Internet Article.- Interaction Analysis for Adaptive User Interfaces.- Unsupervised Subjectivity-Lexicon Generation Based on Vector Space Model for Multi-Dimensional Opinion Analysis in Blogosphere.- Enhancing Negation-Aware Sentiment Classification on Product Reviews via Multi-Unigram Feature Generation.- Building Associated Semantic Overlay for Discovering Associated Services.- Artificial Life and Artificial Immune Systems.- Immunity-Based Model for Malicious Code Detection.- Sparse Representation-Based Face Recognition for One Training Image per Person.- Semi-supervised Local Discriminant Embedding.- Orthogonal Discriminant Local Tangent Space Alignment.- Intelligent Computing in Image Processing.- Separating Pigment Components of Leaf Color Image Using FastICA.- Fast Algorithm for Multisource Image Registration Based on Geometric Feature of Corners.- Newborn Footprint Recognition Using Subspace Learning Methods.- Plant Classification Using Leaf Image Based on 2D Linear Discriminant Analysis.- Palmprint Recognition Combining LBP and Cellular Automata.- Dual Unsupervised Discriminant Projection for Face Recognition.- Applying Wikipedia-Based Explicit Semantic Analysis for Query-Biased Document Summarization.- Special Session on New Hand-Based Biometric Methods.- A New Approach for Vein Pattern-Based Recognition.- Study of Hand-Dorsa Vein Recognition.- DHV Image Registration Using Boundary Optimization.- Special Session on Recent Advances in Image Segmentation.- A Novel Level Set Model Based on Local Information.- A Multi-Descriptor, Multi-Nearest Neighbor Approach for Image Classification.- Orthogonal Locally Discriminant Projection for Palmprint Recognition.- Special Session on Theories and Applications in Advanced Intelligent Computing.- OPC UA Based Information Modeling for Distributed Industrial Systems.- Voting-Averaged Combination Method for Regressor Ensemble.- Face Recognition Using the Feature Fusion Technique Based on LNMF and NNSC Algorithms.- A PDOC Method for Topology Optimization Design.- Special Session on Search Based Software Engineering.- A Decision Support System Based on GIS for Grain Logistics Vehicle Routing Problem.- On Database Normalization Using User Interface Normal Form.- Special Session on Bio-inspired Computing and Applications.- Improved Particle Swarm Optimizers with Application on Constrained Portfolio Selection.- An Improved Image Rectification Algorithm Based on Particle Swarm Optimization.- Particle Swarm Optimizer Based on Small-World Topology and Comprehensive Learning.- Multi-Objective PSO Based on Evolutionary Programming.- Special Session on Advance in Dimensionality Reduction Methods and Its Applications.- Two-Dimensional Sparse Principal Component Analysis for Palmprint Recognition.- Discovery of Protein's Multifunction and Diversity of Information Transmission.- Special Session on Protein and Gene Bioinformatics: Methods and Applications.- Identification and Analysis of Binding Site Residues in Protein Complexes: Energy Based Approach.- Density Based Merging Search of Functional Modules in Protein-Protein Interaction (PPI) Networks.- Topology Prediction of ?-Helical and ?-Barrel Transmembrane Proteins Using RBF Networks.- Palmprint Recognition Based on Neighborhood Rough Set.- Increasing Reliability of Protein Interactome by Combining Heterogeneous Data Sources with Weighted Network Topological Metrics.- Predicting Protein Stability Change upon Double Mutation from Partial Sequence Information Using Data Mining Approach.- Inference of Gene Expression Regulation via microRNA Transfection.- A Residual Level Potential of Mean Force Based Approach to Predict Protein-Protein Interaction Affinity.
• (source: Nielsen Book Data)

The International Conference on Intelligent Computing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intel- gence, machine learning, pattern recognition, image processing, bioinformatics, and computational biology. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the m- tifaceted aspects of intelligent computing. ICIC 2010, held in Changsha, China, August 18-21, 2010, constituted the 6th - ternational Conference on Intelligent Computing. It built upon the success of ICIC 2009, ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005 that were held in Ulsan, Korea, Shanghai, Qingdao, Kunming and Hefei, China, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
(source: Nielsen Book Data)

• Machine generated contents note: Nursing-Task Recognition Using Accelerometers and RFID / Henrik Christensen / Emil Petriu / Frans Groen
• Recognizing Nonverbal Affective Behavior in Humanoid Robots / Lounell Bahoy Gueta / Jun Ota / Yasuko Kitajima / Miwa Hirata / Jukai Maeda / Noriaki Kuwahara / Masako Kanai-Pak / Yoshihiro Takebe
• Feasibility of Interactive Localization and Navigation of People with Visual Impairments / Ronald C. Arkin / Lilia Moshkina / Sunghyun Park
• Sequentially Greedy Unsupervised Learning of Gaussian Mixture Models by Means of a Binary Tree Structure / Navid Fallah / Kostas E. Bekris / Eelke Folmer / Ilias Apostolopoulos
• On Ambiguity in Robot Learning from Demonstration / Nicola Greggio / José Santos-Victor / Alexandre Bernardino
• Recognition and Motion Primitives for Autonomous Clothes Unfolding of Humanoid Robot / Thomas Hellström / Suna Bensch -- ^ 3D Sensing System for Tracked Vehicle Robot Using Movable Laser Range Finder / Youhei Kakiuchi / Kei Okada / Masayuki Inaba / Hiroko Kobori
• Simulating Robotic Auditory Environments / Toyomi Fujita / Yuya Kondo
• Development of a Person Following Robot and Its Experimental Evaluation / Eric Martinson
• Evaluating Pose Estimation Methods for Stereo Visual Odometry on Robots / Jun Miura / Junji Satake / Masaya Chiba / Yuki Ishikawa / Kenta Kitajima / Hiroaki Masuzawa
• Object Search on a Mobile Robot Using Relational Spatial Information / Hatem Alismail / Brett Browning / M. Bernardine Dias
• Safe Navigation on a Mobile Robot Using Local and Temporal Visual Cues / Alper Aydemir / Kristoffer Sjöö / Patric Jensfelt
• Representing Spatial Knowledge in Mobile Cognitive Systems / Xiang Li / Mohan Sridharan
• Landmark Rating and Selection Considering the Observability Regions / Andrzej Pronobis / Kristoffer Sjö

This volume contains the proceedings of the eleventh International Conference on Intelligent Autonomous Systems (IAS-11) at the University of Ottawa in Canada. As ever, the purpose of the IAS conference is to bring together leading international researchers with an interest in all aspects of the autonomy and adaptivity of artificial systems. This year the conference reflects in particular a current trend: the symbiotic interaction of humans with intelligent systems. Of the 35 papers submitted for this yea's conference, 25 have been accepted for presentation. These papers cover a wide spectrum of research in autonomous intelligent systems including interactive systems, learning, perception, localization and mapping, navigation planning and distributed systms. This proceedings includes all the accepted papers and reflects the wide variety of topics of current concern to all those involved in the use, research and development of intelligent autonomous systems.

• Preface
• 1. The smallest free number
• 2. A surpassing problem
• 3. Improving on saddleback search
• 4. A selection problem
• 5. Sorting pairwise sums
• 6. Making a century
• 7. Building a tree with minimum height
• 8. Unravelling greedy algorithms
• 9. Finding celebrities
• 10. Removing duplicates
• 11. Not the maximum segment sum
• 12. Ranking suffixes
• 13. The Burrows-Wheeler transform
• 14. The last tail
• 15. All the common prefixes
• 16. The Boyer-Moore algorithm
• 17. The Knuth-Morris-Pratt algorithm
• 18. Planning solves the rush hour problem
• 19. A simple Sudoku solver
• 20. The countdown problem
• 21. Hylomorphisms and nexuses
• 22. Three ways of computing determinants
• 23. Inside the convex hull
• 24. Rational arithmetic coding
• 25. Integer arithmetic coding
• 26. The Schorr-Waite algorithm
• 27. Orderly insertion
• 28. Loopless functional algorithms
• 29. The Johnson-Trotter algorithm
• 30. Spider spinning for dummies
• Index.
• (source: Nielsen Book Data)

Richard Bird takes a radical approach to algorithm design, namely, design by calculation. These 30 short chapters each deal with a particular programming problem drawn from sources as diverse as games and puzzles, intriguing combinatorial tasks, and more familiar areas such as data compression and string matching. Each pearl starts with the statement of the problem expressed using the functional programming language Haskell, a powerful yet succinct language for capturing algorithmic ideas clearly and simply. The novel aspect of the book is that each solution is calculated from an initial formulation of the problem in Haskell by appealing to the laws of functional programming. Pearls of Functional Algorithm Design will appeal to the aspiring functional programmer, students and teachers interested in the principles of algorithm design, and anyone seeking to master the techniques of reasoning about programs in an equational style.
(source: Nielsen Book Data)

• Cover; Copyright; Credits; Foreword; The Kettle Project; About the Author; About the Reviewers; Table of Contents; Preface; Chapter 1: Getting started with Pentaho Data Integration; Pentaho Data Integration and Pentaho BI Suite; Exploring the Pentaho Demo; Pentaho Data Integration; Using PDI in real world scenarios; Loading data warehouses or data marts; Integrating data; Data cleansing; Migrating information; Exporting data; Integrating PDI using Pentaho BI; Installing PDI; Time for action
• installing PDI; Launching the PDI graphical designer: Spoon

• Time for action
• starting and customizing SpoonSpoon; Setting preferences in the Options window; Storing transformations and jobs in a repository; Creating your first transformation; Time for action
• creating a hello world transformation; Directing the Kettle engine with transformations; Exploring the Spoon interface; Running and previewing the transformation; Time for action
• running and previewing the hello_world; transformation; Installing MySQL; Time for action
• installing MySQL on Windows; Time for action
• installing MySQL on Ubuntu; Summary

• Chapter 2: Getting Started with TransformationsReading data from files; Time for action
• reading results of football matches from files; Input files; Input steps; Reading several files at once; Time for action
• reading all your files at a time using a single; Text file input step; Time for action
• reading all your files at a time using a single; Text file input step and regular expressions; Regular expressions; Grids; Sending data to files; Time for action
• sending the results of matches to a plain file; Output files; Output steps; Some data definitions; Rowset; Streams

• The Select values stepGetting system information; Time for action
• updating a file with news about examinations; Getting information by using Get System Info step; Data types; Date fields; Numeric fields; Running transformations from a terminal window; Time for action
• running the examination transformation from; a terminal window; XML files; Time for action
• getting data from an XML file with information; about countries; What is XML; PDI transformation files; Getting data from XML files; XPath; Configuring the Get data from XML step; Kettle variables; How and when you can use variables

• Time for action
• finding out which language people speak

As part of Packt's Beginner's Guide, this book focuses on teaching by example. The book walks you through every aspect of PDI, giving step-by-step instructions in a friendly style, allowing you to learn in front of your computer, playing with the tool. The extensive use of drawings and screenshots make the process of learning PDI easy. Throughout the book numerous tips and helpful hints are provided that you will not find anywhere else. The book provides short, practical examples and also builds from scratch a small datamart intended to reinforce the learned concepts and to teach you the basics of data warehousing. This book is for software developers, database administrators, IT students, and everyone involved or interested in developing ETL solutions, or, more generally, doing any kind of data manipulation. If you have never used PDI before, this will be a perfect book to start with. You will find this book is a good starting point if you are a database administrator, data warehouse designer, architect, or any person who is responsible for data warehouse projects and need to load data into them. You don't need to have any prior data warehouse or database experience to read this book. Fundamental database and data warehouse technical terms and concepts are explained in easy-to-understand language.
(source: Nielsen Book Data)

• Agents and Multiagent Systems.- A Security Proposal Based on a Real Time Agent to Protect Web Services Against DoS Attack.- Approaching Real-Time Intrusion Detection through MOVICAB-IDS.- Hybrid Dynamic Planning Mechanism for Virtual Organizations.- Combinatorial Auctions for Coordination and Control of Manufacturing MAS: Updating Prices Methods.- Intelligent Systems.- A Software Tool for Harmonic Distortion Simulation Caused by Non-linear Household Loads.- A Multiobjective Variable Neighborhood Search for Solving the Motif Discovery Problem.- Solving the Routing and Wavelength Assignment Problem in WDM Networks by Using a Multiobjective Variable Neighborhood Search Algorithm.- iGenda: An Event Scheduler for Common Users and Centralised Systems.- Scalable Intelligence and Adaptation in Scheduling DSS.- Evolutionary Computing.- A Parallel Cooperative Evolutionary Strategy for Solving the Reporting Cells Problem.- Optimization of Parallel Manipulators Using Evolutionary Algorithms.- Multi-criteria Manipulator Trajectory Optimization Based on Evolutionary Algorithms.- Combining Heuristics Backtracking and Genetic Algorithm to Solve the Container Loading Problem with Weight Distribution.- A Decision Support System for Logistics Operations.- Energy and Environmental Applications.- Greenhouse Heat Load Prediction Using a Support Vector Regression Model.- Evaluating the Low Quality Measurements in Lighting Control Systems.- Soft Computing Models for an Environmental Application.- Hybrid Systems.- GRASP Algorithm for Optimization of Grids for Multiple Classifier System.- A Scatter Search Based Approach to Solve the Reporting Cells Problem.- Fuzzy Optimization of Start-Up Operations for Combined Cycle Power Plants.- Catalog Segmentation by Implementing Fuzzy Clustering and Mathematical Programming Model.- Multi-Network-Feedback-Error-Learning with Automatic Insertion.- Applications.- An Optimized 3D Surface Reconstruction Method Using Spatial Kalman Filtering of Projected Line Patterns.- Decision Making and Quality-of-Information.- The Gene Expression Programming Applied to Demand Forecast.- Brain Magnetic Resonance Spectroscopy Classifiers.- A Bio-inspired Ensemble Model for Food Industry Applications.- Hybrid Intelligent Systems and Applications.- Implementation of a New Hybrid Methodology for Fault Signal Classification Using Short -Time Fourier Transform and Support Vector Machines.- Advances in Clustering Search.- WSAN QoS Driven Control Model for Building Operations.- Intelligent Hybrid Control Model for Lighting Systems Using Constraint-Based Optimisation.
• (source: Nielsen Book Data)

This volume of Advances in Intelligent and Soft Computing contains accepted - pers presented at SOCO 2010 held in the beautiful and historic city of Guimaraes, Portugal, June 2010. The global purpose of SOCO conferences has been to provide a broad and - terdisciplinary forum for soft computing and associated paradigms, which are playing increasingly important roles in an important number of industrial and - vironmental applications fields. Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which - vestigate, simulate and analyze very complex issues and phenomena. This wo- shop is mainly focused on its industrial and environmental applications. th SOCO 2010 is the 5 International Workshop on Soft Computing Models in Industrial Applications and provides interesting opportunities to present and d- cuss the latest theoretical advances and real world applications in this multidis- plinary research field. This volume presents the papers accepted for the 2010 edition, both for the main event and the Special Sessions. SOCO 2010 Special Sessions are a very u- ful tool in order to complement the regular program with new or emerging topics of particular interest to the participating community. Special Sessions that emp- size on multi-disciplinary and transversal aspects, as well as cutting-edge topics were especially encouraged and welcome. SOCO 2010 included a total of 3 Special Sessions: Ensemble Learning and - formation Fusion for Industrial Applications; Soft Computing for Service M- agement; Hybrid Intelligent Systems and Applications.
(source: Nielsen Book Data)

• Introduction
• State of the art
• MORPHEUS architecture overview
• Flexeos embedded FPGA solution
• The dream digital signal processor
• XPP-III
• The hardware services
• The MORPHEUS data communication and storage infrastructure
• Overall MORPHEUS toolset flow
• The Molen organisation and programming paradigm
• Control of dynamic reconfiguration
• Specification tools for spatial design
• Spatial design
• Real-time digital film processing
• Ethernet based in-service reconfiguration of socs in telecommunication networks
• Homeland security
• image processing for intelligent cameras
• PHY-layer of 802.16 mobile wireless on a hardware accelerated SoC
• Conclusions
• Training
• Dissemination of MORPHEUS results
• Exploitation from the MORPHEUS project
• Project management.

Dynamic System Reconfiguration in Heterogeneous Platformsdefines the MORPHEUSplatform that can join the performance density advantage of reconfigurable technologies and the easy control capabilities of general purpose processors. It consists of a System-on-Chip made of a scalable system infrastructure hosting heterogeneous reconfigurable accelerators, providing dynamic reconfiguration capabilities and data-stream management capabilities. Moreover a toolset which offers a software-oriented approach for implementing data intensive applications on the chip is presented. The toolset provides application design process based on high level programming languages as much as possible. Various applications from differnt domains have been selected to drive the development of the project in assessing the MORPHEUS concept about its computing performance, utilization flexibility and productivity. The emerging IEEE 802.16j standard for Mobile Broadband Wireless Access systems is the base for a first type of applications. The second application is in the area of telecommunication networks which requires data rates up to 40 Gbit/s per single line. A third application is about huge digital postprocessing of the films captured by digital camera or film scanners in resolutions up to 4K. The fourth application is about image processing for intelligent cameras.

• Cyber System.- Cyber-Physical Systems: A New Frontier.- Security.- Misleading Learners: Co-opting Your Spam Filter.- Survey of Machine Learning Methods for Database Security.- Identifying Threats Using Graph-based Anomaly Detection.- On the Performance of Online Learning Methods for Detecting Malicious Executables.- Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems.- A Non-Intrusive Approach to Enhance Legacy Embedded Control Systems with Cyber Protection Features.- Image Encryption and Chaotic Cellular Neural Network.- Privacy.- From Data Privacy to Location Privacy.- Privacy Preserving Nearest Neighbor Search.- Reliability.- High-Confidence Compositional Reliability Assessment of SOA-Based Systems Using Machine Learning Techniques.- Model, Properties, and Applications of Context-Aware Web Services.
• (source: Nielsen Book Data)

Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems is a fertile ground where many tasks can be formulated as learning problems and approached in terms of machine learning algorithms. This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the reliability, security, performance, and privacy issues of cyber space. It enables readers to discover what types of learning methods are at their disposal, summarizing the state-of-the-practice in this significant area, and giving a classification of existing work.Those working in the field of cyber-based systems, including industrial managers, researchers, engineers, and graduate and senior undergraduate students will find this an indispensable guide in creating systems resistant to and tolerant of cyber attacks.
(source: Nielsen Book Data)

• Evolutionary Computation: from Genetic Algorithms to Genetic Programming.- Automatically Defined Functions in Gene Expression Programming.- Evolving Intrusion Detection Systems.- Evolutionary Pattern Matching Using Genetic Programming.- Genetic Programming in Data Modelling.- Stock Market Modeling Using Genetic Programming Ensembles.- Evolutionary Digital Circuit Design Using Genetic Programming.- Evolving Complex Robotic Behaviors Using Genetic Programming.- Automatic Synthesis of Microcontroller Assembly Code Through Linear Genetic Programming.
• (source: Nielsen Book Data)

Designing complex programs such as operating systems, compilers, filing systems, data base systems, etc. is an old ever lasting research area. Genetic programming is a relatively new promising and growing research area. Among other uses, it provides efficient tools to deal with hard problems by evolving creative and competitive solutions. Systems Programming is generally strewn with such hard problems. This book is devoted to reporting innovative and significant progress about the contribution of genetic programming in systems programming. The contributions of this book clearly demonstrate that genetic programming is very effective in solving hard and yet-open problems in systems programming. Followed by an introductory chapter, in the remaining contributed chapters, the reader can easily learn about systems where genetic programming can be applied successfully. These include but are not limited to, information security systems, compilers, data mining systems, stock market prediction systems, robots and automatic programming.
(source: Nielsen Book Data)

• Title page; Preface; Conference Organization; Contents; Invited Talks; Neural Networks; Computer Vision; Applications; Machine Learning; Reasoning; Planning and Robotics; Multiagent Systems; Author Index.

Provides a representative selection of papers promoting synergies in the research community and includes papers on: Neural Networks, Computer Vision, Applications, Machine Learning, Reasoning, Planning and Robotics, and Multi-Agent Systems. These papers are useful to those computer scientists' or engineers' interested in Artificial Intelligence.

• Ch. 1. Complex-valued neural networks: an introduction / Akira Hirose
• ch. 2. Orthogonal decision boundaries and generalization of complex-valued neural networks / Tohru Nitta
• ch. 3. Complex-valued neural associative memories: network stability and learning algorithm / Donq-Liang Lee
• ch. 4. A model of complex-valued associative memories and its dynamics / Yasuaki Kuroe
• ch. 5. Clifford networks / Justin Pearson
• ch. 6. Complex associative memory and complex single neuron model / Iku Nemoto
• ch. 7. Data-reusing algorithm for complex-valued adaptive filters / Damlo P. Mandic, Su Lee Goh and Andrew Hanna
• ch. 8. Instantaneously trained neural networks with complex inputs / Pritam Rajagopal and Subhash Kak
• ch. 9. Applications of complex-valued neural networks for image processing / Hiroyuki Aoki
• ch. 10. Memorization of melodies using complex-valued recurrent neural network / Makoto Kinouchi and Masafumi Hagiwara
• ch. 11. Complex-valued generalized Hebbian algorithm and its applications to sensor array signal processing / Yanwu Zhang
• ch. 12. Phasor model with application to multiuser communication / Teruyuki Miyajima and Kazuo Yamanaka
• ch. 13. Adaptive interferometric radar image processing by using complex-valued neural network / Andriyan Bayu Suksmono and Akira Hirose
• ch. 14. Complex neural network model with analogy to self-oscillation generated in an optical phase-conjugate resonator / Mitsuo Takeda and Takaaki Kishigami
• ch. 15. Coherent lightwave neural network systems: use of frequency domain / Sotaro Kawata and Akira Hirose.

In recent years, complex-valued neural networks have widened the scope of application in optoelectronics, imaging, remote sensing, quantum neural devices and systems, spatiotemporal analysis of physiological neural systems, and artificial neural information processing. In this first-ever book on complex-valued neural networks, the most active scientists at the forefront of the field describe theories and applications from various points of view to provide academic and industrial researchers with a comprehensive understanding of the fundamentals, features and prospects of the powerful complex-valued networks.

• Introduction to agent engineering, J.-M. Liu et al
• why autonomy makes the agent, S. Joseph and T. Kawamura
• knowledge granularity spectrum, action pyramid and the scaling problem, Y.-M. Ye and J.K. Tsotsos
• the motivation for dynamic decision-making frameworks in multi-agent systems, K.S. Barber and C.E. Martin
• dynamically organizing KDD processes in a multi-agent KDD system, N. Zhong et al
• self-organized intelligence, J.-M. Liu
• valuation-based coalition formation in multi-agent systems, S.J. Johansson
• simulating how to cooperate in iterated chicken and prisoner's dilemma games, B. Carlsson
• training intelligent agents using human data collected on the Internet, E. Sklar et al
• agent dynamics - soap paradigm, F.W.K. Lor.
• (source: Nielsen Book Data)

Agent engineering concerns the development of autonomous computational or physical entities capable of perceiving, reasoning, adapting, learning, cooperating and delegating in a dynamic environment. It is one of the most promising areas of research and development in information technology, computer science and engineering.This book addresses some of the key issues in agent engineering: What is meant by "autonomous agents"? How can we build agents with autonomy? What are the desirable capabilities of agents with respect to surviving (they will not die) and living (they will furthermore enjoy their being or existence)? How can agents cooperate among themselves? In order to achieve the optimal performance at the global level, how much optimization at the local, individual level and how much at the global level would be necessary?
(source: Nielsen Book Data)

• pt. I. Technology. Robust layered control system for a mobile robot
• Robot that walks: emergent behaviors from a carefully evolved network
• Learning a distributed map representation based on navigation behaviors
• New approaches to robotics. pt. II. Philosophy. Intelligence without representation
• Planning is just a way of avoiding figuring out what to do next
• Elephants don't play chess
• Intelligence without reason.

Until the mid-1980s, AI researchers assumed that an intelligent system doing high-level reasoning was necessary for the coupling of perception and action. In this traditional model, cognition mediates between perception and plans of action. Realizing that this core AI, as it was known, was illusory, Rodney A. Brooks turned the field of AI on its head by introducing the behavior-based approach to robotics. The cornerstone of behavior-based robotics is the realization that the coupling of perception and action gives rise to all the power of intelligence and that cognition is only in the eye of an observer. Behavior-based robotics has been the basis of successful applications in entertainment, service industries, agriculture, mining, and the home. It has given rise to both autonomous mobile robots and more recent humanoid robots such as Brooks' Cog. This book represents Brooks' initial formulation of and contributions to the development of the behavior-based approach to robotics. It presents all of the key philosophical and technical ideas that put this "bottom-up" approach at the forefront of current research in not only AI but all of cognitive science.
(source: Nielsen Book Data)

• Albertini and Pra, Recurrent Neural Networks: Identification and Other System Theoretic Properties. Anderson and Titterington, Boltzmann Machines: Statistical Associations and Algorithms for Training Anderson and Titterington. Campbell, Constructive Learning Techniques for Designing Neural Network Systems. Mehrotra and Mohan, Modular Neural Networks. Xu and Kwong, Associative Memories. Fry and Sova, A Logical Basis for Neural Network Design.Hoekstra, Duin, and Kraaijveld, Neural Networks Applied to Data Analysis. Zhang and Wang, Multi-Mode Single Neuron Arithmetics.
• (source: Nielsen Book Data)

Optimization Techniques is a unique reference source to a diverse array of methods for achieving optimization, and includes both systems structures and computational methods. The text devotes broad coverage toa unified view of optimal learning, orthogonal transformation techniques, sequential constructive techniques, fast back propagation algorithms, techniques for neural networks with nonstationary or dynamic outputs, applications to constraint satisfaction, optimization issues and techniques for unsupervised learning neural networks, optimum Cerebellar Model of Articulation Controller systems, a new statistical theory of optimum neural learning, and the role of the Radial Basis Function in nonlinear dynamical systems.This volume is useful for practitioners, researchers, and students in industrial, manufacturing, mechanical, electrical, and computer engineering.
(source: Nielsen Book Data)

• Fuzzy compensation principles
• normal fuzzy reasoning methodology
• compensatory genetic fuzzy neural networks
• fuzzy knowledge rediscovery in fuzzy rule bases
• fuzzy cat-pole balancing control systems
• fuzzy knowledge compression and expansion
• highly nonlinear system modelling and prediction
• fuzzy moves in fuzzy games
• genetic neuro-fuzzy pattern recognition
• constructive approach to modelling fuzzy systems.
• (source: Nielsen Book Data)

This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games and pattern recognition. This effective soft computing system is able to perform both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also proposes various novel soft computing techniques.
(source: Nielsen Book Data)

• Introduction, C.H. Chen
• combined neural-net/knowledge-based adaptive systems for large scale dynamic control, A.D.C. Holden and S.C. Suddarth
• a connectionist incremental expert system combining production systems and associative memory, H.F. Yin and P. Liang
• optimal hidden units for two-layer nonlinear feedforward networks, T.D. Sanger
• an incremental fine adjustment algorithm for the design of optimal interpolating networks, S.K. Sin and R.J.P. deFigueiredo
• on the asymptotic properties of recurrent neural networks for optimization, J. Wang
• a real-time image segmentation system using a connectionist classifier architecture, W.E. Blanz and S.L. Gish
• segmentation of ultrasonic images with neural network technology's on automatic active sonar classifier development, T.B. Haley
• on the relationships between statistical pattern recognition and artificial neural networks, C.H. Chen.
• (source: Nielsen Book Data)

The revitalization of neural network research in the past few years has already had a great impact on research and development in pattern recognition and artificial intelligence. Although neural network functions are not limited to pattern recognition, there is no doubt that a renewed progress in pattern recognition and its applications now critically depends on neural networks. This volume specially brings together outstanding original research papers in the area and aims to help the continued progress in pattern recognition and its applications.
(source: Nielsen Book Data)

• Introduction to Fuzzy Harmony Search.- Theory of the Original Harmony Search Method.- Proposed Fuzzy Harmony Search Method.- Study Cases.- Conclusion.
• (source: Nielsen Book Data)

This book focuses on the fields of fuzzy logic and metaheuristic algorithms, particularly the harmony search algorithm and fuzzy control. There are currently several types of metaheuristics used to solve a range of real-world of problems, and these metaheuristics contain parameters that are usually fixed throughout the iterations. However, a number of techniques are also available that dynamically adjust the parameters of an algorithm, such as probabilistic fuzzy logic. This book proposes a method of addressing the problem of parameter adaptation in the original harmony search algorithm using type-1, interval type-2 and generalized type-2 fuzzy logic. The authors applied this methodology to the resolution of problems of classical benchmark mathematical functions, CEC 2015, CEC2017 functions and to the optimization of various fuzzy logic control cases, and tested the method using six benchmark control problems - four of the Mamdani type: the problem of filling a water tank, the problem of controlling the temperature of a shower, the problem of controlling the trajectory of an autonomous mobile robot and the problem of controlling the speed of an engine; and two of the Sugeno type: the problem of controlling the balance of a bar and ball, and the problem of controlling control the balance of an inverted pendulum. When the interval type-2 fuzzy logic system is used to model the behavior of the systems, the results show better stabilization because the uncertainty analysis is better. As such, the authors conclude that the proposed method, based on fuzzy systems, fuzzy controllers and the harmony search optimization algorithm, improves the behavior of complex control plants.
(source: Nielsen Book Data)

• Preface.- 1 Linear Algebra and Optimization: An Introduction.- 2 Linear Transformations and Linear Systems.- 3 Eigenvectors and Diagonalizable Matrices.- 4 Optimization Basics: A Machine Learning View.- 5 Advanced Optimization Solutions.- 6 Constrained Optimization and Duality.- 7 Singular Value Decomposition.- 8 Matrix Factorization.- 9 The Linear Algebra of Similarity.- 10 The Linear Algebra of Graphs.- 11 Optimization in Computational Graphs.- Index.
• (source: Nielsen Book Data)

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution's manual. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The "parent problem" of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.
(source: Nielsen Book Data)

• 1. Unsupervised Visual Learning: from Pixels to Seeing.-
• 2. Unsupervised Learning of Graph and Hypergraph Matching.-
• 3. Unsupervised Learning of Graph and Hypergraph Clustering.-
• 4. Feature Selection meets Unsupervised Learning.-
• 5. Unsupervised Learning of Object Segmentation in Video with Highly Probable Positive Features.-
• 6. Coupling Appearance and Motion: Unsupervised Clustering for Object Segmentation through Space and Time.-
• 7. Unsupervised Learning in Space and Time over Several Generations of Teacher and Student Networks.-
• 8. Unsupervised Learning Towards the Future.
• (source: Nielsen Book Data)

This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way. Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines. .
(source: Nielsen Book Data)

• Part I: Evolutionary algorithms
• Introduction to Evolutionary Single-objective Optimisation
• Particle Swarm Optimisation
• Ant Colony Optimization
• Genetic Algorithm
• Biogeography-Based Optimization
• Part II: Evolutionary Neural Networks
• Evolutionary Feedforward Neural Networks
• Evolutionary Multi-Layer Perceptron
• Evolutionary Radial Basis Function Networks
• Evolutionary Deep Neural Networks.

This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.
(source: Nielsen Book Data)

• Learning for vision.- Computational photography.- Human analysis.- Human sensing.- Stereo and reconstruction.- Optimization.- Matching and recognition.- Video attention.- Poster sessions.
• (source: Nielsen Book Data)

The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
(source: Nielsen Book Data)

• Learning for vision.- Computational photography.- Human analysis.- Human sensing.- Stereo and reconstruction.- Optimization.- Matching and recognition.- Video attention.- Poster sessions.
• (source: Nielsen Book Data)

The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
(source: Nielsen Book Data)

• Sliding Mode Control Design for Some Classes of Chaotic Systems.- Sliding Mode Based Control and Synchronization of Chaotic Systems in Presence of Parametric Uncertainties.- Chattering Free Sliding Mode Controller Design for a Quadrotor Unmanned Aerial Vehicle.- Terminal Sliding Mode Controller Design for a Quadrotor Unmanned Aerial Vehicle.- Insensibility of the Second Order Sliding Mode Control via Measurement Noises: Application to a Robot Manipulator Surveillance Camera.- Robust Control of a Photovoltaic Battery System via Fuzzy Sliding Mode Approach.- Particle Swarm Optimization based Sliding Mode Control Design: Application to a Quadrotor Vehicle.- Global Stabilization of Nonlinear Systems via Novel Second Order Sliding Mode Control with an Application to a Novel Highly Chaotic System.- Complete Synchronization of Chaotic Systems via Novel Second Order Sliding Mode Control with an Application to a Novel Three-Scroll Chaotic System.- Novel Second-Order Sliding Mode Control Design for the Anti-Synchronization of Chaotic Systems with an Application to a Novel Four-Wing Chaotic System.- Control and Synchronization of a Novel Hyperchaotic Two-Disk Dynamo System via Adaptive Integral Sliding Mode Control.- Adaptive Integral Sliding Mode Controller Design for the Control and Synchronization of a Rod-Type Plasma Torch Chaotic System.- Adaptive Integral Sliding Mode Controller Design for the Regulation and Synchronization of a Novel Hyperchaotic Finance System with a Stable Equilibrium.- Adaptive Integral Sliding Mode Controller Design for the Control of a Novel 6-D Coupled Double Convection Hyperchaotic System.- A Memristor-Based Hyperchaotic System with Hidden Attractor and its Sliding Mode Control.- Adaptive Integral Sliding Mode Control of a Chemical Chaotic Reactor System.- Adaptive Integral Sliding Mode Controller Design for the Control and Synchronization of a Novel Jerk Chaotic System.- Sliding Mode Control Design for a Sensorless Sun Tracker.- Super-Twisting Sliding Mode Control of the Enzymes-Substrates Biological Chaotic System.- Super-Twisting Sliding Mode Control and Synchronization of Moore-Spiegel Thermo-Mechanical Chaotic System.
• (source: Nielsen Book Data)

Gathering 20 chapters contributed by respected experts, this book reports on the latest advances in and applications of sliding mode control in science and engineering. The respective chapters address applications of sliding mode control in the broad areas of chaos theory, robotics, electrical engineering, physics, chemical engineering, memristors, mechanical engineering, environmental engineering, finance, and biology. Special emphasis has been given to papers that offer practical solutions, and which examine design and modeling involving new types of sliding mode control such as higher order sliding mode control, terminal sliding mode control, super-twisting sliding mode control, and integral sliding mode control. This book serves as a unique reference guide to sliding mode control and its recent applications for graduate students and researchers with a basic knowledge of electrical and control systems engineering.
(source: Nielsen Book Data)

• CHAPTER 1 Introduction.-
• CHAPTER 2 Time Series Modelling.-
• CHAPTER 3 Options and Options Pricing Models.-
• CHAPTER 4 Neural Networks and Financial Forecasting.-
• CHAPTER 5 Important Problems in Financial Forecasting.-
• CHAPTER 6 Volatility Forecasting.-
• CHAPTER 7 Option Pricing.-
• CHAPTER 8 Value-at-Risk.-
• CHAPTER 9 Conclusion and Discussion.
• (source: Nielsen Book Data)

This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling. These features mean that they can be applied to market-risk problems to overcome classic problems associated with statistical models. .
(source: Nielsen Book Data)

Build simple, maintainable, and easy to deploy machine learning applications. About This Book * Build simple, but powerful, machine learning applications that leverage Go's standard library along with popular Go packages. * Learn the statistics, algorithms, and techniques needed to successfully implement machine learning in Go * Understand when and how to integrate certain types of machine learning model in Go applications. Who This Book Is For This book is for Go developers who are familiar with the Go syntax and can develop, build, and run basic Go programs. If you want to explore the field of machine learning and you love Go, then this book is for you! Machine Learning with Go will give readers the practical skills to perform the most common machine learning tasks with Go. Familiarity with some statistics and math topics is necessary. What You Will Learn * Learn about data gathering, organization, parsing, and cleaning. * Explore matrices, linear algebra, statistics, and probability. * See how to evaluate and validate models. * Look at regression, classification, clustering. * Learn about neural networks and deep learning * Utilize times series models and anomaly detection. * Get to grip with techniques for deploying and distributing analyses and models. * Optimize machine learning workflow techniques In Detail The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios. Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages. Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations. Style and approach This book connects the fundamental, theoretical concepts behind Machine Learning to practical implementations using the Go programming language.
(source: Nielsen Book Data)

• Image Capture and Representation
• Image Re-processing
• Global and Regional Features
• Local Feature Design Concepts
• Taxonomy of Feature Description Attributes
• Interest Point Detector and Feature Descriptor Survey
• Ground Truth Data, Content, Metrics, and Analysis
• Vision Pipeline and Optimizations
• Feature Learning Architecture Taxonomy and Neuroscience Background
• Feature Learning and Deep Learning Architecture Survey. .

Based on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods and deep learning. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics and deep learning architectures. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neural networks, and detailed example architectures to illustrate computer vision hardware and software optimization methods. To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized. The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCV and other imaging and deep learning tools.

• Intro; Preface; Contents; Introduction; 1 Exploring Grouping Problems in Industry; 1.1 Introduction; 1.2 Identifying Grouping Problems in Industry; 1.2.1 Cell Formation in Manufacturing Systems; 1.2.2 Assembly Line Balancing; 1.2.3 Job Shop Scheduling; 1.2.4 Vehicle Routing Problem; 1.2.5 Home Healthcare Worker Scheduling; 1.2.6 Bin Packing Problem; 1.2.7 Task Assignment Problem; 1.2.8 Modular Product Design; 1.2.9 Group Maintenance Planning; 1.2.10 Order Batching; 1.2.11 Team Formation; 1.2.12 Earnings Management; 1.2.13 Economies of Scale; 1.2.14 Timetabling

• 1.2.15 Student Grouping for Cooperative Learning1.2.16 Other Problems; 1.3 Extant Modeling Approaches to Grouping Problems; 1.4 Structure of the Book; References; 2 Complicating Features in Industrial Grouping Problems; 2.1 Introduction; 2.2 Research Methodology; 2.3 Research Findings; 2.4 Complicating Features; 2.4.1 Model Conceptualization; 2.4.2 Myriad of Constraints; 2.4.2.1 Intra-Group Relationship; 2.4.2.2 Inter-Group Relationship; 2.4.2.3 Group Size Limits; 2.4.2.4 Grouping Limit; 2.4.3 Fuzzy Management Goals; 2.4.4 Computational Complexity; 2.5 Suggested Solution Approaches

• 2.6 SummaryReferences; Grouping Genetic Algorithms; 3 Grouping Genetic Algorithms: Advances for Real-World Grouping Problems; 3.1 Introduction; 3.2 Grouping Genetic Algorithm: An Overview; 3.2.1 Group Encoding; 3.3 Crossover; 3.3.1 Mutation; 3.3.2 Inversion; 3.4 Grouping Genetic Algorithms: Advances and Innovations; 3.4.1 Group Encoding Strategies; 3.4.1.1 Encoding Strategy 1; 3.4.1.2 Encoding Strategy 2; 3.4.2 Initialization; 3.4.2.1 User-Generated Seeds; 3.4.2.2 Random Generation; 3.4.2.3 Constructive Heuristics; 3.4.3 Selection Strategies; 3.4.3.1 Stochastic Sampling Without Replacement

• 3.4.4 Rank-Based Wheel Selection Strategy3.4.5 Crossover Strategies; 3.4.5.1 Two-Point Group Crossover; 3.4.5.2 Adaptive Crossover; 3.4.6 Mutation Strategies; 3.4.6.1 Swap Mutation; 3.4.6.2 Split Mutation; 3.4.6.3 Merge Mutation; 3.4.6.4 Adaptive Mutation; 3.4.7 Inversion; 3.4.7.1 Two-Point Inversion; 3.4.7.2 Single-Point Inversion; 3.4.7.3 Adaptive Inversion; 3.4.8 Replacement Strategies; 3.4.9 Termination Strategies; 3.4.9.1 Iteration Count (ItCount); 3.4.9.2 Iterations Without Improvement (ItWithoutImp); 3.4.9.3 Hybrid Criteria; 3.5 Application Areas; 3.6 Summary; References

• 4 Fuzzy Grouping Genetic Algorithms: Advances for Real-World Grouping Problems4.1 Introduction; 4.2 Preliminaries: Fuzzy Logic Control; 4.3 Fuzzy Grouping Genetic Algorithms: Advances and Innovations; 4.3.1 FGGA Coding Scheme; 4.3.2 Initialization; 4.3.3 Fuzzy Fitness Evaluation; 4.3.3.1 Multifactor Evaluation; 4.3.3.2 Fuzzy Goal-Oriented Fitness Evaluation; 4.3.4 Fuzzy Genetic Operators; 4.3.4.1 Fuzzy Controlled Genetic Parameters; Convergence Measure; Diversity Measure; Crossover Probability; Mutation Probability; Inversion Probability; 4.3.4.2 Fuzzy Logic Controlled Crossover

This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision criteria. Examples of such problems are: vehicle routing problems, team formation problems, timetabling problems, assembly line balancing, group maintenance planning, modular design, and task assignment. A wide range of industrial grouping problems, drawn from diverse fields such as logistics, supply chain management, project management, manufacturing systems, engineering design and healthcare, are presented. Typical complex industrial grouping problems, with multiple decision criteria and constraints, are clearly described using illustrative diagrams and formulations. The problems are mapped into a common group structure that can conveniently be used as an input scheme to specific variants of grouping genetic algorithms. Unique heuristic grouping techniques are developed to handle grouping problems efficiently and effectively. Illustrative examples and computational results are presented in tables and graphs to demonstrate the efficiency and effectiveness of the algorithms. Researchers, decision analysts, software developers, and graduate students from various disciplines will find this in-depth reader-friendly exposition of advances and applications of grouping genetic algorithms an interesting, informative and valuable resource.

• Advanced Computing.- Language Semantic And Content Technologies.- Signal And Image Processing.- Optimisation And Intelligent Control.
• (source: Nielsen Book Data)

This volume is a selected collection of papers presented and discussed at the International Conference "Advanced Computing for Innovation (AComIn 2015)". The Conference was held at 10th -11th of November, 2015 in Sofia, Bulgaria and was aimed at providing a forum for international scientific exchange between Central/Eastern Europe and the rest of the world on several fundamental topics of computational intelligence. The papers report innovative approaches and solutions in hot topics of computational intelligence - advanced computing, language and semantic technologies, signal and image processing, as well as optimization and intelligent control.
(source: Nielsen Book Data)

• Cloud Computing
• Social Networking
• Monitoring
• Literature Management.

This book presents some of the emerging techniques and technologies used to handle Web data management. Authors present novel software architectures and emerging technologies and then validate using experimental data and real world applications. The contents of this book are focused on four popular thematic categories of intelligent Web data management: cloud computing, social networking, monitoring and literature management. The Volume will be a valuable reference to researchers, students and practitioners in the field of Web data management, cloud computing, social networks using advanced intelligence tools.

• Intro
• Foreword
• Preface
• Organization of the Chapters
• Acknowledgments
• Contents
• Contributors
• Part I Methodology
• 1 SOMA
• Self-organizing Migrating Algorithm
• Abstract
• 1 Introduction
• 2 Historical Background and Algorithm Classification
• 2.1 SOMA in the Context of Selected Evolutionary Algorithms
• 3 SOMA Applicability
• 4 SOMA Principles and Control Parameters
• 5 SOMA Strategies
• 5.1 SOMA Parameters
• 5.2 Standard Evolutionary Operations in SOMA
• 5.2.1 Population
• 5.2.2 Mutations
• 5.2.3 Crossover
• 5.2.4 Constraint Handling

• 5.2.5 Boundary Constraints
• 5.2.6 Constraint Functions
• 5.2.7 Handling of Integer and Discrete Variables
• 6 Parameter Dependence
• 7 SOMA and Cost Function Evaluations
• 8 Selected SOMA Applications
• 9 SOMA in Computer Games
• 10 SOMA and Interdisciplinary Research
• 11 Conclusion
• Acknowledgments
• References
• 2 DSOMA
• Discrete Self Organising Migrating Algorithm
• Abstract
• 1 Introduction
• 2 Discrete Self-organising Migrating Algorithm
• 3 Initialisation
• 4 Creating Jump Sequences
• 5 Constructing Trial Individuals
• 6 Repairing Trial Individuals

• 7 Population Update
• 8 Iteration
• 9 Migrations
• 10 2 Opt Local Search
• 11 Conclusion
• Acknowledgments
• References
• Part II Implementation
• 3 SOMA and Strange Dynamics
• Abstract
• 1 Introduction
• 2 SOMA and Chaos
• 2.1 Chaos Synthesis
• 2.2 Chaos Control
• 2.3 Chaos Identification
• 2.4 SOMA Powered by Pseudorandom, Chaos and Deterministic Dynamics
• 3 SOMA and Fractal Geometry
• 4 SOMA Dynamics as a Complex Networks
• 5 Conclusion
• Acknowledgment
• References
• 4 Multi-objective Self-organizing Migrating Algorithm
• Abstract

• 1 Introduction to Multi-objective Optimization
• 2 MOSOMA
• 2.1 Controlling Parameters
• 2.2 Migration of Agents
• 2.3 Final Non-dominated Set Choice
• 3 Appendix I
• Evaluation Metrics
• 4 Appendix II
• Benchmark Problems
• Acknowledgements
• References
• 5 Multi-objective Design of EM Components
• Abstract
• 1 Design of EM Components
• 1.1 Yagi-Uda Antenna Design
• 1.2 Dielectric Layered Filter Design
• 1.3 Adaptive Beamforming in Time Domain
• Acknowledgements
• References
• 6 Utilization of Parallel Computing for Discrete Self-organizing Migration Algorithm
• Abstract

• 1 Introduction
• 2 Levels of Parallelization
• 3 Hardware and Software Options for Parallelization
• 3.1 OpenMP
• 3.2 Message Passing Interface
• 3.2.1 Brief Introduction into Kaira
• 3.3 GPU Computing with CUDA
• 4 Parallelization of DSOMA
• 4.1 OpenMP Implementation of DSOMA
• 4.2 Distributed Island Model Implementation of DSOMA
• 4.3 GPU Implementation
• 4.3.1 Data Storage, Transfers and Alignment
• 4.3.2 Data Level Prallelism
• 4.3.3 Single Thread Computation
• 4.3.4 Block/Warp Computation
• 5 Experiments
• 5.1 OpenMP Experiments
• 5.2 CUDA Experiments

This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA.

• Introduction.-
• On the Role of Compliance and Geometry in Mechanical Stability of the Humans and Robots.-
• Teleimpedance Control of a Robotic Arm.-
• Human-like Impedance Control of a Dual-Arm Manipulator.-
• Teleimpedance Control of a Robotic Hand.-
• Teleimpedance Control of a Compliant Knee Exoskeleton.
• (source: Nielsen Book Data)

This book introduces novel thinking and techniques to the control of robotic manipulation. In particular, the concept of teleimpedance control as an alternative method to bilateral force-reflecting teleoperation control for robotic manipulation is introduced. In teleimpedance control, a compound reference command is sent to the slave robot including both the desired motion trajectory and impedance profile, which are then realized by the remote controller. This concept forms a basis for the development of the controllers for a robotic arm, a dual-arm setup, a synergy-driven robotic hand, and a compliant exoskeleton for improved interaction performance.
(source: Nielsen Book Data)

• Newton-Like Methods on Generalized Banach Spaces and Fractional Calculus
• Semilocal Convegence of Newton-Like Methods and Fractional Calculus
• Convergence of Iterative Methods and Generalized Fractional Calculus
• Fixed Point Techniques And Generalized Right Fractional Calculus
• Approximating Fixed Points And K-Fractional Calculus
• Iterative Methods And Generalized G-Fractional Calculus
• Unified Convergence Analysis For Iterative Algorithms And Fractional Calculus
• Convergence Analysis For Extended Iterative Algorithms And Fractional And Vector Calculus
• Convergence Analysis For Extended Iterative Algorithms And Fractional Calculus
• Secant-Like Methods And Fractional Calculus
• Secant-Like Methods And Modified G- Fractional Calculus
• Secant-Like Algorithms And Generalized Fractional Calculus
• Secant-Like Methods And Generalized G-Fractional Calculus Of Canavati-Type
• Iterative Algorithms And Left-Right Caputo Fractional Derivatives
• Iterative Methods On Banach Spaces With A Convergence Structure And Fractional Calculus
• Inexact Gauss-Newton Method For Singular Equations
• The Asymptotic Mesh Independence Principle
• Ball Convergence Of A Sixth Order Iterative Method
• Broyden's Method With Regularily Continuous Divided Differences
• Left General Fractional Monotone Approximation
• Right General Fractional Monotone Approximation Theor
• Left Generalized High Order Fractional Monotone Approximation
• Right Generalized High Order Fractional Monotone Approximation
• Advanced Fractional Taylor's Formulae
• Generalized Canavati Type Fractional Taylor's Formulae.

In this monograph the authors present Newton-type, Newton-like and other numerical methods, which involve fractional derivatives and fractional integral operators, for the first time studied in the literature. All for the purpose to solve numerically equations whose associated functions can be also non-differentiable in the ordinary sense. That is among others extending the classical Newton method theory which requires usual differentiability of function. Chapters are self-contained and can be read independently and several advanced courses can be taught out of this book. An extensive list of references is given per chapter. The book's results are expected to find applications in many areas of applied mathematics, stochastics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also to be in all science and engineering libraries.

• Chaos Modeling, Control and Synchronization.- Control Systems and Applications.- Intelligent Control Applications.
• (source: Nielsen Book Data)

The development of computational intelligence (CI) systems was inspired by observable and imitable aspects of intelligent activity of human being and nature. The essence of the systems based on computational intelligence is to process and interpret data of various nature so that that CI is strictly connected with the increase of available data as well as capabilities of their processing, mutually supportive factors. Developed theories of computational intelligence were quickly applied in many fields of engineering, data analysis, forecasting, biomedicine and others. They are used in images and sounds processing and identifying, signals processing, multidimensional data visualization, steering of objects, analysis of lexicographic data, requesting systems in banking, diagnostic systems, expert systems and many other practical implementations. This book consists of 16 contributed chapters by subject experts who are specialized in the various topics addressed in this book. The special chapters have been brought out in the broad areas of Control Systems, Power Electronics, Computer Science, Information Technology, modeling and engineering applications. Special importance was given to chapters offering practical solutions and novel methods for the recent research problems in the main areas of this book, viz. Control Systems, Modeling, Computer Science, IT and engineering applications. This book will serve as a reference book for graduate students and researchers with a basic knowledge of control theory, computer science and soft-computing techniques. The resulting design procedures are emphasized using Matlab/Simulink software.
(source: Nielsen Book Data)

• Preface. Acknowledgments.
• Chapter 1. Introduction. 1.1 The Machine Intelligence Research. 1.2 The Two-Fold Objectives: Data-Driven and Biologically-Inspired Approaches. 1.3 How to Read this Book. 1.4 Summary and Further Reading. References.
• Chapter 2. Incremental Learning. 2.1 Introduction. 2.2 Problem Foundation. 2.3 An Adaptive Incremental Learning Framework. 2.4 Design of the Mapping Function. 2.5 Case Study. 2.6 Summary.
• Chapter 3. Imbalanced Learning. 3.1 Introduction. 3.2 Nature of the Imbalanced Learning. 3.3 Solutions for Imbalanced Learning. 3.4 Assessment Metrics for Imbalanced Learning. 3.5 Opportunities and Challenges. 3.6 Case Study. 3.7 Summary.
• Chapter 4. Ensemble Learning. 4.1 Introduction. 4.2 Hypothesis Diversity. 4.3 Developing Multiple Hypotheses. 4.4 Integrating Multiple Hypotheses. 4.5 Case Study. 4.6 Summary.
• Chapter 5. Adaptive Dynamic Programming for Machine Intelligence. 5.1 Introduction. 5.2 Fundamental Objectives: Optimization and Prediction. 5.3 ADP for Machine Intelligence. 5.4 Case Study. 5.5 Summary.
• Chapter 6. Associative Learning. 6.1 Introduction. 6.2 Associative Learning Mechanism. 6.3 Associative Learning in Hierarchical Neural Networks. 6.4 Case Study. 6.5 Summary.
• Chapter 7. Sequence Learning. 7.1 Introduction. 7.2 Foundations for Sequence Learning. 7.3 Sequence Learning in Hierarchical Neural Structure. 7.4 Level 0: A Modified Hebbian Learning Architecture. 7.5 Level 1 to Level N: Sequence Storage, Prediction and Retrieval. 7.6 Memory Requirement. 7.7 Learning and Anticipation of Multiple Sequences. 7.8 Case Study. 7.9 Summary.
• Chapter 8. Hardware Design for Machine Intelligence. 8.1 A Final Comment. References.
• (source: Nielsen Book Data)

This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This willprovide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications.
(source: Nielsen Book Data)

• Introduction to Statistical Machine Learning Overview of Semi-Supervised Learning Mixture Models and EM Co-Training Graph-Based Semi-Supervised Learning Semi-Supervised Support Vector Machines Human Semi-Supervised Learning Theory and Outlook.
• (source: Nielsen Book Data)

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field.
(source: Nielsen Book Data)

• Preface
• 1. Basic notions in classical data analysis
• 2. Linear multivariate statistical analysis
• 3. Basic time series analysis
• 4. Feed-forward neural network models
• 5. Nonlinear optimization
• 6. Learning and generalization
• 7. Kernel methods
• 8. Nonlinear classification
• 9. Nonlinear regression
• 10. Nonlinear principal component analysis
• 11. Nonlinear canonical correlation analysis
• 12. Applications in environmental sciences
• Appendix A. Sources for data and codes
• Appendix B. Lagrange multipliers
• Bibliography
• Index.
• (source: Nielsen Book Data)

Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the environmental sciences. Due to their powerful nonlinear modelling capability, machine learning methods today are used in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and monitoring of snow, ice and forests. The book includes end-of-chapter review questions and an appendix listing websites for downloading computer code and data sources. A resources website contains datasets for exercises, and password-protected solutions are available. The book is suitable for first-year graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in environmental sciences interested in applying these new methods to their own work.
(source: Nielsen Book Data)

• Local Ordinal Classification.- Using Genetic Algorithms and Decision Trees for a posteriori Analysis and Evaluation of Tutoring Practices based on Student Failure Models.- Exploiting Decision Trees in Product-based Fuzzy Neural Modeling to Generate Rules with Dynamically Reduced Dimensionality.- Retraining the Neural Network for Data Visualization.- Rule-Based Adaptive Navigation for an Intelligent Educational Mobile Robot.- BRWM: A relevance feedback mechanism for web page clustering.- Bagged Averaging of Regression Models.- Argument-based User Support Systems using Defeasible Logic Programming.- Knowledge Modelling Using The UML Profile.- Optimized Multi-Domain Secure Interoperation using Soft Constraints.- Learning context models for the recognition of scenarios.- MDA-Based Architecture of a Description Logics Reasoner.- Incremental guideline formalization with tool support.- Accessing data in the semantic web: An intelligent data integration and navigation approaches.- An Expert System Delivery Environment for the World Wide Web.- A Formally Specified Ontology Management API as a Registry for Ubiquitous Computing Systems.- Intelligent Configurable Electronic Shop Platform based on Ontologies and 3D Visualization.- Adapting User Interfaces to the User Interaction Requirements in Real Time.- On supporting young students in visual logic modeling.- Attentional Model for Perceiving Social Context in Intelligent Environments.- Penguin Quart - Slovak Digit Speech Recognition Game Based on HMM.- Impact of Face Registration Errors on Recognition.- Unsupervised Segmentation of Meeting Configurations and Activities using Speech Activity Detection.- A Model of Real-Time Indoor Surveillance System using Behavior Detection.- A Filter Module Used in Pedestrian Detection System.- User Localization for Intelligent Crisis Management.- An Intrusion Detection System for Network-Initiated Attacks Using a Hybrid Neural Network.- Which Adequate Trust Model for Trust Networks?.- XML Systems for Intelligent Management of Pervasive Computing Resources.- A constraint based approach for aiding heat treatment operation design and distortion evaluation.- Applying AI to Cooperating Agricultural Robots.- Capacity Evaluation of an Indoor Smart Antenna System at 60 GHz.- Steady State Contingency analysis of electrical networks using machine learning techniques.- Robust Multimodal Audio-Visual Processing for Advanced Context Awareness in Smart Spaces.- Toward supporting group dynamics.- Multimodal Integration of Sensor Network.- Multimodal Identity Tracking in a Smartroom.- Multimodal Focus Attention and Stress Detection and feedback in an Augmented Driver Simulator.- A Fuzzy Expert System for the Early Warning of Accidents Due to Driver Hypo-Vigilance.- Mixed Reality Cane Simulation.- 3D content-based search using sketches.- Manual Annotation and Automatic Image Processing of Multimodal Emotional Behaviors in TV Interviews.- MPEG-4 Facial Expression Synthesis based on Appraisal Theory.- Towards On- and Off-line Search, Browse and Replay of Home Activities.- Engineering an interoperable adaptive hypermedia testing tool supporting user adaptable strategies.- Teaching a Computer Science Course using Adaptable Instructional Images.- e-Class Personalized: Design and Evaluation of an Adaptive Learning Content Management System.- The Use of Psychophysiological Measures for Designing Adaptive Learning Systems.- Developing Personalized E-Books: A Multi-Layered Approach.- Designing a Solver for Arithmetic Constraints to Support Education in Mathematics.- A Tutoring System Discovering Gaps in the Current Body of Students' Knowledge.- Sequencing Parametric Exercises for an Operating System Course.- A gene expression analysis system for medical diagnosis.- Recording, Monitoring and Interrelating Changes of Invivo Bio-cells from Video (Biosignatures).- An Archetype for MRI guided Tele-interventions.- Differential Evolution Algorithms for Finding Predictive Gene Subsets in Microarray Data.- Feature Selection for Microarray Data Analysis Using Mutual Information and Rough Set Theory.- A Support Vector Machine Approach to Breast Cancer Diagnosis and Prognosis.- Source Code Author Identification Based on N-gram Author Profiles.- AJA - Tool for Programming Adaptable Agents.- Investigating the Predictability of Empirical Software Failure Data with Artificial Neural Networks and Hybrid Models.- Selecting the Appropriate Machine Learning Techniques for the Prediction of Software Development Costs.- On the Idea of Using Nature-Inspired Metaphors to Improve Software Testing.- Fast Video Object Tracking using Affine Invariant Normalization.- Knowledge Acquisition from Multimedia Content using an Evolution Framework.- Exploratory Search: Image Retrieval without Deep Semantics.- Word Senses: The Stepping Stones in Semantic-Based Natural Language Processing.- Space-Time Tubes and Motion Representation.- Semantic Concept Detection from News Videos with Self-Organizing Maps.- Analysis of Semantic Information Available in an Image Collection Augmented with Auxiliary Data.- Supporting Semi-Automatic Semantic Annotation of Multimedia Resources.- A Simulation Tool for Modelling Pedestrian Dynamics during Evacuation of Large Areas.- Radar Imaging by Range Density Functions.- A Method for Incremental Data Fusion in Distributed Sensor Networks.- Information Society: the two faces of Janus.- Ant Seeker: An algorithm for enhanced web search.- Increasing Intelligent Wireless Sensor Networks Survivability by Applying Energy-Efficient Schemes.- A Review of Video Watermarking and a Benchmarking Framework.- Chaotic Encryption Driven Watermarking of Human Video Objects Based on Hu Moments.- Semi-Fragile Watermarking Authentication with Local and Global Watermarks.- Decentralising the Digital Rights Management Value Chain by means of Distributed License Catalogues.- AXMEDIS architectural solution for interoperable content and DRM on multichannel distribution.- Computer Aided Diagnosis of CT Focal Liver Lesions based on Texture Features, Feature Selection and Ensembles of Classifiers.- Texture Analysis for Classification of Endometrial Tissue in Gray Scale Transvaginal Ultrasonography.- Wavelet-based Feature Analysis for Classification of Breast Masses from Normal Dense Tissue.- Microcalcification Features Extracted from Principal Component Analysis in the Wavelet Domain.- Classification of Atherosclerotic Carotid Plaques Using Gray Level Morphological Analysis on Ultrasound images.
• (source: Nielsen Book Data)

Artificial Intelligence applications build on a rich and proven theoretical background to provide solutions to a wide range of real life problems. The ever expanding abundance of information and computing power enables researchers and users to tackle higly interesting issues for the first time, such as applications providing personalized access and interactivity to multimodal information based on preferences and semantic concepts or human-machine interface systems utilizing information on the affective state of the user. The purpose of the 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI) is to bring together researchers, engineers, and practitioners interested in the technical advances and business and industrial applications of intelligent systems. AIAI 2006 is focused on providing insights on how AI can be implemented in real world applications.
(source: Nielsen Book Data)

• Title page; Preface; Conference Organization; Contents; Full Papers; Cognitive Learning with Automatic Goal Acquisition; Semantics of Alan; A Compact Argumentation System for Agent System Specification; Social Responsibility Among Deliberative Agents; Knowledge Base Extraction for Fuzzy Diagnosis of Mental Retardation Level; Tuning the Feature Space for Content-Based Music Retrieval; Personalizing Trust in Online Auctions; An Hybrid Soft Computing Approach for Automated Computer Design; FUNEUS: A Neurofuzzy Approach Based on Fuzzy Adaline Neurons; Empirical Evaluation of Scoring Methods

• Binarization Algorithms for Approximate Updating in Credal NetsOn Generalizing the AGM Postulates; The Two-Variable Situation Calculus; Base Belief Change and Optimized Recovery; Unsupervised Word Sense Disambiguation Using the WWW; Relational Descriptive Analysis of Gene Expression Data; Solving Fuzzy PERT Using Gradual Real Numbers; Approaches to Efficient Resource-Constrained Project Rescheduling; A Comparison of Web Service Interface Similarity Measures; Finding Alternatives Web Services to Parry Breakdowns; Posters; Smart Ride Seeker Introductory Plan

• Spam Filtering: The Influence of the Temporal Distribution of Training DataAn Approach for Evaluating User Model Data in an Interoperability Scenario; On the Improvement of Brain Tumour Data Clustering Using Class Information; Endowing BDI Agents with Capability for Modularizing; Rational Agents Under ASP in Games Theory; Automatic Generation of Natural Language Parsers from Declarative Specifications; Reconsideration on Non-Linear Base Orderings; Dynamic Abstraction for Hierarchical Problem Solving and Execution in Stochastic Dynamic Environments

• A Comparison of Two Machine-Learning Techniques to Focus the Diagnosis TaskArgumentation Semantics for Temporal Defeasible Logic; NEWPAR: An Optimized Feature Selection and Weighting Schema for Category Ranking; Challenges and Solutions for Hierarchical Task Network Planning in E-Learning; Invited Talks; Artificial Intelligence and Unmanned Aerial Vehicles; Writing a Good Grant Proposal; Author Index

STAIRS 2006 is the third European Starting AI Researcher Symposium, an international meeting aimed at AI researchers, from all countries, at the beginning of their career: PhD students or people holding a PhD for less than one year. The topics of the papers included range from traditional AI areas to AI applications, such as Agents, Automated Reasoning, Belief Revision, Case-based Reasoning, Constraints, Data Mining & Information Extraction, Genetic Algorithms, Human Computer Interaction, Interactive Sensry Systems (Speech, Multi-Model Processing), Knowledge Representation, Logic Programming, Machine Learning, Natural Language Processing, Neural Networks, Nonmonotonic Reasoning, Planning & Scheduling, Reasoning about Action and Change, Robotics, Search, Smantic Web, Spatial & Temporal Reasoning and Uncertainty.

Annotation Intelligent Technologies including neural network, evolutionary computations, fuzzy approach and mainly hybrid approaches are very promising tools to build intelligent technologies in general. The progress of each theory or application is provided by a number of various theoretical as well as applicational experiments. Machine intelligence is the only alternative how to increase the level of technology to make technology more human-centred and more effective for society. This book includes theoretical as well as applicational papers in the field of neural networks, fuzzy systems and mainly evolutionary computations which application potential was increased by enormous progress in computer power. Hybrid technologies are still progressing and are trying to make some more applications with their ability to learn and process fuzzy information. Neurogenetic systems are very interesting approach to make systems re-configurable and on-line systems for real-world applications. The book is presenting papers from Japan, USA, Hungary, Poland, Germany, Finland, France, Slovakia, United Kingdom, Czech Republic and some other countries. This publication provides the latest state of the art in the field and could be contributed to theory and applications in the machine intelligence tools and their wide application potential in current and future technologies within the Information Society.

The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior-thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI, " and "behavior-based AI." This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.
(source: Nielsen Book Data)

• Helicopter flight control with fuzzy logic and genetic algorithms, C. Philips et al
• skill acquisition and skill-based motion planning for hierarchical intelligent control of a redundant manipulator, T. Shibata
• a creative design of fuzzy logic controller using a genetic algorithm, T. Hashiyama et al
• automatic fuzzy tuning and its applications, H. Ishigami et al
• an evolutionary algorithm for fuzzy controller synthesis and optimization based on SGS-Thomson's W.A.R.P. fuzzy processor, R. Poluzzi et al
• on-line self-structuring fuzzy inference systems for function approximation, H. Bersini
• fuzzy classification based on adaptive networks and genetic algorithms, C.-T. Sun and J.-S. Jang
• intelligent systems for fraud detection, J. Kingdon
• genetic algorithms for query optimization in information retrieval - relevance feedback, D.H. Kraft et al
• fuzzy fitness assignment in an interactive genetic algorithm for a cartoon face search, K. Nishio et al
• an evolutionary approach to simulate cognitive feedback learning in medical domain, H.S. Lopes et al
• a classified review on the combination fuzzy logic-genetic algorithms bibliography - 1989-1995, O. Cordon et al.
• (source: Nielsen Book Data)

Ever since fuzzy logic was introduced by Lotfi Zadeh in the mid-sixties and genetic algorithms by John Holland in the early seventies, these two fields widely been subjects of academic research the world over. During the last few years, they have been experiencing extremely rapid growth in the industrial world, where they have been shown to be very effective in solving real-world problems. These two substantial fields, together with neurocomputing techniques, are recognized as major parts of soft computing: a set of computing technologies already riding the waves of the next century to produce the human-centered intelligent systems of tomorrow; the collection of papers presented in this book shows the way. The book also contains an extensive bibliography on fuzzy logic and genetic algorithms.
(source: Nielsen Book Data)

• The Road to Intelligent Machines
• The Power and Limitations of Perceptrons
• Neural Logic Networks
• Probabilistic Neural Logic Networks
• Fuzzy Neural Logic Networks
• Temporal Neural Logic Networks
• Neural Logic Programming
• Connectionist Expert Systems
• Fuzzy Knowledge Processing
• The Art of Guessing.
• (source: Nielsen Book Data)

This book is the first of a series of technical reports of a key research project of the Real-World Computing Program supported by the MITI of Japan.The main goal of the project is to model human intelligence by a special class of mathematical systems called neural logic networks.The book consists of three parts. Part 1 describes the general theory of neural logic networks and their potential applications. Part 2 discusses a new logic called Neural Logic which attempts to emulate more closely the logical thinking process of human. Part 3 studies the special features of neural logic networks which resemble the human intuition process.This book should appeal to researchers in artificial intelligence, neural computings and logic, as well as graduate and advance undergraduate students in computer science.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 1st International Congress on Blockchain and Applications 2020, BLOCKCHAIN'20, held in L'Aquila, Italy, in October 2020. Among the scientific community, blockchain and artificial intelligence are a promising combination that will transform the production and manufacturing industry, media, finance, insurance, e-government, etc. Nevertheless, there is no consensus with schemes or best practices that would specify how blockchain and artificial intelligence should be used together. The 21 full papers presented were carefully reviewed and selected from over 40 submissions. They contain the latest advances on blockchain and artificial intelligence and on their application domains, exploring innovative ideas, guidelines, theories, models, technologies, and tools, and identifying critical issues and challenges that researchers and practitioners must deal with in future research. .
(source: Nielsen Book Data)

• Chapter 1- Perceptrons * Introduction to Perceptrons * Working of a Perceptron * Program to understand the working of a Perceptron Chapter 2: Neural Networks * Introduction to Neural Networks * Types of Neural Networks * How each neural network works * Program to understand the working of Neural Networks Chapter 3: Project 1- DJ Neuron * About the Project: Creating Music Using Neural Networks * Requirements * Explanation of concepts used * Architecture of the Neural Network * Source code with line by line instructions Chapter 4: Project 2- Artistic Neurons * About the Project: Adding colour to black and white images * Requirements * Explanation of concepts used * Architecture of the Neural Network * Source code with line by line instructions Chapter 5: Project 3- Go HD * About the Project: Restoration of images for better quality * Requirements
• * Explanation of concepts used * Architecture of the Neural Network * Source code with line by line instructions Chapter 6: Project 4- Voice Experiments
• * About the Project: Voice Manipulation * Requirements
• * Explanation of concepts used * Architecture of the Neural Network * Source code with line by line instructions Chapter 7: Project 5- Imposters * About the Project: Fake Image Recognition * Requirements * Explanation of concepts used * Architecture of the Neural Network * Source code with line by line instructions Chapter 8: Project 6 - Gaming is Fun * About the Project: MI-agent training using Unity. Learn to create Artificially Intelligent Characters.
• Requirements
• Explanation of concepts used
• Architecture of the Neural Network
• Source code with line by line instructions.
• (source: Nielsen Book Data)

Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to deep fakes and so much more. It is crucial for aspiring data scientists or those who want to enter the field of AI to understand deep learning concepts. The best way to learn is by doing. You'll develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. You'll also work with Neural Networks and other deep learning concepts. By the end of the book, you'll have a collection of unique projects that you can add to your GitHub profiles and expand on for professional application. What You'll Learn Grasp the basic process of neural networks through projects, such as creating music Restore and colorize black and white images with deep learning processes Who This Book Is For Beginners new to TensorFlow and Python. .
(source: Nielsen Book Data)

• From the content: Theoretical Computer Science: Computability, Decidability and Logic.- Theoretical Computer Science: Computational Complexity.- Databases and Artificial Intelligence.
• (source: Nielsen Book Data)

The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This third volume is dedicated to the interfaces of AI with various fields, with which strong links exist either at the methodological or at the applicative levels. The foreword of this volume reminds us that AI was born for a large part from cybernetics. Chapters are devoted to disciplines that are historically sisters of AI: natural language processing, pattern recognition and computer vision, and robotics. Also close and complementary to AI due to their direct links with information are databases, the semantic web, information retrieval and human-computer interaction. All these disciplines are privileged places for applications of AI methods. This is also the case for bioinformatics, biological modeling and computational neurosciences. The developments of AI have also led to a dialogue with theoretical computer science in particular regarding computability and complexity. Besides, AI research and findings have renewed philosophical and epistemological questions, while their cognitive validity raises questions to psychology. The volume also discusses some of the interactions between science and artistic creation in literature and in music. Lastly, an epilogue concludes the three volumes of this Guided Tour of AI Research by providing an overview of what has been achieved by AI, emphasizing AI as a science, and not just as an innovative technology, and trying to dispel some misunderstandings.
(source: Nielsen Book Data)

• A Prescriptive Analytical Logic Model Incorporated with Analytic Hierarchy Process for Software Application Error Analysis
• Simplified Framework of Natural Language Processing for Structure Management of Current-Age Data
• Design and Software Implementation of Heuristic and Suboptimal Strategies for the Mancala/Kalah Game
• Sector-Selective Hybrid Scheme Facilitating Hardware Supportability Over Image Compression
• Performance Evaluation of Joint Rate-Distortion Model of Video Codec
• To Defeat DDoS Attacks in Cloud Computing Environment Using Software Defined Networking (SDN)
• Oriented Petri Nets as Means of Describing a Human-Computer Interaction
• FPGA Based Transient Fault Generate and Fault Tolerant for Asynchronous and Synchronous Circuits
• Designing an Energy Efficient Routing for Subsystems Sensors in Internet of Things Eco-System Using Distributed Approach
• The Compare of Solo Programming and Pair Programming Strategies in a Scrum Team: A Multi-agent Simulation
• Fair Random Access with Track-Based SNR Scheduling for Full-Duplex Wireless Powered Communication Networks
• A DTN Gateway-Based Architecture for Web Access in Space Internet
• Multi-objective Scheduling Optimization for ETL Tasks in Cluster Environment
• An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries
• Analysis of Social Engineering Attacks Using Exploit Kits
• Bipolar Disorder: A Pathway Towards Research Progress in Identification and Classification
• Managing Electronic Waste with Recycling: A Review of Developing and Developed Regions
• Trajectory Modeling in a Pursuit Problem with Curvature Restrictions
• An Approach to Using Templates for Modeling Exceptions in Terms of Petri Nets
• Performance Bottleneck Analysis and Optimization Framework in Concurrent Environments
• Matlab Code Generation and Consumption
• The Problem of Preventing the Development of Critical Combinations of Events in Large-Scale Systems
• Using Software Package "Multimeat-Expert" for Modeling and Optimization of Composition Chopped Meat Product with Vegetable Additive
• Application of Machine Learning for Document Classification and Processing in Adaptive Information Systems
• SNR-Based TDMA Scheduling with Continuous Energy Transfer for Wireless Powered Communication Networks
• Comparative Analysis of Software Development Life Cycle Models (SDLC). Plus 27 more papers.

This book gathers the refereed proceedings of the Intelligent Algorithms in Software Engineering Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Software engineering research and its applications to intelligent algorithms have now assumed an essential role in computer science research. In this book, modern research methods, together with applications of machine and statistical learning in software engineering research, are presented. .
(source: Nielsen Book Data)

• Chapter 1. Contraction methods for correlation clustering: the order is important (Laszlo Aszalos).-
• Chapter 2. Optimisation of Preparedness and Response of Health services in major crises using the IMPRESS platform (Nina Dobrinkova).-
• Chapter 3. The new approach for dynamic optimization with variability constraints (Pawel Drag).-
• Chapter 4. Intercriteria Analisys of ACO Performance for Workforce Planning Problem (Olympia Roeva).-
• Chapter 5. Is Prufer Code encoding always a bad idea? (Hanno Hildmann).-
• Chapter 6. A model for wireless-access network topology and a PSO-based approach for its optimization (Hanno Hildmann).-
• Chapter 7. InterCriteria Analysis Approach for Comparison of Simple and Multi-population Genetic Algorithms Performance (Maria Angelova).-
• Chapter 8. Structure Optimization and Learning of Fuzzy Cognitive Map with the use of Evolutionary Algorithm and Graph Theory Metrics (Katarzyna Poczeta).-
• Chapter 9. Fuzziness in the Berth Allocation Problem (Flabio Gutierrez).-
• Chapter 10. Identifying Clusters in Spatial Data via Sequential Importance Sampling (Nishanthi Raveendran).-
• Chapter 11. Multiobjective Optimization Grover Adaptive Search (Benjamin Baran.-
• Chapter 12. Discovering Knowledge from Predominantly Repetitive Data by InterCriteria Analysis (Olympia Roeva).
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Introduction.- Testing and Learning.- Extensions of Automata Learning.- Integrative Approaches.
• (source: Nielsen Book Data)

Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts. This volume originates from a Dagstuhl Seminar entitled "Machine Learning for Dynamic Software Analysis: Potentials and Limits" held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities. The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches.
(source: Nielsen Book Data)

• Part I: Smart Cities.- Defensive Driving Strategy and Control for Autonomous Ground Vehicle in Mixed Traffic.- Augmenting the LSA Technique to Evaluate Ubicomp Environments.- Mixed Integer Programming Formulation for the Energy-Efficient Train Timetables Problem.- Distributing Computing in the Internet of Things: Cloud, Fog and Edge Computing Overview.- Part II: Search, Optimization and Hybrid Algorithms.- Integer Programming Models and Heuristics for Non-Crossing Euclidean 3-Matchings.- A Multi-Objective Robust Ellipse Fitting Algorithm.- Gradient-Based Multiobjective Optimization with Uncertainties.- A New Local Search Heuristic for the Multidimensional Assignment Problem.- Part III: Electronics and Embedded Systems.- A Multi-Objective and Multidisciplinary Optimisation Algorithm for Microelectromechanical Systems.- Coefficients Estimation of MPM through LSE, ORLS and SLS for RF-PA Modeling and DPD.- Optimal Sizing of Amplifiers by Evolutionary Algorithms with Integer Encoding and gm/ID Design Method.- Index. .
• (source: Nielsen Book Data)

This volume comprises a selection of works presented at the Numerical and Evolutionary Optimization (NEO 2016) workshop held in September 2016 in Tlalnepantla, Mexico. The development of powerful search and optimization techniques is of great importance in today's world and requires researchers and practitioners to tackle a growing number of challenging real-world problems. In particular, there are two well-established and widely known fields that are commonly applied in this area: (i) traditional numerical optimization techniques and (ii) comparatively recent bio-inspired heuristics. Both paradigms have their unique strengths and weaknesses, allowing them to solve some challenging problems while still failing in others. The goal of the NEO workshop series is to bring together experts from these and related fields to discuss, compare and merge their complementary perspectives in order to develop fast and reliable hybrid methods that maximize the strengths and minimize the weaknesses of the underlying paradigms. In doing so, NEO promotes the development of new techniques that are applicable to a broader class of problems. Moreover, NEO fosters the understanding and adequate treatment of real-world problems particularly in emerging fields that affect all of us, such as healthcare, smart cities, big data, among many others. The extended papers presented in the book contribute to achieving this goal. .
(source: Nielsen Book Data)

• Table of Contents Recognizing traffic signs using Convnets Annotating Images with Object Detection API Caption generation for images Building GANs for Conditional Image Creation Stock Price Prediction with LSTM Create & Train Machine Translation Systems Train and set up a Chatbot, able to discuss like a human Detecting Duplicate Quora Questions Building a TensorFlow Recommender Systems Video Games by Reinforcement learning.
• (source: Nielsen Book Data)

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios Key Features Build efficient deep learning pipelines using the popular Tensorflow framework Train neural networks such as ConvNets, generative models, and LSTMs Includes projects related to Computer Vision, stock prediction, chatbots and more Book DescriptionTensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently. What you will learn Set up the TensorFlow environment for deep learning Construct your own ConvNets for effective image processing Use LSTMs for image caption generation Forecast stock prediction accurately with an LSTM architecture Learn what semantic matching is by detecting duplicate Quora questions Set up an AWS instance with TensorFlow to train GANs Train and set up a chatbot to understand and interpret human input Build an AI capable of playing a video game by itself -and win it! Who this book is forThis book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.
(source: Nielsen Book Data)

• Mathematical Aspects of Using Neural Approaches for InformationRetrieval.- A Fuzzy Kwan- Cai Neural Network for Determining Image Similarity and for the Face Recognition.- Predicting Human Personality from Social Media using a Fuzzy Neural Network.- Modern Neural Methods for Function Approximation.- A Fuzzy Gaussian Clifford Neural Network.- Concurrent Fuzzy Neural Networks.- A New Interval Arithmetic Based Neural Network.- A Recurrent Neural Fuzzy Network.
• (source: Nielsen Book Data)

This book highlights the ability of neural networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR), which is based on index term matching. The book defines a new NN-based method for learning image similarity and describes how to use fuzzy Gaussian neural networks to predict personality.It introduces the fuzzy Clifford Gaussian network, and two concurrent neural models: (1) concurrent fuzzy nonlinear perceptron modules, and (2) concurrent fuzzy Gaussian neural network modules.Furthermore, it explains the design of a new model of fuzzy nonlinear perceptron based on alpha level sets and describes a recurrent fuzzy neural network model with a learning algorithm based on the improved particle swarm optimization method.
(source: Nielsen Book Data)

• Multiagent Systems.- Distributed Artificial Intelligence.- Intelligent Agents.- Agent Architectures.- Multiagent Planning.- Scheduling.- Autonomous Coordination.- Machine Learning.- Agent-based Manufacturing.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 15th German Conference on Multiagent System Technologies, MATES 2017, held in Lepzig, Germany, in August 2017. The 17 full papers presented in this volume were carefully reviewed and selected from 24 submissions for inclusion in the proceedings. Over these 15 years, the MATES conference series has been aiming at the promotion of and the cross-fertilization between theory and application of intelligent agents and multi-agent systems.
(source: Nielsen Book Data)

• DNA Computing
• Membrane Computing
• Neural Computing
• Machine Learning.

The two-volume set, CCIS 681 and CCIS 682, constitutes the proceedings of the 11th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2016, held in Xi'an, China, in October 2016. The 115 revised full papers presented were carefully reviewed and selected from 343 submissions. The papers of Part I are organized in topical sections on DNA Computing; Membrane Computing; Neural Computing; Machine Learning. The papers of Part II are organized in topical sections on Evolutionary Computing; Multi-objective Optimization; Pattern Recognition; Others.

• Evolutionary Computing
• Multi-objective Optimization
• Pattern Recognition
• Others.

The two-volume set, CCIS 681 and CCIS 682, constitutes the proceedings of the 11th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2016, held in Xi'an, China, in October 2016. The 115 revised full papers presented were carefully reviewed and selected from 343 submissions. The papers of Part I are organized in topical sections on DNA Computing; Membrane Computing; Neural Computing; Machine Learning. The papers of Part II are organized in topical sections on Evolutionary Computing; Multi-objective Optimization; Pattern Recognition; Others.

• From the Content: From Multi-valued Logics to Fuzzy Logic
• A Dialogue Concerning Contradiction And Reasoning
• Some Entertainments Dealing with Three Valued Logic
• Fuzziness as an Experimental Science: an Homage to Claudio Moraga
• Some reflections on the use of interval fuzzy sets for dealing with Fuzzy Deformable Prototypes
• The way to the BliZ.

The book is an authoritative collection of contributions by leading experts on the topics of fuzzy logic, multi-valued logic and neural network. Originally written as an homage to Claudio Moraga, seen by his colleagues as an example of concentration, discipline and passion for science, the book also represents a timely reference guide for advance students and researchers in the field of soft computing, and multiple-valued logic. .

• Fast Output-Sensitive Approach for Minimum Convex Hulls Formation
• Local Search Algorithms for Portfolio Selection: Search Space and Correlation Analysis
• Optimization of Fuel Consumption in Firefighting Water Capsule Flights of a Helicopter
• Practical Application of OFN arithmetics in a Crisis Control Center Monitoring
• Forecasting Indoor Temperature Using Fuzzy Cognitive Maps with Structure Optimization Genetic Algorithm
• Correlation Clustering by Contraction, a More Effective Method
• Synthesis of Power Aware Adaptive Embedded Software Using Developmental Genetic Programming
• Flow Design and Evaluation in Photonic Data Transport Network
• Introducing the Environment in Ant Colony Optimization
• Fast Preconditioned Solver for Truncated Saddle Point Problem in Nonsmooth Cahn-Hilliard Model
• The Constraints Aggregation Technique for Control of Ethanol Production
• InterCriteria Analysis by Pairs and Triples of Genetic Algorithms Application for Models Identification
• Genetic Algorithms for Constrained Tree Problems
• InterCriteria Analysis of Genetic Algorithms Performance
• Exploring Sparse Covariance Estimation Techniques in Evolution Strategies
• Parallel Metaheuristics for Robust Graph Coloring Problem.

This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2015. It presents recent advances in computational optimization. The volume includes important real life problems like parameter settings for controlling processes in bioreactor, control of ethanol production, minimal convex hill with application in routing algorithms, graph coloring, flow design in photonic data transport system, predicting indoor temperature, crisis control center monitoring, fuel consumption of helicopters, portfolio selection, GPS surveying and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics, medicine and other domains can be formulated as optimization problems.

• An Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.
• (source: Nielsen Book Data)

This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: - Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. - Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. - Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.
(source: Nielsen Book Data)

• Introduction.-Fundamentals
• Control Tasks Based on Artificial Potential Fields
• Redundancy Resolution by Null Space Projections
• Stability Analysis
• Whole-Body Coordination
• Integration of the Whole-Body Controller into a Higher-Level Framework
• Summary.

Introducing mobile humanoid robots into human environments requires the systems to physically interact and execute multiple concurrent tasks. The monograph at hand presents a whole-body torque controller for dexterous and safe robotic manipulation. This control approach enables a mobile humanoid robot to simultaneously meet several control objectives with different pre-defined levels of priority, while providing the skills for compliant physical contacts with humans and the environment. After a general introduction into the topic of whole-body control, several essential reactive tasks are developed to extend the repertoire of robotic control objectives. Additionally, the classical Cartesian impedance is extended to the case of mobile robots. All of these tasks are then combined and integrated into an overall, priority-based control law. Besides the experimental validation of the approach, the formal proof of asymptotic stability for this hierarchical controller is presented. By interconnecting the whole-body controller with an artificial intelligence, the immense potential of the integrated approach for complex real-world applications is shown. Several typical household chores, such as autonomously wiping a window or sweeping the floor with a broom, are successfully performed on the mobile humanoid robot Rollin? Justin of the German Aerospace Center (DLR). The results suggest the presented controller for a large variety of fields of application such as service robotics, human-robot cooperation in industry, telepresence in medical applications, space robotics scenarios, and the operation of mobile robots in dangerous and hazardous environments.

• Part I Computational Models and Knowledge Discovery
• Part II Communications, Networks, and Cloud Computing
• Part III Computer-Based Systems
• Part IV Data-Oriented and Software-Intensive Systems.

This carefully edited and reviewed volume addresses the increasingly popular demand for seeking more clarity in the data that we are immersed in. It offers excellent examples of the intelligent ubiquitous computation, as well as recent advances in systems engineering and informatics. The content represents state-of-the-art foundations for researchers in the domain of modern computation, computer science, system engineering and networking, with many examples that are set in industrial application context. The book includes the carefully selected best contributions to APCASE 2014, the 2nd Asia-Pacific Conference on Computer Aided System Engineering, held February 10-12, 2014 in South Kuta, Bali, Indonesia. The book consists of four main parts that cover data-oriented engineering science research in a wide range of applications: computational models and knowledge discovery; communications networks and cloud computing; computer-based systems; and data-oriented and software-intensive systems.

• PART 1: Linguistic resources.-
• PART 2: Tools and related methodologies.
• (source: Nielsen Book Data)

The papers collected in this volume are selected as a sample of the progress in Natural Language Processing (NLP) performed within the Italian NLP community and especially attested by the PARLI project. PARLI (Portale per l'Accesso alle Risorse in Lingua Italiana) is a project partially funded by the Ministero Italiano per l'Universita e la Ricerca (PRIN 2008) from 2008 to 2012 for monitoring and fostering the harmonic growth and coordination of the activities of Italian NLP. It was proposed by various teams of researchers working in Italian universities and research institutions. According to the spirit of the PARLI project, most of the resources and tools created within the project and here described are freely distributed and they did not terminate their life at the end of the project itself, hoping they could be a key factor in future development of computational linguistics.
(source: Nielsen Book Data)

• Discovering Lisp
• Lists everywhere.- Functions in Lisp
• Lisp Programming
• From Crisp Sets to Fuzzy Sets
• From Fuzzy Sets to Linguistic Variables
• Fuzzy Logic
• Practical Projects using FuzzyLisp.-

This book makes use of the LISP programming language to provide readers with the necessary background to understand and use fuzzy logic to solve simple to medium-complexity real-world problems. It introduces the basics of LISP required to use a Fuzzy LISP programming toolbox, which was specifically implemented by the author to "teach" the theory behind fuzzy logic and at the same time equip readers to use their newly-acquired knowledge to build fuzzy models of increasing complexity. The book fills an important gap in the literature, providing readers with a practice-oriented reference guide to fuzzy logic that offers more complexity than popular books yet is more accessible than other mathematical treatises on the topic. As such, students in first-year university courses with a basic tertiary mathematical background and no previous experience with programming should be able to easily follow the content. The book is intended for students and professionals in the fields of computer science and engineering, as well as disciplines including astronomy, biology, medicine and earth sciences. Software developers may also benefit from this book, which is intended as both an introductory textbook and self-study reference guide to fuzzy logic and its applications. The complete set of functions that make up the Fuzzy LISP programming toolbox can be downloaded from a companion book's website

• Deep Learning of Representations / Yoshua Bengio, Aaron Courville
• Recurrent Neural Networks / Sajid A. Marhon, Christopher J.F. Cameron
• Supervised Neural Network Models for Processing Graphs / Monica Bianchini, Marco Maggini
• Topics on Cellular Neural Networks / Liviu Goraş, Ion Vornicu, Paul Ungureanu
• Approximating Multivariable Functions by Feedforward Neural Nets / Paul C. Kainen, Věra Kůrková
• Bochner Integrals and Neural Networks / Paul C. Kainen, Andrew Vogt
• Semi-supervised Learning / Mohamed Farouk Abdel Hady, Friedhelm Schwenker
• Statistical Relational Learning / Hendrik Blockeel
• Kernel Methods for Structured Data / Andrea Passerini
• Multiple Classifier Systems: Theory, Applications and Tools / Francesco Gargiulo, Claudio Mazzariello
• Self Organisation and Modal Learning: Algorithms and Applications / Dominic Palmer-Brown, Chrisina Jayne
• Bayesian Networks, Introduction and Practical Applications / Wim Wiegerinck, Willem Burgers, Bert Kappen
• Relevance Feedback in Content-Based Image Retrieval: A Survey / Jing Li, Nigel M. Allinson
• Learning Structural Representations of Text Documents in Large Document Collections / Ah Chung Tsoi, Markus Hagenbuchner, Milly Kc
• Neural Networks in Bioinformatics / Masood Zamani, Stefan C. Kremer.

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformat.

• Stochastic Algorithms for 3D Node Localization in Anisotropic Wireless Sensor Networks
• An Evaluation of Classification Algorithms Using Mc Nemar's Test
• Permitting Features in P Systems Generating Picture Arrays
• An ACO Framework for Single Track Railway Scheduling Problem
• Bio-Inspired Soft-Computational Framework for Speech and Image Application
• Leukocyte Classification in Skin Tissue Images
• Solving Application Oriented Graph Theoretical Problems with DNA Computing
• Human Identification Using Heartbeat Interval Features and ECG Morphology
• Improved Real-Time Discretize Network Intrusion Detection System
• Identification and Impact Assessment of High-Priority Field Failures in Passenger Vehicles Using Evolutionary Optimization
• Automatic Agricultural Leaves Recognition System
• Non-Uniform Mapping in Binary-Coded Genetic Algorithms.

• Control Words of Transition P Systems
• Iso-Array Splicing Grammar System
• GA based Dimension Reduction for enhancing performance of k-Means and Fuzzy k-Means: A Case Study for Categorization of Medical Dataset
• A Computational Intelligence based Approach to Telecom Customer Classification for Value Added Services
• An Efficient Approach on Rare Association Rule Mining
• A Hybrid Multiobjective Particle Swarm Optimization Approach for Non-redundant Gene Marker Selection
• Application of High Quality Amino Acid Indices to AMS 3.0: A Update Note
• Constructive Solid Geometry Based Topology Optimization Using Evolutionary Algorithm
• Array P Systems with Hybrid Teams
• An Approach for the Ordering of Evaluation of Objectives in Multiobjective Optimization
• Extended Forma: Analysis and an Operator Exploiting it
• Incorporating Great Deluge with Harmony Search for Global Optimization Problems.

• Boundary Handling Approaches in Particle Swarm Optimization
• Diversity Measures in Artificial Bee Colony
• Digital Video Watermarking Using Scene Detection
• Self Adaptive Acceleration Factor in Particle Swarm Optimization
• Applying Case Based Reasoning in Cuckoo Search for the Expedition of Groundwater Exploration
• Reversible OR Logic Gate Design Using DNA
• Performance Enhanced Hybrid Artificial Neural Network for Abnormal Retinal Image Classification
• Algorithmic Tile Self-assembly Model for the Minimum Dominating Set Problem
• Semantic Sub-tree Crossover Operator for Postfix Genetic Programming
• Exploration Enhanced Particle Swarm Optimization using Guided Re-Initialization
• Using Firefly Algorithm to Solve Resource Constrained Project Scheduling Problem
• Analysis of Cellular Automata and Genetic Algorithm based Test Pattern Generators for Built In Self Test
• Ant Colony-based System for Retinal Blood Vessels Segmentation.

• An Efficient Neural Network Based Background Subtraction Method
• JustThink: Smart BCI Applications
• Interpretability Issues in Evolutionary Multi-Objective Fuzzy Knowledge Base Systems
• Hybrid Firefly Based Simultaneous Gene Selection and Cancer Classification Using Support Vector Machines and Random Forests
• Recognition of Online Handwritten Gurmukhi Strokes Using Support Vector Machine
• An Optimal Fuzzy Logic Controller Tuned with Artificial Immune System
• Comparisons of Different Feature Sets for Predicting Carbohydrate-Binding Proteins From Amino Acid Sequences Using Support Vector Machine
• A PSO Based Smart Unit Commitment Strategy for Power Systems Including Solar Energy
• A User-Oriented Content Based Recommender System Based on Reclusive Methods and Interactive Genetic Algorithm.

The book is a collection of high quality peer reviewed research papers presented in Seventh International Conference on Bio-Inspired Computing (BIC-TA 2012) held at ABV-IIITM Gwalior, India. These research papers provide the latest developments in the broad area of "Computational Intelligence". The book discusses wide variety of industrial, engineering and scientific applications of nature/bio-inspired computing and presents invited papers from the inventors/originators of novel computational techniques

• Prior Knowledge in the Classification of Biomedical Data.-Estimation of a Simple Genetic Algorithm Applied to a Laboratory Experiment
• A Comparison of Robust Methods for Pareto Tail Modeling in the Case of Laeken Indicators
• R Code for Hausdorff and Simplex Dispersion Orderings in the 2D Case On Some Confidence Regions to Estimate a Linear Regression
• Model for Interval Data Possibilistic Coding: Error Detection vs. Error Correction . Coherent Correction for Conditional Probability Assessments
• with RInferential Rules for Weak Graphoid.-Fast Factorization of Probability Trees and its Application to Recursive Trees Learning
• Option Pricing in Incomplete Markets Based on Partial Information
• Lorenz Curves of extrema
• Likelihood in a Possibilistic and Probabilistic Context: A Comparison
• Nonparametric Predictive Inference for Order Statistics of Future Observations
• Expected Pair-Wise Comparison of the Outcomes of a Fuzzy Random Variable
• The Behavioral Meaning of the Median Functional Classification and the Random Tukey Depth. Practical Issues
• On Concordance Measures and Copulas with Fractal Support
• Factorisation Properties of the Strong Product etc. ...

Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of randomness (stochastic uncertainty) but also in the form of imprecision/fuzziness is but one point underlining the need for a widening of statistical tools. Most such extensions originate in a "softening" of classical methods, allowing, in particular, to work with imprecise or vague data, considering imprecise or generalized probabilities and fuzzy events, etc. About ten years ago the idea of establishing a recurrent forum for discussing new trends in the before-mentioned context was born and resulted in the first International Conference on Soft Methods in Probability and Statistics (SMPS) that was held in Warsaw in 2002. In the following years the conference took place in Oviedo (2004), in Bristol (2006) and in Toulouse (2008). In the current edition the conference returns to Oviedo. This edited volume is a collection of papers presented at the SMPS 2010 conference held in Mieres and Oviedo. It gives a comprehensive overview of current research into the fusion of soft methods with probability and statistics

• Preface.- Introduction.- Security and Dependability in Ambient Intelligence (AmI).- Security and Dependability Engineering.- The Use of Patterns in Security and Dependability Engineering: An Overview.- The SERENITY vision.- Realising the Vision Specification of S&D Properties.- Representation of Security and Dependability Solutions.- Organisational Level S&D Patterns.- S&D patterns for Web Services and Workflows.- Network and Communication S&D Patterns.- Pattern Integration Schemes.- SERENITY S&D Solution Development Process.- SERENITY Trust Model.- Verification Using the Secure Tropos Approach.- Verification Using APA-SHVT.- Verification Using Avispa.- Complementing Solutions with Monitoring.- Transforming Verification Results into Models.- Exploiting S&D Patterns in System Development.- SERENITY System Development Process.- Pattern Libraries and their Use System Development.- Exploiting S&D Patterns at Runtime.- The SERENITY Framework.- Pattern Driven Selection and Configuration of S&D Mechanisms at Runtime.- Pattern Driven Detection of S&D Intrusions and Threats.- Diagnosing S&D Iintrusions.- Recovery and Control for S&D Intrusions and Threats.- Supporting the Evolution of S&D Patterns.- Experiences and Future Directions.- Applications of SERENITY Framework on Industrial Case Studies - Experiences and Challenges.- Standardization.- Realizing the Potential of SERENITY in Emerging AmI Ecosystems: Implications and Challenges.- Index.
• (source: Nielsen Book Data)

Security and Dependability for Ambient Intelligence is the primary publication of the SERENITY approach, which provides security and dependability (S&D) solutions for dynamic, highly distributed, heterogeneous systems. The objective of SERENITY is to enhance the security and dependability of ambient intelligence systems by providing a framework supporting the automated integration, configuration, monitoring and adaptation of security and dependability mechanisms. An edited volume contributed by world leaders in the field, this book covers the problems that the highly dynamic and heterogeneous nature of ambient intelligence systems poses to security and dependability and presents solutions to these problems. Security and Dependability for Ambient Intelligence is designed for researchers and practitioners focusing on the dynamic integration, deployment and verification of security and dependability solutions in highly distributed systems incorporating ambient intelligence features. It is also suitable as a reference or secondary text for advanced-level students in computer science and computer or electrical engineering.
(source: Nielsen Book Data)

• Introduction : The mechanical mind / Philip Husbands, Michael Wheeler, and Owen Holland
• Charles Babbage and the emergence of automated reason / Seth Bullock
• D'Arcy Thompson : a grandfather of A-life / Margaret A. Boden
• Alan Turing's mind machines / Donald Michie
• What did Alan Turing mean by "machine"? / Andrew Hodges
• The ratio club : a hub of British cybernetics / Philip Husbands and Owen Holland
• From mechanisms of adaptation to intelligence amplifiers : the philosophy of W. Ross Ashby / Peter M. Asaro
• Gordon Pask and his maverick machines / Jon Bird and Ezequiel Di Paolo
• Santiago dreaming / Andy Beckett
• Steps toward the synthetic method : symbolic information processing and self-organizing systems in early artificial intelligence modeling / Roberto Cordeschi
• The mechanization of art / Paul Brown
• The robot story : why robots were born and how they grew up / Jana Horáková and Jozef Kelemen
• God's machines : Descartes on the mechanization of mind / Michael Wheeler
• Why Heideggerian AI failed and how fixing it would require making it more Heideggerian / Hubert L. Dreyfus
• An interview with John Maynard Smith
• An interview with John Holland
• An interview with Oliver Selfridge
• An interview with Horace Barlow
• An interview with Jack Cowan.

The idea of intelligent machines has become part of popular culture. But tracing the history of the actual science of machine intelligence reveals a rich network of cross-disciplinary contributions--the unrecognized origins of ideas now central to artificial intelligence, artificial life, cognitive science, and neuroscience. In The Mechanization of Mind in History, scientists, artists, historians, and philosophers discuss the multidisciplinary quest to formalize and understand the generation of intelligent behavior in natural and artificial systems as a wholly mechanical process. The contributions illustrate the diverse and interacting notions that chart the evolution of the idea of the mechanical mind. They describe the mechanized mind as, among other things, an analogue system, an organized suite of chemical interactions, a self-organizing electromechanical device, an automated general-purpose information processor, and an integrated collection of symbol manipulating mechanisms. They investigate the views of pivotal figures that range from Descartes and Heidegger to Alan Turing and Charles Babbage, and they emphasize such frequently overlooked areas as British cybernetic and pre-cybernetic thinkers. The volume concludes with the personal insights of five highly influential figures in the field: John Maynard Smith, John Holland, Oliver Selfridge, Horace Barlow, and Jack Cowan.Philip Husbands is Professor of Computer Science and Artificial Intelligence in the Department of Informatics at the University of Sussex and Codirector of the Sussex Centre for Computational Neuroscience and Robotics. Owen Holland is Professor in the Department of Computer Science at the University of Essex. Michael Wheeler is Reader in Philosophy at the University of Stirling. He is the author of Reconstructing the Cognitive World: The Next Step (MIT Press, 2005).ContributorsPeter Asaro, Horace Barlow, Andy Beckett, Margaret Boden, Jon Bird, Paul Brown, Seth Bullock, Roberto Cordeschi, Jack Cowan, Ezequiel Di Paolo, Hubert Dreyfus, Andrew Hodges, Owen Holland, Jana Horakova, Philip Husbands, Jozef Kelemen, John Maynard Smith, Donald Michie, Oliver Selfridge, Michael Wheeler.
(source: Nielsen Book Data)

• Robustness in Computer Hardware, Software, Networks, and Protocols.- Robustness in Digital Hardware.- Multiagent-Based Fault Tolerance Management for Robustness.- A Two-Level Robustness Model for Self-Managing Software Systems.- Robustness in Network Protocols and Distributed Applications of the Internet.- Robustness in Biology Inspired Systems.- Detecting Danger: The Dendritic Cell Algorithm.- Non-invasive Brain-Computer Interfaces for Semi-autonomous Assistive Devices.- Robust Learning of High-dimensional Biological Networks with Bayesian Networks.- Robustness in Artificial Intelligence Systems.- Robustness in Nature as a Design Principle for Artificial Intelligence.- Feedback Structures as a Key Requirement for Robustness: Case Studies in Image Processing.- Exploiting Motor Modules in Modular Contexts in Humanoid Robotics.- Robustness in Space Applications.- Robustness as Key to Success for Space Missions.- Robust and Automated Space System Design.- Robust Bio-regenerative Life Support Systems Control.
• (source: Nielsen Book Data)

Our time recognizes robustness as an important, all-pervading feature in the world around us. Despite its omnipresence, robustness is not entirely understood, rather dif?cult to de?ne, and, despite its obvious value in many situations, rather dif?cult to achieve. One of the goals of this edited book is to report on the topic of robustness from a variety and diverse range of ?elds and perspectives. We are interested, for instance, in fundamental strategies nature applies to make systems robust-and arguably "intelligent"-and how these strategies may hold as general design principles in modern technology. A particular focus is on computer-based systems and appli- tions. This in mind, the book has four main sections: Part I has a look at robustness in terms of underlying technologies and infrastr- tures upon which many computer-based "intelligent" systems reside and inves- gates robustness on the hardware and software level, but also in larger environments such as the Internet and self-managing systems. The contributions in Part II target robustness in research areas that are inspired by biology, including brain-computer interfaces, biological networks, and biological immune systems, for example. Part III involves the exciting ?eld of arti?cial intelligence. The chapters here discuss the value of robustness as a general design principle for arti?cial intelligence, stressing its potential in areas such as humanoid robotics and image processing.
(source: Nielsen Book Data)

• A Bayesian Approach to Causal Discovery.- A Tutorial on Learning Causal Influence.- Learning Based Programming.- N-1 Experiments Suffice to Determine the Causal Relations Among N Variables.- Support Vector Inductive Logic Programming.- Neural Probabilistic Language Models.- Computational Grammatical Inference.- On Kernel Target Alignment.- The Structure of Version Space.
• (source: Nielsen Book Data)

Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.
(source: Nielsen Book Data)

• Multi-Objective Clustering, Feature Extraction and Feature Selection.- Feature Selection Using Rough Sets.- Multi-Objective Clustering and Cluster Validation.- Feature Selection for Ensembles Using the Multi-Objective Optimization Approach.- Feature Extraction Using Multi-Objective Genetic Programming.- Multi-Objective Learning for Accuracy Improvement.- Regression Error Characteristic Optimisation of Non-Linear Models.- Regularization for Parameter Identification Using Multi-Objective Optimization.- Multi-Objective Algorithms for Neural Networks Learning.- Generating Support Vector Machines Using Multi-Objective Optimization and Goal Programming.- Multi-Objective Optimization of Support Vector Machines.- Multi-Objective Evolutionary Algorithm for Radial Basis Function Neural Network Design.- Minimizing Structural Risk on Decision Tree Classification.- Multi-objective Learning Classifier Systems.- Multi-Objective Learning for Interpretability Improvement.- Simultaneous Generation of Accurate and Interpretable Neural Network Classifiers.- GA-Based Pareto Optimization for Rule Extraction from Neural Networks.- Agent Based Multi-Objective Approach to Generating Interpretable Fuzzy Systems.- Multi-objective Evolutionary Algorithm for Temporal Linguistic Rule Extraction.- Multiple Objective Learning for Constructing Interpretable Takagi-Sugeno Fuzzy Model.- Multi-Objective Ensemble Generation.- Pareto-Optimal Approaches to Neuro-Ensemble Learning.- Trade-Off Between Diversity and Accuracy in Ensemble Generation.- Cooperative Coevolution of Neural Networks and Ensembles of Neural Networks.- Multi-Objective Structure Selection for RBF Networks and Its Application to Nonlinear System Identification.- Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection.- Applications of Multi-Objective Machine Learning.- Multi-Objective Optimisation for Receiver Operating Characteristic Analysis.- Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination.- Fuzzy Tuning for the Docking Maneuver Controller of an Automated Guided Vehicle.- A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments.- Multi-Objective Neural Network Optimization for Visual Object Detection.
• (source: Nielsen Book Data)

Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
(source: Nielsen Book Data)

• Keynote Papers.- Generalized Theory of Uncertainty (GTU) - Principal Concepts and Ideas.- Reasoning with Vague Probability Assessments.- Soft Methods in Earth Systems Engineering.- Statistical Data Processing under Interval Uncertainty: Algorithms and Computational Complexity.- Soft Methods in Statistics and Random Information Systems.- On Testing Fuzzy Independence.- Variance Decomposition of Fuzzy Random Variables.- Fuzzy Histograms and Density Estimation.- Graded Stochastic Dominance as a Tool for Ranking the Elements of a Poset.- On Neyman-Pearson Lemma for Crisp, Random and Fuzzy Hypotheses.- Fuzzy Probability Distributions Induced by Fuzzy Random Vectors.- On the Identifiability of TSK Additive Fuzzy Rule-Based Models.- An Asymptotic Test for Symmetry of Random Variables Based on Fuzzy Tools.- Exploratory Analysis of Random Variables Based on Fuzzifications.- A Method to Simulate Fuzzy Random Variables.- Friedman's Test for Ambiguous and Missing Data.- Probability of Imprecisely-Valued Random Elements with Applications.- Measure-Free Martingales with Application to Classical Martingales.- A Note on Random Upper Semicontinuous Functions.- Optional Sampling Theorem and Representation of Set-Valued Amart.- On a Choquet Theorem for Random Upper Semicontinuous Functions.- A General Law of Large Numbers, with Applications.- Applications and Modelling of Imprecise Operators.- Fuzzy Production Planning Model for Automobile Seat Assembling.- Optimal Selection of Proportional Bounding Quantifiers in Linguistic Data Summarization.- A Linguistic Quantifier Based Aggregation for a Human Consistent Summarization of Time Series.- Efficient Evaluation of Similarity Quantified Expressions in the Temporal Domain.- Imprecise Probability Theory.- Conditional Lower Previsions for Unbounded Random Quantities.- Extreme Lower Probabilities.- Equivalence Between Bayesian and Credal Nets on an Updating Problem.- Varying Parameter in Classification Based on Imprecise Probabilities.- Comparing Proportions Data with Few Successes.- A Unified View of Some Representations of Imprecise Probabilities.- Possibility, Evidence and Interval Methods.- Estimating an Uncertain Probability Density.- Theory of Evidence with Imperfect Information.- Conditional IF-probability.- On Two Ways for the Probability Theory on IF-sets.- A Stratification of Possibilistic Partial Explanations.- Finite Discrete Time Markov Chains with Interval Probabilities.- Evidence and Compositionality.- High Level Fuzzy Labels for Vague Concepts.- Integrated Uncertainty Modelling in Applications.- Possibilistic Channels for DNA Word Design.- Transformation of Possibility Functions in a Climate Model of Intermediate Complexity.- Fuzzy Logic for Stochastic Modeling.- A CUSUM Control Chart for Fuzzy Quality Data.- A Fuzzy Synset-Based Hidden Markov Model for Automatic Text Segmentation.- Applying Fuzzy Measures for Considering Interaction Effects in Fine Root Dispersal Models.- Scoring Feature Subsets for Separation Power in Supervised Bayes Classification.- Interval Random Variables and Their Application in Queueing Systems with Long-Tailed Service Times.- Online Learning for Fuzzy Bayesian Prediction.
• (source: Nielsen Book Data)

The idea of soft computing emerged in the early 1990s from the fuzzy systems c- munity, and refers to an understanding that the uncertainty, imprecision and ig- rance present in a problem should be explicitly represented and possibly even - ploited rather than either eliminated or ignored in computations. For instance, Zadeh de?ned `Soft Computing' as follows: Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role model for soft computing is the human mind. Recently soft computing has, to some extent, become synonymous with a hybrid approach combining AI techniques including fuzzy systems, neural networks, and biologically inspired methods such as genetic algorithms. Here, however, we adopt a more straightforward de?nition consistent with the original concept. Hence, soft methods are understood as those uncertainty formalisms not part of mainstream s- tistics and probability theory which have typically been developed within the AI and decisionanalysiscommunity.Thesearemathematicallysounduncertainty modelling methodologies which are complementary to conventional statistics and probability theory.
(source: Nielsen Book Data)

• Title page; Table of Contents; Introduction; Interval Constraints; Constraint Satisfaction Problems; Interval Analysis; Constraint Propagation; Partial Consistencies; Global Hull-Consistency; Local Search; Experimental Results; Interval Constraints for Differential Equations; Ordinary Differential Equations; Constraint Satisfaction Differential Problems; Solving a CSDP; Biomedical Decision Support with ODEs; Conclusions and Future Work; References; Appendix A: Interval Analysis Theorems; Appendix B: Constraint Propagation Theorems.

Focuses on the integration of ordinary differential equations within the interval constraints framework, which for this purpose is extended with the formalism of Constraint Satisfaction Differential Problems. Such a framework allows the specification of ordinary differential equations by means of constraints.

• Genetic Programming Theory and Practice.- Discovering Financial Technical Trading Rules Using.- Abstraction GP.- Using Genetic Programming in Industrial Statistical Model Building Population Sizing for Genetic Programming.- Considering the Roles of Structure in Problem Solving by Computer.- Lessons Learned using Genetic Programming in a Stock Picking Context Favourable Biasing of Function Sets.- Toward Automated Design of Industrial-Strength Analog Circuits by Means of Genetic Programming.- Topological Synthesis of Robust Systems.- Does Genetic Programming Inherently Adopt Structured DesignTechniques?- Genetic Programming of an Algorithmic Chemistry.- ACGP: Adaptable Constrained Genetic Programming.- Searching for Supply Chain Reordering Policies.- Cartesian Genetic Programming and the Post Docking Filtering Problem.- Listening to Data: Tuning a Genetic Programming System.- Incident Detection on Highways.- Pareto-Front Exploitation in Symbolic Regression.- An Evolved Antenna for a NASA Mission.
• (source: Nielsen Book Data)

The work described in this book was first presented at the Second Workshop on Genetic Programming, Theory and Practice, organized by the Center for the Study of Complex Systems at the University of Michigan, Ann Arbor, 13-15 May 2004. The goal of this workshop series is to promote the exchange of research results and ideas between those who focus on Genetic Programming (GP) theory and those who focus on the application of GP to various re- world problems. In order to facilitate these interactions, the number of talks and participants was small and the time for discussion was large. Further, participants were asked to review each other's chapters before the workshop. Those reviewer comments, as well as discussion at the workshop, are reflected in the chapters presented in this book. Additional information about the workshop, addendums to chapters, and a site for continuing discussions by participants and by others can be found at http://cscs.umich.edu:8000/GPTP-20041. We thank all the workshop participants for making the workshop an exciting and productive three days. In particular we thank all the authors, without whose hard work and creative talents, neither the workshop nor the book would be possible. We also thank our keynote speakers Lawrence ("Dave") Davis of NuTech Solutions, Inc., Jordan Pollack of Brandeis University, and Richard Lenski of Michigan State University, who delivered three thought-provoking speeches that inspired a great deal of discussion among the participants.
(source: Nielsen Book Data)

• 1. Introduction. 1.1. Support vector machine: data processing method for problems of small sample size. 1.2. Support vector machine: data processing method for complicated data sets in chemistry. 1.3. Underfitting and overfitting: problems of machine learning. 1.4. Theory of overfitting and underfitting control, ERM and SRM principles of statistical learning theory. 1.5. Concept of large margin
• a basic concept of SVM. 1.6. Kernel functions: technique for nonlinear data processing by linear algorithm. 1.7. Support vector regression: regression based on principle of statistical learning theory. 1.8. Other machine learning methods related to statistical learning theory. 1.9. Some comments on the application of SVM in chemistry
• 2. Support Vector Machine. 2.1. Margin and optimal separating plane. 2.2. Interpretation by statistical learning therory. 2.3. Support vector classification. 2.4. Support vector regression. 2.5 V-SVM
• 3. Kernel functions. 3.1. Introduction. 3.2. Mercer kernel. 3.3. Properties of kernel. 3.4. Kernel selection
• 4. Feature selection using support vector machine. 4.1. Significance and difficulty of feature selection in chemical data processing. 4.2. SVM-BFS
• application of wrapper method and floating search method. 4.3. SVM-RFE: application of optimal brain damage and recursive feature elimination. 4.4. Multitask learning. 4.5. Computer experiments: feature selection of artificially generated data set
• 5. Principle of atomic or molecular parameter-data processing method. 5.1. Two different strategies for structure-property relationship investigation. 5.2. Number of valence electrons of atoms. 5.3. Ionization potential of atoms. 5.4. Atomic radii and ionic radii. 5.5. Electronegativity. 5.6. Charge-radius ratio. 5.7. Topological parameters of molecules and 3-D molecular descriptors. 5.8. Atomic parameters for ionic systems. 5.9. Atomic parameters for covalent compounds. 5.10. Atomic parameters for metallic systems
• 6. SVM applied to phase diagram assessment and prediction. 6.1. Comprehensive assessment and computerized prediction of phase diagrams. 6.2. Atomic parameter-pattern recognition method for phase diagram prediction. 6.3. Prediction of intermediate compound formation. 6.4. Prediction of formation of extended solid solutions. 6.5. Prediction of melting types of intermediate compounds. 6.6. Modeling of melting points or decomposition temperature of intermediate compounds. 6.7. Prediction of crystal types of intermediate compounds. 6.8. Modeling of liquid-liquid immiscibility of inorganic systems. 6.9. SVM applied to intelligent database of phase diagrams.

• 7. SVM applied to thermodynamic property prediction. 7.1. Significance of estimation of thermodynamic properties of chemical substances. 7.2. Modeling of enthalpy of formation of compounds. 7.3. Modeling of free energy of mixing of liquid alloy systems. 7.4 Prediction of activity coefficient of concentrated electrolyte solutions. 7.5. Regularity of the solubility of C[symbol] in organic solvents
• 8. SVM applied to molecular and materials design. 8.1. concepts of molecular design and materials design. 8.2. SVM applied to new compound synthesis problems. 8.3. SVM applied to the computerized prediction of properties of materials. 8.4. SVM applied to process design for materials preparation
• 9. SVM applied to structure-activity relationships. 9.1. Concept of Structure-Activity Relationships (SAR). 9.2. Brief Introduction to some of chemometric methods used in SAR. 9.3. Brief introduction to molecular descriptors used in SAR. 9.4 SAR of N-(3-Oxo-3,4-dihydro-2H-benzo[l, 4]oxazine-6-carbonyl) guanidines. 9.5. SAR of triazole-derivatives. 9.6. SAR of the 5-hydroxytryptamine receptor antagonists. 9.7. QSAR of N-phenylacetamides as herbicides
• 10. SVM applied to data of trace element analysis. 10.1. Trace element science and chemical data processing. 10.2. SVM applied to trace element analysis of human hair. 10.3. SVM applied to trace elements analysis of cigarettes. 10.4. SVM applied to trace element analysis of tea
• 11. SVM applied to archeological chemistry of ancient ceramics. 11.1. SVM applied to archeological data processing. 11.2. Identification of Jun Wares of Song Dynasty. 11.3. Modeling of official Ru Wares. 11.4. Modeling of composition of Yue Wares. 11.5. Modeling of composition of blue and white porcelain samples. 11.6. Archeological research of ancient porcelain kilns. 11.7. Period discrimination of ancient samples
• 12. SVM applied to cancer research. 12.1. SVM applied to cancer epidemiology. 12.2. Carcinogenic and environmental behaviors of polycyclic aromatic hydrocarbons. 12.3. SVM applied to cancer diagnosis
• 13. SVM applied to some topics of chemical analysis. 13.1. Multivariate calibration in chemical analysis. 13.2. Retention indices estimation in chromatography. 13.3. Detection of hidden explosives
• 14. SVM applied to chemical and metallurgical technology. 14.1. Physico-chemical basis of modeling of chemical processes. 14.2. Characteristics of data processing for industrial process modeling. 14.3. Optimal zone: strategy of large margin search. 14.4. Application of strategy of large margin search. 14.5. Optimal control for target maximization or minimization. 14.6. Optimal control for problem of restricted response. 14.7. Materials properties estimation for production process. 14.8. Comprehensive strategy for industrial optimization.

In recent years, the support vector machine (SVM), a new data processing method, has been applied to many fields of chemistry and chemical technology. Compared with some other data processing methods, SVM is especially suitable for solving problems of small sample size, with superior prediction performance. SVM is fast becoming a powerful tool of chemometrics. This book provides a systematic approach to the principles and algorithms of SVM, and demonstrates the application examples of SVM in QSAR/QSPR work, materials and experimental design, phase diagram prediction, modeling for the optimal control of chemical industry, and other branches in chemistry and chemical technology.
(source: Nielsen Book Data)