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• Preface
• Semi-supervised Learning
• Local Tangent Space Laplacian Eigenmaps
• Multi-Step Model of Switching Reinforcement Learning to Mimic Infants` Motor Development
• Reverse Engineering Networks as Ordinary Differential Equations Systems
• The Relevance of Bayesian Experimental Design for Modeling in Systems Biology
• Topographic Maps for clustering & fast identification of bacteria using 16S housekeeping gene
• Cognitive Modelling of Language Acquisition with Complex Networks
• Process Mining & Visual Analytics: Breathing Life into Business Process Models.
• (source: Nielsen Book Data)

Computational intelligence aims to emulate aspects of biological systems for developing software and/or hardware that learns and adapts. Such systems primarily include neural networks, fuzzy logic systems and evolutionary computation each of them including multiple active and fast growing research directions. This book presents new and ground-breaking research from world-renowned experts focusing on various topics related to computational intelligence. What makes this book particularly interesting is the variety of subjects attained, which offer a global perspective on this central research field in the Knowledge Age.
(source: Nielsen Book Data)

• Foreword vii Preface ix Acknowledgement xi
• Chapter 1: Introduction 1-20 1.1 Computational Intelligence 1 1.2 Paradigms of Computational Intelligence 2 1.3 Synergies of Computational Intelligence Techniques 11 1.4 Applications of Computational Intelligence 13 1.5 Grand Challenges of Computational Intelligence 14 1.6 Overview of the Book 14 1.7 Matlab Basics 16 1.8 Bibliography 17
• Chapter 2: Fuzzy Logic 21-78 2.1 Introduction 21 2.2 Fuzzy Logic 23 2.3 Fuzzy Sets 24 2.4 Membership Functions 25 2.5 Features of MFs 30 2.6 Operations on Fuzzy sets 32 2.7 Linguistic Variables 39 2.8 Linguistic Hedges 42 2.9 Fuzzy Relations 45 2.10 Fuzzy If-Then Rules 48 2.11 Fuzzification 52 2.12 Defuzzification 54 2.13 Inference Mechanism 59 2.13.1 Mamdani Fuzzy Inference 60 2.13.2 Sugeno Fuzzy Inference 61 2.13.3 Tsukamoto Fuzzy Inference 65 2.14 Worked out Examples 67 2.15 Matlab Programs 76 2.16 Bibliography 77
• Chapter 3: Fuzzy Systems and Applications 79-128 3.1 Introduction 79 3.2 Fuzzy System 80 3.3 Fuzzy Modelling 81 3.3.1 Structure Identification 82 3.3.2 Parameter Identification 85 3.3.3 Construction of parameterised Membership Functions 86 3.4 Fuzzy Control 92 3.4.1 Fuzzification 93 3.4.2 Inference Mechanism 93 3.4.3 Rule-base 98 3.4.4 Defuzzification 100 3.5 Design of Fuzzy Controller 101 3.5.1 Input-output Selection 102 3.5.2 Choice of Membership Functions 102 3.5.3 Creation of Rule-base 103 3.5.4 Types of Fuzzy Controller 104 3.6 Modular Fuzzy Controller .121 3.7 Matlab Programs 124 3.8 Bibliography 125
• Chapter 4: Neural Networks 129-201 4.1 Introduction 129 4.2 Artificial Neuron Model 130 4.3 Activation Functions 132 4.4 Network Architecture 134 4.4.1 Feedforward Networks 134 4.4.1.1 Multilayer Perceptron (MLP) Networks 136 4.4.1.2 Radial Basis Function (RBF) Networks 138 4.4.1.3 General Regression Neural Networks 142 4.4.1.4 Probabilistic Neural Network 146 4.4.1.5 Belief Network 149 4.4.1.6 Hamming Network 150 4.4.1.7 Stochastic Networks 153 4.5 Learning in Neural Networks 153 4.5.1 Supervised learning 154 4.5.1.1 Widro-Hoff Learning Algorithm 155 4.5.1.2 Gradient Descent Rule 4.5.1.3 Generalised Delta Learning Rule 162 4.5.1.4 Backpropagation Learning Algorithm 165 4.5.1.5 Cohen-Grossberg Learning Rule 171 4.5.1.6 Adaptive Conjugate Gradient Model of Adeli and Hung 173 4.5.2 Unsupervised Learning 173 4.5.2.1 Hebbian Learning Rule 174 4.5.2.2 Kohonen Learning 178 4.6 Recurrent Neural Networks 187 4.6.1 Elman Networks 189 4.6.2 Jordan Networks 192 4.6.3 Hopfield Networks 194 4.7 Matlab Programs 198 4.8 Bibliography 198
• Chapter 5: Neural Systems 202-232 5.1 Introduction 200 5.2 System Identification and Control 201 5.2.1 System Description 201 5.2.2 System Identification 202 5.2.3 System Control ..203 5.3 Neural Networks for Control 205 5.3.1 System Identification 206 5.3.2 Neural Networks for Control Design 208 5.3.2.1 NN-based direct (or specialised learning) control 209 5.3.2.2 NN-based indirect control .210 5.3.2.3 Backpropagation-through time control 211 5.3.2.4 NN-based direct inverse control 212 5.3.2.5 Model Predictive Control 214 5.3.2.6 NN-based Adaptive Control 216 5.3.2.7 NARMA-L2 (Feedback Linearization) Control 223 5.4 Matlab Programs 226 5.5 Bibliography 227
• Chapter 6: Evolutionary Computation 233-304 6.1 Introduction 233 6.2 Evolutionary Computing 234 6.3 Terminologies of Evolutionary Computing 235 6.3.1 Chromosome Representation 235 6.3.2 Encoding Scheme 236 6.3.3 Population 243 6.3.4 Evaluation (or Fitness) Functions 245 6.3.5 Fitness Scaling 246 6.4 Genetic Operators 247 6.4.1 Selection Operators 247 6.4.2 Crossover Operators 252 6.4.3 Mutation Operators 261 6.5 Performance Measure of EA 264 6.6 Evolutionary Algorithms 265 6.6.1 Evolutionary Programming 265 6.6.2 Evolution Strategies 271 6.6.3 Genetic Algorithms 277 6.6.4 Genetic Programming 283 6.6.5 Differential Evolution 294 6.6.6 Cultural Algorithm 299 6.7 Matlab Programs 300 6.8 Bibliography 301
• Chapter 7: Evolutionary Systems 305-340 7.1 Optimisation .305 7.2 Multi-objective Optimisation ..310 7.2.1 Vector Evaluated GA 315 7.2.2 Multi-objective GA 315 7.2.3 Niched Pareto GA .316 7.2.4 Non-dominated Sorting GA 316 7.2.5 Strength Pareto Evolutionary Algorithm 318 7.3 Co-evolution .319 7.3.1 Cooperative Co-evolution 324 7.3.2 Competitive Co-evolution .326 7.4 Parallel Evolutionary Algorithms 328 7.4.1 Global GA 329 7.4.2 Migration (or Island) Model GA 330 7.4.3 Diffusion GA .331 7.4.4 Hybrid Parallel GA 334 7.5 Bibliography .336
• Chapter 8: Evolutionary Fuzzy Systems 341-392 8.1 Introduction 341 8.2 Evolutionary Adaptive Fuzzy Systems 343 8.2.1 Evolutionary Tuning of Fuzzy Systems 345 8.2.2 Evolutionary Learning of Fuzzy Systems 361 8.3 Objective Functions and Evaluation 368 8.3.1 Objective Functions 368 8.3.2 Evaluation 370 8.4 Fuzzy Adaptive Evolutionary Algorithms 371 8.4.1 Fuzzy Logic based Control of EA Parameters 374 8.4.2 Fuzzy Logic based Genetic Operators of EA 387 8.5 Bibliography 388
• Chapter 9: Evolutionary Neural Systems 393-455 9.1 Introduction 393 9.2 Supportive Combinations 395 9.2.1 NN-EA Supportive Combination 395 9.2.2 EA-NN Supportive Combination 398 9.3 Collaborative Combinations 406 9.3.1 EA for NN Connection Weight Training 408 9.3.2 EA for NN Architectures 416 9.3.3 EA for NN Node Transfer Functions 430 9.3.4 EA for NN Weight, Architecture and Transfer Function Training 434 9.4 Amalgamated Combination 437 9.5 Competing Conventions 440 9.6 Bibliography 447
• Chapter 10: Neuro Fuzzy Systems 455-530 10.1 Introduction 455 10.2 Combination of Neural and Fuzzy Systems 458 10.3 Cooperative Neuro-Fuzzy Systems 459 10.3.1 Cooperative FS-NN Systems 460 10.3.2 Cooperative NN-FS Systems 461 10.4 Concurrent Neuro-Fuzzy Systems 470 10.5 Hybrid Neuro-Fuzzy Systems 471 10.5.1 Fuzzy Neural Networks with Mamdani-type Fuzzy Inference System 472 10.5.2 Fuzzy Neural Networks with Takagi-Sugeno-type Fuzzy Inference System 474 10.5.3 Fuzzy Neural Networks with Tsukamoto-type Fuzzy Inference System 476 10.5.4 Neural Network based Fuzzy System (Sigma-Pi Network) 480 10.5.5 Fuzzy-Neural System Architecture with Ellipsoid Input Space 484 10.5.6 Fuzzy Adaptive Learning Control Network (FALCON) 487 10.5.7 Approximate Reasoning based Intelligent Control (ARIC) 490 10.5.8 Generalised ARIC (GARIC) 495 10.5.9 Fuzzy Basis Function Networks (FBFN) 502 10.5.10 FUzzy Net (FUN) 505 10.5.11 Combination of Fuzzy Inference and Neural Network in Fuzzy Inference Software (FINEST) 507 10.5.12 Neuro-Fuzzy Controller (NEFCON) 510 10.5.13 Self-constructing Neural Fuzzy Inference Network (SONFIN) 512 10.6 Adaptive Neuro-Fuzzy System 515 10.6.1 Adaptive Neuro-Fuzzy Inference System (ANFIS) 516 10.6.2 Coactive Neuro-Fuzzy Inference System (CANFIS) 519 10.7 Fuzzy Neurons 523 10.8 Matlab Programs 526 10.9 Bibliography 527 Appendix531-606 Index.
• (source: Nielsen Book Data)

• Foreword xiii Preface xv Acknowledgements xix
• 1 Introduction to Computational Intelligence 1 1.1 Computational Intelligence 1 1.2 Paradigms of Computational Intelligence 2 1.3 Approaches to Computational Intelligence 3 1.4 Synergies of Computational Intelligence Techniques 11 1.5 Applications of Computational Intelligence 12 1.6 Grand Challenges of Computational Intelligence 13 1.7 Overview of the Book 13 1.8 MATLAB R - Basics 14 References 15
• 2 Introduction to Fuzzy Logic 19 2.1 Introduction 19 2.2 Fuzzy Logic 20 2.3 Fuzzy Sets 21 2.4 Membership Functions 22 2.5 Features of MFs 27 2.6 Operations on Fuzzy Sets 29 2.7 Linguistic Variables 33 2.8 Linguistic Hedges 35 2.9 Fuzzy Relations 37 2.10 Fuzzy If Then Rules 39 2.11 Fuzzification 43 2.12 Defuzzification 44 2.13 Inference Mechanism 48 2.14 Worked Examples 54 2.15 MATLAB R - Programs 61 References 61
• 3 Fuzzy Systems and Applications 65 3.1 Introduction 65 3.2 Fuzzy System 66 3.3 Fuzzy Modelling 67 3.4 Fuzzy Control 75 3.5 Design of Fuzzy Controller 81 3.6 Modular Fuzzy Controller 97 3.7 MATLAB R - Programs 99 References 100
• 4 Neural Networks 103 4.1 Introduction 103 4.2 Artificial Neuron Model 106 4.3 Activation Functions 107 4.4 Network Architecture 108 4.5 Learning in Neural Networks 124 4.6 Recurrent Neural Networks 149 4.7 MATLAB R - Programs 155 References 156
• 5 Neural Systems and Applications 159 5.1 Introduction 159 5.2 System Identification and Control 160 5.3 Neural Networks for Control 163 5.4 MATLAB R - Programs 179 References 180
• 6 Evolutionary Computing 183 6.1 Introduction 183 6.2 Evolutionary Computing 183 6.3 Terminologies of Evolutionary Computing 185 6.4 Genetic Operators 194 6.5 Performance Measures of EA 208 6.6 Evolutionary Algorithms 209 6.7 MATLAB R - Programs 234 References 235
• 7 Evolutionary Systems 239 7.1 Introduction 239 7.2 Multi-objective Optimization 243 7.3 Co-evolution 250 7.4 Parallel Evolutionary Algorithm 256 References 262
• 8 Evolutionary Fuzzy Systems 265 8.1 Introduction 265 8.2 Evolutionary Adaptive Fuzzy Systems 267 8.3 Objective Functions and Evaluation 287 8.4 Fuzzy Adaptive Evolutionary Algorithms 290 References 303
• 9 Evolutionary Neural Networks 307 9.1 Introduction 307 9.2 Supportive Combinations 309 9.3 Collaborative Combinations 318 9.4 Amalgamated Combination 343 9.5 Competing Conventions 345 References 351
• 10 Neural Fuzzy Systems 357 10.1 Introduction 357 10.2 Combination of Neural and Fuzzy Systems 359 10.3 Cooperative Neuro-Fuzzy Systems 360 10.4 Concurrent Neuro-Fuzzy Systems 369 10.5 Hybrid Neuro-Fuzzy Systems 369 10.6 Adaptive Neuro-Fuzzy System 404 10.7 Fuzzy Neurons 409 10.8 MATLAB R - Programs 411 References 412 Appendix A: MATLAB R - Basics 415 Appendix B: MATLAB R - Programs for Fuzzy Logic 433 Appendix C: MATLAB R - Programs for Fuzzy Systems 443 Appendix D: MATLAB R - Programs for Neural Systems 461 Appendix E: MATLAB R - Programs for Neural Control Design 473 Appendix F: MATLAB R - Programs for Evolutionary Algorithms 489 Appendix G: MATLAB R - Programs for Neuro-Fuzzy Systems 497 Index 507.
• (source: Nielsen Book Data)

Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB(R) exercises and applications in each chapter Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems Considers real world problems in the domain of systems modelling, control and optimization Contains a foreword written by Lotfi Zadeh Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.
(source: Nielsen Book Data)

• ADVANCES IN KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS; Frontiers in Artificial Intelligence and Applications; Preface; Contents; Set of Experience and Experiential Decisional DNA; An Estimation of Distribution Algorithm for Solving the Quay Crane Scheduling Problem with Availability Constraints; Performance comparison of non-RNN and RNN in Emergence of Discrete Decision Making through Reinforcement Learning; Genetic Algorithm Solving Orienteering Problem in Large Networks; Optimisation of Ensemble Classifiers using Genetic Algorithm.

• A Learning Based Evolutionary Algorithm For Distributed Multi-Depot VRPPositive Predictive Value based dynamic K-Nearest Neighbor; An Analysis of Clustering Approaches to Distributed Learning on Heterogeneously Distributed Datasets; Unsupervised Discretization Method based on Adjustable Intervals; Evaluation of Random Subspace and Random Forest Regression Models Based on Genetic Fuzzy Syste; A logic for strategies in persuasion dialogue games; Multi-Agent Logic based on Temporary Logic TS4Kn serving Web Search; LMT: A Lightweight Logical Framework for Multi-agent Systems.

• Adaptive organization for cooperative systemsAn optimal tactic for intelligent agents to conduct search & detection operations based on multiple look angles; A complex system approach for a reliable Smart Grid modeling; A Comparison Analysis of Consensus Determining Using One and Two-level Methods; Multi-Agent Logic with Distances, Uncertainty and Interaction Based on Linear Temporal Frames; A framework for handling fuzzy temporal events; On the Continuous Evaluation of the Macrostructure of Sleep.

• Entropic Dimensionality Reduction in Discriminating Between Alzheimer's Disease and Vascular DementiaMeaning Judgment Method for Alphabet Abbreviation Using the Association Mechanism; Method of Constructing the Integral OLAP-model based on Formal Concept Analysis; Semantically Enhanced Text Stemmer (SETS) for Document Clustering; Prosaico: Characterisation of objectives within the scope of an intelligent system for sport advising; Exploiting the Self-Organizing Financial Stability Map; Knowledge-Driven Method for Object Qualification in 3D Point Cloud Data.

• SAC3̂
• A Rule-Based System to Include Context in the Durability Analysis of Civil StructuresPredicting the Final Result of Sporting Events Based on Changes in Bookmaker Odds; A Novel Channel Estimation Scheme Combining Adaptive Supervised and Unsupervised Algorithms; Reference signal cancellation in passive radar using Volterra-Wiener class filter with dynamic structure; Prosodic feature normalization for emotionre cognition by using synthesized speech; Regularity Dimension of Medical Images; Automatic Scoring of Shooting Targets with Tournament Precision.

• Evolutionary computation and its applications. 1. Maximal margin algorithms for pose estimation / Ying Guo and Jiaming Li. 2. Polynomial modeling in a dynamic environment based on a particle swarm optimization / Kit Yan Chan and Tharam S. Dillon. 3. Restoration of half-toned color-quantized images using particle swarm optimization with multi-wavelet mutation / Frank H.F. Leung, Benny C.W. Yeung and Y.H. Chan
• Fuzzy logics and their applications. 4. Hypoglycemia detection for insulin-dependent diabetes mellitus: evolved fuzzy inference system approach / S.H. Ling, P.P. San and H.T. Nguyen
• Neural networks and their applications. 5. Study of limit cycle behavior of weights of perceptron / C.Y.F. Ho and B.W.K. Ling. 6. Artificial neural network modeling with application to nonlinear dynamics / Yi Zhao. 7. Solving Eigen-problems of matrices by neural networks / Yiguang Liu [and others]. 8. Automated screw insertion monitoring using neural networks: a computational intelligence approach to assembly in manufacturing / Bruno Lara, Lakmal D. Seneviratne and Kaspar Althoefer
• Support vector machines and their applications. 9. On the applications of heart disease risk classification and hand-written character recognition using support vector machines / S.R. Alty, H.K. Lam and J. Prada. 10. Nonlinear modeling using support vector machine for heart rate response to exercise / Weidong Chen [and others]. 11. Machine learning-based nonlinear model predictive control for heart rate response to exercise / Yi Zhang [and others]. 12. Intelligent fault detection and isolation of HVAC system based on online support vector machine / Davood Dehestani [and others].

This book focuses on computational intelligence techniques and their applications - fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches.

• Evolutionary computation and its applications. 1. Maximal margin algorithms for pose estimation / Ying Guo and Jiaming Li. 2. Polynomial modeling in a dynamic environment based on a particle swarm optimization / Kit Yan Chan and Tharam S. Dillon. 3. Restoration of half-toned color-quantized images using particle swarm optimization with multi-wavelet mutation / Frank H.F. Leung, Benny C.W. Yeung and Y.H. Chan
• Fuzzy logics and their applications. 4. Hypoglycemia detection for insulin-dependent diabetes mellitus: evolved fuzzy inference system approach / S.H. Ling, P.P. San and H.T. Nguyen
• Neural networks and their applications. 5. Study of limit cycle behavior of weights of perceptron / C.Y.F. Ho and B.W.K. Ling. 6. Artificial neural network modeling with application to nonlinear dynamics / Yi Zhao. 7. Solving Eigen-problems of matrices by neural networks / Yiguang Liu [and others]. 8. Automated screw insertion monitoring using neural networks: a computational intelligence approach to assembly in manufacturing / Bruno Lara, Lakmal D. Seneviratne and Kaspar Althoefer
• Support vector machines and their applications. 9. On the applications of heart disease risk classification and hand-written character recognition using support vector machines / S.R. Alty, H.K. Lam and J. Prada. 10. Nonlinear modeling using support vector machine for heart rate response to exercise / Weidong Chen [and others]. 11. Machine learning-based nonlinear model predictive control for heart rate response to exercise / Yi Zhang [and others]. 12. Intelligent fault detection and isolation of HVAC system based on online support vector machine / Davood Dehestani [and others].

This book focuses on computational intelligence techniques and their applications - fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches.

• Machine generated contents note: ch. 1 Introduction
• 1.1. Brief History of AI
• 1.2. Cognitive Issues of AI
• 1.3. Hierarchical Model of Thought
• 1.4. Symbolic Intelligence
• 1.5. Research Approaches of Artificial Intelligence
• 1.6. Automated Reasoning
• 1.7. Machine Learning
• 1.8. Distributed Artificial Intelligence
• 1.9. Artificial Thought Model
• 1.10. Knowledge Based Systems
• Exercises
• ch. 2 Logic Foundation of Artificial Intelligence
• 2.1. Introduction
• 2.2. Logic Programming
• 2.3. Nonmonotonic Logic
• 2.4. Closed World Assumption
• 2.5. Default Logic
• 2.6. Circumscription Logic
• 2.7. Nonmonotonic Logic NML
• 2.8. Autoepistemic Logic
• 2.9. Truth Maintenance System
• 2.10. Situation Calculus
• 2.11. Frame Problem
• 2.12. Dynamic Description Logic
• Exercises
• ch. 3 Constraint Reasoning
• 3.1. Introduction
• 3.2. Backtracking
• 3.3. Constraint Propagation
• 3.4. Constraint Propagation in Tree Search
• 3.5. Intelligent Backtracking and Truth Maintenance.

• 3.6. Variable Instantiation Ordering and Assignment Ordering
• 3.7. Local Revision Search
• 3.8. Graph-based Backjumping
• 3.9. Influence-based Backjumping
• 3.10. Constraint Relation Processing
• 3.11. Constraint Reasoning System COPS
• 3.12. ILOG Solver
• Exercise
• ch. 4 Qualitative Reasoning
• 4.1. Introduction
• 4.2. Basic approaches in qualitative reasoning
• 4.3. Qualitative Model
• 4.4. Qualitative Process
• 4.5. Qualitative Simulation Reasoning
• 4.6. Algebra Approach
• 4.7. Spatial Geometric Qualitative Reasoning
• Exercises
• ch. 5 Case-Based Reasoning
• 5.1. Overview
• 5.2. Basic Notations
• 5.3. Process Model
• 5.4. Case Representation
• 5.5. Case Indexing
• 5.6. Case Retrieval
• 5.7. Similarity Relations in CBR
• 5.8. Case Reuse
• 5.9. Case Retainion
• 5.10. Instance-Based Learning
• 5.11. Forecast System for Central Fishing Ground
• Exercises
• ch. 6 Probabilistic Reasoning
• 6.1. Introduction
• 6.2. Foundation of Bayesian Probability
• 6.3. Bayesian Problem Solving
• 6.4. Naive Bayesian Learning Model.

• 6.5. Construction of Bayesian Network
• 6.6. Bayesian Latent Semantic Model
• 6.7. Semi-supervised Text Mining Algorithms
• Exercises
• ch. 7 Inductive Learning
• 7.1. Introduction
• 7.2. Logic Foundation of Inductive Learning
• 7.3. Inductive Bias
• 7.4. Version Space
• 7.5. AQ Algorithm for Inductive Learning
• 7.6. Constructing Decision Trees
• 7.7. ID3 Learning Algorithm
• 7.8. Bias Shift Based Decision Tree Algorithm
• 7.9. Computational Theories of Inductive Learning
• Exercises
• ch. 8 Support Vector Machine
• 8.1. Statistical Learning Problem
• 8.2. Consistency of Learning Processes
• 8.3. Structural Risk Minimization Inductive Principle
• 8.4. Support Vector Machine
• 8.5. Kernel Function
• Exercises
• ch. 9 Explanation-Based Learning
• 9.1. Introduction
• 9.2. Model for EBL
• 9.3. Explanation-Based Generalization
• 9.4. Explanation Generalization using Global Substitutions
• 9.5. Explanation-Based Specialization
• 9.6. Logic Program of Explanation-Based Generalization
• 9.7. SOAR Based on Memory Chunks.

• 9.8. Operationalization
• 9.9. EBL with imperfect domain theory
• Exercises
• ch. 10 Reinforcement Learning
• 10.1. Introduction
• 10.2. Reinforcement Learning Model
• 10.3. Dynamic Programming
• 10.4. Monte Carlo Methods
• 10.5. Temporal-Difference Learning
• 10.6. Q-Learning
• 10.7. Function Approximation
• 10.8. Reinforcement Learning Applications
• Exercises
• ch. 11 Rough Set
• 11.1. Introduction
• 11.2. Reduction of Knowledge
• 11.3. Decision Logic
• 11.4. Reduction of Decision Tables
• 11.5. Extended Model of Rough Sets
• 11.6. Experimental Systems of Rough Sets
• 11.7. Granular Computing
• 11.8. Future Trends of Rough Set Theory
• Exercises
• ch. 12 Association Rules
• 12.1. Introduction
• 12.2. The Apriori Algorithm
• 12.3. FP-Growth Algorithm
• 12.4. CFP-Tree Algorithm
• 12.5. Mining General Fuzzy Association Rules
• 12.6. Distributed Mining Algorithm For Association Rules
• 12.7. Parallel Mining of Association Rules
• Exercises
• ch. 13 Evolutionary Computation
• 13.1. Introduction
• 13.2. Formal Model of Evolution System Theory.

• 13.3. Darwin's Evolutionary Algorithm
• 13.4. Classifier System
• 13.5. Bucket Brigade Algorithm
• 13.6. Genetic Algorithm
• 13.7. Parallel Genetic Algorithm
• 13.8. Classifier System Boole
• 13.9. Rule Discovery System
• 13.10. Evolutionary Strategy
• 13.11. Evolutionary Programming
• Exercises
• ch. 14 Distributed Intelligence
• 14.1. Introduction
• 14.2. The Essence of Agent
• 14.3. Agent Architecture
• 14.4. Agent Communication Language ACL
• 14.5. Coordination and Cooperation
• 14.6. Mobile Agent
• 14.7. Multi-Agent Environment MAGE
• 14.8. Agent Grid Intelligence Platform
• Exercises
• ch. 15 Artificial Life
• 15.1. Introduction
• 15.2. Exploration of Artificial Life
• 15.3. Artificial Life Model
• 15.4. Research Approach of Artificial Life
• 15.5. Cellular Automata
• 15.6. Morphogenesis Theory
• 15.7. Chaos Theories
• 15.8. Experimental Systems of Artificial Life
• Exercises.

Artificial intelligence is a branch of computer science and a discipline in the study of machine intelligence, that is, developing intelligent machines or intelligent systems imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behavior.Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel, reflects the research updates in this field, and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. It is also suitable as a reference book for relevant scientific and technical personnel.
(source: Nielsen Book Data)

• Preface
• Swarm Intelligence & Fuzzy Systems
• Evolutionary Strategies to Find Pareto Fronts in Multiobjective Problems
• Particle Swarm Optimization Applied to Real-World Combinatorial Problems: The Case Study of the In-Core Fuel Management Optimization
• Swarm Intelligence & Artificial Neural Networks
• Application of Particle Swarm Optimization Method to Inverse Heat Radiation Problem
• Ant Colony Optimization for Fuzzy System Parameter Optimization: From Discrete to Continuous Space
• Particle Swarm Optimization: A Survey
• Application of PSO to Electromagnetic & Radar-Related Problems in Non Cooperative Target Identification
• Ant Colony Optimization: A Powerful Strategy for Biomarker Feature Selection
• Swarm Intelligence Based Anonymous Authentication Protocol for Dynamic Group Management in EHRM System
• Index.
• (source: Nielsen Book Data)

Swarm Intelligence (SI) describes the evolving collective intelligence of population/groups of autonomous agents with a low level of intelligence. The population of agents interacts with each other or their environment locally using decentralised and self-organisational aspects in their decision making. SI and related sub-methods that follow its principles are used for problem solving in a variety of areas, such as robotics and forecasting. This book discusses swarm intelligence techniques and fuzzy logic as useful tools for solving practical engineering problems and the utilisation of a swarm intelligence algorithm to obtain the optimum neural network structure. Also explored is the application of Particle Swarm Optimization (PSO) methods to inverse heat radiation problems and PSO as a technique in computational electromagnetism problems.
(source: Nielsen Book Data)

• Preface
• Using Mono-Objective & Multi-Objective Particle Swarm Optimization for the Tuning of Process Control Laws
• Study on Vehicle Routing Problem with Time Windows Based on Enhanced Particle Swarm Optimization Approach
• Reliability Optimization Problems using Adaptive Genetic Algorithm & Improved Particle Swarm Optimization
• Convergence Issues in Particle Swarm Optimization
• Globally Convergent Modifications of Particle Swarm Optimization for Unconstrained Optimization
• Nonlinear 0-1 Programming through Particle Swarm Optimization using Decoding Algorithms
• Comparative Study of Different Approaches to Particle Swarm Optimization in Theory & Practice
• Particle Swarm Optimization for Computer Graphics & Geometric Modeling: Recent Trends
• The Singly-Linked Ring Topology for the Particle Swarm Optimization Algorithm
• PSO Assisted Multiuse Detection for DS-CDMA Communication Systems
• Optimization of Abrasive Flow Machining Process Parameters using Particle Swarm Optimization & Simulated Annealing Algorithms
• Index.
• (source: Nielsen Book Data)

Particle swarm optimisation (PSO) is an algorithm modelled on swarm intelligence that finds a solution to an optimisation problem in a search space or model and predicts social behaviour in the presence of objectives. The PSO is a stochastic, population-based computer algorithm modelled on swarm intelligence. Swarm intelligence is based on social-psychological principles and provides insights into social behaviour, as well as contributing to engineering applications. This book presents information on particle swarm optimisation such as using mono-objective and multi-objective particle swarm optimisation for the tuning of process control laws; convergence issues in particle swarm optimisation; study on vehicle routing problems using enhanced particle swarm optimisation and others.
(source: Nielsen Book Data)

• Relatedness of Fuzzy Sets, Fuzzification of Allen's Temporal Interval Relation
• Reasoning About Qualitative Relations Between Fuzzy Intervals
• Temporal Information Retrieval with Vague Events
• Representation and Composition of Fuzzy Spatial Relations
• Fuzzification of the Region Connection Calculus
• Reasoning in the Fuzzy Region Connection Calculus
• Geographic Information Retrieval with Vague Regions.
• (source: Nielsen Book Data)

This book comprehensively studies fuzzy temporal and spatial information, starting from the basics on fuzzy set theory and temporal/spatial reasoning, the development of a new model to represent fuzzy temporal/spatial information, the study of efficient and complete reasoning algorithms, and their application in an information retrieval context. This useful volume presents the first approach that goes beyond merely representing information, by thoroughly addressing a variety of reasoning tasks. It also focuses on applications in the domain of information retrieval, and demonstrating the practical importance of the proposed framework.
(source: Nielsen Book Data)

• 1. Why kernels for structured data? 1.1. Supervised machine learning. 1.2. Kernel methods. 1.3. Representing structured data. 1.4. Goals and contributions. 1.5. Outline. 1.6. Bibliographical notes
• 2. Kernel methods in a nutshell. 2.1. Mathematical foundations. 2.2. Recognising patterns with kernels. 2.3. Foundations of kernel methods. 2.4. Kernel machines. 2.5. Summary
• 3. Kernel design. 3.1. General remarks on kernels and examples. 3.2. Kernel functions. 3.3. Introduction to kernels for structured data. 3.4. Prior work. 3.5. Summary
• 4. Basic term kernels. 4.1. Logics for learning. 4.2. Kernels for basic terms. 4.3. Multi-instance learning. 4.4. Related work. 4.5. Applications and experiments
• 5. Graph kernels. 5.1. Motivation and approach. 5.2. Labelled directed graphs. 5.3. Complete graph kernels. 5.4. Walk kernels. 5.5. Cyclic pattern kernels. 5.6. Related work. 5.7. Relational reinforcement learning. 5.8. Molecule classification. 5.9 Summary
• 6. Conclusions.

This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.

• Introduction and Role of Artificial Neural Networks
• Fundamentals of Biological Neural Networks
• Basic Principles of ANNs and Their Early Structures
• The Perceptron
• The Madaline
• Back Propagation
• Hopfield Networks
• Counter Propagation
• Adaptive Resonance Theory
• The Cognitron and the Neocogntiron
• Statistical Training
• Recurrent (Time Cycling) Back Propagation Networks
• Large Scale Memory Storage and Retrieval (LAMSTAR) Network.
• (source: Nielsen Book Data)

The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural network approaches and architectures with the theories, this text presents detailed case studies for each of the approaches, accompanied with complete computer codes and the corresponding computed results. The case studies are designed to allow easy comparison of network performance to illustrate strengths and weaknesses of the different networks.
(source: Nielsen Book Data)

• Web Intelligence, World Knowledge and Fuzzy Logic.- Towards More Powerful Information Technology via Computing with Words and Perceptions: Precisiated Natural Language, Protoforms and Linguistic Data Summaries.- Enhancing the Power of Search Engines and Navigations Based on Conceptual Model: Web Intelligence.- Soft Computing for Perception-Based Decision Processing and Analysis: Web-Based BISC-DSS.- Evaluating Ontology Based Search Strategies.- Soft Computing for Perception Based Information Processing.- Distributed Architecture for Modeling and Simulation of Autonomous Multi-agent Multi-Physics Systems.- Fuzzy Thesauri for and from the WWW.- Consumer Profiling Using Fuzzy Query and Social Network Techniques.- A Trial to Represent Dynamic Concepts.- SORE (Self Organizable Regulating Engine) - An Example of a Possible Building Block for a "Biologizing" Control System.- Multivariate Non-Linear Feature Selection with Kernel Methods.- A New Fuzzy Spectral Approach to Information Integration in a Search Engine.- Towards Irreducible Modeling of Structures and Functions of Protein Sequences.- Mining Fuzzy Association Rules: An Overview.- A Foundation for Computing with Words: Meta-Linguistic Axioms.- Augmented Fuzzy Cognitive Maps Supplemented with Case Based Reasoning for Advanced Medical Decision Support.- Pruning, Selective Binding and Emergence of Internal Models: Applications to ICA and Analogical Reasoning.- Evolution of the Laws That Deal with the Utilization of Information Networks.- Intelligent Type-2 Fuzzy Inference for Web Information Search Task.- Causality In An Inherently III Defined World.
• (source: Nielsen Book Data)

Search engines, with Google at the top, have become the most heavily used online service, with millions of searches performed every day and many remarkable capabilities. Soft Computing for Information Processing and Analysis includes reports from the front of soft computing in the internet industry and imparts knowledge and understanding of the significance of the field's accomplishments, new developments and future directions. This carefully edited book has evolved from presentations made by the participants of a meeting entitled "Fuzzy Logic and the Internet: Enhancing the Power of the Internet", organized by the Berkeley Initiative in Soft Computing (BISC), University of California, Berkeley. It addresses the important topics of modern search engines such as fuzzy query, decision analysis and support systems, including articles about topics such as Web Intelligence, World Knowledge and Fuzzy Logic (by Lotfi A. Zadeh), perception based information processing, or web intelligence.
(source: Nielsen Book Data)

• Mathematical Tools for Machine Intelligence
• Advanced Applications with Machine Intelligence
• Machine Intelligence for High Level Intelligent Systems.
• (source: Nielsen Book Data)

This book brings together the contributions of leading researchers in the field of machine intelligence, covering areas such as fuzzy logic, neural networks, evolutionary computation and hybrid systems.There is wide coverage of the subject - from simple tools, through industrial applications, to applications in high-level intelligent systems which are biologically motivated, such as humanoid robots (and selected parts of these systems, like the visual cortex). Readers will gain a comprehensive overview of the issues in machine intelligence, a field which promises to play a very important role in the information society of the future.
(source: Nielsen Book Data)

• Mathematical Tools for Machine Intelligence
• Advanced Applications with Machine Intelligence
• Machine Intelligence for High Level Intelligent Systems.
• (source: Nielsen Book Data)

This book brings together the contributions of leading researchers in the field of machine intelligence, covering areas such as fuzzy logic, neural networks, evolutionary computation and hybrid systems.There is wide coverage of the subject - from simple tools, through industrial applications, to applications in high-level intelligent systems which are biologically motivated, such as humanoid robots (and selected parts of these systems, like the visual cortex). Readers will gain a comprehensive overview of the issues in machine intelligence, a field which promises to play a very important role in the information society of the future.
(source: Nielsen Book Data)

For centuries, people have been fascinated by the possibility of building an artificial system that behaves intelligently. Now there is a new entry in this arena - neural networks. Naturally Intelligent Systems offers a comprehensive introduction to these exciting systems. It provides a technically accurate, yet down-to-earth discussion of neural networks, clearly explaining the underlying concepts of key neural network designs, how they are trained, and why they work. Throughout, the authors present actual applications that illustrate neural networks' utility in the new world.
(source: Nielsen Book Data)
Naturally Intelligent Systems offers a comprehensive introduction to neural networks.
(source: Nielsen Book Data)
For centuries, people have been fascinated by the possibility of building an artificial system that behaves intelligently. From Mary Shelley's Frankenstein monster to the computer intelligence of HAL in 2001, scientists have been cast in the role of creator of such devices. Now there is a new entry into this arena, neural networks, and "Naturally Intelligent Systems explores these systems to see how they work and what they can do. Neural networks are not computers in any traditional sense, and they have little in common with earlier approaches to the problem of fabricating intelligent behavior. Instead, they are information processing systems that are physically modeled after the structure of the brain and that are "trained to perform a task rather than programmed like a computer. Neural networks, in fact, provide a tool with problemsolving capabilities - and limitations - strikingly similar to those of animals and people. In particular, they are successful in applications such as speech, vision, robotics, and pattern recognition. "Naturally Intelligent Systems offers a comprehensive introduction to these exciting systems. It provides a technically accurate, yet down-to-earth discussion of neural networks. No particular mathematical background is necessary; it is written for all interested readers. "Naturally Intelligent Systents clearly explains the underlying concepts of key neural network designs, how they are trained, and why they work. It compares their behavior to the natural intelligence found in animals - and people. Throughout, Caudill and Butler bring the field into focus by presenting actual applications that illustrate neural networks' utility in the real world. MaureenCaudill is President of Adaptics, a neural network consulting company in San Diego and author of the popular "Neural Network Primer" articles that appear regularly in "AI Expert. Charles Butler is a Senior Principal Scientist at Physical Sciences in Alexandria, Virginia. He is a specialist in neural network application development. A Bradford Book.
(source: Nielsen Book Data)

• Machine Learning Models.- Convex Optimization Theory.- Deterministic Convex Optimization.- Stochastic Convex Optimization.- Convex Finite-sum and Distributed Optimization.- Nonconvex Optimization.- Projection-free Methods.- Operator Sliding and Decentralized Optimization.
• (source: Nielsen Book Data)

This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
(source: Nielsen Book Data)

• Introduction
• e-Learning so far
• MOOCs, Crowdsourcing and Social Networks
• User Profiling and Personalisation
• Personal Learning Networks, Portfolios and Environments
• Customised e-Learning
• Looking Ahead.

This book reviews a blend of artificial intelligence (AI) approaches that can take e-learning to the next level by adding value through customization. It investigates three methods: crowdsourcing via social networks; user profiling through machine learning techniques, and personal learning portfolios using learning analytics. Technology and education have drawn closer together over the years as they complement each other within the domain of e-learning, and different generations of online education reflect the evolution of new technologies as researcher and developers continuously seek to optimize the electronic medium to enhance the effectiveness of e-learning. Artificial intelligence (AI) for e-learning promises personalized online education through a combination of different intelligent techniques that are grounded in established learning theories while at the same time addressing a number of common e-learning issues. This book is intended for education technologists and e-learning researchers as well as for a general readership interested in the evolution of online education based on techniques like machine learning, crowdsourcing, and learner profiling that can be merged to characterize the future of personalized e-learning.
(source: Nielsen Book Data)

• Motivation.- Closing the Loop: Placing Ontology Usage Analysis in the Ontology Development and Deployment Lifecycle.- Ontology Usage Analysis Framework (OUSAF).- Identification Phase : Ontology Usage Network Analysis Framework (OUN-AF).- Investigation Phase: Empirical Analysis of Domain Ontology Usage (EMP-AF).
• (source: Nielsen Book Data)

This unique book succinctly summarizes the need to measure how ontologies (one of the building blocks of the Semantic Web) are currently being utilized, providing insights for various stakeholders. Where possible it improves and reuses terms in existing vocabularies/ontologies, as recommended by the Linked Data community. Recent advances in the Semantic Web have led to a proliferation of Resource Description Framework (RDF) data, which employ ontologies to semantically describe the information on the Web making it equally understandable for both humans and machines. However, to create a network effect, it is important that selective ontologies are used by more data publishers to improve the value of that ontology. For this to happen, it is vital to discover what is being used from an ontology to semantically annotate the information on the Web specific to a given domain. Answers to such basic but crucial questions can only be achieved by ascertaining how ontologies in the current semantic web are being utilized and adopted. The proposed frameworks to obtain such insights are explained with real-world examples to provide a clear and detailed description of ontology usage analysis. Both theoretical and practical, the book is of value to academics and professionals working in industry. Specifically, it is of primary interest to researchers, graduate students and practitioners in the area of the Semantic Web and its various real-world applications.
(source: Nielsen Book Data)

• Part I Global Robot Path Planning
• 1.
• Introduction to Mobile Robot Path Planning
• 2.
• Background on Artificial Intelligence Algorithms for Global Path Planning
• 3.
• Design and Evaluation of Intelligent Global Path Planning Algorithms 4.
• Integration of Global Path Planners in ROS
• 5.
• Robot Path Planning using Cloud Computing for Large Grid Maps
• Part II Multi-Robot Task Allocation
• 6.
• General Background on Multi-Robot Task Allocation
• 7.
• Different Approaches to Solve the MRTA Problem
• 8.
• Performance Analysis of the MRTA Approaches for Autonomous Mobile Robot .
• (source: Nielsen Book Data)

This book presents extensive research on two main problems in robotics: the path planning problem and the multi-robot task allocation problem. It is the first book to provide a comprehensive solution for using these techniques in large-scale environments containing randomly scattered obstacles. The research conducted resulted in tangible results both in theory and in practice. For path planning, new algorithms for large-scale problems are devised and implemented and integrated into the Robot Operating System (ROS). The book also discusses the parallelism advantage of cloud computing techniques to solve the path planning problem, and, for multi-robot task allocation, it addresses the task assignment problem and the multiple traveling salesman problem for mobile robots applications. In addition, four new algorithms have been devised to investigate the cooperation issues with extensive simulations and comparative performance evaluation. The algorithms are implemented and simulated in MATLAB and Webots.
(source: Nielsen Book Data)

• Temporal Costs of Computing Unit Redundancy in Steady and Transient State.- SIPE: A Domain-Specific Language for Specifying Interactive Programming Exercises.- Managing Software Complexity by Exploiting Software Similarity Patterns.- A Prototype Tool for Semantic Validation of UML class Diagrams with the Use of Domain Ontologies Expressed in OWL 2.- Ensuring the Strong Exception Safety.- Efficient Testing of Time-dependent, Asynchronous Code.
• (source: Nielsen Book Data)

This book reports on recent advances in software engineering research and practice. Divided into 15 chapters, it addresses: languages and tools; development processes; modelling, simulation and verification; and education. In the first category, the book includes chapters on domain-specific languages, software complexity, testing and tools. In the second, it reports on test-driven development, processing of business rules, and software management. In turn, subsequent chapters address modelling, simulation and verification of real-time systems, mobile systems and computer networks, and a scrum-based framework. The book was written by researchers and practitioners, the goal being to achieve a synergistic combination of research results achieved in academia and best practices used in the industry, and to provide a valuable reference guide for both groups.
(source: Nielsen Book Data)

• Database and Graph Theory [16].- Crossover and Permutation.- Similarity Between Graphs in Database by Permutations.- Morphogenetic and Morpheme Network to Structured Worlds.- Formal Description and References in Graph Theory.
• (source: Nielsen Book Data)

This book offers a concise introduction to morphogenetic computing, showing that its use makes global and local relations, defects in crystal non-Euclidean geometry databases with source and sink, genetic algorithms, and neural networks more stable and efficient. It also presents applications to database, language, nanotechnology with defects, biological genetic structure, electrical circuit, and big data structure. In Turing machines, input and output states form a system - when the system is in one state, the input is transformed into output. This computation is always deterministic and without any possible contradiction or defects. In natural computation there are defects and contradictions that have to be solved to give a coherent and effective computation. The new computation generates the morphology of the system that assumes different forms in time. Genetic process is the prototype of the morphogenetic computing. At the Boolean logic truth value, we substitute a set of truth (active sets) values with possible contradictions. The value of a proposition is a set of true and false values. The aim of morphogenetic computing is to use and solve the contradictions in order to transform systems to allow classical computation.
(source: Nielsen Book Data)

• Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using ROS.- Design of Fuzzy Logic Controllers to ROS-based UAVs.- Flying Multiple UAVs Using ROS.- SkiROS
• A skill-based robot control architecture on top of ROS.- Control of Mobile Robots using ActionLib.- Parametric Identification of the Dynamics of Mobile Robots and Its Application for the Tuning of Controllers in ROS.- ROSLink: Bridging ROS with the Internet-of-Things for Cloud Robotics.- A ROS Package for Dynamic Bandwidth Management in Multi-Robot Systems.- An autonomous companion UAV for the SpaceBot Cup competition 2015. .
• (source: Nielsen Book Data)

This second volume is a continuation of the successful first volume of this Springer book, and as well as addressing broader topics it puts a particular focus on unmanned aerial vehicles (UAVs) with Robot Operating System (ROS). Consisting of three types of chapters: tutorials, cases studies, and research papers, it provides comprehensive additional material on ROS and the aspects of developing robotics systems, algorithms, frameworks, and applications with ROS. ROS is being increasingly integrated in almost all kinds of robots and is becoming the de-facto standard for developing applications and systems for robotics. Although the research community is actively developing applications with ROS and extending its features, amount of literature references is not representative of the huge amount of work being done. The book includes 19 chapters organized into six parts: Part 1 presents the control of UAVs with ROS, while in Part 2, three chapters deal with control of mobile robots. Part 3 provides recent work toward integrating ROS with Internet, cloud and distributed systems. Part 4 offers five case studies of service robots and field experiments. Part 5 presents signal-processing tools for perception and sensing, and lastly, Part 6 introduces advanced simulation frameworks. The diversity of topics in the book makes it a unique and valuable reference resource for ROS users, researchers, learners and developers. .
(source: Nielsen Book Data)

The objective of this book is to provide the reader with a comprehensive coverage on the Robot Operating Systems (ROS) and latest related systems, which is currently considered as the main development framework for robotics applications. The book includes twenty-seven chapters organized into eight parts. Part 1 presents the basics and foundations of ROS. In Part 2, four chapters deal with navigation, motion and planning. Part 3 provides four examples of service and experimental robots. Part 4 deals with real-world deployment of applications. Part 5 presents signal-processing tools for perception and sensing. Part 6 provides software engineering methodologies to design complex software with ROS. Simulations frameworks are presented in Part 7. Finally, Part 8 presents advanced tools and frameworks for ROS including multi-master extension, network introspection, controllers and cognitive systems. This book will be a valuable companion for ROS users and developers to learn more ROS capabilities and features.

• Learning and evolution
• Human interaction
• Robotic simulation.

This book constitutes the refereed proceedings of the First International Symposium on Artificial Life and Intelligent Agents, ALIA 2014, held in Bangor, UK, in November 2014. The 10 revised full papers were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections on learning and evolution; human interaction; robotic simulation.

• Part I. Multi-Robots Systems
• Part II. Data Fusion, Localization and Mapping
• Part III. Security and Dependability
• Part IV. Mobility.

This book compiles some of the latest research in cooperation between robots and sensor networks. Structured in twelve chapters, this book addresses fundamental, theoretical, implementation and experimentation issues. The chapters are organized into four parts namely multi-robots systems, data fusion and localization, security and dependability, and mobility.

• Introduction.- Respondent Privacy.- User Privacy.
• (source: Nielsen Book Data)

This book provides an overview of the research work on data privacy and privacy enhancing technologies carried by the participants of the ARES project. ARES (Advanced Research in Privacy an Security, CSD2007-00004) has been one of the most important research projects funded by the Spanish Government in the fields of computer security and privacy. It is part of the now extinct CONSOLIDER INGENIO 2010 program, a highly competitive program which aimed to advance knowledge and open new research lines among top Spanish research groups. The project started in 2007 and will finish this 2014. Composed by 6 research groups from 6 different institutions, it has gathered an important number of researchers during its lifetime. Among the work produced by the ARES project, one specific work package has been related to privacy. This books gathers works produced by members of the project related to data privacy and privacy enhancing technologies. The presented works not only summarize important research carried in the project but also serve as an overview of the state of the art in current research on data privacy and privacy enhancing technologies.
(source: Nielsen Book Data)

Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.
(source: Nielsen Book Data)

Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.
(source: Nielsen Book Data)

• Preface
• Chapter 1: Understanding and Modifying Data Sources
• Chapter 2: Using Essbase Studio
• Chapter 3: Building the BSO Cube
• Chapter 4: Building the ASO Cube
• Chapter 5: Using EAS for Development
• Chapter 6: Creating Calculation Scripts
• Chapter 7: Using MaxL to Automate Process
• Chapter 8: Data Integration
• Chapter 9: Provisioning Security Using MaxL Editor or Shared Services
• Chapter 10: Developing Dynamic Reports Index.
• (source: Nielsen Book Data)

This cookbook is full of immediately useable recipes showing you the advanced development techniques when building Essbase Applications and how to take these applications further. This cookbook offers practical, task-based, and immediately usable recipes covering a wide range of advanced development techniques to build Essbase Applications and take them further. In addition to its cookbook style, which ensures the solutions are presented in a clear step-by-step manner, its explanations go into great detail, which makes it good learning material for everyone who has experience in Essbase and wants to improve. The book is designed in such a way that you can either read it chapter by chapter or refer to recipes that you want in no particular order. If you are an experienced Essbase developer, Essbase Database Designer or Database Administrator, then this book is for you. This book assumes that you have good knowledge of Oracle Essbase.
(source: Nielsen Book Data)

• Preface
• Special Topics in Pattern Recognition with Applications in Nonprofileration
• Manufacturing Feature Recognition for Mould & Die Designs: Current Status & Future Directions
• Pattern-Recognition Receptors of Oral Epithelia
• Generating-Kernel Based Nonlinear Feature Extraction Methods
• Damage Assessment Based on Pattern Recognition
• Artificial Intelligence Techniques for Assisting the Decision of Making or Postponing the Embryo Transfer
• New Perspectives on a Pattern Recognition Algorithm Based on Haken's Synergetic Computer Network- With a Comment on Artificial Intelligence & Physical Intelligence
• Active Contours for Real Time Applications
• Class Distribution Estimation in Imprecise Domains Based on Supervised Learning
• Quantitative Bioimage Analysis Using Pattern Recognition
• Advances in Mining Emerging Patterns for Supervised Classification
• On the Geometrical Aspect of Biometric Authentication
• Pattern Recognition as a New Method of Numerical Research of the Concrete Dynamic System
• Pattern Recognition from ICA Mixture Modeling
• Index.
• (source: Nielsen Book Data)

Pattern recognition involves tasks such as pattern identification, feature extraction from them and pattern classification in categories, based on the extracted features. This book presents topical research in the study of pattern recognition, including pattern recognition applications in non-proliferation; pattern recognition of machine feature identifications; pattern recognition receptors of oral epithelia; construction damage assessment based on pattern recognition; Haken's pattern recognition algorithm as applied in engineering sciences; active contours for real time applications and quantitative bioimage analysis using pattern recognition.
(source: Nielsen Book Data)

• Multimodal Human Spacecraft Interaction in Remote Environments
• A Framework for Collaborative Aspects of Intelligent Service Robot
• Piecewise Bezier Curves Path Planning with Continuous Curvature Constraint for Autonomous Driving
• Combined Heuristic Approach to Resource-Constrained Project Scheduling Problem
• A Development of Data-Logger for Indoor Environment
• Multiobjective Evolutionary Optimization and Machine Learning: Application to Renewable Energy Predictions
• Hybriding Intelligent Host-Based and Network-Based Stepping Stone Detections
• Open Source Software Use in City Government
• Pheromone-Balance Driven Ant Colony Optimization with Greedy Mechanism
• Study of Pitchfork Bifurcation in Discrete Hopfield Neural Network
• Grammatical Evolution and STE Criterion
• Data Quality in ANFIS Based Soft Sensors
• The Meccano Method for Automatic Volume Parametrization of Solids
• A Buck Converter Model for Multi-Domain Simulations
• The Computer Simulation of Shaping in Rotating Electrical Discharge Machining
• Parameter Identification of a Nonlinear Two Mass System Using Prior Knowledge
• Adaptive and Neural Learning for Biped Robot Actuator Control
• Modeling, Simulation, and Analysis for Battery Electric Vehicles
• Modeling Confined Jets with Particles and Swril
• Robust Tracking and Control of MIMO Processes with Input Saturation and Unknown Disturbance
• Analysis of Priority Rule-Based Scheduling in Dual-Resource-Constrained Shop-Floor Scenarios
• A Hybrid Framework for Servo-Actuated Systems Fault Diagnosis
• Multigrid Finite Volume Method for FGF-2 Transport and Binding
• Integrated Mining Fuzzy Association Rules For Mineral Processing State Identification
• A Combined Cycle Power Plant Simulator: A Powerful, Competitive, and Useful Tool for Operator's Training
• Texture Features Extraction in Mammograms Using Non-Shannon Entropies
• A Wideband DOA Estimation Method Based on Arbitrary Group Delay
• Spatial Speaker Spatial Positioning of Synthesized Speech in Java
• Commercial Break Detection and Content Based Video Retrieval
• ClusterDAM: Clustering Mechanism for Delivery of Adaptive Multimedia Content in Two-Hop Wireless Networks
• Ranking Intervals in Complex Stochastic Boolean Systems Using Intrinsic Ordering
• Predicting Memory Phases
• Information Security Enhancement to Public-Key Cryptosystem Through Magic Squares
• Resource Allocation for Grid Applications: An Economy Model
• A Free and Didactic Implementation of the SEND Protocol for IPv6
• A Survey of Network Benchmark Tools
• Hybrid Stock Investment Strategy Decision Support System
• Towards Performance Analysis of Ad hoc Multimedia Network
• Towards the Performance Optimization of Public-key Algorithms Using Fuzzy Modular Arithematic and Addition Chain
• RBDT-1 Method: Combining Rules and Decision Tree Capabilities
• Computational and Theoretical Concepts for Regulating Stem Cells Using Viral and Physical Methods
• DFA, a Biomedical Checking Tool for the Heart Control System
• Generalizations in Mathematical Epidemiology
• Review of Daily Physical Activity Monitoring System Based on Single Triaxial Accelerometer and Portable Data Measurement Unit
• A Study of the Protein Folding Problem by a Simulation Model
• Analysing Multiobjective Fitness Function with Finite State Automata.

A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.

• What Is Computational Intelligence and Where Is It Going?.- New Millennium AI and the Convergence of History.- The Challenges of Building Computational Cognitive Architectures.- Programming a Parallel Computer: The Ersatz Brain Project.- The Human Brain as a Hierarchical Intelligent Control System.- Artificial Brain and OfficeMate TR based on Brain Information Processing Mechanism.- Natural Intelligence and Artificial Intelligence: Bridging the Gap between Neurons and Neuro-Imaging to Understand Intelligent Behaviour.- Computational Scene Analysis.- Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities.- The Science of Pattern Recognition. Achievements and Perspectives.- Towards Comprehensive Foundations of Computational Intelligence.- Knowledge-Based Clustering in Computational Intelligence.- Generalization in Learning from Examples.- A Trend on Regularization and Model Selection in Statistical Learning: A Bayesian Ying Yang Learning Perspective.- Computational Intelligence in Mind Games.- Computer Go: A Grand Challenge to AI.- Noisy Chaotic Neural Networks for Combinatorial Optimization.
• (source: Nielsen Book Data)

In recent years computational intelligence has been extended by adding many other subdisciplines and this new field requires a series of challenging problems that will give it a sense of direction in order to ensure that research efforts are not wasted. This book written by top experts in computational intelligence provides such clear directions and a much-needed focus on the most important and challenging research issues.
(source: Nielsen Book Data)

• Introduction
• Dualism
• Behaviourism
• Neuroanatomy
• Australian materialism
• Functionalism
• Formal systems
• Computability
• Universal machines
• Computationalism
• Search
• Games
• Machine reasoning
• Machines and language
• Human reasoning
• Human language
• Meaning
• Representation
• Artificial neural networks
• Minds and computers.

Could a computer have a mind? What kind of machine would this be? Exactly what do we mean by 'mind' anyway? The notion of the 'intelligent' machine, whilst continuing to feature in numerous entertaining and frightening fictions, has also been the focus of a serious and dedicated research tradition. Reflecting on these fictions, and on the research tradition that pursues 'Artificial Intelligence', raises a number of vexing philosophical issues. Minds and Computers introduces readers to these issues by offering an engaging, coherent, and highly approachable interdisciplinary introduction to the Philosophy of Artificial Intelligence. Readers are presented with introductory material from each of the disciplines which constitute Cognitive Science: Philosophy, Neuroscience, Psychology, Computer Science, and Linguistics. Throughout, readers are encouraged to consider the implications of this disparate and wide-ranging material for the possibility of developing machines with minds. And they can expect to develop a foundation for philosophically responsible engagement with A.I., a sound understanding of Philosophy of Mind and of computational theory, and a good feel for cross-disciplinary analysis. Features: *A solid foundation in the Philosophy of Mind *A broadly interdisciplinary purview *A directed philosophical focus *A clear and accessible explanation of technical material with abundant exercises *A glossary of terms.
(source: Nielsen Book Data)
Could a computer have a mind? What kind of machine would this be? Exactly what do we mean by OCymindOCO anyway? The notion of the OCyintelligentOCO machine, whilst continuing to feature in numerous entertaining and frightening fictions, has also been the focus of a serious and dedicated research tradition. Reflecting on these fictions, and on the research tradition that pursues OCyArtificial IntelligenceOCO, raises a number of vexing philosophical issues. Minds and Computers introduces readers to these issues by offering an engaging, coherent, and highly approachable interdisciplinary introduction to the Philosophy of Artificial Intelligence. Readers are presented with introductory material from each of the disciplines which constitute Cognitive Science: Philosophy, Neuroscience, Psychology, Computer Science, and Linguistics".
(source: Nielsen Book Data)

• Prerequisites.- Simple Learning Classifier Systems.- The XCS Classifier System.- How XCS Works: Ensuring Effective Evolutionary Pressures.- When XCS Works: Towards Computational Complexity.- Effective XCS Search: Building Block Processing.- XCS in Binary Classification Problems.- XCS in Multi-Valued Problems.- XCS in Reinforcement Learning Problems.- Facetwise LCS Design.- Towards Cognitive Learning Classifier Systems.- Summary and Conclusions.
• (source: Nielsen Book Data)

Rule-basedevolutionaryonlinelearningsystems, oftenreferredtoasMichig- style learning classi?er systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generali- tion capabilities of genetic algorithms promising a ?exible, online general- ing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with a- mal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in di?erent problem types, problem structures, c- ceptspaces, andhypothesisspacesstayednearlyunpredictable. Thisbookhas the following three major objectives: (1) to establish a facetwise theory - proachforLCSsthatpromotessystemanalysis, understanding, anddesign; (2) to analyze, evaluate, and enhance the XCS classi?er system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding of LCS functioning that enables the successful application of LCSs to diverse problem types and problem domains. The quantitative analysis of XCS shows that the inter- tive, evolutionary-based online learning mechanism works machine learning competitively yielding a low-order polynomial learning complexity. Moreover, the facetwise analysis approach facilitates the successful design of more - vanced LCSs including Holland's originally envisioned cognitive systems. Martin V.
(source: Nielsen Book Data)

• Scott Consequence Relations
• Biconsequence Relations
• Four-Valued Logics
• Nonmonotonic Semantics
• Default Consequence Relations
• Argumentation Theory
• Production and Causal Inference
• Epistemic Consequence Relations
• Modal Nonmonotonic Logics.
• (source: Nielsen Book Data)

Many approaches in the field of nonmonotonic and "commonsense" reasoning are actually different representations of the same basic ideas and constructions. This book gives a logical formalization of the original, explanatory approach to nonmonotonic reasoning. It uses the basic formalism of biconsequence relations, as well as derived systems of default, autoepistemic and causal inference, to cover in a single framework such diverse systems as default logic, autoepistemic and modal nonmonotonic logics, input/output and causal logics, argumentation theory, and semantics of general logic programs with negation as failure. This approach provides a clear separation between logical (monotonic) and nonmonotonic aspects of nonmonotonic reasoning. The separation allows, in particular, to single out the logics underlying modern logic programming and restore thereby the connection between logic programming and logic.
(source: Nielsen Book Data)

• Paper Sessions Applications 1.- Artificial Intelligence Systems in Micromechanics.- Integrating Two Artificial Intelligence Theories in a Medical Diagnosis Application.- Artificial Intelligence and Law.- Virtual Market Environment for Trade.- Neural Networks and Fuzzy Systems.- An Artificial Neural Networks Approach to the Estimation of Physical Stellar Parameters.- Evolutionary Robot Behaviors Based on Natural Selection and Neural Network.- Control of Overhead Crane by Fuzzy-Pid with Genetic Optimisation.- Creative Design of Fuzzy Logic Controller.- On-Line Extraction of Fuzzy rules in a Wastewater Treatment Plant.- Agents.- An Autonomous Intelligent Agent Architecture Based on Constructivist AI.- Finding Manufacturing Expertise Using Ontologies and Cooperative Agents.- Using Agents in the Exchange of Product Data.- Applications 2.- A Pervasive Identification and Adaptation System for the Smart House.- Deductive Diagnosis of Digital Circuits.- Verification of NASA Emergent Systems.- Theory.- Knowledge Base Structure.- Learning Bayesian Metanetworks from Data with Multilevel Uncertainty.- Using Organizational Structures Emergence for Maintaining Functional Integrity in Embedded Systems Networks.- Efficient Attribute Reduction Algorithm.- Using Relative Logic for Pattern Recognition.- Intelligent Tutoring and Collaboration.- Mathtutor: A Multi-Agent Intelligent Tutoring System.- Analysis and Intelligent Support of Learning Communities in Semi-Structured Discussion Environments.- An Adaptive Assessment System to Evaluate Student Ability Level.- Forming the Optimal Team of Experts for Collaborative Work.- Internet.- Impact on Performance of Hypertext Classification of Selective Rich HTML Capture.- Introducing a Star Topology into Latent Class Models for Collaborative Filtering.- Dialoguing with an Online Assistant in a Financial Domain: The VIP-Advisor Approach.- An Agency for Semantic-Based Automatic Discovery of Web-Services.- Genetic Algorithms.- GESOS: A Multi-Objective Genetic Tool for Project Management Considering Technical and Non-Technical Constraints.- Using Genetic Algorithms and Tabu Search Parallel Models to Solve the Scheduling Problem.- Modelling Document Categories by Evolutionary Learning of Text Centroids.- Ontologies and Data Mining.- ODEval: A Tool for Evaluating RDF(S), DAML+OIL, and OWL Concept Taxonomies.- Air - A Platform for Intelligent Systems.- Swissanalyst.- Data Mining by MOUCLAS Algorithm for Petroleum Reservoir Characterization from Well Logging Data.- Reasoning and Scheduling.- Verification of Procedural Reasoning System (PRS) Programs Using Coloured Petri Nets (CPN).- On-Line Possibilistic Diagnosis Based on Expert Knowledge for Engine Dyno Test Benches.- CBR and Micro-Architecture Anti-Patterns Based Software Design Improvement.- A Decision Support System (DSS) for the Railway Scheduling Problem.- An Interactive Multicriteria Optimisation Approach to Scheduling.
• (source: Nielsen Book Data)

Artificial Intelligence and Innovations (AIAI) will interest researchers, IT professionals and consultants by examining technologies and applications of demonstrable value. The conference focused on profitable intelligent systems and technologies. AIAI focuses on real world applications; therefore authors should highlight the benefits of AI technology for industry and services. Novel approaches solving business and industrial problems, using AI, will emerge from this conference.
(source: Nielsen Book Data)

• Neural network performance metrics
• noise in neural implementations
• simulation requirements and environment
• fault tolerance
• generalisation ability
• learning trajectory and speed.
• (source: Nielsen Book Data)

Hardware inaccuracy and imprecision are important considerations when implementing neural algorithms. This book presents a study of synaptic weight noise as a typical fault model for analogue VLSI realisations of MLP neural networks and examines the implications for learning and network performance. The aim of the book is to present a study of how including an imprecision model into a learning scheme as a"fault tolerance hint" can aid understanding of accuracy and precision requirements for a particular implementation. In addition the study shows how such a scheme can give rise to significant performance enhancement.
(source: Nielsen Book Data)

• Towards 'onlife' education. How technology is forcing us to rethink pedagogy.- On Blended Learning Flexibility: An Educational Approach.- The policy approach of b-learning. The university model of education in the public-private binomial. .
• (source: Nielsen Book Data)

This book focuses on essential aspects of the theoretical foundations that support blended learning (BL) as a teaching training modality in tertiary education. Analyzing the changes in the world of education that lead to new ways of thinking and learning, it redefines the concept of blended learning at a time of constant growth in many universities around the world. This involves a shared reflection on the role of technology in the current university teacher education programs, as well as on the role that pedagogy plays in increasingly technology-driven contexts. Furthermore, the book presents pedagogical approaches to guide university professors in the design and implementation of blended learning courses. To this end, it describes some of the major models and approaches to BL instructional design, and examines issues related to the quality of BL training and the indicators to measure it, in order to identify those models that contribute to a better understanding of the dimensions that increase its effectiveness.
(source: Nielsen Book Data)

• Chapter 1. GUI Testing for Introductory Object-Oriented Programming Exercises (Ushio Inoue).-
• Chapter 2. Python Deserialization Denial of Services Attacks and their Mitigations (Kousei Tanaka).-
• Chapter 3. A Branch-and-Bound Based Exact Algorithm for the Maximum Edge-Weight Clique Problem (Satoshi Shimizu).-
• Chapter 4. A Software Model for Precision Agriculture Framework Based on Smart Farming System and Application of IoT Gateway (Symphorien Karl Yoki Donzia).-
• Chapter 5. Components of Mobile Integration in Social Business and E-commerce Application (Mechelle Grace Zaragoza).
• (source: Nielsen Book Data)

• Proposed Framework Application for a Quality Mobile Application Measurement and Evaluation.- Proposal and Development of Artificial Personality (AP) application using the "Requesting" Mechanism.- Load Experiment of the vDACS Scheme in case of the 300 Simultaneous Connection.- Hearing-Dog Robot to wake People up using its Bumping Action.- Implementation of Document Production Support System with Obsession Mechanism.- Detecting Outliners in Terms of Errors in Embedded Software Development Projects Using Imbalance Data Classification.- Development of Congestion State Guiding System for University Cafeteria.- Analog Learning Neural Circuit with Switched Capacitor and the Design of Deep Learning Model.- Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning.- Improvement of "Multiple Sightseeing Spot Scheduling System".- Advertising in the Webtoon of Cosmetics Brand -Focusing on 'tn' Youth Cosmetics Brands
• Testing Driven Development of Mobile Application using Automatic Bug Management Systems.- Shape Recovery of Polyp from Endoscope Image Using Blood Vessel Information.- Design of Agent Development Framework for RoboCupRescue Simulation.
• (source: Nielsen Book Data)

This book presents the outcomes of the 5th ACIS International Conference on Computational Science/Intelligence & Applied Informatics (CSII 2018), which was held on July 10-12, 2018 in Yonago, Japan. 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)
This book presents the scientific outcome of the 4th ACIS International Conference on Computational Science/Intelligence & Applied Informatics (CSII 2017), which was held on July 9-13, 2017 in Hamamatsu, Japan. 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 includes research findings concerning all aspects (theory, applications and tools) of computer and information science, and discusses the practical challenges encountered and the solutions adopted to address them. The book features 16 of the conference's most promising papers, written by researchers who are expected to make significant contributions in the field of computer and information science.
(source: Nielsen Book Data)

• Machine Learning Paradigms: Introduction to Deep Learning-Based Technological Applications
• Part I Deep Learning in Sensing
• Vision to Language: Methods, Metrics and Datasets
• Deep Learning Techniques for Geospatial Data Analysis
• Deep Learning Approaches in Food Recognition
• Part II Deep Learning in Social Media and IOT
• Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding
• A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks
• Part III Deep Learning in the Medical Field
• Survey on Deep Learning Techniques for Medical Imaging Application Area
• Deep Learning Methods in Electroencephalography
• Part IV Deep Learning in Systems Control
• The Implementation and the Design of a Hybriddigital PI Control Strategy Based on MISO Adaptive Neural Network Fuzzy Inference System Models-A MIMO Centrifugal Chiller case study
• A Review of Deep Reinforcement Learning Algorithms and Comparative Results on Inverted Pendulum System
• Part V Deep Learning in Feature Vector Processing
• Stock Market Forecasting by Using Support Vector Machines
• An Experimental Exploration of Machine Deep Learning for Drone Conflict Prediction
• Deep Dense Neural Network for Early Prediction of Failure-Prone Students
• Part VI Evaluation of Algorithm Performance
• Non-parametric Performance Measurement with Artificial Neural Networks
• A Comprehensive Survey on the Applications of Swarm Intelligence and Bio-Inspired Evolutionary Strategies
• Detecting Magnetic Field Levels Emitted by Tablet Computers via Clustering Algorithms.

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
(source: Nielsen Book Data)

• Part I. Intelligence and decision making system
• A Context-Based Video Compression: A Quantum-Inspired Vector Quantization Approach
• An Enhanced Database Recovery Model Based on Game Theory for Mobile Applications
• Location Estimation of RF Emitting Source Using Supervised Machine Learning Technique
• An Effective Offloading Model Based on Genetic Markov Process for Cloud Mobile Applications
• Toward an Efficient CRWSN Node Based on Stochastic Threshold Spectrum Sensing
• Video Captioning Using Attention Based Visual Fusion with Bi-temporal Context and Bi-modal Semantic Feature Learning
• Matchmoving Previsualization Based on Artificial Marker Detection
• Research Method of Blind Path Recognition Based on DCGAN
• The Impact of the Behavioral Factors on Investment Decision-Making: A Systemic Review on Financial Institutions
• Part II. Deep learning technology and applications (4 papers)
• Part III. Document and sentiment analysis (8 papers)
• Part IV. Blockchain and Cyber Physical System (6 papers)
• Part V Health Informatics and AI Against COVID-19 (7 papers)
• Part VI. Big Data Analytics and Service Quality (4 papers)
• Part VII. Data Mining, Decision Making, and Intelligent Systems (7 papers)
• Part VIII. Power and control systems (10 papers)
• Part IX. Business Intelligence (6 papers)
• Part X. Social media and Digital transformation (5 papers)
• Part XI. Robotic, Control Design and Smart Systems (12 papers).

This book presents the proceedings of the 6th International Conference on Advanced Intelligent Systems and Informatics 2020 (AISI2020), which took place in Cairo, Egypt, from October 19 to 21, 2020. This international and interdisciplinary conference, which highlighted essential research and developments in the fields of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into several sections, covering the following topics: Intelligent Systems, Deep Learning Technology, Document and Sentiment Analysis, Blockchain and Cyber Physical System, Health Informatics and AI against COVID-19, Data Mining, Power and Control Systems, Business Intelligence, Social Media and Digital Transformation, Robotic, Control Design, and Smart Systems.
(source: Nielsen Book Data)

• SFactors Affecting Satisfaction of NCS Based Educational System
• Model-Driven Development of Mobile Applications Allowing Role-Driven Variants
• E-Learning Adaptation and Mobile Learning for Education
• Design and Evaluation of Soil pH IoT Sensor Attribute for Rice Agriculture in Central Africa
• A Study on the Architecture of Mixed Reality Application for Architectural Design Collaboration
• Exploring the Improvement of the Defense Information System
• A Modern Solution for Identifying, Monitoring, and Selecting Configurations for SSL/TLS Deployment
• Analyses of Characteristics of Changes in Cerebral Activation Status, Depending on Blood Types, in Response to Auditory Stimulation
• Development of Infant Care System Application.

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

From making faster, better decisions to automating rote work to enabling robots to respond to emotions, AI and machine learning are already reshaping business and society. What should you and your company be doing today to ensure that you're poised for success and keeping up with your competitors in the age of AI' Artificial Intelligence: The Insights You Need from Harvard Business Review brings you today's most essential thinking on AI and explains how to launch the right initiatives at your company to capitalize on the opportunity of the machine intelligence revolution. Business is changing. Will you adapt or be left behind' Get up to speed and deepen your understanding of the topics that are shaping your company's future with the Insights You Need from Harvard Business Review series. Featuring HBR's smartest thinking on fast-moving issues-blockchain, cybersecurity, AI, and more-each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow. You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas-and prepare you and your company for the future.

• Possibilistic Framework for Multi-objective Optimization under Uncertainty.- Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU. Phase Equilibrium Description of a Supercritical Extraction System using Metaheuristic Optimization Algorithms.- Intrusion Detection System based on a behavioral approach.- A new hybrid method to solve the multi-objective optimization problem for a composite hat-stiffened panel.- Storage yard management: modelling and solving.- Multi-capacitated location problem : A new resolution method combining exact and heuristic approaches based on set partitioning.- Application of genetic algorithm for solving bilevel linear programming problems.- Adapted Bin-Packing algorithm for the yard optimization problem.- Hidden Markov Model classifier for the adaptive ACS-TSP pheromone parameters.
• (source: Nielsen Book Data)

This book presents recent research on bioinspired heuristics for optimization. Learning- based and black-box optimization exhibit some properties of intrinsic parallelization, and can be used for various optimizations problems. Featuring the most relevant work presented at the 6th International Conference on Metaheuristics and Nature Inspired Computing, held at Marrakech (Morocco) from 27th to 31st October 2016, the book presents solutions, methods, algorithms, case studies, and software. It is a valuable resource for research academics and industrial practitioners.
(source: Nielsen Book Data)

• Chapter 1. GUI Testing for Introductory Object-Oriented Programming Exercises (Ushio Inoue).-
• Chapter 2. Python Deserialization Denial of Services Attacks and their Mitigations (Kousei Tanaka).-
• Chapter 3. A Branch-and-Bound Based Exact Algorithm for the Maximum Edge-Weight Clique Problem (Satoshi Shimizu).-
• Chapter 4. A Software Model for Precision Agriculture Framework Based on Smart Farming System and Application of IoT Gateway (Symphorien Karl Yoki Donzia).-
• Chapter 5. Components of Mobile Integration in Social Business and E-commerce Application (Mechelle Grace Zaragoza).
• (source: Nielsen Book Data)

This book presents the outcomes of the 5th ACIS International Conference on Computational Science/Intelligence & Applied Informatics (CSII 2018), which was held on July 10-12, 2018 in Yonago, Japan. 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)

• Deterministic Parameter Control in Differential Evolution with Combined Variants for Constrained Search Spaces.- A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems.- Evaluating Memetic Building Spatial Design Optimisation Using Hypervolume Indicator Gradient Ascent.- Fitting Multiple Ellipses with PEARL and a Multi-objective Genetic Algorithm.- Analyzing Evolutionary Art Audience Interaction by Means of a Kinect Based Non-Intrusive Method.- Applying Control Theory to Optimize the Inventory Holding Costs in Supply Chains.- On the Selection of Tuning Parameters in Predictive Controllers Based on NSGA-II.- IDA-PBC Controller Tuning Using Steepest Descent.- Self-Tuning for a SISO-Type Fuzzy Control Based on the Relay Feedback Approach.- Optimal Design Of Sliding Mode Control Combined with Positive Position Feedback.- Biot's Parameters Estimation In Ultrasound Propagation Through Cancellous Bone.- Optimal Sizing of Low-DropOut Voltage Regulators by NSGA-II and PVT Analysis.- Genetic Optimization of Fuzzy Systems for the Classification of Treated Water Quality.- Stabilization Based on Fuzzy System for Structures Affected by External Disturbances.- Comparison of Two Methods for I/Q Imbalance Compensation Applied in RF Power Amplifiers.- An Application of Data Envelopment Analysis to the Performance Assessment of Online Social Networks Usage in Mazatlan Hotel Organizations. .
• (source: Nielsen Book Data)

This book features 15 chapters based on the Numerical and Evolutionary Optimization (NEO 2017) workshop, held from September 27 to 29 in the city of Tijuana, Mexico. The event gathered researchers from two complimentary fields to discuss the theory, development and application of state-of-the-art techniques to address search and optimization problems. The lively event included 7 invited talks and 64 regular talks covering a wide range of topics, from evolutionary computer vision and machine learning with evolutionary computation, to set oriented numeric and steepest descent techniques. Including research submitted by the NEO community, the book provides informative and stimulating material for future research in the field.
(source: Nielsen Book Data)

• Meal-Assistance Robot Operated by Head Movement.- Decision Tree Analysis in Game Informatics.- Binary Blockchain: Solving the Mining Congestion Problem by Dynamically Adjusting the Mining Capacity.- An Efficient Signature Scheme for Anonymous Credentials.- Improve Example-Based Machine Translation Quality for Low-Resource Language Using Ontology.- A Fast Area Labeling Method using Auxiliary Lines.- Heuristic Test Case Generation Techniques Using Extended Place/Transistion Net.- Risk Assessment of Security Requirements of Banking Information Systems Based on Attack Patterns.- mCITYPASS: Privacy-preserving Secure Access to Federated Touristic Services with Mobile Devices.- Heuristic-Based Usability Evaluation Tool for Android Applications.- Automated Essay Scoring System Based on Rubric.- Mobile Development Tools and Method Integration.
• (source: Nielsen Book Data)

This book presents the scientific outcome of the 5th International Conference on Applied Computing and Information Technology (ACIT 2017), which was held on July 9-13, 2017 in Hamamatsu, Japan. 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 includes research findings on 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. This book features 12 of the conference's most promising papers, written by authors who are expected to make important contributions to the field of computer and information science.
(source: Nielsen Book Data)

• Learning for vision
• Computational photography
• Human analysis
• Human sensing
• Stereo and reconstruction
• Optimization
• Matching and recognition
• Video attention
• Poster sessions.

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.

• Introduction.- Background.- Benchmarking.- Computation of Large Datasets.- Optimized Online Scheduling Algorithms.
• (source: Nielsen Book Data)

This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.
(source: Nielsen Book Data)

• Business Sustainability Conceptualization.- A Replicated Study on Relationship between Code Quality and Method Comments.- A Predictive Model for Standardized Test Performance in Michigan Schools.- A Development Technique for Mobile Applications Program.- Identification Method Applications Development using Adapting Component Model.- Development of Guiding Walking Support Device for Visually Impaired People with the GPS.- User Evaluation Prediction Models Based on Conjoint Analysis and Neural Networks for Interactive Evolutionary Computation.- Emotional Video Scene Retrieval Using Multilayer Convolutional Network.- Proactive Approach for the Prevention of DDoS Attacks in Cloud Computing Environments.- Practical Uses of Memory Storage Extension.- How to Build a High Quality Mobile Applications Based on Improved Process.- A New Hybrid Discrete Firefly Algorithm for Solving the Traveling Salesman Problem.
• (source: Nielsen Book Data)

This edited book presents scientific results of the 4th International Conference on Applied Computing and Information Technology (ACIT 2016) which was held on December 12-14, 2016 in Las Vegas, USA. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. The aim of this conference was also to bring out the research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the Program Committee, and underwent further rigorous rounds of review. This book captures 11 of the conference's most promising papers, and the readers impatiently await the important contributions that they know these authors are going to bring to the field of computer and information science.
(source: Nielsen Book Data)

• Cover
• Copyright
• Credits
• About the Author
• About the Reviewer
• www.PacktPub.com
• Customer Feedback
• Table of Contents
• Preface
• Chapter 1: Getting Started with Microsoft Cognitive Services
• Cognitive Services in action for fun and life changing purposes
• Setting up boilerplate code
• Detecting faces with the Face API
• Overview of what we are dealing with
• Vision
• Computer Vision
• Emotion
• Face
• Video
• Speech
• Bing Speech
• Speaker Recognition
• Custom Recognition
• Language
• Bing Spell Check
• Language Understanding Intelligent Service (LUIS)
• Linguistic Analysis
• Text Analysis
• Web Language Model
• Knowledge
• Academic
• Entity Linking
• Knowledge Exploration
• Recommendations
• Search
• Bing Web Search
• Bing Image Search
• Bing Video Search
• Bing News Search
• Bing Autosuggest
• Getting feedback on detected faces
• Summary
• Chapter 2: Analyzing Images to Recognize a Face
• Learning what an image is about using Computer Vision API
• Setting up a chapter example project
• Generic image analysis
• Recognizing celebrities using domain models
• Utilizing Optical Character Recognition
• Generating image thumbnails
• Diving deep into the Face API
• Retrieving more information from the detected faces
• Deciding whether two faces belong to the same person
• Finding similar faces
• Grouping similar faces
• Adding identification to our Smart-House application
• Creating our Smart-House application
• Adding people to be identified
• Identifying a person
• Summary
• Chapter 3: Analyzing Videos
• Knowing your mood using the Emotion API
• Getting images from a web camera
• Letting the smart-house know your mood
• Diving into the Video API
• Video operations as common code
• Getting operation results
• Wiring up execution in the ViewModel.

• Detecting and tracking faces in videos
• Detecting motion
• Stabilizing shaky videos
• Generating video thumbnails
• Analyzing emotions in videos
• Summary
• Chapter 4: Letting Applications Understand Commands
• Creating language-understanding models
• Register an account and get a license key
• Creating an application
• Recognizing key data using entities
• Understanding what the user wants using intents
• Simplifying development using pre-built models
• Pre-built applications
• Training a model
• Training and publishing the model
• Connecting to the smart-house application
• Model improvement through active usage
• Visualizing performance
• Resolving performance problems
• Adding model features
• Adding labeled utterances
• Looking for incorrect utterance labels
• Changing the schema
• Active learning
• Executing operations based on commands
• Maintaining conversations from unclear utterances
• Completing actions from intents
• Action fulfillment
• Summary
• Chapter 5: Speak with Your Application
• Converting text to audio and vice versa
• Speaking to the application
• Letting the application speak back
• Audio output format
• Error codes
• Supported languages
• Utilizing LUIS based on spoken commands
• Knowing who is speaking
• Adding speaker profiles
• Enrolling a profile
• Identifying the speaker
• Verifying a person through speech
• Customizing speech recognition
• Creating a custom acoustic model
• Creating a custom language model
• Deploying the application
• Summary
• Chapter 6: Understanding Text
• Setting up a common core
• New project
• Web requests
• Data contracts
• Correcting spelling errors
• Natural Language Processing using the Web Language Model
• Breaking a word into several
• Generating the next word in a sequence of words.

• Learning if a word is likely to follow a sequence of words
• Learning if certain words is likely to appear together
• Extracting information through textual analysis
• Detecting language
• Extracting key phrases from text
• Learning if a text is positive or negative
• Exploring text using linguistic analysis
• Introduction to linguistic analysis
• Analyzing text from a linguistic viewpoint
• Summary
• Chapter 7: Extending Knowledge Based on Context
• Linking entities based on context
• Providing personalized recommendations
• Creating a model
• Importing catalog data
• Importing usage data
• Building a model
• Consuming recommendations
• Recommending items based on prior activities
• Summary
• Chapter 8: Querying Structured Data in a Natural Way
• Tapping into academic content using the Academic API
• Setting up an example project
• Interpreting natural language queries
• Finding academic entities from query expressions
• Calculating the distribution of attributes from academic entities
• Entity attributes
• Creating the backend using the Knowledge Exploration Service
• Defining attributes
• Adding data
• Building the index
• Understanding natural language
• Local hosting and testing
• Going for scale
• Hooking into Microsoft Azure
• Deploying the service
• Answering FAQs using QnA Maker
• Creating a knowledge base from frequently asked questions
• Training the model
• Publishing the model
• Improving the model
• Summary
• Chapter 9: Adding Specialized Searches
• Searching the Web from the Smart-House application
• Preparing the application for web searches
• Searching the Web
• Getting the news
• News from queries
• News from categories
• Trending news
• Searching for images and videos
• Using a common user interface
• Searching for images
• Searching for videos.

• Helping the user with auto suggestions
• Adding Autosuggest to the user interface
• Suggesting queries
• Search commonalities
• Languages
• Pagination
• Filters
• Safe search
• Freshness
• Errors
• Summary
• Chapter 10: Connecting the Pieces
• Connecting the pieces
• Creating an intent
• Updating the code
• Executing actions from intents
• Searching news on command
• Describing news images
• Real-life applications using Microsoft Cognitive Services
• Uber
• DutchCrafters
• CelebsLike.me
• Pivothead
• wearable glasses
• Zero Keyboard
• The common theme
• Where to go from here
• Summary
• Appendix A: LUIS Entities and Intents
• LUIS pre-built intents
• LUIS pre-built entities
• Appendix B: Additional Information on Linguistic Analysis
• Part-of-Speech Tags
• Phrase types
• Appendix C: License Information
• Video Frame Analyzer
• OpenCvSharp3
• Newtonsoft. Json
• NAudio
• Definitions
• Grant of Rights
• Conditions and Limitations
• Index.

Learn to build interactive and efficient applications by leveraging 24 effective cognitive services APIs powered by Microsoft About This Book * Explore the capabilities of 24 of the APIs released as part of the Cognitive Services platform * Build intelligent apps that combine the power of computer vision, speech recognition, and language processing * Give your apps human-like cognitive intelligence with this hands-on guide Who This Book Is For .NET developers who want to add AI capabilities to their applications will find this book useful. No knowledge of machine learning or AI is necessary to work through this book. What You Will Learn * Identify a person through visual inspection and audio * Reduce user effort by utilizing AI-like capabilities * Understand how to analyze images and text in different ways * Find out how to analyze images using Vision APIs * Add video analysis to applications using Vision APIs * Utilize Search to find anything you want * Analyze text to extract information and explore text structure In Detail Microsoft has revamped its Project Oxford to launch the all new Cognitive Services platform-a set of 30 APIs to add speech, vision, language, and knowledge capabilities to apps. This book will introduce you to 24 of the APIs released as part of Cognitive Services platform and show you how to leverage their capabilities. More importantly, you'll see how the power of these APIs can be combined to build real-world apps that have cognitive capabilities. The book is split into three sections: computer vision, speech recognition and language processing, and knowledge and search. You will be taken through the vision APIs at first as this is very visual, and not too complex. The next part revolves around speech and language, which are somewhat connected. The last part is about adding real-world intelligence to apps by connecting them to Knowledge and Search APIs. By the end of this book, you will be in a position to understand what Microsoft Cognitive Service can offer and how to use the different APIs. Style and approach This book takes you through essential API capabilities and shows how to utilize them to suit the needs of your application.
(source: Nielsen Book Data)

• Cooperative Games.- Fuzzy Coalitions and Fuzziness of Games.- Games with a Fuzzy Bilateral Relation among the Players.- Fuzzy Communication.- A Priori Fuzzy Unions
• Fuzzy Permission.
• (source: Nielsen Book Data)

This book offers a comprehensive introduction to cooperative game theory and a practice-oriented reference guide to new models and tools for studying bilateral fuzzy relations among several agents or players. It introduces the reader to several fuzzy models, each of which is first analyzed in the context of classical games (crisp games) and subsequently in the context of fuzzy games. Special emphasis is given to the value of Shapley, which is presented for the first time in the context of fuzzy games. Students and researchers will find here a self-contained reference guide to cooperative fuzzy games, characterized by a wealth of examples, descriptions of a wide range of possible situations, step-by-step explanations of the basic mathematical concepts involved, and easy-to-follow information on axioms and properties.
(source: Nielsen Book Data)

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book * Skill up and implement tricky neural networks using Google's TensorFlow 1.x * An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. * Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn * Install TensorFlow and use it for CPU and GPU operations * Implement DNNs and apply them to solve different AI-driven problems. * Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. * Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. * Use different regression techniques for prediction and classification problems * Build single and multilayer perceptrons in TensorFlow * Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. * Learn how restricted Boltzmann Machines can be used to recommend movies. * Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. * Master the different reinforcement learning methods to implement game playing agents. * GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more. Style and approach This book consists of hands-on recipes where you'll deal with real-world problems. You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x. Your one-stop solution for common and not-so-common pain points, this is a book that you must have on the shelf.
(source: Nielsen Book Data)

• Part I Literature Review and State of the Art
• Part II Model for Adaptive Semantics Visualization
• Part III Proof of the Conceptual Model.

This book introduces a novel approach for intelligent visualizations that adapts the different visual variables and data processing to human's behavior and given tasks. Thereby a number of new algorithms and methods are introduced to satisfy the human need of information and knowledge and enable a usable and attractive way of information acquisition. Each method and algorithm is illustrated in a replicable way to enable the reproduction of the entire "SemaVis" system or parts of it. The introduced evaluation is scientifically well-designed and performed with more than enough participants to validate the benefits of the methods. Beside the introduced new approaches and algorithms, readers may find a sophisticated literature review in Information Visualization and Visual Analytics, Semantics and information extraction, and intelligent and adaptive systems. This book is based on an awarded and distinguished doctoral thesis in computer science

• Advances in Business ICT: New Ideas from Ongoing Research.- Verification of Temporal Knowledge Bases as an Important Aspect of Knowledge Management Processes in Organization.- The role of simulation performance in software-in-the-loop simulations.- Cognitum Ontorion: Knowledge Representation and Reasoning System.- Overview of Selected Business Process Semantization Techniques.- Selected Approaches Towards Taxonomy of Business Process Anomalies.- Hybrid framework for investment project portfolio selection.- Towards Predicting Stock Price Moves with Aid of Sentiment Analysis of Twitter Social Network Data and Big Data Processing Environment.- On a Property of Phase Correlation and Possibilities to Reduce the Walsh Function System.
• (source: Nielsen Book Data)

This book discusses the effective use of modern ICT solutions for business needs, including the efficient use of IT resources, decision support systems, business intelligence, data mining and advanced data processing algorithms, as well as the processing of large datasets (inter alia social networking such as Twitter and Facebook, etc.). The ability to generate, record and process qualitative and quantitative data, including in the area of big data, the Internet of Things (IoT) and cloud computing offers a real prospect of significant improvements for business, as well as the operation of a company within Industry 4.0. The book presents new ideas, approaches, solutions and algorithms in the area of knowledge representation, management and processing, quantitative and qualitative data processing (including sentiment analysis), problems of simulation performance, and the use of advanced signal processing to increase the speed of computation. The solutions presented are also aimed at the effective use of business process modeling and notation (BPMN), business process semantization and investment project portfolio selection. It is a valuable resource for researchers, data analysts, entrepreneurs and IT professionals alike, and the research findings presented make it possible to reduce costs, increase the accuracy of investment, optimize resources and streamline operations and marketing.
(source: Nielsen Book Data)

• Artificial Neural Networks Applications: An Introduction
• Order in the Black Box: Consistency And Robustness Of Neuron Activation Of Feed Forward Neural Networks And Its Use In Efficient Optimization Of Network Structure
• Artificial Neural Networks as Models of Robustness in Development And Regeneration: Stability of Memory During Morphological Remodeling
• A Structure Optimization Algorithm of Neural Networks For Pattern Learning from Educational Data
• Stochastic Neural Networks for Modelling Random Processes from Observed Data
• Curvelet Interaction with Artificial Neural Networks
• Hybrid Wavelet Neural Network Approaches.-Quantification of Prediction Uncertainty in Artificial Neural Network Models
• Classifying Calpain Inhibitors for The Treatment of Cataracts: A Self Organising Map (SOM) ANN/KM Approach in Drug Discovery
• Improved Ultrasound Based Computer Aided Diagnosis System for Breast Cancer Using Neural Networks Incorporating a Novel Effective Feature
• Degree of Central Regularity of Mass.-SOM Clustering and Modelling of Australian Railway Drivers' Sleep, Wake, Duty Profiles
• A Neural Approach to Electricity Demand Forecasting
• Development of Artificial Intelligence Based Regional Flood Estimation Techniques for Eastern Australia
• Artificial Neural Networks in Precipitation Nowcasting: An Australian Case Study
• Construction of Pmx Concentration Surfaces Using Neural Evolutionary Fuzzy Models of Semi Physical Class
• Application of Artificial Neural Network in Social Media Data Analysis: A Case of Lodging Business in Philadelphia
• Sentiment Analysis on Morphologically Rich Languages
• An Artificial Neural Network (ANN) Approach
• Predicting Stock Price Movements with News Sentiment: An Artificial Neural Networks Approach
• Modelling Mode Choice of Individual In Linked Trips with Artificial Neural Networks and Fuzzy Representation
• Artificial Neural Network (ANN) Pricing Model for Natural Rubber Products Based on Climate Dependencies
• A Hybrid Artificial Neural Network (ANN) Approach to Spatial and Non-Spatial Attribute Data Mining: A Case Study Experience.

This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.

• Aquatic I
• Vision I
• Planetary
• Vision II
• Poster Session
• Aerial
• Aquatic II
• Underground
• Systems.

This book contains the proceedings of the 10th FSR, (Field and Service Robotics) which is the leading single-track conference on applications of robotics in challenging environments. The 10th FSR was held in Toronto, Canada from 23-26 June 2015. The book contains 42 full-length, peer-reviewed papers organized into a variety of topics: Aquatic, Vision, Planetary, Aerial, Underground, and Systems. The goal of the book and the conference is to report and encourage the development and experimental evaluation of field and service robots, and to generate a vibrant exchange and discussion in the community. Field robots are non-factory robots, typically mobile, that operate in complex and dynamic environments: on the ground (Earth or other planets), under the ground, underwater, in the air or in space. Service robots are those that work closely with humans to help them with their lives. The first FSR was held in Canberra, Australia, in 1997. Since that first meeting, FSR has been held roughly every two years, cycling through Asia, Americas, Europe.

• Fuzzy Logic and the Linz Seminar:Themes and some Personal Reminiscences
• How I Saw and How I See Fuzzy Sets
• Modules in the Category
• A Geometric Approach to MV-algebras
• On the Equational Characterization of Continuous t-norms
• The Semantics of Fuzzy Logics: Two Approaches to Finite Tomonoids
• Structure of Uninorms with Continuous Diagonal Functions
• The Notions of Overlap and Grouping Functions
• Asymmetric Copulas and Their Application in Design of Experiments
• Copulæ of Processes Related to the Brownian Motion: a Brief Survey
• Extensions of Capacities
• Multi-source Information Fusion Using Measure Representations
• Bases and Transforms of Set Functions
• Conditioning for Boolean Subsets, Indicator Functions, and Fuzzy Subsets
• Multivalued Functions Integration from Additive to Arbitrary Non-Negative Set Function.

The book is a collection of contributions by leading experts, developed around traditional themes discussed at the annual Linz Seminars on Fuzzy Set Theory. The different chapters have been written by former PhD students, colleagues, co-authors and friends of Peter Klement, a leading researcher and the organizer of the Linz Seminars on Fuzzy Set Theory. The book also includes advanced findings on topics inspired by Klement's research activities, concerning copulas, measures and integrals, as well as aggregation problems. Some of the chapters reflect personal views and controversial aspects of traditional topics, while others deal with deep mathematical theories, such as the algebraic and logical foundations of fuzzy set theory and fuzzy logic. Originally thought as an homage to Peter Klement, the book also represents an advanced reference guide to the mathematical theories related to fuzzy logic and fuzzy set theory with the potential to stimulate important discussions on new research directions in the field.

• Autonomous mobile robots.- Tele-operated and telepresence robots.- Human-robot interaction.- Trajectory control for mobile robots.- Autonomous vehicles.- Service-oriented robotic Systems.- Semantic mapping.- Environment mapping.- Visual odometry.- Applications of RGB-D sensors.- Humanoid and biped robots.- Robocup soccer robots.- Robot control.- Path planning.- Multiple vehicles and teams of robots.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Joint Conference on Robotics, LARS, SBR, Robocontrol 2014, held in Sao Carlos, Brazil, in October 2014. The 8 revised full papers presented were carefully reviewed and selected from 76 submissions. The selected papers present a complete and solid reference of the state-of-the-art of intelligent robotics and automation research, covering the following areas: autonomous mobile robots, tele-operated and telepresence robots, human-robot interaction, trajectory control for mobile robots, autonomous vehicles, service-oriented robotic systems, semantic mapping, environment mapping, visual odometry, applications of RGB-D sensors, humanoid and biped robots, Robocup soccer robots, robot control, path planning, multiple vehicles and teams of robots.
(source: Nielsen Book Data)

• Introduction The Case-Base Reasoning and Decision Making Learning Formal Aspects Summary and Beyond.
• (source: Nielsen Book Data)

Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the design of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges.
(source: Nielsen Book Data)

• Cover; Title; EUL; Contents; Foreword; Preface; List of Contributors; Keywords; Chapter 01; Chapter 02; Chapter 03; Chapter 04; Chapter 05; Chapter 06; Chapter 07; Chapter 08; Chapter 09; Chapter 10; Chapter 11; Index.

This book dives into the heart of how to design distributed control architectures for heterogeneous teams of humans, robots, and automated systems, enabling them to achieve greater cooperation and autonomy through the use of network technologiesReaders can envision a realistic view of what can be expected from a networked human robot cooperative environment in the next decade.

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Preface
• Progressive Organization of Co-Operating Colonies/Collections of Ants/Agents (POOCA) for Competent Pheromone-Based Navigation & Multi-Agent Learning
• Ant Colony Solution to the Optimal Transformer Sizing & Efficiency Problem in Power Systems
• Distributed Decisions: New Insights from Radio-Tagged Ants
• Ant Colony Optimization used in No Wavefront Sensor Adaptive Optics Systems for Solid-State Lasers
• Any Colony Optimization Agents & Path Routing: The Cases of Construction Scheduling & Urban Water Distribution Pipe Networks
• KANTS: A Self-Organized Ant System for Pattern Clustering & Classification
• A Hybrid System Based in Ant Colony & Paraconsistent Logic
• Ant Colony Optimization: A Powerful Strategy for Biomarker Feature Selection
• Any Colony Optimization Based Message Authentication for Wireless Networks
• Index.
• (source: Nielsen Book Data)

Ant colony optimisation is a population-based general-search technique that can be used for the solution of difficult combinatorial problems. Ant colony optimisation uses the knowledge-reinforcing mechanism used by real-life ants while traversing possible paths in search of food, depositing a chemical substance (called pheromone), en-route and subsequently forming trails which can then be followed by other ants in the colony. With its strong mathematical foundation and its simplicity of use, ant colony optimisation provides an alternative method to routing optimisations with a wide range of applications. This book presents topical research in the study of ant colonies, their behaviour and application in computer technologies.
(source: Nielsen Book Data)

• Defeasible Planning through Multi-Agent Argumentation .-Operator behavior modelling in a submarine .-Automatic Wrapper Adaptation by Tree Edit Distance Matching
• Representing Temporal Knowledge in the Semantic Web: The Extended 4D Fluents Approach
• Combining a Multi-Document Update Summarization System -CBSEAS- with a Genetic Algorithm
• Extraction of Essential Events with Application to Damage Evaluation on Fuel Cells
• Detecting car accidents based on traffic flow measurements using machine learning techniques
• Next Generation Environments for Context-aware Learning Design
• Neurules-A Type of Neuro-Symbolic Rules: An Overview.

The combination of different intelligent methods is a very active research area in Artificial Intelligence (AI). The aim is to create integrated or hybrid methods that benefit from each of their components. Some of the existing efforts combine soft computing methods either among themselves or with more traditional AI methods such as logic and rules. Another stream of efforts integrates machine learning with soft-computing or traditional AI methods. Yet another integrates agent-based approaches with logic and also non-symbolic approaches. Some of the combinations have been quite important and more extensively used, like neuro-symbolic methods, neuro-fuzzy methods and methods combining rule-based and case-based reasoning. However, there are other combinations that are still under investigation, such as those related to the Semantic Web. The 2nd Workshop on?Combinations of Intelligent Methods and Applications? (CIMA 2010) was intended to become a forum for exchanging experience and ideas among researchers and practitioners who are dealing with combining intelligent methods either based on first principles or in the context of specific applications. CIMA 2010 was held in conjunction with the 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2010). Also, a special track was organized in ICTAI 2010, under the same title. This volume includes revised versions of the papers presented in CIMA 2010 and one of the short papers presented in the corresponding ICTAI 2010 special track. It also includes a paper of the editors as invited.

• Theoretical Foundations of Both SI and ANN
• Advances of SI and ANN
• Hybridization of SI and ANN
• Applications.
• (source: Nielsen Book Data)

This book provides a new forum for the dissemination of knowledge in both theoretical and applied research on swarm intelligence (SI) and artificial neural network (ANN). It accelerates interaction between the two bodies of knowledge and fosters a unified development in the next generation of computational model for machine learning.To the best of our knowledge, the integration of SI and ANN is the first attempt to integrate various aspects of both the independent research area into a single volume.
(source: Nielsen Book Data)

• Chapter 1 Introduction 1
• 1.1 Logic, foundations of computer science, and applications of logic to computer science 1
• 1.2 On the utility of logic for computer engineers 3
• Chapter 2 A Few Thoughts Before the Formalization 7
• 2.1 What is logic? 7
• 2.1.1 Logic and paradoxes 8
• 2.1.2 Paradoxes and set theory 9
• l2.1.2.1 The answer 10
• 2.1.3 Paradoxes in arithmetic and set theory 13
• 2.1.3.1 The halting problem 13
• 2.1.4 On formalisms and well-known notions 15
• 2.1.4.1 Some "well-known" notions that could turn out to be difficult to analyze 19
• 2.1.5 Back to the definition of logic 23
• 2.1.5.1 Some definitions of logic for all 24
• 2.1.5.2 A few more technical definitions 24
• 2.1.5.3 Theory and meta-theory (language and meta-language) 30
• 2.1.6 A few thoughts about logic and computer science 30
• 2.2 Some historic landmarks 32
• Chapter 3 Propositional Logic 39
• 3.1 Syntax and semantics 40
• 3.1.1 Language and meta-language 43
• 3.1.2 Transformation rules for cnf and dnf 49
• 3.2 The method of semantic tableaux 54
• 13.2.1 A slightly different formalism: signed tableaux 58
• 3.3 Formal systems 64
• 3.3.1 A capital notion: the notion of proof 64
• 3.3.2 What do we learn from the way we do mathematics? 72
• 3.4 A formal system for PL (PC) 78
• 3.4.1 Some properties of formal systems 84
• 3.4.2 Another formal system for PL (PC) 86
• 3.4.3 Another formal system 86
• 3.5 The method of Davis and Putnam 92
• 3.5.1 The Davis-Putnam method and the SAT problem 95
• 3.6 Semantic trees in PL 96
• 3.7 The resolution method in PL 101
• 3.8 Problems, strategies, and statements 109
• 3.8.1 Strategies 110
• 3.9 Horn clauses 113
• 3.10 Algebraic point of view of propositional logic 114
• Chapter 4 First-order Terms 121
• 4.1 Matching and unification 121
• 4.1.1 A motivation for searching for a matching algorithm 121
• 4.1.2 A classification of trees 123
• 4.2 First-order terms, substitutions, unification 125
• Chapter 5 First-Order Logic (FOL) or Predicate Logic (PL1, PC1) 131
• 5.1 Syntax 133
• 5.2 Semantics 137
• 5.2.1 The notions of truth and satisfaction 139
• 5.2.2 A variant: multi-sorted structures 150
• 5.2.2.1 Expressive power, sort reduction 150
• 5.2.3 Theories and their models 152
• 5.2.3.1 How can we reason in FOL? 153
• 5.3 Semantic tableaux in FOL 154
• 5.4 Unification in the method of semantic tableaux 166
• 5.5 Toward a semi-decision procedure for FOL 169
• 5.5.1 Prenex normal form 169
• 5.5.1.1 Skolemization 174
• 5.5.2 Skolem normal form 176
• 5.6 Semantic trees in FOL 186
• 5.6.1 Skolemization and clausal form 188
• 5.7 The resolution method in FOL 190
• 5.7.1 Variables must be renamed 201
• 5.8 A decidable class: the monadic class 202
• 5.8.1 Some decidable classes 205
• 5.9 Limits: Gödel's (first) incompleteness theorem 206
• Chapter 6 Foundations of Logic Programming 213
• 6.1 Specifications and programming 213
• 6.2 Toward a logic programming language 219
• 6.3 Logic programming: examples 222
• 6.3.1 Acting on the execution control: cut"/" 229
• 6.3.1.1 Translation of imperative structures 231
• 6.3.2 Negation as failure (NAF) 232
• 6.3.2.1 Some remarks about the strategy used by LP and negation as failure 238
• 6.3.2.2 Can we simply deduce instead of using NAF? 239
• 6.4 Computability and Horn clauses 241
• Chapter 7 Artificial Intelligence 245
• 7.1 Intelligent systems: AI 245
• 7.2 What approaches to study AI? 249
• 7.3 Toward an operational definition of intelligence 249
• 7.3.1 The imitation game proposed by Turing 250
• 7.4 Can we identify human intelligence with mechanical intelligence? 251
• 7.4.1 Chinese room argument 252
• 7.5 Some history 254
• 7.5.1 Prehistory 254
• 7.5.2 History 255
• 7.6 Some undisputed themes in AI 256
• Chapter 8 Inference 259
• 8.1 Deductive inference 260
• 8.2 An important concept: clause subsumption 266
• 8.2.1 An important problem 268
• 8.3 Abduction 273
• 8.3.1 Discovery of explanatory theories 274
• 8.3.1.1 Required conditions 275
• 8.4 Inductive inference 278
• 8.4.1 Deductive inference 279
• 8.4.2 Inductive inference 280
• 8.4.3 Hempel's paradox (1945) 280
• 8.5 Generalization: the generation of inductive hypotheses 284
• 8.5.1 Generalization from examples and counter examples 288
• Chapter 9 Problem Specification in Logical Languages 291
• 9.1 Equality 291
• 9.1.1 When is it used? 292
• 9.1.2 Some questions about equality 292
• 9.1.3 Why is equality needed? 293
• 9.1.4 Whatis equality? 293
• 9.1.5 How to reason with equality? 295
• 9.1.6 Specification without equality 296
• 9.1.7 Axiomatization of equality 297
• 9.1.8 Adding the definition of = and using the resolution method 297
• 9.1.9 By adding specialized rules to the method of semantic tableaux 299
• 9.1.10 By adding specialized rules to resolution 300
• 9.1.10.1 Paramodulation and demodulation 300
• 9.2 Constraints 309
• 9.3 Second Order Logic (SOL): a few notions 319
• 9.3.1 Syntax and semantics 324
• 9.3.1.1 Vocabulary 324
• 9.3.1.2 Syntax 325
• 9.3.1.3 Semantics 325
• Chapter 10 Non-classical Logics 327
• l0.l Many-valued logics 327
• 10.1.1 How to reason with p-valued logics? 334
• 10.1.1.1 Semantic tableaux for p-valued logics 334
• 10.2 Inaccurate concepts: fuzzy logic 337
• 10.2.1 Inference in FL 348
• 10.2.1.1 Syntax 349
• 10.2.1.2 Semantics 349
• 10.2.2 Herbrand's method in FL 350
• 10.2.2.1 Resolution andFL 351
• 10.3 Modal logics 353
• 10.3.1 Toward a semantics 355
• 10.3.1.1 Syntax (language of modal logic) 357
• 10.3.1.2 Semantics 358
• 10.3.2 How to reason with modallogics? 360
• 10.3.2.1 Formal systems approach 360
• 10.3.2.2 Translation approach 361
• 10.4 Some elements of temporal logic 371
• 10.4.1 Temporal operators and semantics 374
• 10.4.1.1 A famous argument 375
• 10.4.2 A temporal logic 377
• 10.4.3 How to reason with temporal logics? 378
• 10.4.3.1 The method of semantic tableaux 379
• 10.4.4 An example of a PL for linear and discrete time; PTL (or PLTL) 381
• 10.4.4.1 Syntax 331
• 10.4.4.2 Semantics 382
• 10.4.4.3 Method of semantic tableaux for PLTL (direct method) 333
• Chapter 11 Knowledge and Logic: Some Notions 385
• 11.1 What is knowledge? 335
• 11.2 Knowledge and modal logic 389
• 11.2.1 Toward a formalization 389
• 11.2.2 Syntax 339
• 11.2.2.1 What expressive power? An example 389
• 11.2.2.2 Semantics 339
• 11.2.3 New modal operators 391
• 11.2.3.1 Syntax (extension) 391
• 11.2.3.2 Semantics (extension) 391
• 11.2.4 Application examples 392
• 11.2.4.1 Modeling the muddy children puzzle 392
• 11.2.4.2 Corresponding Kripke worlds 392
• 11.2.4.3 Properties of the (formalization chosen for the) knowledge 394
• Chapter 12 Solutions to the Exercises 395.

Logic and its components (propositional, first-order, non-classical) play a key role in Computer Science and Artificial Intelligence. While a large amount of information exists scattered throughout various media (books, journal articles, webpages, etc.), the diffuse nature of these sources is problematic and logic as a topic benefits from a unified approach. Logic for Computer Science and Artificial Intelligence utilizes this format, surveying the tableaux, resolution, Davis and Putnam methods, logic programming, as well as for example unification and subsumption. For non-classical logics, the translation method is detailed. Logic for Computer Science and Artificial Intelligence is the classroom-tested result of several years of teaching at Grenoble INP (Ensimag). It is conceived to allow self-instruction for a beginner with basic knowledge in Mathematics and Computer Science, but is also highly suitable for use in traditional courses. The reader is guided by clearly motivated concepts, introductions, historical remarks, side notes concerning connections with other disciplines, and numerous exercises, complete with detailed solutions, The title provides the reader with the tools needed to arrive naturally at practical implementations of the concepts and techniques discussed, allowing for the design of algorithms to solve problems.
(source: Nielsen Book Data)

• Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces.- Renyi's Entropy, Divergence and Their Nonparametric Estimators.- Adaptive Information Filtering with Error Entropy and Error Correntropy Criteria.- Algorithms for Entropy and Correntropy Adaptation with Applications to Linear Systems.- Nonlinear Adaptive Filtering with MEE, MCC, and Applications.- Classification with EEC, Divergence Measures, and Error Bounds.- Clustering with ITL Principles.- Self-Organizing ITL Principles for Unsupervised Learning.- A Reproducing Kernel Hilbert Space Framework for ITL.- Correntropy for Random Variables: Properties and Applications in Statistical Inference.- Correntropy for Random Processes: Properties and Applications in Signal Processing.
• (source: Nielsen Book Data)

This bookisan outgrowthoften yearsof researchatthe Universityof Florida Computational NeuroEngineering Laboratory (CNEL) in the general area of statistical signal processing and machine learning. One of the goals of writing the book is exactly to bridge the two ?elds that share so many common problems and techniques but are not yet e?ectively collaborating. Unlikeotherbooks thatcoverthe state ofthe artinagiven?eld, this book cuts across engineering (signal processing) and statistics (machine learning) withacommontheme:learningseenfromthepointofviewofinformationt- orywithanemphasisonRenyi'sde?nitionofinformation.Thebasicapproac h is to utilize the information theory descriptors of entropy and divergence as nonparametric cost functions for the design of adaptive systems in unsup- vised or supervised training modes. Hence the title: Information-Theoretic Learning (ITL). In the course of these studies, we discovered that the main idea enabling a synergistic view as well as algorithmic implementations, does not involve the conventional central moments of the data (mean and covariance). Rather, the core concept is the ?-norm of the PDF, in part- ular its expected value (? = 2), which we call the information potential. This operator and related nonparametric estimators link information theory, optimization of adaptive systems, and reproducing kernel Hilbert spaces in a simple and unconventional way.
(source: Nielsen Book Data)

• Investigating Universal Computability of Conventional Cellular Automata Problems on an Organic Molecular Matrix Noise-Based Logic and Computing: From Boolean Logic Gates to Brain Circuitry and Its Possible Hardware Realization Models and Mechanisms for Artificial Morphogenesis Biologically-Inspired Network Architecture for Future Networks Foraging Behaviors and Potential Computational Ability of Problem-Solving in an Amoeba Two Molecular Information Processing Systems Based on Catalytic Nucleic Acids The Effect of Community on Distributed Bio-inspired Service Composition Efficient Computation in Brownian Cellular Automata A Molecular Communication System Properties of Threshold Coupled Chaotic Neuronal Maps Implementation of Rotary Element with Quantum Cellular Automata Universal 2-State Asynchronous Cellular Automaton with Inner-Independent Transitions Effect of Population Size in Extended Parameter-Free Genetic Algorithm Temperature Effects on Olive Fruit Fly Infestation in the FlySim Cellular Automata Model Computing by Observing Changes Robustness of the Critical Behaviour in a Discrete Stochastic Reaction-Diffusion Medium Quantifying the Severity of the Permutation Problem in Neuroevolution Extending the Geometrical Design of DNA Nanostructures An Optical Solution for the Subset Sum Problem Design of True Random One-Time Pads in DNA XOR Cryptosystem On Designing Gliders in Three-Dimensional Larger than Life Cellular Automata Instability of Collective Flow in Two-Dimensional Optimal Velocity Model A New Differential Evolution for Multiobjective Optimization by Uniform Design and Minimum Reduce Hypervolume Noise Effects on Chaos in Chaotic Neuron Model Application of Improved Grammatical Evolution to Santa Fe Trail Problems Limit Theorem for a Time-Dependent Coined Quantum Walk onthe Line Top-Predator Survivor Region Is Affected by Bottom-Prey Mortality Rate on the Monte-Carlo Simulation in Lattice Model Simulation and Theoretical Comparison between 'Zipper' and 'Non-Zipper' Merging Universality of 2-State 3-Symbol Reversible Logic Elements - A Direct Simulation Method of a Rotary Element Pump Current as a Signal Transformation Evaluation of Generation Alternation Models in Evolutionary Robotics Photonic Switching of DNA's Position That Represents the Internal State in Photonic DNA Automaton Fluctuation Induced Structure in Chemical Reaction with Small Number of Molecules Parallel Retrieval of Nanometer-Scale Light-Matter Interactions for Nanophotonic Systems A Compressible Fluid Model for Traffic Flow and Nonlinear Saturation of Perturbation Growth Functional Sized Population Magnetic Optimization Algorithm Emergence and Collapse of Order in Ad Hoc Cellular Automata A Transition Rule Set for the First 2-D Optimum-Time Synchronization Algorithm A Two-Dimensional Optimum-Time Firing Squad Synchronization Algorithm and Its Implementation Quaternion Based Thermal Condition Monitoring System Firing Correlation in Spiking Neurons with Watts-Strogatz Rewiring Methods for Shortening Waiting Time in Walking-Distance Introduced Queueing Systems Effect of Mutation to Distribution of Optimum Solution in Genetic Algorithm.
• (source: Nielsen Book Data)

This book is the refereed proceedings of the Fourth International Workshop on Natural Computing, IWNC 2009, held in Himeji International Exchange Center, HIMEJI, JAPAN on September 2009. IWNC aims to bring together computer scientists, biologists, mathematicians, electronic engineers, physicists, and humanitarians, to critically assess present findings in the field, and to outline future developments in nature-inspired computing.
(source: Nielsen Book Data)

• Part 1 Keynote Speakers
• Part 2 Quantitative Logic
• Part 3 Fuzzy Sets and Systems
• Part 4 Soft Computing.

This book is the proceedings of the 2nd International Conference on Quantitative Logic and Soft Computing (QL & SC 2010) from Oct. 22-25, 2010 in Xiamen, China. Only high-quality papers are included. The 2nd International Conference on Quantitative Logic and Soft Computing (QL & SC 2010), built on the success of previous conferences, the QL & QS 2009 (Shanghai, China), is a major symposium for scientists, engineers and practitioners in China to present their updated results, ideas, developments and applications in all areas of quantitative logic and soft computing. It aims to strengthen relations between industry research laboratories and universities, and to create a primary symposium for world scientists in quantitative logic and soft computing fields as follows: 1) Quantitative Logic. 2) Fuzzy Sets and Systems. 3) Soft Computing.

• 1. Programming-by-Demonstration of Robot Motions
• 2. Grasp Recognition by Fuzzy Modeling and Hidden Markov Models
• 3. Distributed Adaptive Coordinated Control of Multi-Manipulator Systems Using Neural Networks
• 4. A New Framework for View-invariant Human Action Recognition
• 5. Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions
• 6. Obstacle Detection using Cross-ratio and Disparity Velocity
• 7. Learning and Vision-based Obstacle Avoidance and Navigation
• 8. A Fraction Distortion Model for Accurate Camera Calibration and Correction
• 9. A Leader-follower Flocking System Based on Estimated Flocking Center
• 10. A Behavior Based Control System for Surveillance
• 11. Hierarchical Composite Anti-Disturbance Control for Robotic Systems Using Robust Disturbance Observer
• 12. Autonomous Navigation for Mobile Robots with Human-Robot Interaction
• 13. Prediction-based Perceptual System of a Partner Robot for Natural Communication
• Index.

Robot Intelligence is an exciting interdisciplinary field including engineering, information technology, machine learning, biological science and psychology. Its dramatic growth in practical applications is driven by both real-world requirements and maturity of related disciplines such as intelligent algorithms. It is expected that perception, understanding and reasoning capabilities play a crucial role in robot-assisted tasks and enable robots to exhibit similar performance on executing various tasks in both constrained and unconstrained environments. Robot Intelligence is a rare collection of chapters reflecting recent robotics developments from the advanced knowledge processing perspective. It also provides a comprehensive introduction and methodology to selected robotics topics including human robot interaction, human motion analysis, robot vision and advanced control. The robot intelligence methods presented enable readers to address many complex problems involving a wide range of robot sensors for the perception and understanding of the environment and the reasoning of the subsequent actions. The book's comprehensive coverage will prove invaluable to senior undergraduates, PhD students, researchers and practitioners.

• Introduction to Approaches in the Development of Heterogeneous Catalysts
• Methods of Searching for Optimal Catalytic Materials
• Analysis and Mining of Data Gathered in Catalytic Experiments
• Artificial Neural Networks in the Study of Catalytic Performance.
• (source: Nielsen Book Data)

The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade - evolutionary optimization and artificial neural networks - are described. The book is unique in that it describes evolutionary optimization in a broader context of methods of searching for optimal catalytic materials, including statistical design of experiments, as well as presents neural networks in a broader context of data analysis. It is the first book that demystifies the attractiveness of artificial neural networks, explaining its rational fundamental - their universal approximation capability. At the same time, it shows the limitations of that capability and describes two methods for how it can be improved. The book is also the first that presents two other important topics pertaining to evolutionary optimization and artificial neural networks: automatic generating of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. Both are not only theoretically explained, but also well illustrated through detailed case studies.
(source: Nielsen Book Data)

• Introduction to Approaches in the Development of Heterogeneous Catalysts
• Methods of Searching for Optimal Catalytic Materials
• Analysis and Mining of Data Gathered in Catalytic Experiments
• Artificial Neural Networks in the Study of Catalytic Performance.
• (source: Nielsen Book Data)

The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade - evolutionary optimization and artificial neural networks - are described. The book is unique in that it describes evolutionary optimization in a broader context of methods of searching for optimal catalytic materials, including statistical design of experiments, as well as presents neural networks in a broader context of data analysis. It is the first book that demystifies the attractiveness of artificial neural networks, explaining its rational fundamental - their universal approximation capability. At the same time, it shows the limitations of that capability and describes two methods for how it can be improved. The book is also the first that presents two other important topics pertaining to evolutionary optimization and artificial neural networks: automatic generating of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. Both are not only theoretically explained, but also well illustrated through detailed case studies.
(source: Nielsen Book Data)

• Humanoid Robotics.- Time-Varying Affective Response for Humanoid Robots.- The Co-simulation of Humanoid Robot Based on Solidworks, ADAMS and Simulink.- From RoboNova to HUBO: Platforms for Robot Dance.- BunnyBot: Humanoid Platform for Research and Teaching.- Teen Sized Humanoid Robot: Archie.- Interdisciplinary Construction and Implementation of a Human Sized Humanoid Robot by Master Students.- Human Robot Interaction.- Safety Aspects in a Human-Robot Interaction Scenario: A Human Worker Is Co-operating with an Industrial Robot.- Integration of a RFID System in a Social Robot.- A Practical Study on the Design of a User-Interface Robot Application.- Infrared Remote Control with a Social Robot.- BlogRobot: Mobile Terminal for Blog Browse Using Physical Representation.- An Exploratory Investigation into the Effects of Adaptation in Child-Robot Interaction.- Devious Chatbots - Interactive Malware with a Plot.- Towards Better Human Robot Interaction: Understand Human Computer Interaction in Social Gaming Using a Video-Enhanced Diary Method.- Promotion of Efficient Cooperation by Sharing Environment with an Agent Having a Body in Real World.- Interaction Design for a Pet-Like Remote Control.- Experiences with a Barista Robot, FusionBot.- Mutually Augmented Cognition.- How Humans Optimize Their Interaction with the Environment: The Impact of Action Context on Human Perception.- Development of a Virtual Presence Sharing System Using a Telework Chair.- PLEXIL-DL: Language and Runtime for Context-Aware Robot Behaviour.- Ambient Intelligence in a Smart Home for Energy Efficiency and Eldercare.- Education and Entertainment.- Intelligent Technologies for Edutainment Using Multiple Robots.- Remote Education Based on Robot Edutainment.- Not Just "Teaching Robotics" but "Teaching through Robotics".- A Proposal of Autonomous Robotic Systems Educative Environment.- Mechatronics Education: From Paper Design to Product Prototype Using LEGO NXT Parts.- Fostering Development of Students' Collective and Self-efficacy in Robotics Projects.- From an Idea to a Working Robot Prototype: Distributing Knowledge of Robotics through Science Museum Workshops.- Teaching Electronics through Constructing Sensors and Operating Robots.- Learning from Analogies between Robotic World and Natural Phenomena.- Integrating Robot Design Competitions into the Curriculum and K-12 Outreach Activities.- Teamwork and Robot Competitions in the Undergraduate Program at the Copenhagen University College of Engineering.- Cooperative Robotics.- Multiagents System with Dynamic Box Change for MiroSot.- Multi Block Localization of Multiple Robots.- Soty-Segment: Robust Color Patch Design to Lighting Condition Variation.- Task-Based Flocking Algorithm for Mobile Robot Cooperation.- Analysis of Spatially Limited Local Communication for Multi-Robot Foraging.- AMiRESot - A New Robot Soccer League with Autonomous Miniature Robots.- Robotic System Design.- BeBot: A Modular Mobile Miniature Robot Platform Supporting Hardware Reconfiguration and Multi-standard Communication.- System Design for Semi-automatic AndroSot.- Learning, Optimization, Communication.- Extended TA Algorithm for Adapting a Situation Ontology.- An Integer-Coded Chaotic Particle Swarm Optimization for Traveling Salesman Problem.- USAR Robot Communication Using ZigBee Technology.
• (source: Nielsen Book Data)

th This volume is an edition of the papers selected from the 12 FIRA RoboWorld C- gress, held in Incheon, Korea, August 16-18, 2009. The Federation of International Robosoccer Association (FIRA - www. fira. net) is a non-profit organization, which organizes robotic competitions and meetings around the globe annually. The RoboSoccer competitions started in 1996 and FIRA was - tablished on June 5, 1997. The Robot Soccer competitions are aimed at promoting the spirit of science and technology to the younger generation. The congress is a forum in which to share ideas and future directions of technologies, and to enlarge the human networks in robotics area. The objectives of the FIRA Cup and Congress are to explore the technical dev- opment and achievement in the field of robotics, and provide participants with a robot festival including technical presentations, robot soccer competitions and exhibits - der the theme "Where Theory and Practice Meet. " th Under the umbrella of the 12 FIRA RoboWorld Incheon Congress 2009, six int- national conferences were held for greater impact and scientific exchange: th * 6 International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS) th * 5 International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE) * International Conference on Social Robotics (ICSR) * International Conference on Advanced Humanoid Robotics Research (ICAHRR) * International Conference on Entertainment Robotics (ICER) * International Robotics Education Forum (IREF) This volume consists of selected quality papers from the six conferences.
(source: Nielsen Book Data)

• A Review of Search and Resource Discovery Techniques in Peer-to-Peer Networks (S Botros & S Waterhouse)
• Adaptive Content Mapping for Internet Navigation (R W Brause & M Ueberall)
• Flexible Queries to XML Information (E Damiani et al.)
• Agent-Based Hypermedia Models (W Balzano et al.)
• Self-Organizing Neural Networks Application for Information Organization (R Rizzo)
• Emotion-Orientated Intelligent Systems (T Ichimura et al.)
• Public Opinion Channel: A Network-Based Interactive Broadcasting System for Supporting a Knowledge-Creating Community (T Fukuhara et al.)
• A New Era of Intelligent E-Commerce Based on Intelligent Java Agent-Based Development Environment (iJADE) (R S T Lee)
• Automated Internet Trading Based on Optimized Physics Models of Markets (L Ingber & R P Mondescu)
• Implementing and Maintaining a Web Case-Based Reasoning System for Heating Ventilation and Air Conditioning Systems Sales Support (I Watson).
• (source: Nielsen Book Data)

The Internet/WWW has made it possible to easily access quantities of information never available before. However, both the amount of information and the variation in quality pose obstacles to the efficient use of the medium. Artificial intelligence techniques can be useful tools in this context. Intelligent systems can be applied to searching the Internet and data-mining, interpreting Internet-derived material, the human-Web interface, remote condition monitoring and many other areas.This volume presents the latest research on the interaction between intelligent systems (neural networks, adaptive and connectionist paradigms, fuzzy and rule-based systems, intelligent agents) and the Internet/WWW. It surveys both the employment of intelligent systems to facilitate and enhance the use of the Internet, and applications where the Internet is a channel through which intelligent techniques are applied.
(source: Nielsen Book Data)

• pt. I. Pattern Recognition
• Ch. 1. Multiple Classifier Systems
• Ch. 2. Building Decision Trees from the Fourier Spectrum of a Tree Ensemble
• Ch. 3. Clustering Large Data Sets
• Ch. 4. Multi-objective Variable String Genetic Classifier: Application to Remote Sensing Imagery
• pt. II. Image Processing and Vision
• Ch. 5. Dissimilarity Measures Between Fuzzy Sets or Fuzzy Structures
• Ch. 6. Early Vision: Concepts and Algorithms
• Ch. 7. Self-organizing Neural Network for Multi-level Image Segmentation
• Ch. 8. Geometric Transformation by Moment Method with Wavelet Matrix
• Ch. 9. New Computationally Efficient Algorithms for Video Coding.

• An introduction to artificial intelligence, N.G. Bourbakis
• fundamental methods for horn logic and AI applications, E. Kounalis and P. Marquis
• applications of genetic algorithms to permutation problems, F. Petry and B. Buckles
• extracting procedural knowledge from software systems using inductive learning in the PM system, R. Reynolds and E. Zannoni
• resource oriented parallel planning, S. Lee and K. Chung
• advanced parsing technology for knowledge based shells, J. Kipps
• analysis and synthesis of intelligent systems, W. Arden
• document analysis and recognition, S.N. Srihari et al
• signal understanding - an AI approach to modulation and classification, J.E. Whelchel et al
• and others.
• (source: Nielsen Book Data)

This volume is the first in a series which deals with the challenge of AI issues, gives updates of AI methods and applications, and promotes high quality new ideas, techniques and methodologies in AI. This volume contains articles by 38 specialists in various AI subfields covering theoretical and application issues.
(source: Nielsen Book Data)

• Table of Contents An Overview of Amazon EMR Exploring the Architecture and Deployment Options Common Use Cases and Architecture Patterns Big Data Applications and Notebooks Available in Amazon EMR Setting Up and Configuring EMR Clusters Monitoring, Scaling, and High Availability Understanding Security in Amazon EMR Understanding Data Governance in Amazon EMR Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark Implementing Real-Time Streaming with Amazon EMR and Spark Streaming Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA Migrating On-Premises Hadoop Workloads to Amazon EMR Best Practices and Cost Optimization Techniques.
• (source: Nielsen Book Data)

Design scalable big data solutions using Hadoop, Spark, and AWS cloud native services Key Features Build data pipelines that require distributed processing capabilities on a large volume of data Discover the security features of EMR such as data protection and granular permission management Explore best practices and optimization techniques for building data analytics solutions in Amazon EMR Book DescriptionAmazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS. What you will learn Explore Amazon EMR features, architecture, Hadoop interfaces, and EMR Studio Configure, deploy, and orchestrate Hadoop or Spark jobs in production Implement the security, data governance, and monitoring capabilities of EMR Build applications for batch and real-time streaming data analytics solutions Perform interactive development with a persistent EMR cluster and Notebook Orchestrate an EMR Spark job using AWS Step Functions and Apache Airflow Who this book is forThis book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and Amazon EMR. Prior experience in either Python programming, Scala, or the Java programming language and a basic understanding of Hadoop and AWS will help you make the most out of this book.
(source: Nielsen Book Data)

• Table of Contents Getting Started with TinyML Prototyping with Microcontrollers Building a Weather Station with TensorFlow Lite for Microcontrollers Voice Controlling LEDs with Edge Impulse Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano Building a Gesture-Based Interface for YouTube Playback Running a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OS Toward the Next TinyML Generation with microNPU.
• (source: Nielsen Book Data)

Work through over 50 recipes to develop smart applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico using the power of machine learning Key Features Train and deploy ML models on Arduino Nano 33 BLE Sense and Raspberry Pi Pico Work with different ML frameworks such as TensorFlow Lite for Microcontrollers and Edge Impulse Explore cutting-edge technologies such as microTVM and Arm Ethos-U55 microNPU Book DescriptionThis book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers. The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you'll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you'll cover recipes relating to temperature, humidity, and the three "V" sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you'll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you'll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game. By the end of this book, you'll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase. What you will learn Understand the relevant microcontroller programming fundamentals Work with real-world sensors such as the microphone, camera, and accelerometer Run on-device machine learning with TensorFlow Lite for Microcontrollers Implement an app that responds to human voice with Edge Impulse Leverage transfer learning to classify indoor rooms with Arduino Nano 33 BLE Sense Create a gesture-recognition app with Raspberry Pi Pico Design a CIFAR-10 model for memory-constrained microcontrollers Run an image classifier on a virtual Arm Ethos-U55 microNPU with microTVM Who this book is forThis book is for machine learning developers/engineers interested in developing machine learning applications on microcontrollers through practical examples quickly. Basic familiarity with C/C++, the Python programming language, and the command-line interface (CLI) is required. However, no prior knowledge of microcontrollers is necessary.
(source: Nielsen Book Data)

• Chapter 1
• Welcome to TensorFlow.js Headings
• What is TensorFlow.js?
• TensorFlow.js API
• Tensors Operations Variables
• How to install it
• Use cases Chapter 2 Building your First Model Headings
• Building a logistic regression classification model
• Building a linear regression model
• Doing unsupervised learning with k-means
• Dimensionality reduction and visualization with t-SNE and d3.js
• Our first neural network Chapter 3 Create a drawing app to predict handwritten digits using Convolutional Neural Networks and MNIST Headings
• Convolutional Neural Networks
• The MNIST Dataset
• Design the model architecture
• Train the model
• Evaluate the model
• Build the drawing app
• Integrate the model within the app
• Chapter 4 "Move your body!" A game featuring PoseNet, a pose estimator model Headings
• What is PoseNet?
• Loading the model
• Interpreting the result
• Building a game around it Chapter 5 Detect yourself in real-time using an object detection model trained in Google Cloud's AutoML Headings
• TensorFlow Object Detection API
• Google Cloud's AutoML
• Training the model
• Exporting the model and importing it in TensorFlow.js
• Building the webcam app Chapter 6 Transfer Learning with Image Classifier and Voice Recognition Headings
• What's Transfer Learning?
• MobileNet and ImageNet (MobileNet is the base model and ImageNet is the training set)
• Transferring the knowledge
• Re-training the model
• Testing the model with a video Chapter 7 Censor food you do not like with pix2pix, Generative Adversarial Networks, and ml5.js Headings
• Introduction to Generative Adversarial Networks
• What is image translation?
• Training your custom image translator with pix2pix
• Deploying the model with ml5.js Chapter 8 Detect toxic words from a Chrome Extension using a Universal Sentence Encoder Headings
• Toxicity classifier
• Training the model
• Testing the model
• Integrating the model in a Chrome Extension Chapter 9 Time Series Analysis and Text Generation with Recurrent Neural Networks Headings
• Recurrent Neural Networks
• Example 1: Building an RNN for time series analysis
• Example 2: Building an RNN to generate text Chapter 10 Best practices, integrations with other platforms, remarks and final words Headings
• Best practices
• Integration with other platforms
• Materials for further practice
• Conclusion.
• (source: Nielsen Book Data)

Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow. js is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard , ml5js , tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow. js to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN). Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. What You'll Learn Build deep learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis Who This Book Is For Programmers developing deep learning solutions for the web and those who want to learn TensorFlow.js with at least minimal programming and software development knowledge. No prior JavaScript knowledge is required, but familiarity with it is helpful.
(source: Nielsen Book Data)

• Introduction to Fuzzy Sets.- Interval-Valued Fuzzy Relations.- Applications.- Summary and Open Problem.
• (source: Nielsen Book Data)

This book offers an introduction to fuzzy sets theory and their operations, with a special focus on aggregation and negation functions. Particular attention is given to interval-valued fuzzy sets and Atanassov's intuitionistic fuzzy sets and their use in uncertainty models involving imperfect or unknown information. The theory and application of interval-values fuzzy sets to various decision making problems represent the central core of this book, which describes in detail aggregation operators and their use with imprecise data represented as intervals. Interval-valued fuzzy relations, compatibility measures of interval and the transitivity property are thoroughly covered. With its good balance between theoretical considerations and applications of originally developed algorithms to real-world problem, the book offers a timely, inspiring guide to mathematicians and engineers developing new decision making models or implementing/applying existing ones to a wide range of applications involving imprecise or incomplete data. .
(source: Nielsen Book Data)

• Part I: Type-2 Fuzzy Logic in Metaheuristics.- A comparative study of dynamic adaptation of parameters in the GWO algorithm using type-1 and interval type-2 fuzzy logic.- Ensemble Neural Network optimization using a gravitational search algorithm with Interval Type-1 and Type-2 fuzzy parameter adaptation in pattern recognition applications.- Improved method based on type-2 fuzzy logic for the adaptive harmony search algorithm.- Comparison of bio-inspired methods with parameter adaptation through interval type-2 fuzzy logic.- Differential Evolution algorithm with Interval type-2 fuzzy logic for the optimization of the mutation parameter.- Part II: Neural Networks Theory and Applications.- Person recognition with modular deep neural network using the iris biometric measure.- Neuro-evolutionary Neural Network for the Estimation of Melting Point of Ionic Liquids.- A proposal to classify ways of walking patterns using spik-ing neural networks.- Partially-connected Artificial Neural Networks developed by Grammatical Evolution for pattern recognition problems.- Part III: Metaheuristics: Theory and Applications.- Bio-inspired Metaheuristics for Hyper-parameter Tuning of Support Vector Machine Classifiers.
• (source: Nielsen Book Data)

This book comprises papers on diverse aspects of fuzzy logic, neural networks, and nature-inspired optimization meta-heuristics and their application in various areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book is organized into seven main parts, each with a collection of papers on a similar subject. The first part presents new concepts and algorithms based on type-2 fuzzy logic for dynamic parameter adaptation in meta-heuristics. The second part discusses network theory and applications, and includes papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The third part addresses the theory and practice of meta-heuristics in different areas of application, while the fourth part describes diverse fuzzy logic applications in the control area, which can be considered as intelligent controllers. The next two parts explore applications in areas, such as time series prediction, and pattern recognition and new optimization and evolutionary algorithms and their applications respectively. Lastly, the seventh part addresses the design and application of different hybrid intelligent systems.
(source: Nielsen Book Data)

• Co-occurrence Relation" and "Ingredient Category" Recommend Alternative-ingredients.- Design and Initial Evaluation of Bluetooth Low Energy Separate Channel Fingerprinting.- Toward Sustainable Smart Mobility Information Infrastructure Platform :-Project Overview-.- Model-based methodology Establishing Traceability between Requirements, Design and Operation Information in Lifecycle-Oriented Architecture.- Sports game summarization based on sub-events and game-changing phrases.- Headline Generation with Recurrent Neural Network.- Customer State Analysis with Enthusiasm Analysis.
• (source: Nielsen Book Data)

This book examines theoretical and practical aspects of emerging technologies in e-service and artificial intelligence from an academic and professional viewpoint. To do so, it focuses on three major areas: the development of novel user support systems; development of smart mobility; and emerging technologies in Artificial Intelligence (AI). With regard to the development of novel user support systems, Chapter 1 introduces alternative ingredients recommendation using data on co-occurrence relation and ingredients categories to support cooking, while Chapter 2 introduces a study on location information inference using data acquired by low-energy Bluetooth devices. Turning to the development of smart mobility, Chapter 3 highlights a sustainable information infrastructure project for smart mobility systems. In addition, Chapter 4 presents a lifecycle-oriented development process to improve requirements and design in terms of uncertainties to provide sustainable information architectures for smart mobility. In the book's third and last part - emerging technologies in AI - Chapter 5 presents a summarization task for sports events on Twitter, focusing on an abstractive approach based on sub-events during the sports event. Chapter 6 discusses the generation of headlines using a recurrent neural network based on a machine translation approach. Lastly, Chapter 7 explores customer behavior analysis using enthusiasm analysis, an approach that estimates customers' activation levels. The book gathers a selection of the highest-quality papers presented at the 4th International Congress on Advanced Applied Informatics, held on July 12-16, 2015 in Okayama, Japan. Given the breadth of its coverage, it offers a valuable resource for practitioners, researchers and students alike.
(source: Nielsen Book Data)

• Phraseology in translation and contrastive studies.- Lexicography and terminography.- Exploitation of corpora in phraseological studies.- Development of corpora for phraseological studies.- Phraseology and language learning.- Cognitive and cultural aspects of phraseology.- Theoretical and descriptive approaches to phraseology.- Computational approaches to phraseology.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the International Conference on Computational and Corpus-Based Phraseology, Europhras 2017, held in London, UK, in November 2017. The 31 full papers presented were carefully reviewed and selected from numerous submissions and are organized into the following thematic sessions: Phraseology in translation and contrastive studies, Lexicography and terminography, Exploitation of corpora in phraseological studies, Development of corpora for phraseological studies, Phraseology and language learning, Cognitive and cultural aspects of phraseology, Theoretical and descriptive approaches to phraseology, and Computational approaches to phraseology. The chapter 'Frequency Consolidation Among Word N-Grams' is available open access under a CC BY 4.0 license.
(source: Nielsen Book Data)

• Preface.- A Genetic Programming Approach to Cost-Sensitive Control in Resource Constrained Sensor Systems.- A Study on Performance of the Hill Climbing Heuristic Method for Router Placement in Mesh Networks.- An Automated Irrigation System Based on a Low- Cost Micro-controller for Tomato Production in South India.- Artificial-Neural-Network-based Real-Time Urban Road Traffic State Estimation Framework.- Attack Detection using Evolutionary Computation.- Computational Intelligence based Security in WSNs - Technologies and Design Challenges.- Efficient Anomaly Detection System for Video Surveillance Application in WVSN with Particle Swarm Optimization.- Planning Robust Sensor Relocation Trajectories for a Mobile Robot with Evolutionary Multi-Objective Optimization.
• (source: Nielsen Book Data)

This book emphasizes the increasingly important role that Computational Intelligence (CI) methods are playing in solving a myriad of entangled Wireless Sensor Networks (WSN) related problems. The book serves as a guide for surveying several state-of-the-art WSN scenarios in which CI approaches have been employed. The reader finds in this book how CI has contributed to solve a wide range of challenging problems, ranging from balancing the cost and accuracy of heterogeneous sensor deployments to recovering from real-time sensor failures to detecting attacks launched by malicious sensor nodes and enacting CI-based security schemes. Network managers, industry experts, academicians and practitioners alike (mostly in computer engineering, computer science or applied mathematics) benefit from th e spectrum of successful applications reported in this book. Senior undergraduate or graduate students may discover in this book some problems well suited for their own research endeavors.
(source: Nielsen Book Data)

Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book * An overview of modern Data Science and Machine Learning libraries available in Java * Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. * Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn * Get a solid understanding of the data processing toolbox available in Java * Explore the data science ecosystem available in Java * Find out how to approach different machine learning problems with Java * Process unstructured information such as natural language text or images * Create your own search engine * Get state-of-the-art performance with XGBoost * Learn how to build deep neural networks with DeepLearning4j * Build applications that scale and process large amounts of data * Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. Style and approach This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples.
(source: Nielsen Book Data)

• Remote Material Handling systems
• Industrial Robotics
• Mobile Robotics
• Adaptive control
• Vision system
• Smart materials
• Tele-operation.

This book constitutes the thoroughly refereed proceedings of the First International Conference of Advancements in Automation, Robotics and Sensing, ICAARS 2016, held in Coimbatore, India, in June 2016. The 83 revised selected papers were selected from 159 submissions and focus on industrial robotics, mobile robotics, adaptive control, vision system, smart materials, and teleoperation.

• Part I Fundamental Theory.- Part II Applications.
• (source: Nielsen Book Data)

This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.
(source: Nielsen Book Data)

• Intro; Preface; Contents; 1 The Role of Computational Intelligence in Quantitative Software Engineering; 1 Introduction
• Software Development and the Art of Cappuccino; 2 Persistent Problems in Software Development; 3 Uncertainty; 4 Irreversibility; 5 Complexity; 6 Handling Uncertainty, Irreversibility, and Complexity 2026 and Cappuccino!; 7 The Pivotal Role of Computational Intelligence in Quantitative Software Engineering; 8 Conclusions; References; 2 Computational Intelligence: An Introduction; Abstract; 1 Introduction

• 2 Computational Intelligence: An Agenda of Synergy of Algorithms of Learning, Optimization and Knowledge Representation3 Neural Networks and Neurocomputing; 4 Evolutionary and Biologically Inspired Computing: Towards a Holistic View at Global Optimization; 5 Information Granularity and Granular Computing; 6 Formal Platforms of Information Granularity; 6.1 Information Granules of Higher Type and Higher Order; 6.2 Hybrid Models of Information Granules; 7 The Concept of Information Granulation-Degranulation; 8 Clustering as a Means of Design of Information Granules

• 8.1 Unsupervised Learning with Fuzzy Sets8.2 Fuzzy C-Means as an Algorithmic Vehicle of Data Reduction Through Fuzzy Clusters; 8.3 Knowledge-Based Clustering; 9 Computational Intelligence and Software Engineering; 10 Conclusions; References; 3 Towards Benchmarking Feature Subset Selection Methods for Software Fault Prediction; Abstract; 1 Introduction; 2 Related Work; 3 Feature Subset Selection (FSS) Methods; 3.1 Information Gain (IG) Attribute Ranking; 3.2 Relief (RLF); 3.3 Principal Component Analysis (PCA); 3.4 Correlation-Based Feature Selection (CFS)

• 3.5 Consistency-Based Subset Evaluation (CNS)3.6 Wrapper Subset Evaluation (WRP); 3.7 Genetic Programming (GP); 4 Experimental Setup; 5 Results and Analysis; 6 Validity Evaluation; 7 Conclusions; References; 4 Evolutionary Computation for Software Product Line Testing: An Overview and Open Challenges; Abstract; 1 Introduction; 2 Background; 2.1 SPL Foundations
• Feature Models and Running Example; 2.2 Basics of Evolutionary Algorithms; 3 Overview of SPL Testing; 4 Combinatorial Interaction Testing for Software Product Lines; 4.1 Basic Terminology; 4.2 SPL Genetic Solver (SPLGS)

• 4.3 State of the Art CIT for SPL Testing5 Multi-objective SPL Testing; 5.1 Multi-objective Optimization Formalization; 5.2 An Example Scenario; 5.3 Computation of Exact Pareto Fronts; 5.4 Sate of the Art in Evolutionary Multi-objective Optimization for SPL Testing; 6 Evolutionary Testing of SPLs in Practice; 7 Open Challenges and Questions; 8 Conclusions; Acknowledgments; References; 5 Metaheuristic Optimisation and Mutation-Driven Test Data Generation; Abstract; 1 Introduction; 2 Test Data Generation; 3 Mutation Analysis; 4 Metaheuristic Optimisation

In a down-to-the earth manner, the volume lucidly presents how the fundamental concepts, methodology, and algorithms of Computational Intelligence are efficiently exploited in Software Engineering and opens up a novel and promising avenue of a comprehensive analysis and advanced design of software artifacts. It shows how the paradigm and the best practices of Computational Intelligence can be creatively explored to carry out comprehensive software requirement analysis, support design, testing, and maintenance. Software Engineering is an intensive knowledge-based endeavor of inherent human-centric nature, which profoundly relies on acquiring semiformal knowledge and then processing it to produce a running system. The knowledge spans a wide variety of artifacts, from requirements, captured in the interaction with customers, to design practices, testing, and code management strategies, which rely on the knowledge of the running system. This volume consists of contributions written by widely acknowledged experts in the field who reveal how the Software Engineering benefits from the key foundations and synergistically existing technologies of Computational Intelligence being focused on knowledge representation, learning mechanisms, and population-based global optimization strategies. This book can serve as a highly useful reference material for researchers, software engineers and graduate students and senior undergraduate students in Software Engineering and its sub-disciplines, Internet engineering, Computational Intelligence, management, operations research, and knowledge-based systems.

This book constitutes the proceedings of the 10th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2015, held in Hefei, China, in September 2015. The 63 revised full papers presented were carefully reviewed and selected from 182 submissions. The papers deal with the following main topics: evolutionary computing, neural computing, DNA computing, and membrane computing.

• Intro; Preface; Organization; Contents; Utilizing Permission Norms in BDI Practical Normative Reasoning; 1 Introduction; 2 Background; 2.1 Jason; 2.2 Event Calculus; 3 Norm Representation; 3.1 Norms; 4 BDI Agent Normative Reasoning; 5 Experiments; 5.1 Gold and Silver Mining Society; 6 Summary; References; Holonic Institutions for Multi-scale Polycentric Self-governance; 1 Introduction; 2 Background and Motivation; 2.1 Formalising Ostrom's Principles; 2.2 The Eighth Principle: Nested Enterprises; 2.3 An Example; 3 Institutions and Holons; 3.1 Institutions: An Informal Overview

• 3.2 Self-organising Electronic Institutions3.3 Holonic Systems; 4 Holonic Institutions; 5 Case Study for Community Energy Systems; 5.1 Smart Houses; 5.2 Decentralised Community Energy Systems; 5.3 Holonic Institutions; 5.4 Polycentric Self-Governance; 6 Related and Future Work; 7 Summary and Conclusions; References; Contextualized Planning Using Social Practices; 1 Introduction; 2 Background; 2.1 2APL; 2.2 Case-Based Reasoning; 2.3 Work Practice Simulation; 3 Social Practices; 3.1 Social Intelligence and Social Practices; 3.2 Characteristics of Social Practices

• 3.3 Social Practices in Deliberation4 Scenario; 5 Discussion and Conclusion; References; Modelling the Impact of Role Specialisation on Cooperative Behaviour in Historic Trader Scenarios; 1 Introduction; 2 Dynamic Deontics; 2.1 Characteristics; 2.2 Operationalisation; 3 Historic Trader Scenarios; 4 Model; 5 Simulation Results; 5.1 Role Unification Without Norm Enforcement; 5.2 Role Unification with Norm Enforcement; 5.3 Role Specialisation Without Norm Enforcement; 5.4 Role Specialisation with Norm Enforcement; 6 Discussion, Conclusions and Outlook; References

• Severity-Sensitive Robustness Analysis in Normative Systems1 Introduction; 2 ISR Scenario; 3 Formalization; 4 Normative Ranking of Possible Worlds; 4.1 Computing Pw; 4.2 Computing the Ranking; 5 Detailed Example; 6 Checking Robustness; 7 Discussion; 8 Conclusions; Building an Artificial Primitive Human Society: An Agent-Based Approach; Abstract; 1 Introduction; 2 Background; 2.1 Simulation Environment and Settings; 3 Experimental Settings and Results; 4 Discussion; 5 Conclusion and Future Direction; References; Designing for Planned Emergence in Multi-agent Systems; 1 Introduction

• 2 OJAzzIC Overview3 Design Considerations; 3.1 Scenario; 3.2 Design Questions; 3.3 Designing an OJAzzIC Based System; 4 Incident Response Demonstration System; 4.1 Define the Goal Model; 4.2 Define the Organizational Model; 4.3 Define the Agent Capabilities Model; 4.4 Define the Role Model; 4.5 Establish Social Policies; 5 Observations; 6 Related Work; 7 Conclusion; References; Supporting Request Acceptance with Use Policies; 1 Introduction; 2 Related Work; 3 Overview; 4 Requests and Use Policies; 4.1 Representations; 4.2 Conflict; 5 The Event Calculus Normative Model; 5.1 Event Calculus

This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Workshops on Coordination, Organizations, Institutions and Norms in Agent Systems, COIN 2014. The workshops were co-located with AAMAS 2014, held in Paris, France, in May 2014, and with PRICAI 2014, held in Gold Coast, QLD, Australia, in December 2014. The 16 full papers were carefully reviewed and selected for inclusion in this volume. The papers cover a wide range of topics from work on formal aspects of normative and team based systems, to software engineering with organisational concepts, to applications of COIN based systems, and to philosophical issues surrounding socio-technical systems. They highlight not only the richness of existing work in the field, but also point out the challenges and exciting research that remains to be done in the area.
(source: Nielsen Book Data)

• Malware and Machine Learning
• Soft Computing Based Epidemical Crisis Prediction
• An ACP-Based Approach to Intelligence and Security Informatics
• Microfiles as a Potential Source of Confidential Information Leakage
• Decision Support in Open Source Intelligence
• Information Fusion Process Design Issues for Hard and Soft Information: Developing an Initial Prototype
• Intrusion Detection with Type-2 Fuzzy Ontologies and Similarity Measures
• A multi-objective genetic algorithm based approach for effective intrusion detection using neural networks
• Cyber Insider Mission Detection for Situation Awareness
• A Game Theoretic Engine for Cyber Warfare
• Mission Impact Assessment for Cyber Warfare
• Uncertainty modeling: the Computational Economists' View on Cyberwarfare.

Cyberwarfare has become an important concern for governmental agencies as well businesses of various types. This timely volume, with contributions from some of the internationally recognized, leaders in the field, gives readers a glimpse of the new and emerging ways that Computational Intelligence and Machine Learning methods can be applied to address problems related to cyberwarfare. The book includes a number of chapters that can be conceptually divided into three topics: chapters describing different data analysis methodologies with their applications to cyberwarfare, chapters presenting a number of intrusion detection approaches, and chapters dedicated to analysis of possible cyber attacks and their impact. The book provides the readers with a variety of methods and techniques, based on computational intelligence, which can be applied to the broad domain of cyberwarfare.

• Improvement Model of Business Continuity Management on E-Learning / Gang Chen
• Using Computer Simulation to Improve Teaching and Learning Effectiveness on DC/DC Converter / LiPing Fan
• The Improved Diagonal-Matrix-Weight IMM Algorithm Based on LDU Factorization / Liang Gao, Jianping Xing, Fan Shi
• Performance Analysis of VCN and IVCN Localization Algorithms in WSN / Yang Liu, Jianping Xing, Hua Wu, Xiaoming Wu
• Impact Analysis of Environment Factors on Iterative Calculation of Secondary Grid Division (ICSGD) Localization Scheme for Wireless Sensor Networks / Yuxin Ren, Jianping Xing, Yang Liu, Can Sun
• Research on Data-Driven in Business Workflow System Design / ChuanSheng Zhou
• Research on XML Data-Driven Based Web Page Design / ChuanSheng Zhou
• Research and Application of Data-Driven in Web System Design / ChuanSheng Zhou
• Cogitation of Information Technology Construction in University Library / Ning Xu.

• Study on E-Commerce Application in Manufacturing Enterprises: A Case Study of Ruijie Network Co., Ltd. / Ling Tian, Xiaohong Wang, Liwei Li
• Discussion on Occupation Moral Education in Higher Vocational Colleges / Jushun Li
• Dynamic Analysis and System Design Based on Diluted Asymmetric Discrete-Time Hopfield Network / Li Tu, Chi Zhang
• Evaluation Method on the Scientific Research of Local University / Xiaomin Huang
• A Random Endogenous Economic Growth Model and Equilibrium Analysis / Zhongcheng Zhang
• Try to Talk about the Background of the Information Network Moral Construction / LiYan Yang
• Construction of Library Information Resources Depending on Internet Environment / LiYan Yang
• A General Fuzzified CMAC and Its Function Approximation / Zhipeng Shen, Chen Guo
• Discussion on Young Teachers' Teaching Method / Peng Qin, Yinghua Wang, Zhihai Qin
• Reform of Basic Computer Teaching in Universities and Its Measures under New Circumstances / Jianguo Yu, Shuanghong Liu.

• Exploration of the Building Model about Theme_Based Learning and Shared Community Based Python Add Django / Xiang Li, Mei Zhu, Xuan Zhan
• Principles and Methods in Teaching English with Multimedia / YiFei Zhu
• Research on the Difference of Attitudinal Changes between Ambivalent Consumers / Minxue Huang, Xiaoliang Feng, Chao Wang
• Study on Chinese Error Checking / Chongwen Wang, Bo Yuan
• Double-Sources Dijkstra Algorithm within Typical Urban Road Networks / Shiming Wang, Jianping Xing, Yong Wu
• Development and Practice of Knowledge Service Platform Based on DSpace / Lu Han, Yi Ding
• Application of Hopfield Network in Grayscale Image Recognition / Li Tu, Zhiping Liao, Chi Zhang
• Design of Mobile Learning-Recommendation Services Supporting Small-Screen Display Interfaces / Hong-Ren Chen
• The Effect of Perceived Organizational Support on Employee Work Engagement: A Case Study Analysis / TingJun Kou
• Web-Based Data Mining in Computer Culture Basic Course in College Using an Improved GA / Jianxin Bi.

• Teaching Mode Innovation and Practice of "Foundation of Computer Programming" / Yaqing Shi, Meijuan Wang, Cheng Hu
• MBR Phrase Scoring and Pruning for SMT / Nan Duan
• Application of Computer Aided Instruction (CAI) in English Teaching for Non-English-Major Postgraduates Based on Constructivism / Xueyao Chen
• Discussion on Digital Storytelling in Comprehensive Practice Courses Application / JiangYan Zheng, Hao Zhang, Liang Hong
• GCM Data Analysis Using Dimensionality Reduction / Zuoling Li, Guirong Weng
• Application of the BP Neural Network PID Algorithm in Heat Transfer Station Control / Yang Yu, Dongmei Yin
• The Influence of Human Resource Management Information System on Human Resource Management / Yan-Hong Yu
• The Empirical Study of Investment Opportunities in the Value-Relevance of Accounting Earnings / Yuanyang Fu, He Guo, Danping Wang
• On Software Professional Studio Construction in Higher Vocational Schools and Its Meaning / ShuYan Yu.

• The Research on Teaching Ideas of "Data Structure and Algorithm" in Non-computer Major / Zhao Wang
• Application-Oriented Intercultural Engineering Education in Automotive Engineering and Service / Yanfen Mao, Ming Chen, Hans Wiedmann
• Research of Private Courier Enterprise Based on E-Commerce / Bo Zhang
• The Considering about Chinese Enterprises to Establish E-Training Platform / Jianlin Qiu
• Empirical Study on Undergraduate Employability and Training Mechanism in Chinese Independent Institutes / Yong Chen
• Research and Practice on the Open Teaching for the Authentication of AutoCAD Senior Application Engineers / Qibing Wang
• The Application Research on Determination Method of Poor University Students Based on the Interval Density Cluster / Huang Zhenzhen
• Course Design and Practices of Teaching Undergraduate's Database System / Hongji Wang, Qingqin Yan
• The Impact of Use Context on Mobile Payment Acceptance: An Empirical Study in China / Luzhuang Wang, Yongzheng Yi.

• An Empirical Research on Correlation of Communication Companies' Compensation Gap with Enterprise's Performance / Luzhuang Wang, Hui Yu
• On the Principles of College Chinese Textbooks Compilation / Zhonghua Li
• The Secondary Hash MM Algorithm Based on Probability Factor / Yan Niu, Lele Huan
• An Optimal Model of Selecting Players for Mathematics Competition / Ying Wei
• Gestalt Psychology Theory Based Design of User's Experience on Online Trade Websites / Peng Bian, Nairen Zhang
• Exploration and Practice on Construction of Curriculum Group of Numerical Analysis / Datian Niu, Xuegang Yuan, Jia Jiao, Wei Zhao
• College Chinese and Humanities Education / Zhonghua Li
• Distributed Detecting and Real-Time Advertising System of Equipment Failure Based on MANET / Xia Qin, Tian Yi
• Strengthen Study Motivations of College Students via Learning Environment Improvement / Xin Zhang, Libo Huang.

• The Mediating Role of Entrepreneurial Efficiency on the Relationship between Human Resource Management and Corporate Entrepreneurship / ChunLi Liu, HongLi Wang
• M-Learning Activity Design for College English Courses / Lin Hu, Honghua Xu, Shuying Zhuang
• A Novel Approach to Online Image Education Resource Retrieval by Semantic Network and Interactive Filtering / Xianlin Peng, Ling Gao, Yuwen Ning
• A Study on the Establishment of Interpretation Teaching Model Based on Multi-media Network / JinBao Cai, Jing Xiao, Ying Lin
• Inter-domain Communication Mechanism Design and Implementation for High Performance / Tan Jie, Huang Wei
• The Study on College Library Electronic Resources Integration Based on Internet / Min Ren
• A Multimedia Based Natural Forest Park for Ecological Education / Zhen Liu, YanJie Chai
• Web-Based Interactive Animation for Children's Safety Education: From 2D to 3D / Zhen Liu, YanJie Chai
• The Evaluation on Ideological and Political Teachers' Competency / Yongfang Liu.

• The Application of Set Pair Analysis in College Ideological and Political Education / Yongfang Liu, Zhikai Yun
• The Improvement of Enterprise-Oriented Scale-Free Network Model / Yongxian Chen, Dichong Wu, Canlin Mo
• Research on Model of Ontology-Based Semantic Information Retrieval / Yu Cheng, Ying Xiong
• Research of ICE-Based Unified Identity Authentication Model / Yue Zhang, BingBing Xia
• Research of Key Technologies in Development in Thesis Management System / Huadong Wang
• The Localization Algorithm for Wireless Sensor Networks Based on Distance Clustering / Xiaohui Chen, Jinpeng Chen, Bangjun Lei
• Research and Analysis of the Social Access to College Sports Venues in Wuhan / Han Jiang
• Prediction of Zenith Tropospheric Delay Based on BP Neural Network / Yong Wang, Lihui Zhang, Jing Yang
• Advance in Scence Change-Based H.264 Rate Control Methods / Feifei Lu, Xiao Chen
• Optimizing the Network Topology in Gnutella P2P Networks / Jin Bo.

CSE2011 is an integrated conference concentration its focus on computer science and education. In the proceeding, you can learn much more knowledge about computer science and education of researchers from all around the world. The main role of the proceeding is to be used as an exchange pillar for researchers who are working in the mentioned fields. In order to meet the high quality of Springer, AISC series, the organization committee has made their efforts to do the following things. Firstly, poor quality paper has been refused after reviewing course by anonymous referee experts. Secondly, periodically review meetings have been held around the reviewers about five times for exchanging reviewing suggestions. Finally, the conference organizers had several preliminary sessions before the conference. Through efforts of different people and departments, the conference will be successful and fruitful.

• The Process of Industrial Bioethanol Production Explained by Self-Organised Maps / Miguel A. Sanz-Bobi, Pablo Ruiz and Julio Montes
• Towards a Further Understanding of the Robotic Darwinian PSO / Micael S. Couceiro, Fernando M.L. Martins, Filipe Clemente, Rui P. Rocha and Nuno M.F. Ferreira
• A Comparison Study Between Two Hyperspectral Clustering Methods: KFCM and PSO-FCM / Amin Alizadeh Naeini, Saeid Niazmardi, Shahin Rahmatollahi Namin, Farhad Samadzadegan and Saeid Homayouni
• Comparison of Classification Methods for Golf Putting Performance Analysis / J. Miguel A. Luz, Micael S. Couceiro, David Portugal, Rui P. Rocha and Hélder Araújo, et al.
• Switched Unfalsified Multicontroller Nonparametric Model Based Design / Fernando Coito, Luís Brito Palma and Fernando Costa
• Evolving Fuzzy Uncalibrated Visual Servoing for Mobile Robots / P.J.S. Gonçalves, P.J.F. Lopes, P.M.B. Torres and J.M.R. Sequeira.

• Evaluating the Potential of Particle Swarm Optimization for Hyperspectral Image Clustering in Minimum Noise Fraction Feature Space / Shahin Rahmatollahi Namin, Amin Alizadeh Naeini and Farhad Samadzadegan
• On a Ball's Trajectory Model for Putting's Evaluation / Gonçalo Dias, Rui Mendes, Micael S. Couceiro, Carlos M. Figueiredo and J. Miguel A. Luz
• Efficient Discriminative Models for Proteomics with Simple and Optimized Features / Lionel Morgado, Carlos Pereira, Paula Veríssimo and António Dourado
• Meta-heuristics Self-Parameterization in a Multi-agent Scheduling System Using Case-Based Reasoning / Ivo Pereira, Ana Madureira and Paulo de Moura Oliveira
• Haptic-Based Robot Teleoperation: Interacting with Real Environments / Pedro Neto, Nélio Mourato and J. Norberto Pires
• Multi-agent Predictive Control with Application in Intelligent Infrastructures / J.M. Igreja, S.J. Costa, J.M. Lemos and F.M. Cadete
• Single-Objective Spreading Algorithm / E.J. Solteiro Pires, Luís Mendes, António M. Lopes, P.B. de Moura Oliveira and J.A. Tenreiro Machado.

• Fault Tolerant Control Based on Adaptive LQG and Fuzzy Controllers / Carla Viveiros, Luis Brito Palma and José Manuel Igreja
• P2P Web Service Based System for Supporting Decision-Making in Cellular Manufacturing Scheduling / Maria Leonilde R. Varela, Rui Barbosa and Susana Costa
• Web-Based Decision Support System for Orders Planning / António Arrais-Castro, Maria Leonilde R. Varela and Sílvio Carmo-Silva
• Product Documentation Management Through REST-Based Web Service / Filipe Rocha, Maria Leonilde R. Varela and Sílvio Carmo-Silva
• Fuzzy Web Platform for Electrical Energy Losses Management / Gaspar Gonçalves Vieira, Maria Leonilde R. Varela and Rita A. Ribeiro
• Web System for Supporting Project Management / Cátia Filipa Veiga Alves, André Filipe Nogueira da Silva and Maria Leonilde R. Varela
• Generation Capacity Expansion Planning in Restructured Electricity Markets Using Genetic Algorithms / Adelino J.C. Pereira and João Tomé Saraiva.

• Decision Making in Maintainability of High Risk Industrial Equipment / José Sobral and Luis Ferreira
• The Classification Platform Applied to Mammographic Images / P.J.S. Gonçalves
• On an Optimization Model for Approximate Nonnegative Matrix Factorization / Ana Maria de Almeida
• Random Walks in Electric Networks / D.M.L.D. Rasteiro
• Business Intelligence Tools / Jorge Bernardino and Marco Tereso
• Food Service Management Web Platform Based on XML Specification and Web Services / Pedro Sabioni, Vinícius Carneiro and Maria Leonilde R. Varela
• Differential Evolution on the Minimization of Thermal Residual Stresses in Functionally Graded Structures / T.A.N. Silva and M.A.R. Loja
• Magnetic Wheeled Climbing Robot: Design and Implementation / M.F. Silva, R.S. Barbosa and A.L.C. Oliveira
• Development of an AGV Controlled by Fuzzy Logic / Ramiro S. Barbosa, Manuel F. Silva and Dário J. Osório
• Affect Recognition / Raquel Faria and Ana Almeida
• Web 2.0: Tagging Usefulness / Joaquim Filipe P. Santos and Ana Almeida.

• Multidimensional Scaling Analysis of Electricity Market Prices / Filipe Azevedo and J. Tenreiro Machado
• PCMAT Metadata Authoring Tool / Paulo Couto, Constantino Martins, Luiz Faria, Marta Fernandes and Eurico Carrapatoso
• Collaborative Broker for Distributed Energy Resources / João Carlos Ferreira, Alberto Rodrigues da Silva, Vítor Monteiro and João L. Afonso
• A Multidimensional Scaling Classification of Robotic Sensors / Miguel F.M. Lima and J.A. Tenreiro Machado
• Rough Set Theory: Data Mining Technique Applied to the Electrical Power System / C.I. Faustino Agreira, C.M. Machado Ferreira and F.P. Maciel Barbosa
• Tuning a Fractional Order Controller from a Heat Diffusion System Using a PSO Algorithm / Isabel S. Jesus and Ramiro S. Barbosa
• A Tool for Biomedical -- Documents Classification Using Support Vector Machines / João Oliveira, Daniel Correia, Carlos Pereira, Paula Veríssimo and António Dourado.

• Conflicts Management in Retail Systems with Self-Regulation / Bruno Magalhães and Ana Madureira
• Adaptive e-Learning Systems Foundational Issues of the ADAPT Project / Eduardo Pratas and Viriato M. Marques
• Recognizing Music Styles -- An Approach Based on the Zipf-Mandelbrot Law / Viriato M. Marques and Cecília Reis
• A Platform for Peptidase Detection Based on Text Mining Techniques and Support Vector Machines / Daniel Correia, Carlos Pereira, Paula Veríssimo and António Dourado
• Optimal Configuration of Uniplanar-Unilateral External Fixators in Tibia Fractures / Luis Roseiro and Augusta Neto
• Identification of the Forces in the Suspension System of a Race Car Using Artificial Neural Networks / Luis Roseiro, Carlos Alcobia, Pedro Ferreira, Abderrahmane Baïri and Najib Laraqi, et al.
• Combinational Logic Circuits Design Tool for a Learning Management System / Cecília Reis and Viriato M. Marques
• Labeling Methods for the General Case of the Multi-objective Shortest Path Problem -- A Computational Study / J.M. Paixão and J.L. Santos.

This book provides a general overview and original analysis of new developments and applications in several areas of Computational Intelligence and Information Systems. Computational Intelligence has become an important tool for engineers to develop and analyze novel techniques to solve problems in basic sciences such as physics, chemistry, biology, engineering, environment and social sciences. The material contained in this book addresses the foundations and applications of Artificial Intelligence and Decision Support Systems, Complex and Biological Inspired Systems, Simulation and Evolution of Real and Artificial Life Forms, Intelligent Models and Control Systems, Knowledge and Learning Technologies, Web Semantics and Ontologies, Intelligent Tutoring Systems, Intelligent Power Systems, Self-Organized and Distributed Systems, Intelligent Manufacturing Systems and Affective Computing. The contributions have all been written by international experts, who provide current views on the topics discussed and present recent, original insights from their own experience in these fields.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Relational knowledge
• 3. From data to hypotheses
• 4. Clustering
• 5. Information gain
• 6. Knowledge and relations
• 7. Rough set theory
• 8. Inductive logic learning
• 9. Ensemble learning
• 10. The logic of knowledge
• 11. Indexes and bibliography
• Bibliography
• Index.
• (source: Nielsen Book Data)

What is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual properties of different approaches.
(source: Nielsen Book Data)

• pt. 1. A world of annotations
• pt. 2. Leaving a mark ...
• pt. 3. Peeking at the future.

Did you ever read something on a book, felt the need to comment, took up a pencil and scribbled something on the books' text'? If you did, you just annotated a book. But that process has now become something fundamental and revolutionary in these days of computing. Annotation is all about adding further information to text, pictures, movies and even to physical objects. In practice, anything which can be identified either virtually or physically can be annotated. In this book, we will delve into what makes annotations, and analyse their significance for the future evolutions of the web. We will explain why it was thought to be unreasonable to annotate documents manually and how Web 2.0 is making us rethink our beliefs. We will have a look at tools which make use of Artificial Intelligence techniques to support people in the annotation task. Behind these tools, there exists an important property of the web known as redundancy; we will explain what it is and show how it can be exploited. Finally we will gaze into the crystal ball and see what we might expect to see in the future. Until people understand what the web is all about and its grounding in annotation, people cannot start appreciating it. And until they do so, they cannot start creating the web of the future.

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
(source: Nielsen Book Data)

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
(source: Nielsen Book Data)