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• Introduction
• Part I: Neural Networks
• Introduction
• Threshold Logic Units
• General Neural Networks
• Multi-Layer Perceptrons
• Radial Basis Function Networks
• Self-Organizing Maps
• Hopfield Networks
• Recurrent Networks
• Mathematical Remarks for Neural Networks
• Part II: Evolutionary Algorithms
• Introduction to Evolutionary Algorithms
• Elements of Evolutionary Algorithms
• Fundamental Evolutionary Algorithms
• Computational Swarm Intelligence
• Part III: Fuzzy Systems
• Fuzzy Sets and Fuzzy Logic
• The Extension Principle
• Fuzzy Relations
• Similarity Relations
• Fuzzy Control
• Fuzzy Data Analysis
• Part IV: Bayes and Markov Networks
• Introduction to Bayes Networks
• Elements of Probability and Graph Theory
• Decompositions
• Evidence Propagation
• Learning Graphical Models
• Belief Revision
• Decision Graphs.

Computational intelligence comprises concepts, paradigms, algorithms, and implementations of systems that are intended to exhibit intelligent behavior in complex environments. It relies heavily on (at least) nature-inspired methods, which have the advantage that they tolerate incomplete, imprecise and uncertain knowledge and thus also facilitate finding solutions that are approximative, manageable and robust at the same time. Fully updated, this new edition of the authoritative textbook provides a clear and logical introduction to Computational Intelligence, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. Rather than aim for completeness, the goal is to give a methodical introduction, supporting fundamental concepts and their implementations with explanation of the theoretical background of proposed problem solutions. Topics and features: Offers new material on deep learning, scalarization, large-scale optimization algorithms, and collective decision-making algorithms Contains numerous classroom-tested examples and definitions Discusses in detail the classical areas of artificial neural networks, fuzzy systems, evolutionary algorithms, and Bayes and Markov networks Reviews the latest developments, including such topics as ant colony optimization and probabilistic graphical models Provides supplementary material, including module descriptions, lecture slides, exercises with solutions, and software tools This seminal textbook is primarily meant as a companion book for lectures on the covered topics in the area of computational intelligence. However, it is also eminently suitable as a guidebook for self-study by students and practitioners from industry and commerce. Dr. Rudolf Kruse is the former leader of the Computational Intelligence Research Group and now Emeritus Professor of the Department of Computer Science at the University of Magdeburg, Germany. Dr. Sanaz Mostaghim is a full Professor of Computer Science and Dr. Christian Braune is a Senior Lecturer at the same institution. Dr. Christian Borgelt is a Professor of Data Science at the Paris Lodron University of Salzburg, Austria. Dr. Matthias Steinbrecher is a Development Architect at SAP SE, Potsdam, Germany.

• Part I. Innovative Trends in Computational Intelligence.-
• Chapter 1. Data Mining in Healthcare and Prediction Model Using Data-Mining Technique on Covid-19.-
• Chapter 2. Computational Intelligence in Intelligent Transportation Systems: An Overview.-
• Chapter 3. Insights to Computational Intelligence Techniques for Computer Vision.-
• Chapter 4. A two-stage multi-feature selection method to predict healthcare data using neural network.-
• Chapter 5. Computational Approaches for Detection and Classification of Crop Diseases.-
• Chapter 6. Three-Layer Multimodal Biometric Fusion Using SIFT and SURF Descriptors for Improved Accuracy of Authentication of Human Identity.-
• Chapter 7. Applying Computation Intelligence for Improved Computer Vision Capabilities.-
• Chapter 8. ECG Feature Extraction.-
• Chapter 9. Computational Intelligence in Web Min.-
• Chapter 10. A Review on Cognitive Computational Neuroscience: Overview, Models and Applications.-
• Chapter 11. Artificial Neural Network: Models, Applications and Challenges.-
• Chapter 12. Proportional and Multi-Stimulations Haptic Device for Active Upper Limbs Prosthetics Control.
• (source: Nielsen Book Data)

This book addresses the key problems that computational intelligence aims to solve, including (i) the involved computational process might be too complex for mathematical reasoning; (ii) it might contain some uncertainties during the process, or (iii) by nature, the computational process is a randomly determined one (heuristic). The contributors make use of methods that are close to the human's way of reasoning, that is, available information might be inexact or incomplete, yet it would be able to produce controlled actions in an adaptive way. Approaches presented in the book include swarm intelligence, artificial immune systems, image processing, data mining, natural language processing, text mining, and other solutions involving artificial intelligence methodologies. .
(source: Nielsen Book Data)

• Single Identity Clustering-based Data Anonymization in Healthcare.- Optimization Model for Production Planning: Case of an Indian Steel Company.- Vision-based User-Friendly and Contactless Security for Home Appliance via Hand Gestures.- Sentiment Analysis of Healthcare Big Data: A Fundamental Study.- Sleep Apnea Detection using Contact-Based and Non-Contact Based Using Deep Learning Methods.- Drift Compensation of a Low-cost pH Sensor by Artificial Neural Network.- Sentiment Analysis at Online Social Network for Cyber Malicious Post Reviews Using Machine Learning Techniques.- Analysis of various mobility models and their impact on QoS in MANET.- Analysis of Classifier Algorithms to detect Anti Money Laundering.- Design and Development of an ICT Intervention for Early Childhood Development in Minority Ethnic Communities in Bangladesh.
• (source: Nielsen Book Data)

This book covers all core technologies like neural networks, fuzzy systems, and evolutionary computation and their applications in the systems. Computationally intelligent system is a new concept for advanced information processing. The objective of this system is to realize a new approach for analyzing and creating flexible information processing of sensing, learning, recognizing, and action taking. Computational intelligent is a part of artificial intelligence (AI) which includes the study of versatile components to empower or encourage savvy practices in intricate and evolving situations. The computationally intelligent system highly relies on numerical information supplied by manufacturers unlike AI. .
(source: Nielsen Book Data)

• Foreword.- Preface. - Control engineering from classical to intelligent control theory - An overview (Blondin, Saez, Pardalos).- Main metaheuristics used for the optimization of the control of the complex systems (Borne, Gharbi).- Optimal controller parameter tuning from multi/many objective optimization algorithms (Altinoz).- Fuzzy and neuro-fuzzy control for smart structures (Tairidis, Stavroulakis).- Computational intelligence in the desalination industry (Cabrera, Carta).- Control of complex biological systems utilizing the neural network predictor (Bamgbose, Li, Qian).- A real-time big-data control-theoretical framework for cyber-physical-human systems (Gusrialdi, Xi, Qu, Simaan).- Distributed optimization based control on the example of microgrids (Braun, Sauerteig, Worthmann).- Coherency estimation in power systems: A Koopman operator approach (Chamorro, Ordonez, Peng, Gonzalez-Longatt, Sood).- Appliance identification through non-intrusive load monitoring in residences (Gogos, Georgiou).- Management suggestions for process control of semiconductor manufacturing: An operations research and data science perspective (Khakifirooz, Fathi, Chien, Pardalos).- Feedback control algorithms for the dissipation of traffic waves with autonomous vehicles (Monache, Liard, Rat, Stern, Bhadani, Seibold, Sprinkle, Work, Piccoli).- Disturbance rejection run-to-run controller for semiconductor manufacturing (Khakifirooz, Fathi, Pardalos).- Energy management improvement based on fleet learning for hybrid electric buses (Lopez-Ibarra, Herrera, Milo, Gaztanaga, Camblong). .
• (source: Nielsen Book Data)

This volume presents some recent and principal developments related to computational intelligence and optimization methods in control. Theoretical aspects and practical applications of control engineering are covered by 14 self-contained contributions. Additional gems include the discussion of future directions and research perspectives designed to add to the reader's understanding of both the challenges faced in control engineering and the insights into the developing of new techniques. With the knowledge obtained, readers are encouraged to determine the appropriate control method for specific applications.
(source: Nielsen Book Data)

• Introduction
• Part I: Neural Networks
• Introduction
• Threshold Logic Units
• General Neural Networks
• Multi-Layer Perceptrons
• Radial Basis Function Networks
• Self-Organizing Maps
• Hopfield Networks
• Recurrent Networks
• Mathematical Remarks for Neural Networks
• Part II: Evolutionary Algorithms
• Introduction to Evolutionary Algorithms
• Elements of Evolutionary Algorithms
• Fundamental Evolutionary Algorithms
• Computational Swarm Intelligence
• Part III: Fuzzy Systems
• Fuzzy Sets and Fuzzy Logic
• The Extension Principle
• Fuzzy Relations
• Similarity Relations
• Fuzzy Control
• Fuzzy Data Analysis
• Part IV: Bayes and Markov Networks
• Introduction to Bayes Networks
• Elements of Probability and Graph Theory
• Decompositions
• Evidence Propagation
• Learning Graphical Models
• Belief Revision
• Decision Graphs.

This authoritative textbook provides a clear and logical introduction to the field, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. This enhanced second edition to the definitive textbook on Computational Intelligence has been fully revised and expanded with new content on swarm intelligence, deep learning, fuzzy data analysis, and discrete decision graphs. Topics and features: Provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools Contains numerous classroom-tested examples and definitions throughout the text Presents useful insights into all that is necessary for the successful application of computational intelligence methods Explains the theoretical background underpinning proposed solutions to common problems Discusses in great detail the classical areas of artificial neural networks, fuzzy systems and evolutionary algorithms Reviews the latest developments in the field, covering such topics as ant colony optimization and probabilistic graphical models This accessible text is an essential reference for students of artificial intelligence and intelligent systems, and a valuable resource for all researchers and practitioners seeking a self-study primer on computational intelligence. Rudolf Kruse and Sanaz Mostaghim are professors at the Department of Computer Science of the Otto von Guericke University of Magdeburg, Germany. Christian Borgelt is a principal researcher, and Christian Braune is a research assistant at the same institution. Matthias Steinbrecher is with SAP SE, Potsdam, Germany.

• Robustness of Legislative Procedures of the Italian Parliament.- Approval Voting as a Method of Prediction in Political Votings. Case of Polish elections.- The Complexity of Voter Control and Shift Bribery under Parliament Choosing Rules.- National Interests in the European Parliament: Roll Call Vote Analysis.- Voting and Communication when Hiring by Committee.- Power Measures and Public Goods.- Holdout Threats During Wage Bargaining.- Index of implicit power as a measure of reciprocal ownership.- Manipulability Of Voting Procedures: Strategic Voting And Strategic Nomination.- Reflections on the Signifcance of Misrepresenting Preferences.- Fibonacci representations of homogeneous weighted majority games.- Towards a fairness-oriented approach to consensus reaching support under fuzzy preferences and a fuzzy majority via linguistic summaries?.- What Is It That Drives Dynamics: We Don't Believe in Ghosts, Do We?.
• (source: Nielsen Book Data)

These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the semantic Web, social networks, and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies, such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This twenty-third issue contains 14 carefully selected and revised contributions.
(source: Nielsen Book Data)

• Dynamic Topologies for Particle Swarms
• Evaluative Study of PSO/Snake Hybrid Algorithm and Gradient Path Labeling for Calculating Solar Differential Rotation
• The Uncertainty Quandary: A Study in the Context of the Evolutionary Optimization in Games and other Uncertain Environments
• Hybrid Single Node Genetic Programming for Symbolic Regression
• L2 Designer: A Tool for Genetic L-system Programming in Context of Generative Art
• Manifold Learning Approach toward Constructing State Representation for Robot Motion Generation
• The Existence of Two Variant Processes in Human Declarative Memory: Evidence Using Machine Learning Classification Techniques in Retrieval Tasks
• Divide and Conquer Ensemble Method for Time Series Forecasting
• Application areas of Ephemeral Computing: A survey.

These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the semantic Web, social networks, and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies, such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This twenty-forth issue contains 9 carefully selected and revised contributions.p>.

• Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives
• Web Services, Policies, and Context: Concepts and Solutions
• Data Mining with Privacy Preserving in Industrial Systems
• Kernels for Text Analysis
• Discovering Time-Constrained Patterns from Long Sequences
• Gauging Image and Video Quality in Industrial Applications
• Model Construction for Knowledge-Intensive Engineering Tasks
• Artificial Intelligence Applied to the Modeling and Implementation of a Virtual Medical Office
• DICOM-Based Multidisciplinary Platform for Clinical Decision Support: Needs and Direction
• Improving Neural Network Promoter Prediction by Exploiting the Lengths of Coding and Non-Coding Sequences
• Artificial Immune Systems for Self-Nonself Discrimination: Application to Anomaly Detection
• Computational Intelligence Applied to the Automatic Monitoring of Dressing Operations in an Industrial CNC Machine
• Automated Novelty Detection in Industrial Systems
• Multiway Principal Component Analysis (MPCA) for Upstream/Downstream Classification of Voltage Sags Gathered in Distribution Substations
• Applications of Neural Networks to Dynamical System Identification and Adaptive Control
• A Multi-Objective Multi-Colony Ant Algorithm for Solving the Berth Allocation Problem
• Query Rewriting for Semantic Multimedia Data Retrieval.

Computational Intelligence (CI) has emerged as a rapid growing field over the past decade. Its various techniques have been recognized as powerful tools for intelligent information processing, decision making and knowledge management. ""Advances of Computational Intelligence in Industrial Systems"" reports the exploration of CI frontiers with an emphasis on a broad spectrum of real-world applications. Section I a"" Theory and Foundation presents some of the latest developments in CI, e.g. particle swarm optimization, Web services, data mining with privacy protection, kernel methods for text a

• Part I Overview, Background.- Part II Preprocessing
• Visualization
• Systems Integration.- Part III Artificial Intelligence.- Part IV Logic and Reasoning.- Part V Ontology.- Part VI Intelligent Agents.- Part VII Fuzzy Systems.- Part VIII Artificial Neural Networks.- Part IX Evolutionary Approaches.- Part X DNA and Immunity-based Computing.
• (source: Nielsen Book Data)

Computational Intelligence paradigms have proven effective on real-world problems in virtually all disciplines including Science, Engineering, Business, healthcare, Management and Avionics. This handbook includes contributions from the leading international experts in their respective fields. There are twenty five chapters grouped in the following ten parts covering theoretical aspects and practical applications: * Overview * Data pre-processing, system integration and visualization * Artificial Intelligence * Logic and reasoning * Ontology * Intelligent Agents * Fuzzy Systems * Artificial neural networks * Evolutionary paradigms and systems * DNA and immunity-based computing The handbook will be of interest to the students, researchers, professors and practitioner in the area of computational intelligence paradigms and their applications.

• An Introduction to Computational Intelligence Paradigms and their Applications.- A Quest for Adaptable and Interpretable Architectures of Computational Intelligence.- Membership Map.- Advanced Dev of Fuzzy ARTMAP --Pattern Classification Boaz.- Large Margin Methods for Structured Output Prediction.- nsemble Classifier Design.- Functional Principal Points and Functional Cluster Analysis.- Clustering with Size Constraints.- luster Validating Techniques.- Fuzzy Blocking Regression Models.- Support Vector Machines.- Linkage Analysis in Genetic Algorithms.
• (source: Nielsen Book Data)

System designers are faced with a large set of data which has to be analysed and processed efficiently. Advanced computational intelligence paradigms present tremendous advantages by offering capabilities such as learning, generalisation and robustness. These capabilities help in designing complex systems which are intelligent and robust. The book includes a sample of research on the innovative applications of advanced computational intelligence paradigms. The characteristics of computational intelligence paradigms such as learning, generalization based on learned knowledge, knowledge extraction from imprecise and incomplete data are the extremely important for the implementation of intelligent machines. The chapters include architectures of computational intelligence paradigms, knowledge discovery, pattern classification, clusters, support vector machines and gene linkage analysis. We believe that the research on computational intelligence will simulate great interest among designers and researchers of complex systems. It is important to use the fusion of various constituents of computational intelligence to offset the demerits of one paradigm by the merits of another.
(source: Nielsen Book Data)

• Part I Motivations and Theory.- Part II Search and Reasoning.- Part III Optimization.- Part IV Learning.- Part V RealWorld Applications.
• (source: Nielsen Book Data)

This volume is motivated in part by the observation that opposites permeate everything around us, in some form or another. It's study has attracted the attention of countless minds for at least 2500 years. However, due to the lack of an accepted mathematical formalism for opposition it has not been explicitly studied to any great length in fields outside of philosophy and logic. Despite the fact that we observe opposition everywhere in nature, our minds seem to divide the world into entities and opposite entities; indeed we use opposition everyday. We have become so accustomed to opposition that its existence is accepted, not usually questioned and its importance is constantly overlooked.On the one hand, this volume is a fist attempt to bring together researchers who are inquiring into the complementary nature of systems and processes and, on the other hand, it provides some elementary components for a framework to establish a formalism for opposition-based computing. From a computational intelligence perspective, many successful opposition-based concepts have been in existence for a long time. It is not the authors intention to recast these existing methods, rather to elucidate that, while diverse, they all share the commonality of opposition - in one form or another, either implicitly or explicitly. Therefore they have attempted to provide rough guidelines to understand what makes concepts 'oppositional'.
(source: Nielsen Book Data)

• PREFACE ; ACKNOWLEDGEMENTS; CONFILICT OF INTEREST; Introduction ; 1.1. OVERVIEW; 1.2. GOAL; 1.3. OUTLINE;
• Chapter 1 (Introduction);
• Chapter 2 (Background);
• Chapter 3 (Evolutionary Algorithms);
• Chapter 4 (System Specification);
• Chapter 5 (Design and Implementation);
• Chapter 6 (Data Visualization);
• Chapter 7 (User Interface);
• Chapter 8 (Case Study);
• Chapter 9 (Discussion);
• Chapter 10 (Summary and Future); Background ; 2.1. CLUSTERING; 2.1.1. Introduction; 2.1.2. General Definition; 2.1.3. Object Similarity; Proximity Measure for Continuous Values; Proximity Measure for Discrete Values.

• Proximity Measure for Mixed Values2.1
• .4. Clustering Methods; Hierarchical Clustering; Partitional Clustering; Fuzzy Clustering; 2.1
• .5. Cluster Membership; 2.1
• .6. Cluster Validation; Evolutionary Algorithms ; 3
• .1. INTRODUCTION; 3.1
• .1. Data Representation Chromosome; 3.1
• .2. Initial Population; 3.1
• .3. Fitness Function; 3.1
• .4. Selection; 3.1
• .5. Reproduction; 3.1
• .6. Stopping conditions; 3
• .2. MATHEMATICAL OPTIMIZATION; 3.2
• .1. Maxima and Mimima; 3.2
• .2. Optimization Problems; 3
• .3. GENETIC ALGORITHMS; 3.3
• .1. Crossover; 3.3
• .2. Mutation; 3.3
• .3. Control Parameters; 3
• .4. GENETIC PROGRAMMING.

• 3.4
• .1. Tree Based Representation3.4
• .2. Fitness Function; 3.4
• .3. Crossover Operators; 3.4
• .4. Mutation Operators; 3
• .5. EVOLUTIONARY PROGRAMMING; 3.5
• .1. Representation; 3.5
• .2. Mutation Operators; 3.5
• .3. Selection Operators; 3
• .6. EVOLUTION STRATEGIES; 3.6
• .1. Generic Evolution Strategies Algorithm; 3.6
• .2. Strategy Parameter; 3.6
• .3. Selection Operator; 3.6
• .4. Crossover Operators; 3.6
• .5. Mutation Operator; 3
• .7. DIFFERENTIAL EVOLUTION; 3.7
• .1. Mutation Operator; 3.7
• .2. Crossover Operator; 3.7
• .3. Selection; 3.7
• .4. Control Parameters; 3
• .8. CULTURAL ALGORITHMS; 3.8
• .1. Belief Space.

• 3.8
• .2. Acceptance Function3.8
• .3. Influence Function; System Specification ; 4
• .1. INTRODUCTION; 4
• .2. SYSTEM OBJECTIVE; 4
• .3. FUNCTIONAL REQUIREMENTS; 4.3
• .1. System Input; 4.3
• .2. Cluster Analysis; 4.3
• .3. Visualization; 4
• .4. NON-FUNCTIONAL REQUIREMENTS; 4.4
• .1. Functional Correctness; 4.4
• .2. Extensibility; 4.4
• .3. Maintainability; 4.4
• .4. Portability; 4.4
• .1. Usability; Design and Implementation ; 5
• .1. INTRODUCTION; 5
• .2. SYSTEM ARCHITECTURE; 5.2
• .1. Dependency Injection; 5.2
• .2. Open-Closed Principle; 5
• .3. TOOLS AND TECHNOLOGIES; 5.3
• .1. Java; 5.3
• .2. JavaFX; 5.3
• .3. Netbeans; 5.3
• .4. Maven.

• 5.3
• .5. Git and GitHub5.3
• .6. JUnit; 5
• .4. DATA STRUCTURE AND CLUSTERING; 5.4
• .1. Import Data and Data Structure; 5.4
• .2. K-means Algorithm; Complexity of K-Means Operations; 5
• .5. EVOLUTIONARY ALGORITHMS; 5.5
• .1. Genetic Clustering Algorithm; Population Initialization; Fitness Evaluation; Evolve Population; Termination Criteria; Time-Complexity; 5.5
• .2. Differential Evolution Based Clustering Algorithm; Population Initialization; Mutation; Crossover; Termination Criteria; Time-complexity; 5.5
• .3. Selection Operators; Random Selection; Proportional Selection; 5.5
• .4. Mutation Operators.

• 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.
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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.
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• Introduction To Optimization.- Particle Swarm Optimisation.- Artificial Bee Colony Algorithm.- Ant Colony Algorithm.- Grey Wolf Optimizer.- Whale Optimization Algorithm.- Bat Algorithm.- Ant Lion Optimization Algorithm.- Grasshopper Optimisation Algorithm (Goa).- Moths-Flame Optimization Algorithm.- Genetic Algorithm.- Artificial Neural Network.- Future of Nature Inspired Algorithm, Swarm and Computational Intelligence.
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This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.
(source: Nielsen Book Data)

• Intro; Preface; Reviewers; GET Authors; Contents; A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption; 1 Introduction; 2 Computational Intelligent Algorithms; 2.1 Characteristics of Computational Intelligent Algorithms; 3 Big Data Analytics and Energy Consumption by Cluster Computing Systems; 3.1 Big Data Analytics Platforms; 3.2 Energy Consumption Over Big Data Platforms; 3.3 Metrics Used for Measuring Power in Big Data Platforms; 4 Computational Intelligent Algorithms and Big Data Analytics

• 5 Energy Consumption in the Application of Computational Intelligent Algorithms in Big Data Analytics6 A Proposed Framework for Big Data Analytics Using Computational Intelligent Algorithms; 7 Conclusions; References; Artificial Bee Colony for Minimizing the Energy Consumption in Mobile Ad Hoc Network; 1 Introduction; 2 Energy-Aware Routing Protocol; 3 Routing Protocols in MANET; 3.1 Destination-Sequenced Distance-Vector Routing; 3.2 Ad Hoc On-Demand Distance-Vector Routing Protocol; 4 Artificial Bee Colony for AODV and DSDV; 5 Experimental Results; 5.1 Simulation Settings

• 5.2 Performance Metrics5.3 Simulation Results and Performance Comparison; 6 Conclusion; References; A Novel Chicken Swarm Neural Network Model for Crude Oil Price Prediction; 1 Introduction; 2 Artificial Neural Network; 3 Chicken Swarm Optimization; 4 The Proposed Chicken S-NN Algorithm; 5 Results & Discussion; 5.1 Preliminaries; 5.2 Data; 5.3 Discussion; 6 Conclusion; References; Forecasting OPEC Electricity Generation Based on Elman Network Trained by Cuckoo Search Algorithm; 1 Introduction; 2 Elman Network; 3 Cuckoo Search; 4 The Proposed CS Elman Algorithm; 5 Results and Discussion

• 5.1 Discussion6 Conclusions; References; Variable Neighborhood Search-Based Symbiotic Organisms Search Algorithm for Energy-Efficient Scheduling of Virtual Machine in Cloud Data Center; 1 Introduction; 2 Related Works; 3 Energy-Efficient Virtual Machine Scheduling Optimization; 3.1 Problem Definition; 3.2 Basic Concepts of Symbiotic Organisms Search; 4 Performance Evaluation; 4.1 Experimental Setup; 4.2 Results and Discussion; 5 Conclusion and Future Work; References; Energy Savings in Heterogeneous Networks with Self-Organizing Backhauling; 1 Introduction

• 2 Base Station Types in HETNET and Power System Consideration2.1 Base Station Types in HetNet; 2.2 Power System Consideration of BS Sites; 3 Small Cells Deployment and Backhauling Options; 3.1 Wired Backhaul Options for Small Cells; 3.2 Wireless Backhaul Options; 4 System Concept; 5 Backhaul-Energy Model; 6 Results and Discussions; 6.1 Typical Power Consumption of Macro BS and Microwave Backhaul Hub Sites; 6.2 Power Consumption of HetNet and the Break-Even Load; 6.3 Impact of Macro Base Station Load on Power Consumption; 6.4 Energy Savings of Self-Backhauling; 7 Conclusions; References

Addressing the applications of computational intelligence algorithms in energy, this book presents a systematic procedure that illustrates the practical steps required for applying bio-inspired, meta-heuristic algorithms in energy, such as the prediction of oil consumption and other energy products. Contributions include research findings, projects, surveying work and industrial experiences that describe significant advances in the applications of computational intelligence algorithms in energy. For easy understanding, the text provides practical simulation results, convergence and learning curves as well as illustrations and tables. Providing a valuable resource for undergraduate and postgraduate students alike, it is also intended for researchers in the fields of computational intelligence and energy.

• Part I: State-of-the-art.- Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study.- Using Artificial Intelligence against the Phenomenon of Fake News: a Systematic Literature Review.- Fake news detection in internet using deep learning: A review.- Part II: Machine Learning Techniques and Fake News.- Early Detection of Fake News from Social Media Networks using Computational Intelligence Approaches.- Fandet Semantic Model: An OWL Ontology for Context-Based Fake News Detection on Social Media.- Fake News Detection using Machine Learning and Natural Language Processing.- Fake News Detection using Ensemble Learning and Machine Learning Algorithms.- Evaluation of Machine Learning Methods for Fake News Detection.- Credibility and Reliability News Evaluation Based on Artificial Intelligent Service with Feature Segmentation Searching and Dynamic Clustering.- Deep Learning with Self-Attention Mechanism for Fake News Detection.- Modeling and solving the fake news detection scheduling problem.- Part III: Case Studies and Frameworks.- The multiplier effect on the dissemination of false speeches on social networks: Experiment during the silly season in Spain.- Detecting News Influence in a Country: One Step Forward Towards Understanding Fake News.- Factors Affecting the Intention of Using Fintech Services in the Context of Combating of Fake News.- Crowd Sourcing and Blockchain-based Incentive Mechanism to Combat Fake News.- Framework for Fake News Classification using Vectorization and Machine Learning.- Fact Checking: An Automatic end to end Fact Checking System.- Part IV: Fake news and Covid-19 pandemic.- False Information in a Post Covid-19 World.- Applying Fuzzy Logic and Neural Network in Sentiment Analysis for fake news detection: Case of Covid-19.- Analyzing Deep Learning Optimizers for COVID-19 Fake News Detection.- Detecting Fake News On COVID-19 Vaccine from YouTube Videos Using Advanced Machine Learning Approaches.
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This book presents the latest cutting-edge research, theoretical methods, and novel applications in the field of computational intelligence techniques and methods for combating fake news. Fake news is everywhere. Despite the efforts of major social network players such as Facebook and Twitter to fight disinformation, miracle cures and conspiracy theories continue to rain down on the net. Artificial intelligence can be a bulwark against the diversity of fake news on the Internet and social networks. This book discusses new models, practical solutions, and technological advances related to detecting and analyzing fake news based on computational intelligence models and techniques, to help decision-makers, managers, professionals, and researchers design new paradigms considering the unique opportunities associated with computational intelligence techniques. Further, the book helps readers understand computational intelligence techniques combating fake news in a systematic and straightforward way.
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• An Evolving Feature Weighting Framework for Granular Fuzzy Logic Models.- Fuzzy Multi-Criteria Decision-Making: Example of an explainable classification framework.- Rough-Fuzzy Rule Interpolation for Data-Driven Decision Making.- Fuzzy Intelligent System for Improving High Availability of a Docker-Based Hybrid Cloud Infrastructure.- A Study of Neuro-weighted Nearest-neighbour Classification.- Q-Routing with Multiple Soft Requirements.- Avoiding Excess Computation in Asynchronous Evolutionary Algorithms.- Stochasticity improves evolvability in articial gene regulatory networks.- ACO Inspired GA Mutation Applied to the TSP.- Centralised and Decentralised Control of Video Game Agents.
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This book contains the papers presented at the 20th UK Workshop on Computational Intelligence (UKCI 2021), held virtually by Aberystwyth University, 8-10th September 2021. This marks the 20th anniversary of UKCI; a testament to the increasing role and importance of Computational Intelligence (CI) and the continuing interest in its development. UKCI provides a forum for the academic community and industry to share ideas and experience in this field. EDMA 2021, the 4th International Engineering Data- and Model-Driven Applications workshop, is also incorporated and held in conjunction with UKCI 2021. Paper submissions were invited in the areas of fuzzy systems, neural networks, evolutionary computation, machine learning, data mining, cognitive computing, intelligent robotics, hybrid methods, deep learning and applications of CI.
(source: Nielsen Book Data)

• Biomedical Applications
• Anemia Detection using a Full Embedded Mobile Application with YOLO Algorithm
• On the Use of Convolutional Neural Network Architectures for Facial Emotion Recognition
• Automated Preprocessing Pipeline in Visual Imagery Tasks
• Multilabel and Multiclass Approaches Comparison for Respiratory Sounds Classification
• Alternative Proposals and its Applications. - Weighted Hausdor Distance Loss as a Function of Di

This book constitutes revised and extended selected papers of the 4th IEEE Colombian Conference, ColCACI 2021, which was held virtually in Colombia at the end of May 2021. The 7 full papers presented in the volume were carefully reviewed and selected from 22 submissions. The papers are organized in topical sections on biomedical applications and alternative proposals and its applications.

• Algorithm.- Vision.- Robotics and Application.
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This book constitutes the refereed post-conference proceedings of the 6th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2021, held in Suzhou, China, in November 2021.The 41 revised papers presented were carefully reviewed and selected from 105 submissions. The papers are organized in topical sections on algorithm; vision; and robotics and application.
(source: Nielsen Book Data)

• Solving Graph Coloring Problem Using Ant Colony Optimization, Simulated Annealing And Quantum Annealing - A Comparative Study.- Computer Assisted Diagnosis and Neuroimaging of Baby Infants.- Early Prediction of Ebola Virus Using Advanced Recurrent Neural Networks.- A Three-Step Fuzzy based BERT Model for Sentiment Analysis.- Mayfly Algorithm Based PID Controller for LFC of Multi Sources Single Area Power System.- Group Key Management Techniques for Secure Load Balanced Routing Model.- Search Techniques for Data Analytics with focus on Ensemble Methods.- A Survey on Underwater Object Detection.- COVACDISER: A Machine Learning based Web Application to Recommend the Prioritization of COVID-19 Vaccination.- Research of High-Speed Procedures for Defuzzification Based on the Area Ratio Method.- A Single Qubit Quantum Perceptron for OR and XOR Logic.- Societal Gene Acceptance Index Based Crossover in GA for Traveling Salesman Problem.- The method of automated neuro-fuzzy calibration of geometrically distorted images of digital X-ray tomographs.
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This book gathers extended versions of papers presented at DoSIER 2021 (the 2021 Third Doctoral Symposium on Intelligence Enabled Research, held at Cooch Behar Government Engineering College, West Bengal, India, during November 12-13, 2021). The papers address the rapidly expanding research area of computational intelligence, which, no longer limited to specific computational fields, has since made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design, to name but a few. Presenting chapters written by experts active in these areas, the book offers a valuable reference guide for researchers and industrial practitioners alike and inspires future studies.
(source: Nielsen Book Data)