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• Swarm Intelligence and Evolutionary Computation: Overview and Analysis.- Globally convergent hybridization of particle swarm optimization using line search-based derivative-free techniques Fireflies in the Fruits and Vegetables: Combining the Firefly Algorithm with Goal Programming for Setting Optimal Osmotic Dehydration Parameters of Produce.- Hybrid Metaheuristic Algorithms: Past, Present and Future.- Binary Flower Pollination Algorithm and Its Application to Feature Selection.- Bat Algorithm Application for the Single Row Facility Layout Problem.- Discrete Cuckoo Search Applied to Job Shop Scheduling Problem.- Cuckoo Search and Bat Algorithm Applied to Training Feed-Forward Neural Networks.- The Potential of the Firefly Algorithm for Damage Localization and Stiffness Identification.- Synthesizing Cross-Ambiguity Functions Using An Improved Bat Algorithm.- Sustainable Building Design: A Review on Recent Metaheuristic Methods.- Firefly Algorithm for Flow Shop Optimization.- Evaluation of Harmony Search and Differential Evolution Optimization Algorithms on Solving the Booster Station Optimization Problems in Water Distribution Networks.- Web Document Clustering by Using PSO-Based Cuckoo Search Clustering Algorithm.- Analysis of Trusses Using Particle Swarm Optimization.
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This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference for graduates, lecturers, engineers and researchers in computer science, evolutionary computing, artificial intelligence, machine learning, computational intelligence, data mining, engineering optimization and designs.
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• A Performance Analysis of DF Model in the Energy Harvesting Half-duplex and Full-Duplex Relay Networks
• A Model of Swarm Intelligence Based Optimization Framework Adjustable According to Problems
• Domain Model Definition for Domain-Specific RuleGeneration Using Variability Model
• A set-partitioning-based model to save cost on the import processes
• Combining Genetic Algorithm with Variable Neighborhood Search for MAX-SAT
• Enzyme classification on DUD-E database using Logistic Regression Ensemble (Lorens)
• Consolidation of Host-Based Mobility Management Protocols in Wireless Mesh Network
• Application of parallel computing technologies for numerical simulation of air transport in the human nasal cavity
• Genetic Algorithms-based Techniques for Solving Dynamic Optimization Problems with Unknown Active Variables and Boundaries
• Text Segmentation Methods: A Critical Review
• On-Line Power Systems Security Assessment Using Data Stream Random Forest Algorithm Modification
• Enhanced Security of Internet Banking Authentication with Extended Honey Encryption (XHE) Scheme
• An Enhanced Possibilistic Programming Model with Fuzzy Random Confidence-Interval for Multi-Objective Problem
• A Crowdsourcing Approach for Volunteering System
• One Dimensional Vehicle Tracking Analysis in Vehicular Ad hoc Networks
• Parallel Coordinates Visualization Tool on the Air Pollution Data for Northern Malaysia
• Application of Artificial bee colony algorithm for model parameter identification
• A novel weighting scheme applied to improve the text document clustering techniques
• A Methodological Framework To Emulate The Visual Of Malaysian Shadow Play With Computer-Generated Imagery
• Performance Evaluation of Hot Mix Asphalt Concrete by Using Polymeric Waste Polyethylene.

This book presents the latest research of the field of optimization, modeling and algorithms, discussing the real-world application problems associated with new innovative methodologies. The requirements and demands of problem solving have been increasing exponentially and new computer science and engineering technologies have reduced the scope of data coverage worldwide. The recent advances in information communication technology (ICT) have contributed to reducing the gaps in the coverage of domains around the globe. The book is a valuable reference work for researchers in the fields of computer science and engineering with a particular focus on modeling, simulation and optimization as well as for postgraduates, managers, economists and decision makers.
(source: Nielsen Book Data)

• Introduction.- SLAM Front-end.- SLAM Back-end.- Path planning in belief space with Pose SLAM.- Active Pose SLAM.- Conclusions.
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This monograph introduces a unifying framework for mapping, planning and exploration with mobile robots considering uncertainty, linking such problems with a common SLAM approach, adopting Pose SLAM as the basic state estimation machinery. Pose SLAM is the variant of SLAM where only the robot trajectory is estimated and where landmarks are used to produce relative motion measurements between robot poses. With regards to extending the original Pose SLAM formulation, this monograph covers the study of such measurements when they are obtained with stereo cameras, develops the appropriate noise propagation models for such case, extends the Pose SLAM formulation to SE(3), introduces information-theoretic loop closure tests, and presents a technique to compute traversability maps from the 3D volumetric maps obtained with Pose SLAM. A relevant topic covered in this monograph is the introduction of a novel path planning approach that exploits the modeled uncertainties in Pose SLAM to search for the path in the pose graph that allows the robot to navigate to a given goal with the least probability of becoming lost. Another relevant topic is the introduction of an autonomous exploration method that selects the appropriate actions to drive the robot so as to maximize coverage, while minimizing localization and map uncertainties. This monograph is appropriate for readers interested in an information-theoretic unified perspective to the SLAM, path planning and exploration problems, and is a reference book for people who work in mobile robotics research in general.
(source: Nielsen Book Data)

• AI Researchers, Video Games are Your Friends!.- On Routine Evolution of New Replicating Structures in Cellular Automata.- Application of Adaptive Differential Evolution for Model Identification in Furnace Optimized Control System.- Multi-Strategy Genetic Algorithm for Multimodal Optimization.- GPGPU Vs Multiprocessor SPSO Implementations to Solve Electromagnetic Optimization Problems.- Multi-Robot Cooperative Tasks using Combined Nature-Inspired Techniques.- GEML: Evolutionary Unsupervised and Semi-Supervised Learning of Multi-class Classification with Grammatical Evolution.- For Sale or Wanted: Directed Crossover in Adjudicated Space.- Design of an Autonomous Intelligent Demand-Side Management System by using Electric Vehicles as Mobile Energy Storage Units by Means of Evolutionary.- Particle Convergence Time in the PSO Model with Inertia Weight.- A Modified Fuzzy Lee-Carter Method for Modeling Human Mortality.- Synchronization of Uncertain Chaotic Systems using Generalized Predictive Control based on Fuzzy PID Controllers.- An Order Hyperresolution Calculus for Goedel Logic with Truth Constants and Equality, Strict Order, Delta.- Using Evidence Theory in Land Cover Change Prediction to Model Imperfection Propagation with Correlated Inputs Parameters.- Choosing Suitable Similarity Measures to Compare Intuitionistic Fuzzy Sets that Represent Experience-Based Evaluation Sets.- Bayesian Logistic Regression using Vectorial Centroid for Interval Type-2 Fuzzy Sets.- A Comparison of Learning Rules for Mixed Order Hyper Networks.- Pedestrian Action Prediction using Static Image Feature.- Gaussian Nonlinear Line Attractor for Learning Multidimensional Data.- Mining Significant Frequent Patterns in Parallel Episodes with a Graded Notion of Synchrony and Selective Participation.- A Heteroassociative Learning Model Robust to Interference.- An Extended Q Learning System with Emotion State to Make Up an Agent with Individuality.- Inedited SVM Application to Automatically Tracking and Recognizing Arm-and-Hand Visual Signals to Aircraft.- Learning-Based Distance Evaluation in robots' vision: A Comparison of ANFIS, MLP, SVR and Bilinear Interpolation Models.
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This book includes a selection of revised and extended versions of the best papers from the seventh International Joint Conference on Computational Intelligence (IJCCI 2015), held in Lisbon, Portugal, from 12 to 14 November 2015, which was composed of three co-located conferences: The International Conference on Evolutionary Computation Theory and Applications (ECTA), the International Conference on Fuzzy Computation Theory and Applications (FCTA), and the International Conference on Neural Computation Theory and Applications (NCTA). The book presents recent advances in scientific developments and applications in these three areas, reflecting the IJCCI's commitment to high quality standards.
(source: Nielsen Book Data)

• Intelligent Decision Support Systems for Sustainable Computing.- A Genetic Algorithm based Efficient Load Distribution Strategy for Handling Large Scale Workloads on Sustainable Computing Systems.- Efficiency in Energy Decision Support Systems using Soft Computing Techniques.- Computational Intelligence Based Heuristic Approach for Maximizing Energy Efficiency in Internet of Things.- Distributed Algorithm with Inherent Intelligence for Multi-Cloud Resource Provisioning.- Parameter Optimization methods based on Computational Intelligence Techniques in Context of Sustainable Computing.- The Maximum Power Point tracking using Fuzzy Logic Algorithm for DC Motor based Conveyor System.- Differential Evolution Based Significant Data Region Identification on Large Storage Drives.- A Fuzzy Based Power Switching Selection for Residential Application to beat Peak Time Power Demand.- Energy Saving Using Memorization: A Novel Energy-Efficient and Fault Tolerant Algorithm.- Analyzing Slavic Textual Sentiment using Deep Convolutional Neural Networks.- Intelligent Decision Support System for an Integrated Pest Management in Apple Orchard.- Analysis of Error Propagation in Safety Critical Software Systems: An Approach based on UGF.- A Framework for Analyzing Uncertainty in Data using Computational Intelligence Techniques.- .
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This unique book dicusses the latest research, innovative ideas, challenges and computational intelligence (CI) solutions in sustainable computing. It presents novel, in-depth fundamental research on achieving a sustainable lifestyle for society, either from a methodological or from an application perspective. Sustainable computing has expanded to become a significant research area covering the fields of computer science and engineering, electrical engineering and other engineering disciplines, and there has been an increase in the amount of literature on aspects sustainable computing such as energy efficiency and natural resources conservation that emphasizes the role of ICT (information and communications technology) in achieving system design and operation objectives. The energy impact/design of more efficient IT infrastructures is a key challenge in realizing new computing paradigms. The book explores the uses of computational intelligence (CI) techniques for intelligent decision support that can be exploited to create effectual computing systems, and addresses sustainability problems in computing and information processing environments and technologies at the different levels of CI paradigms. An excellent guide to surveying the state of the art in computational intelligence applied to challenging real-world problems in sustainable computing, it is intended for scientists, practitioners, researchers and academicians dealing with the new challenges and advances in area.
(source: Nielsen Book Data)

• Bayesian Methods for Intelligent Task Assignment in Crowdsourcing Systems.- Designing Societies of Robots.- On the Origins of Imperfection and Apparent Non-Rationality.- Lasso Granger Causal Models: Some Strategies and their Efficiency for Gene Expression Regulatory Networks.- Cooperative Feature Selection in Personalized Medicine.- Imperfect Decision Making and Risk Taking are affected by Personality.
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This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selfish decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: * task allocation to maximize "the wisdom of the crowd"; * design of a society of "edutainment" robots who account for one anothers' emotional states; * recognizing and counteracting seemingly non-rational human decision making; * coping with extreme scale when learning causality in networks; * efficiently incorporating expert knowledge in personalized medicine; * the effects of personality on risky decision making. The volume is a valuable source for researchers, graduate students and practitioners in machine learning, stochastic control, robotics, and economics, among other fields.
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• Introduction.- Classification and Architecture.- Geometric and Static Foundations.- Kinematic Codes.- Workspace.- Dynamics.- Kinematics with Nonstandard Cable Models.- Design.- Practice.
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Cable-driven parallel robots are a new kind of lightweight manipulators with excellent scalability in terms of size, payload, and dynamics capacities. For the first time, a comprehensive compendium is presented of the field of cable-driven parallel robots. A thorough theory of cable robots is setup leading the reader from first principles to the latest results in research. The main topics covered in the book are classification, terminology, and fields of application for cable-driven parallel robots. The geometric foundation of the standard cable model is introduced followed by statics, force distribution, and stiffness. Inverse and forward kinematics are addressed by elaborating efficient algorithms. Furthermore, the workspace is introduced and different algorithms are detailed. The book contains the dynamic equations as well as simulation models with applicable parameters. Advanced cable models are described taking into account pulleys, elastic cables, and sagging cables. For practitioner, a descriptive design method is stated including methodology, parameter synthesis, construction design, component selection, and calibration. Rich examples are presented by means of simulation results from sample robots as well as experimental validation on reference demonstrators. The book contains a representative overview of reference demonstrator system. Tables with physical parameters for geometry, cable properties, and robot parameterizations support case studies and are valuable references for building custom cable robots. For scientist, the book provides the starting point to address new scientific challenges as open problems are named and a commented review of the literature on cable robot with more than 500 references are given.
(source: Nielsen Book Data)

• Cyber Physical Systems Security.-Risk Management for CPS Security.-Wireless Sensor Network Security for Cyber Physical Systems.-WSN Security mechanisms for CPS.- ICS/SCADA System Security for CPS.-Embedded Systems Security for Cyber Physical Systems.-Distributed Control Systems Security for CPS.-Standards for CPS.
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This book is a pioneering yet primary general reference resource on cyber physical systems and their security concerns. Providing a fundamental theoretical background, and a clear and comprehensive overview of security issues in the domain of cyber physical systems, it is useful for students in the fields of information technology, computer science, or computer engineering where this topic is a substantial emerging area of study. .
(source: Nielsen Book Data)

• Part I: Main Track.- Long distance in-links for ranking enhancement.- Concept Tracking and Adaptation for Drifting Data Streams under Extreme Verification Latency.- Adversarial Sample Crafting for Time Series Classification with Elastic Similarity Measures.- Slot Co-allocation Optimization in Distributed Computing with Heterogeneous Resources.- About Designing an Observer Pattern-Based Architecture for a Multi-Objective Metaheuristic Optimization Framework.- Scalable Inference of Gene Regulatory Networks with the Spark Distributed Computing Platform.- Finding Best Compiler Options for Critical Software Using Parallel Algorithms.- Drift Detection over Non-stationary Data Streams using Evolving Spiking Neural Networks.- Part II: Energy.- A Hybrid Ensemble of Heterogeneous Regressors for Wind Speed Estimation in Wind Farms.- Bio-inspired approximation to MPPT under real irradiation conditions.- Part III: Industry.- Decision Making in Industry 4.0 Scenarios supported by Imbalanced Data Classification.
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This book gathers a wealth of research contributions on recent advances in intelligent and distributed computing, and which present both architectural and algorithmic findings in these fields. A major focus is placed on new techniques and applications for evolutionary computation, swarm intelligence, multi-agent systems, multi-criteria optimization and Deep/Shallow machine learning models, all of which are approached as technological drivers to enable autonomous reasoning and decision-making in complex distributed environments. Part of the book is also devoted to new scheduling and resource allocation methods for distributed computing systems. The book represents the peer-reviewed proceedings of the 12th International Symposium on Intelligent Distributed Computing (IDC 2018), which was held in Bilbao, Spain, from October 15 to 17, 2018.
(source: Nielsen Book Data)

• From von Neumann architecture and Atanasoff's ABC to Neuro-morphic Computation and Kasabov's NeuCube: Principles and Implementations.- Applications of Computational Intelligence in Industrial and Environmental Scenarios.- Real, Imaginary and Complex Sets.- Intercriteria analysis over patterns.- From Primary to Conventional Science.- Intelligent Two-Level Optimization and Model Predictive Control of Degrading Plants.- Collaborative Clustering: New Perspective to Rank Factor Granules.- Learning through Contstraint Applications.- Autonomous Flight Control and Precise Gestural Positioning of a Small Quadrotor.- Introduction to the Theory of Randomized Machine Learning.- Grid-Type Fuzzy Models for Performance Evaluation and Condition Monitoring of Photovoltaic Systems.- Intelligent Control of Uncertain Switched Nonlinear Plants: NFG Optimum Control Synthesis via Switched Fuzzy Time-delay Systems.- Multidimensional Intuitionistic Fuzzy Quantifiers and Level Operators.- Data Processing and Harmonization for Intelligent Transportation Systems. An Application Scenario on Highway Traffic Flows.
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By presenting the latest advances in fuzzy sets and computing with words from around the globe, this book disseminates recent innovations in advanced intelligent technologies and systems. From intelligent control and intuitionistic fuzzy quantifiers to various data science and industrial applications, it includes a wide range of valuable lessons learned and ideas for future intelligent products and systems.
(source: Nielsen Book Data)

• Part I: Knowledge Engineering and Semantic Web.- A Three-stage Consensus-based Method for Collective Knowledge Determination.- Fuzzy Ontology Modeling by Utilizing Fuzzy Set and Fuzzy Description Logic.- An Approach for Recommending Group Experts on Question and Answering Sites.- A method for uncertainty elicitation of experts using belief function.- Storing Hypergraph-based Data Models in Non-hypergraph Data Storage.- Part II: Natural Language Processing and Text Mining.- A Fuzzy Logic Approach to Predict the Popularity of a Presidential Candidate.- DNA Sequences Representation Derived from Discrete Wavelet Transformation for Text Similarity Recognition.- Tweet integration by finding the shortest paths on a word graph.- Event detection in Twitter: Methodological Evaluation and Structural Analysis of the Bibliometric Data.- Combination of inner approach and context-based approach for extracting feature of medical record data.- A Novel Method to Predict Type for DBpedia Entity.- Context-Based Personalized Predictors of the Length of Written Responses to Open-ended Questions of Elementary School Students.- Part III: Machine Learning and Data Mining.- Robust Scale-Invariant Normalization and Similarity Measurement for Time Series Data.
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This book offers a unique blend of reports on both theoretical models and their applications in the area of Intelligent Information and Database Systems. The reports cover a broad range of research topics, including advanced learning techniques, knowledge engineering, Natural Language Processing (NLP), decision support systems, Internet of things (IoT), computer vision, and tools and techniques for Intelligent Information Systems. They are extended versions of papers presented at the ACIIDS 2018 conference (10th Asian Conference on Intelligent Information and Database Systems), which was held in Dong Hoi City, Vietnam on 19-21 March 2018. What all researchers and students of computer science need is a state-of-the-art report on the latest trends in their respective areas of interest. Over the years, researchers have proposed increasingly complex theoretical models, which provide the theoretical basis for numerous applications. The applications, in turn, have a profound influence on virtually every aspect of human activities, while also allowing us to validate the underlying theoretical concepts.
(source: Nielsen Book Data)

• Data in the 21st Century.- The Understanding of Dependent Structure and Co-Movement of World Stock Exchanges Under the Economic Cycle.- Macro-Econometric Forecasting for During Periods of Economic Cycle Using Bayesian Extreme Value Optimization Algorithm.- Generalize Weighted in Interval Data for Fitting a Vector Autoregressive Model.- Asymmetric Effect with Quantile Regression for Interval-valued Variables.- Emissions, Trade Openness, Urbanisation, and Income in Thailand: An Empirical Analysis.- Does Forecasting Benefit from Mixed-Frequency Data Sampling Model: The Evidence from Forecasting GDP Growth Using Financial Factor in Thailand.- How Better Are Predictive Models: Analysis on the Practically Important Example of Robust Interval Uncertainty.
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This book presents recent research on predictive econometrics and big data. Gathering edited papers presented at the 11th International Conference of the Thailand Econometric Society (TES2018), held in Chiang Mai, Thailand, on January 10-12, 2018, its main focus is on predictive techniques - which directly aim at predicting economic phenomena; and big data techniques - which enable us to handle the enormous amounts of data generated by modern computers in a reasonable time. The book also discusses the applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that employs mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. It is therefore important to develop data processing techniques that explicitly focus on prediction. The more data we have, the better our predictions will be. As such, these techniques are essential to our ability to process huge amounts of available data.
(source: Nielsen Book Data)

• Introduction to Swarm Search.- Cooperative "Swarm Cleaning" of Stationary Domains.- Swarm Search of Expanding Regions in Grids: Lower Bounds.- Swarm Search of Expanding Regions in Grids: Upper Bounds.- The Search Complexity of Collaborative Swarms Expanding Z2 Grid Regions.
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This book offers a comprehensive analysis of the theory and tools needed for the development of an efficient and robust infrastructure for the design of collaborative patrolling unmanned aerial vehicle (UAV) swarms, focusing on its applications for tactical intelligence drones. It discusses frameworks for robustly and near-optimally analyzing flocks of semi-autonomous vehicles designed to efficiently perform the ongoing dynamic patrolling and scanning of pre-defined "search regions". It discusses the theoretical limitations of such systems, as well as the trade-offs between the systems' various economic and operational parameters. Current UAV systems rely mainly on human operators for the design and adaptation of drones' flying routes. However, recent technological advances have introduced new systems, comprised of a small number of self-organizing vehicles, manually guided at the swarm level by a human operator. With the growing complexity of such man-supervised architectures, it is becoming increasingly harder to guarantee a pre-defined level of performance. The use of large scale swarms of UAVs as a combat and reconnaissance platform therefore necessitates the development of an efficient optimization mechanism of their utilization, specifically in the design and maintenance of their patrolling routes. The book is intended for researchers and engineers in the fields of swarms systems and autonomous drones. .
(source: Nielsen Book Data)

• Modelling of Underwater Robots.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.
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A classic in underwater robotics. One of the first volumes in the "Springer Tracts in Advanced Robotics" series, it has been a bestseller through the previous three editions. Fifteen years after the publication of the first edition, the fourth edition comes to print. The book addresses the main control aspects in underwater manipulation tasks. With respect to the third edition, it has been revised, extended and some concepts better clustered. The mathematical model with significant impact on the control strategy is discussed. The problem of controlling a 6-degrees-of-freedoms autonomous underwater vehicle is investigated and a survey of fault detection/tolerant strategies for unmanned underwater vehicles is provided. Inverse kinematics, dynamic and interaction control for underwater vehicle-manipulator systems are then discussed. The code used to generate most of the numerical simulations is made available and briefly discussed.
(source: Nielsen Book Data)

• Introduction.- Part 1:Background and Methodology.- Discrete Consensus Achievement in Artificial Systems.- Modular Design of Strategies for the Best-of-n Problem.- Part 2:Mathematical Modeling and Analysis.- Indirect Modulation of Majority-Based Decisions.- Direct Modulation of Voter-Based Decisions.- Direct Modulation of Majority-Based Decisions.- Part 3:Robot Experiments.- A Robot Experiment in Site Selection.- A Robot Experiment in Collective Perception.- Part 4:Discussion and Annexes.- Conclusions.- Background on Markov Chains.
• (source: Nielsen Book Data)

This book focuses on the design and analysis of collective decision-making strategies for the best-of-n problem. After providing a formalization of the structure of the best-of-n problem supported by a comprehensive survey of the swarm robotics literature, it introduces the functioning of a collective decision-making strategy and identifies a set of mechanisms that are essential for a strategy to solve the best-of-n problem. The best-of-n problem is an abstraction that captures the frequent requirement of a robot swarm to choose one option from of a finite set when optimizing benefits and costs. The book leverages the identification of these mechanisms to develop a modular and model-driven methodology to design collective decision-making strategies and to analyze their performance at different level of abstractions. Lastly, the author provides a series of case studies in which the proposed methodology is used to design different strategies, using robot experiments to show how the designed strategies can be ported to different application scenarios.
(source: Nielsen Book Data)

• Preface.- Acknowledgements.- Chapte
• r1: Introduction.- Chapte
• r2: Selected topics in fuzzy systems designing.- Chapte
• r3: Introduction to fuzzy system interpretability.- Chapte
• r4: Improving fuzzy systems interpretability by appropriate selection of their structure.- Chapte
• r5: Interpretability of fuzzy systems designed in the process of gradient learning.- Chapte
• r6: Interpretability of fuzzy systems designed in the process of evolutionary learning.- Chapte
• r7: Case study: interpretability of fuzzy systems applied to nonlinear modelling and control.- Chapte
• r8: Case study: interpretability of fuzzy systems applied to identity verification.- Chapte
• r9: Concluding remarks and future perspectives.- Index.
• (source: Nielsen Book Data)

This book shows that the term "interpretability" goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.
(source: Nielsen Book Data)

• Chapter 1 Introduction.-
• Chapter 2 Some Theoretical Background.-
• Chapter 3 Spatial Grasp Model.-
• Chapter 4 SGL Detailed Specification.-
• Chapter 5 Main Spatial Mechanisms in SGL.-
• Chapter 6 SGL Networked Interpreter.-
• Chapter 7 Creation, Activation and Management of a Distributed World.-
• Chapter 8 Parallel and Distributed Network Operations.-
• Chapter 9 Solving Social Problems.-
• Chapter 10 Automated Command and Control.-
• Chapter 11 Collective Robotics.-
• Chapter 12 Conclusions.
• (source: Nielsen Book Data)

The book describes a novel ideology and supporting information technology for integral management of both civil and defence-orientated large, distributed dynamic systems. The approach is based on a high-level Spatial Grasp Language, SGL, expressing solutions in physical, virtual, executive and combined environments in the form of active self-evolving and self-propagating patterns spatially matching the systems to be created, modified and controlled. The communicating interpreters of SGL can be installed in key system points, which may be in large numbers (up to millions and billions) and represent equipped humans, robots, laptops, smartphones, smart sensors, etc. Operating under gestalt-inspired scenarios in SGL initially injected from any points, these systems can be effectively converted into goal-driven spatial machines (rather than computers as dealing with physical matter too) capable of responding to numerous challenges caused by growing world dynamics in the 21st century. Including numerous practical examples, the book is a valuable resource for system managers and programmers.
(source: Nielsen Book Data)

• Machine generated contents note: ch. 1 Introduction
• 1.1. Linked Data
• 1.2. Entity Name System
• 1.3. Research Question and Thesis
• 1.4. Dissertation
• 1.5. Contributions
• ch. 2 Background
• 2.1. Structured Data Models
• 2.1.1. Resource Description Framework (RDF)
• 2.1.2. Relational Database (RDB) Model
• 2.1.3. Serializing RDF Data
• 2.2. Instance Matching
• 2.2.1. Blocking Step
• 2.2.2. Similarity Step
• 2.2.3. Evaluating Instance Matching
• 2.3. Heterogeneity
• 2.3.1. Type Heterogeneity
• 2.3.2. Property Heterogeneity
• 2.3.3. Extending the Two-Step Workflow
• 2.4. Scalability
• 2.4.1. Motivation
• 2.4.2. Implementation
• ch. 3 Related Work
• 3.1. Existing Domain-Independent Systems
• 3.1.1. Systems Addressing Automation
• 3.1.2. Systems Addressing Heterogeneity
• 3.1.3. Systems Addressing Scalability
• 3.1.4. Other Systems
• 3.2. Discussion
• 3.2.1. Automation vs. Scalability
• 3.2.2. Issues of Structural Heterogeneity
• 3.3.3. Issues of Unsupervised Blocking
• ch. 4 Type Alignment
• 4.1. Motivating Example and Preliminaries: A Review
• 4.2. Applications of Type Alignment
• 4.3. Approach
• 4.3.1. Possible Strategy Implementations
• 4.4. Evaluations
• 4.4.1. Test Cases
• 4.4.2. Metrics and Methodology
• 4.4.3. Results and Discussion
• ch. 5 Training Set Generation
• 5.1. Intuition
• 5.2. Approach
• 5.3. Evaluations
• 5.3.1. Test Suite
• 5.3.2. Metrics
• 5.3.3. Setup
• 5.3.4. Results and Discussion
• ch. 6 Property Alignment
• 6.1. Approach
• 6.2. Evaluations
• 6.2.1. Setup
• 6.2.2. Results and Discussion
• ch. 7 Blocking and Classification
• 7.1. Approach
• 7.1.1. Feature Generator
• 7.1.2. Learning Procedures
• 7.2. Evaluations
• 7.2.1. Blocking
• 7.2.2. Similarity (non-iterative run)
• 7.2.3. Similarity (iterative run)
• ch. 8 Scalability
• 8.1. Summary of Algorithms
• 8.2. Motivation and Use-Cases
• 8.3. MapReduce Implementations
• 8.3.1. Type Alignment
• 8.3.2. Training Set Generator
• 8.3.3. Property Alignment and Learning Procedures
• 8.3.4. Blocking and Similarity
• ch. 9 Conclusion
• 9.1. Summary
• 9.2. Future Work
• 9.2.1. Linked Data Quality
• 9.2.2. Schema-Free Approaches
• 9.2.3. Transfer Learning.

• Foundations of Intelligent Computing
• Intelligent Techniques in Data Mining
• Multi-Agent Based Technologies
• Intelligent Computing in Decision Support Systems
• Intelligent Text and Data Retrieval: Towards a Better Representation of Users Intentions.

• 4.1 Function ξ in Fermat's Equation4.2 Generalization to Arbitrary Sequences; 5 Conclusions; References; Part IIIntelligent Techniques inData Mining; Forecasting of Short Time Series with Intelligent Computing; 1 Introduction; 2 Recent Trends in Forecasting with Intelligent Computing; 2.1 Fuzzy Time Series; 2.2 Fuzzification of Model Parameters; 2.3 Fuzzy Rule Based Systems; 2.4 Hybrid Systems; 3 Proposed Approach Using Linguistic Summaries; 4 Numerical Results; 4.1 Illustrative Example; 4.2 Comparative Analysis; 5 Conclusion; References; An Improved Adaptive Self-Organizing Map

• On the Identification of α-Asynchronous Cellular Automata in the Case of Partial Observations with Spatially Separated Gaps1 Introduction; 2 Preliminaries; 3 α-Asynchronous CAs; 4 Identification Problem; 5 Identification Algorithm; 5.1 Complete Observations; 5.2 Gap Filling Procedure; 6 Experimental Results; 7 Summary; References; k-Arithmetic Sequences---Theory and Applications; 1 Introduction; 2 k-Arithmetic Sequences; 2.1 Basic Concepts; 2.2 Characterizing Sequence; 3 Another View on Polynomials; 3.1 Characterizing Sequence and Polynomial Coefficients; 3.2 Basis Numbers; 4 Applications

This volume presents recent research, challenging problems and solutions in Intelligent Systems? covering the following disciplines: artificial and computational intelligence, fuzzy logic and other non-classic logics, intelligent database systems, information retrieval, information fusion, intelligent search (engines), data mining, cluster analysis, unsupervised learning, machine learning, intelligent data analysis, (group) decision support systems, intelligent agents and multi-agent systems, knowledge-based systems, imprecision and uncertainty handling, electronic commerce, distributed systems, etc. The book defines a common ground for sometimes seemingly disparate problems and addresses them by using the paradigm of broadly perceived intelligent systems. It presents a broad panorama of a multitude of theoretical and practical problems which have been successfully dealt with using the paradigm of intelligent computing.

• Neural Networks
• . Neural Networks with Feedback and Self-organization Introduction
• Fuzzy Inference Systems and Fuzzy Neural Networks
• Application of Fuzzy Logic Systems and Fuzzy Neural Networks in Forecasting Problems in Macroeconomics and Finance
• Fuzzy Neural Networks in Classification Problems
• Inductive Modeling Method (gmdh) in Problems of Intellectual Data Analysis and Forecasting
• The Cluster Analysis in Intellectual Systems
• Genetic Algorithms and Evolutionary Programing
• Problem of Fuzzy Portfolio optimization Under Uncertainty And Its Solution With Application of Computational Intelligence Methods.

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

• Aerial Vehicles progress On Pico Air Vehicles.- Perception and Mapping.- Planning.- Systems and Integration.- Control.
• (source: Nielsen Book Data)

This volume presents a collection of papers presented at the 15th International Symposium of Robotic Research (ISRR). ISRR is the biennial meeting of the International Foundation of Robotic Research (IFRR) and its 15th edition took place in Flagstaff, Arizona on December 9 to December 12, 2011. As for the previous symposia, ISRR 2011 followed up on the successful concept of a mixture of invited contributions and open submissions. Therefore approximately half of the 37 contributions were invited contributions from outstanding researchers selected by the IFRR officers and the program committee, and the other half were chosen among the open submissions after peer review. This selection process resulted in a truly excellent technical program which featured some of the very best of robotic research. The program was organized around oral presentation in a single-track format and included for the first time a small number of interactive presentations. The symposium contributions contained in this volume report on a variety of new robotics research results covering a broad spectrum including perception, manipulation, grasping, vehicles and design, navigation, control and integration, estimation and SLAM. .
(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)

• The Real Problem of Consciousness
• Consciousness and Subjective Experience
• Perception and Qualia
• From Perception to Consciousness
• Emotions and Consciousness
• Inner Speech and Consciousness
• Qualia and Machine Consciousness
• Testing Consciousness
• Artificial Conscious Cognition
• Associative Information Processing
• Neural Realization of Associative Processing
• Designing a Cognitive Perception System
• Examples of Perception/Response Feedback Loops
• The Transition to Symbolic Processing
• Information Integration with Multiple Modules
• Emotional Significance of Percepts
• The Outline of the Haikonen Cognitive Architecture (HCA)
• Mind Reading Applications
• The Comparison of Some Cognitive Architectures
• Example: An Experimental Robot with the HCA
• Concluding Notes
• Consciousness Explained.
• (source: Nielsen Book Data)

Robots are becoming more human, but could they also become sentient and have human-like consciousness?What is consciousness, exactly?It is a fact that our thoughts and consciousness are based on the neural activity of the brain. It is also a fact that we do not perceive our brain activity as it really is - patterns of neural firings. Instead, we perceive our sensations and thoughts apparently as they are. What kind of condition would transform the neural activity into this kind of internal appearance? This is the basic problem of consciousness.The author proposes an explanation that also provides preconditions for true conscious cognition - the requirement of a direct perceptive system with inherent sub-symbolic and symbolic information processing. Associative neural information processing with distributed signal representations is introduced as a method that satisfies these requirements.Conscious robot cognition also calls for information integration and sensorimotor integration. This requirement is satisfied by the Haikonen Cognitive Architecture (HCA).This book demystifies both the enigmatic philosophical issues of consciousness and the practical engineering issues of conscious robots by presenting them in an easy-to-understand manner for the benefit of students, researchers, philosophers and engineers in the field.
(source: Nielsen Book Data)

• Introduction
• Foundation of Neuro-Physiology
• Neural Computing
• Mind Model
• Perception
• Visual Information Processing
• Audio Information Processing
• Language
• Learning
• Memory
• Thought
• Development of Intelligence
• Emotion
• Immune System
• Consciousness
• Symbolic Logic
• The Machine Proves
• Perspective.
• (source: Nielsen Book Data)

Intelligence Science is an interdisciplinary subject dedicated to joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. Brain science explores the essence of brain research on the principle and model of natural intelligence at the molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. In order to implement machine intelligence, artificial intelligence attempts simulation, extension and expansion of human intelligence using artificial methodology and technology. Research scientists from the above three disciplines work together to explore new concepts, new theories, and methodologies.This book will introduce the concept and methodology of intelligence science systematically. The whole book is divided into 18 chapters altogether. It can be regarded as a textbook in courses of intelligence science, cognitive science, cognitive informatics etc. for senior and graduate students. It has important reference value for researchers engaged in fields such as intelligence science, brain science, cognitive science, neural science, artificial intelligence, psychology and so on.
(source: Nielsen Book Data)

• Preface
• Application of Artificial Intelligence in the Upstream Oil & Gas Industry
• Modeling & Optimization of the Effect of Laser Marking Parameters on Gloss of the Laser Marked Gold Using Artificial Intelligence Approaches
• AI Applications to Metal Stamping Die Design
• Structural Features Simulation on Mechanochemical Synthesis of Al2O3-TiB2 Nanocomposite using ANN with Bayesian Regularization & ANFIS
• An Artificial Intelligence Tool for Predicting Embryos Quality
• Passive System Reliability of the Nuclear Power Plants (NPPs) using Fuzzy Set Theory in Artificial Intelligence
• Emergent Tools in AI
• Neural Networks Applied to Micro-Computed Tomography
• Index.
• (source: Nielsen Book Data)

Artificial Intelligence may be defined as a collection of several analytic tools that collectively attempt to imitate life and has matured to a set of analytic tools that facilitate solving problems which were previously difficult or impossible to solve. In this book, the authors present topical research in the study of the tools and applications of artificial intelligence. Topics discussed include the application of artificial intelligence in the oil and gas industry and in metal stamping die design; using artificial intelligence to predict embryo quality and in biomedical imaging techniques.
(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)

• Intro; Title Page; Acknowledgments; Contents; Abstract; Introduction; Federated SPARQL Query Processing; The Need for Efficient Source Selection; The Need for More Comprehensive SPARQL Benchmarks; Contributions; Chapter Overview; Basic Concepts and Notation; Semantic Web; URIs, RDF; SPARQL Query Language; Triplestore; SPARQL Syntax, Semantic and Notation; State of the Art; Federation systems evaluations; Benchmarks; Federated engines public survey; Survey Design; Discussion of the survey results; Details of selected systems; Overview of the selected approaches; Performance Variables.

• EvaluationExperimental setup; Evaluation criteria; Experimental results; Discussion; Effect of the source selection time; Effect of the data partitioning; Hypergraph-Based Source Selection; Problem Statement; HiBISCuS; Queries as Directed Labelled Hypergraphs; Data Summaries; Source Selection Algorithm; Pruning approach; Evaluation; Experimental Setup; Experimental Results; Trie-based Source Selection; TBSS; TBSS Data Summaries; TBSS Source Selection Algorithm; TBSS Pruning approach; QUETSAL; Quetsal's Architecture; Quetsal's SPARQL 1.1 Query Re-writing; Evaluation; Experimental Setup.

• Experimental ResultsDuplicate-Aware Source Selection; DAW; Min-Wise Independent Permutations (MIPs); DAW Index; DAW Federated Query Processing; Experimental Evaluation; Experimental Setup; Experimental Results; Policy-Aware Source Selection; Motivating Scenario; Methodology and Architecture; Evaluation; Experimental Setup; Experimental Results; Data Distribution-Based Source Selection; Motivation; Biological query example; Methods; Transforming TCGA data to RDF; Linking TCGA to the LOD cloud; TCGA data workflow and schema; Data distribution and load balancing.

• TopFed federated query processing approachSource selection; Results and discussion; Evaluation; Availability of supporting data; LargeRDFBench: SPARQL Federation Benchmark; Background; The Need of More Comprehensive SPARQL Federation Benchmark; Benchmark Description; Benchmark Datasets; Benchmark Queries; Performance Metrics; Evaluation; Experimental Setup; SPARQL 1.0 Experimental Results; SPARQL 1.1 Experimental Results; FEASIBLE: SPARQL Benchmarks Generation Framework; Key SPARQL Features; A Comparison of Existing Triple Stores Benchmarks and Query Logs; FEASIBLE Benchmark Generation.

• Data Set CleaningNormalization of Features Vectors; Query Selection; Complexity Analysis; Evaluation and Results; Composite Error Estimation; Experimental Setup; Experimental Results; Conclusion; HiBISCuS; TBSS/Quetsal; DAW; SAFE; TopFed; LargeRDFBench; FEASIBLE; Bibliography.

• History of Artificial Intelligence.- Symbolic Artificial Intelligence.- Computational Intelligence.- Search Methods.- Evolutionary Computing.- Logic-Based Reasoning.- Structural Models of Knowledge Representation.- Syntactic Pattern Analysis.- Rule-Based Systems.- Pattern Recognition and Cluster Analysis.- Neural Networks.- Reasoning with Imperfect Knowledge.- Defining Vague Notions in Knowledge-Based Systems.- Cognitive Architectures.- Theories of Intelligence in Philosophy and Psychology.- Application Areas of AI Systems.- Prospects of Artificial Intelligence.
• (source: Nielsen Book Data)

In the chapters in Part I of this textbook the author introduces the fundamental ideas of artificial intelligence and computational intelligence. In Part II he explains key AI methods such as search, evolutionary computing, logic-based reasoning, knowledge representation, rule-based systems, pattern recognition, neural networks, and cognitive architectures. Finally, in Part III, he expands the context to discuss theories of intelligence in philosophy and psychology, key applications of AI systems, and the likely future of artificial intelligence. A key feature of the author's approach is historical and biographical footnotes, stressing the multidisciplinary character of the field and its pioneers. The book is appropriate for advanced undergraduate and graduate courses in computer science, engineering, and other applied sciences, and the appendices offer short formal, mathematical models and notes to support the reader.
(source: Nielsen Book Data)

• Introduction.- Generalized Eigenvalue Proximal Support Vector Machines.- Twin Support Vector Machines (TWSVM) for Classification.- TWSVR: Twin Support Vector Machine Based Regression.- Variants of Twin Support Vector Machines: Some More Formulations.- TWSVM for Unsupervised and Semi-Supervised Learning.- Some Additional Topics.- Applications Based on TWSVM.- References.
• (source: Nielsen Book Data)

This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on "Additional Topics" has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.
(source: Nielsen Book Data)

• Case-Studies in Mining User-Generated Reviews for Recommendation
• Mining Newsworthy Topics from Social Media
• Sentiment Analysis Using Supervised Learning with Domain-Adaptation and Sentence-Based Analysis
• Pattern-based Emotion Classification on Social Media
• Entity-based Opinion Mining from Text and Multimedia
• Predicting Emotion Labels for Chinese Microblog Texts.

This volume presents a collection of carefully selected contributions in the area of social media analysis. Each chapter opens up a number of research directions that have the potential to be taken on further in this rapidly growing area of research. The chapters are diverse enough to serve a number of directions of research with Sentiment Analysis as the dominant topic in the book. The authors have provided a broad range of research achievements from multimodal sentiment identification to emotion detection in a Chinese microblogging website. The book will be useful to research students, academics and practitioners in the area of social media analysis.

• Advances in Intelligent Systems (Lakhmi C Jain and Kazumi Nakamatsu)
• Stability, Chaos and Limit Cycles in Recurrent Cognitive Reasoning Systems (Aruna Chakraborty, Amit Konar, Pavel Bhowmik and Atulya K Nagar)
• Some Studies on Data Mining (Dilip Kumar Pratihar)
• Rough Non-Deterministic Information Analysis for Uncertain Information (Hiroshi Sakai, Hitomi Okuma, Mao Wu and Michinori Nakata)
• Metamathematical Limits to Computation (N C A da Costa and F A Doria)
• Hypothesis Refinement: Building Hypotheses in an Intelligent Agent System (Gauvain Bourgne, Nicolas Maudet and Suzanne Pinson)
• A Heuristic Algorithmic Procedure to Solve Allocation Problems with Fuzzy Evaluations (R Bartholo, C A N Cosenza, F A Doria and M R Doria)
• Non-Classical Logics and Intelligent Systems (Seiki Akama)
• A Paraconsistent Annotated Logic Program Before-After EVALPSN and Its Application (Kazumi Nakamatsu and Jair Minoro Abe)
• Inspecting and Preferring Abductive Models (Luis Moniz Pereira, Pierangelo Dell'Acqua, Alexandre Miguel Pinto and Goncalo Lopes)
• Supervised Neural Network Learning: From Vectors to Graphs (Monica Bianchini, Marco Maggini and Lorenzo Sarti)
• Paraconsistent Artificial Neural Networks and Applications (Jair Minoro Abe and Kazumi Nakamatsu)
• Paraconsistent Annotated Evidential Logic E and Applications in Automation and Robotics (Jair Minoro Abe and Kazumi Nakamatsu)
• Adaptive Intelligent Learning System for Online Learning Environments (Fatma Cemile Serce, Ferda Nur Alpaslan and Lakhmi C Jain)
• Automatic Test Program Generation: How Artificial Evolution May Outperform Experience (Danilo Ravotto, Ernesto Sanchez and Giovanni Squillero)
• Discovery of Communications Patterns by the Use of Intelligent Reasoning (J Fulcher, M Zhang, Q Bai and F Ren)
• Adaptive Approach to Quality Enhancement and Storage of Signatures and Fingerprint Images (Roumen Kountchev)
• Knowledge Representation for Electronic Circuits in Logic Programming (Takushi Tanaka)
• An Intelligent CBR Model for Predicting Changes in Tropical Cyclones Intensities (James N K Liu, Simon C K Shiu, Jane You and Leon S K Law)
• Analysis of Sequential Data in Tool Manufacturing of Volkswagen AG (Kemal Ince, Thomas Schneider and Frank Klawonn)
• Reasoning-Based Artificial Agents in Agent-Based Computational Economics (Shu-Heng Chen)
• Reasoning and Knowledge Acquisition from Medical Database Using Lattice SOM and Tree Structure SOM (Takumi Ichimura, Takashi Yamaguchi and Kenneth James Mackin)
• Approximate Processing in Medical Diagnosis by Means of Deductive Agents (G Fenza, D Furno, V Loia and S Senatore).
• (source: Nielsen Book Data)

This book consists of various contributions in conjunction with the keywords "reasoning" and "intelligent systems", which widely covers theoretical to practical aspects of intelligent systems. Therefore, it is suitable for researchers or graduate students who want to study intelligent systems generally.
(source: Nielsen Book Data)

• Part I
• Searching and Sorting
• Overview
• 1 Binary Search
• 2 Insertion Sort
• 3 Fast Sorting Algorithms
• 4 Parallel Sorting
• The Need for Speed
• 5 Topological Sorting
• How Should I Begin to Complete My To Do List?
• 6 Searching Texts
• But Fast! The Boyer-Moore-Horspool Algorithm
• 7 Depth-First Search (Ariadne & Co.)
• 8 Pledge's Algorithm
• How to Escape from a Dark Maze
• 9 Cycles in Graphs
• 10 PageRank
• What Is Really Relevant in the World-Wide Web?
• Part II
• Arithmetic and Encryption
• Overview
• 11 Multiplication of Long Integers
• Faster than Long Multiplication
• 12 The Euclidean Algorithm
• 13 The Sieve of Eratosthenes
• How Fast Can We Compute a Prime Number Table?
• 14 One-Way Functions
• Mind the Trap
• Escape Only for the Initiated
• 15 The One-Time Pad Algorithm
• The Simplest and Most Secure Way to Keep Secrets
• 16 Public-Key Cryptography
• 17 How to Share a Secret
• 18 Playing Poker by Email
• 19 Fingerprinting
• 20 Hashing
• 21 Codes
• Protecting Data Against Errors and Loss
• Part III
• Planning, Coordination and Simulation
• Overview
• 22 Broadcasting
• How Can I Quickly Disseminate Information?
• 23 Coverting Numbers into English Words
• 24 Majority
• Who Gets Elected Class Rep?
• 25 Random Numbers
• How Can We Create Randomness in Computers?
• 26 Winning Strategies for a Matchstick Game
• 27 Scheduling of Tournaments or Sports Leagues
• 28 Eulerian Circuits
• 29 High-Speed Circles
• 30 Gauß-Seidel Iterative Method for the Computation of Physical Problems
• 31 Dynamic Programming
• Evolutionary Distance
• Part IV
• Optimisation
• Overview
• 32 Shortest Paths
• 33 Minimum Spanning Trees
• Sometimes Greed Pays Off
• 34 Maximum Flows
• Towards the Stadium During Rush Hour
• 35 Marriage Broker
• 36 The Smallest Enclosing Circle
• A Contribution to Democracy from Switzerland?
• 37 Online Algorithms
• What Is It Worth to Know the Future?
• 38 Bin Packing
• How Do I Get My Stuff into the Boxes
• 39 The Knapsack Problem
• 40 The Travelling Salesman Problem
• 41 Simulated Annealing.

Algorithms specify the way computers process information and how they execute tasks. Many recent technological innovations and achievements rely on algorithmic ideas - they facilitate new applications in science, medicine, production, logistics, traffic, communication and entertainment. Efficient algorithms not only enable your personal computer to execute the newest generation of games with features unimaginable only a few years ago, they are also key to several recent scientific breakthroughs - for example, the sequencing of the human genome would not have been possible without the invention.

• From Interval (Set) and Probabilistic Granules to Set-and-Probabilistic Granules of Higher Order .- Artificial Intelligence Perspectives on Granular Computing
• Calculi of Approximation Spaces in Intelligent Systems .- Feature Discovery through Hierarchies of Rough Fuzzy Sets .- Comparative Study of Fuzzy Information Processing in Type-2 Fuzzy Systems .- Type-2 Fuzzy Similarity on Partial Truth and Intuitionistic Reasoning .- Decision-Making with Second Order Information Granules .- On the Usefulness of Fuzzy Rule Based Systems based on Hierarchical Linguistic Fuzzy Partitions .- Fuzzy Information Granulation with Multiple Levels of Granularity
• A Rough Set Approach to Building Association Rules and Its Applications .- Fuzzy Modeling with Grey Prediction for Designing Power System Stabilizers .- A Weighted Fuzzy Time Series Based Neural Network Approach to Option Price Forecasting .- A Rough Set Approach to Human Resource Development in IT Corporations .- Environmental Applications of Granular Computing and Intelligent Systems.

Information granules are fundamental conceptual entities facilitating perception of complex phenomena and contributing to the enhancement of human centricity in intelligent systems. The formal frameworks of information granules and information granulation comprise fuzzy sets, interval analysis, probability, rough sets, and shadowed sets, to name only a few representatives. Among current developments of Granular Computing, interesting options concern information granules of higher order and of higher type. The higher order information granularity is concerned with an effective formation of information granules over the space being originally constructed by information granules of lower order. This construct is directly associated with the concept of hierarchy of systems composed of successive processing layers characterized by the increasing levels of abstraction. This idea of layered, hierarchical realization of models of complex systems has gained a significant level of visibility in fuzzy modeling with the well-established concept of hierarchical fuzzy models where one strives to achieve a sound tradeoff between accuracy and a level of detail captured by the model and its level of interpretability. Higher type information granules emerge when the information granules themselves cannot be fully characterized in a purely numerical fashion but instead it becomes convenient to exploit their realization in the form of other types of information granules such as type-2 fuzzy sets, interval-valued fuzzy sets, or probabilistic fuzzy sets. Higher order and higher type of information granules constitute the focus of the studies on Granular Computing presented in this study. The book elaborates on sound methodologies of Granular Computing, algorithmic pursuits and an array of diverse applications and case studies in environmental studies, option price forecasting, and power engineering.

• Foundations of Reinforcement Learning
• Abstraction and Knowledge Transfer in Reinforcement Learning
• Qualitative State Space Abstraction
• Generalization and Transfer Learning with Qualitative Spatial Abstraction
• RLPR
• An Aspectualizable State Space Representation
• Empirical Evaluation
• Summary and Outlook.

Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems. Furthermore, acquired knowledge specific to the learned task, and transfer of knowledge to new tasks is crucial. In this book, the author investigates whether deficiencies of reinforcement learning can be overcome by suitable abstraction methods. He discusses various forms of spatial abstraction, in particular qualitative abstraction, a form.

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

• Frontmatter
• Remerciements
• Sommaire
• Préface
• 1. Qu'est-ce au juste que l'intelligence artificielle?
• 2. Avec l'intelligence artificielle générale pour Graal
• 3. Langage, créativité et émotions
• 4. Les réseaux de neurones artificiels
• 5. Les robots et la vie artificielle (A-life)
• 6. Mais, est-ce réellement de l'intelligence?
• 7. La Singularité
• Références
• Lectures complémentaires
• Index

L'intelligence artificielle, qui consiste à faire faire aux ordinateurs ce que peut faire l'esprit humain et ses applications touchent tous les aspects de notre vie et se trouvent tout autour de nous. Ce petit livre nous rappelle que les résultats de l'intelligence artificielle ont été inestimables pour les biologistes, les psychologues et les linguistes en les aidant à comprendre les processus de la mémoire, de l'apprentissage et du langage sous un angle nouveau. L'intelligence artificielle a aussi alimenté et aiguisé les débats philosophiques concernant la nature de l'esprit, l'intelligence et le caractère unique des êtres humains. Ainsi, Margaret Boden, leader dans le domaine de la recherche en intelligence artificielle, passe en revue ici tous les défis philosophiques et technologiques soulevés par l'intelligence artificielle, et tente de répondre à la question si complexe : les programmes pourront-ils être un jour réellement intelligents, créatifs, ou même conscients ?

• Theoretical Framework.- Evolutionary Computing Techniques in Data Mining.- Ant Colony Decision Tree Approach.- Adaptive Goal Function of the ACDT Algorithm.- Examples of Practical Application.
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This book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation. Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process. The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers. This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R&D.
(source: Nielsen Book Data)

• Optimum Design of Four Mechanical Elements Using Cohort Intelligence Algorithm.- Premier League Championship Algorithm: a multi-population based Algorithm and its Application on Structural Design Optimization.- Socio-inspired Optimization Metaheuristics: A Review.- Social Group Optimization Algorithm for Pattern Optimization in Antenna Arrays.- A Self-organizing Multi-agent Cooperative Robotic System: An Application of Cohort Intelligence Algorithm.- Feature Selection for Vocal Segmentation Using Social Emotional Optimization Algorithm.- Simultaneous Size and Shape Optimization of Dome-shaped Structures Using Improved Cultural Algorithm.- A Socio-Based Cohort Intelligence Algorithm for Integer Discrete and Mixed Design Variables Engineering Problems.- Maximizing Profits in Crop Planning Using Socio Evolution and Learning Optimization.- Application of Cohort- intelligence Variations Designing Fractional PID Controller for Various Systems.
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This book presents the latest insights and developments in the field of socio-cultural inspired algorithms. Akin to evolutionary and swarm-based optimization algorithms, socio-cultural algorithms belong to the category of metaheuristics (problem-independent computational methods) and are inspired by natural and social tendencies observed in humans by which they learn from one another through social interactions. This book is an interesting read for engineers, scientists, and students studying/working in the optimization, evolutionary computation, artificial intelligence (AI) and computational intelligence fields.
(source: Nielsen Book Data)

• Part I: Network measures.- A comparison of approaches to computing betweenness centrality for large graphs.- Cycle-centrality in economic and biological networks.- A game theoretic neighbourhood-based relevance index.- The impact of partially missing communities on the reliability of centrality measures.- Consistent estimation of mixed memberships with successive projections.- Reducing pivots of approximated betweenness computation by hierarchically clustering complex networks.- Power network equivalents: a network science based k-means clustering method integrated with silhouette analysis.- Part II: Link Analysis and Ranking.- Newton's gravitational law for link prediction in social networks.- Efficient outlier detection in hyperedge streams using minHash and locality-sensitive hashing.- Layer-wise model stacking for link prediction in multilayer networks. Case of scientific collaboration networks.- Evolutionary community mining for link prediction in dynamic networks.- Rank aggregation for course sequence discovery.- Part III: Community Structure.- Community-based feature selection for credit card default prediction.- Tracking bitcoin users activity using community detection on a network of weak signals.
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This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the VI International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2017), which took place in Lyon on November 29 - December 1, 2017. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, network dynamics; diffusion, epidemics and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and ecological networks and technological networks.
(source: Nielsen Book Data)

• Fuzzy Set Theory.- Fuzzy Linear Programming.- Linear Programming with Fuzzy Parameters: Simplex Based Approaches.- Linear Programming with Fuzzy Parameters: Non-Simplex Based Approaches.- Fuzzy Transportation Problem.
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The book offers a comprehensive, practice-oriented introduction to the field of fuzzy mathematical programming (FMP) as key topic of modern analytics. FMP plays a fundamental role in dealing with a varied range of problems, such as those concerning smart cities, sustainability, and renewable energies. This book includes an introduction to the basic concepts, together with extensive information on the computational-intelligence-based optimization models and techniques that have been used to date. Special emphasis is given to fuzzy transportation problems. The book is a valuable resource for researchers, data scientists and practitioners dealing with computational-intelligence-based optimization models for analytics.
(source: Nielsen Book Data)

• Type-2 and Intuitionistic Fuzzy Logic: On the Graphical Representation of Intuitionistic Membership Functions for its use in Intuitionistic Fuzzy Inference Systems.- A gravitational search algorithm using type-2 fuzzy logic for parameter adaptation.- General Type-2 Fuzzy edge detection in the preprocessing of a face recognition system.- Interval Type-2 Fuzzy Possibilistic C-Means Optimization using Particle Swarm Optimization.- Optimization of Type-2 and Type-1 Fuzzy Integrator to Ensemble Neural Network with Fuzzy Weights Adjustment.- Choquet integral and Interval Type-2 Fuzzy Choquet integral for edge detection.- An Overview of Granular Computing using Fuzzy Logic Systems.
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This book highlights recent advances in the design of hybrid intelligent systems based on nature-inspired optimization and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction, and optimization of complex problems. The book is divided into seven main parts, the first of which addresses theoretical aspects of and new concepts and algorithms based on type-2 and intuitionistic fuzzy logic systems. The second part focuses on neural network theory, and explores the applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The book's third part presents enhancements to meta-heuristics based on fuzzy logic techniques and describes new nature-inspired optimization algorithms that employ fuzzy dynamic adaptation of parameters, while the fourth part presents diverse applications of nature-inspired optimization algorithms. In turn, the fifth part investigates applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. The sixth part examines new optimization algorithms and their applications. Lastly, the seventh part is dedicated to the design and application of different hybrid intelligent systems.
(source: Nielsen Book Data)

• Processing of Hyperspectral Medical Images: Applications in Dermatology Using Matlab (R).- Foreword.- Preface.- Acknowledgments.- List of selected symbols.- Chapte
• r1: Introduction.- Chapte
• r2: Image acquisition.- Chapte
• r3: Image pre-processing.- Chapte
• r4: Image processing.- Chapte
• r5: Classification.- Chapte
• r6: Sensitivity to parameter changes.- Chapte
• r7: Conclusions.- Appendix.- References.- Summary.
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This book presents new methods of analyzing and processing hyperspectral medical images, which can be used in diagnostics, for example for dermatological images. The algorithms proposed are fully automatic and the results obtained are fully reproducible. Their operation was tested on a set of several thousands of hyperspectral images and they were implemented in Matlab. The presented source code can be used without licensing restrictions. This is a valuable resource for computer scientists, bioengineers, doctoral students, and dermatologists interested in contemporary analysis methods.
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• Preface.- MapReduce: From Elementary Circuits to Cloud.- On the Helmholtz Principle for Data Mining.- A method of introducing weights into OWA operators and other symmetric functions.- Uncertainty Management: Probability, Possibility, Entropy, and Other Paradigms- Relationships between fuzziness, partial truth and probability in the case of repetitive events.- Using extended tree kernels to recognize metalanguage in text.- Relationships between Probability and Possibility Theories.- Modeling extremal events is not easy: why the Extreme Value Theorem cannot be as general as the Central Limit Theorem.- Information quality and uncertainty.- Applying anomalous cluster approach to spatial clustering.- Why is linear quantile regression empirically successful: a possible explanation.- A Rapid Soft Computing Approach to Dimensionality Reduction in Model Construction.- Physics Of The Mind, Dynamic Logic, and Monotone Boolean Functions.- Fuzzy arithmetic type 1 without extension principle and vertical membership functions.- Copula as a bridge between probability theory and fuzzy logic.- Note to the polemics surrounding the second Goedel's theorem.- Ontological Data Mining.
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This book commemorates the 65th birthday of Dr. Boris Kovalerchuk, and reflects many of the research areas covered by his work. It focuses on data processing under uncertainty, especially fuzzy data processing, when uncertainty comes from the imprecision of expert opinions. The book includes 17 authoritative contributions by leading experts.
(source: Nielsen Book Data)

• Locomotion.- Haptics.- Manipulation.- Perception.- Human-Robot Interaction.- Mapping and Localization.- Mechanisms.- Perception and Planning.- Sensor Networks.- Many-Robot Systems.- Interactive Presentations.- Plenary Talk.
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The International Symposium on Experimental Robotics (ISER) is a series of bi-annual meetings which are organized in a rotating fashion around North America, Europe and Asia/Oceania. The goal of ISER is to provide a forum for research in robotics that focuses on novelty of theoretical contributions validated by experimental results. The meetings are conceived to bring together, in a small group setting, researchers from around the world who are in the forefront of experimental robotics research. This unique reference presents the latest advances across the various fields of robotics, with ideas that are not only conceived conceptually but also explored experimentally. It collects robotics contributions on the current developments and new directions in the field of experimental robotics, which are based on the papers presented at the 14th ISER held on June 15-18, 2014 in Marrakech and Essaouira, Morocco. This present fourteenth edition of Experimental Robotics edited by M. Ani Hsieh, Oussama Khatib, and Vijay Kumar offers a collection of a broad range of topics in field and human-ce ntered robotics.
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• Control
• Design.-Intelligence And Learning
• Manipulation
• Planning.

This volume presents a collection of papers presented at the 16th International Symposium of Robotic Research (ISRR). ISRR is the biennial meeting of the International Foundation of Robotic Research (IFRR) and its 16th edition took place in Singapore over the period 16th to 19th December 2013. The ISRR is the longest running series of robotics research meetings and dates back to the very earliest days of robotics as a research discipline. This 16th ISRR meeting was held in the 30th anniversary year of the very first meeting which took place in Bretton Woods (New Hampshire, USA) in August 1983., and represents thirty years at the forefront of ideas in robotics research. As for the previous symposia, ISRR 2013 followed up on the successful concept of a mixture of invited contributions and open submissions. 16 of the contributions were invited contributions from outstanding researchers selected by the IFRR officers and the program committee, and the other contributions were chosen among the open submissions after peer review. This selection process resulted in a truly excellent technical program which featured some of the very best of robotic research. These papers were presented in a single-track interactive format which enables real conversations between speakers and the audience. The symposium contributions contained in this volume report on a variety of new robotics research results covering a broad spectrum organized into traditional ISRR categories: control; design; intelligence and learning; manipulation; perception; and planning.

• Computer Input by Human Eyes Only and it's Applications
• New Ideas for Brain Modelling 2
• Neural-like Growing Networks the Artificial Intelligence Basic Structure
• Detection of Privilege Abuse in RBACAdministered Database
• Learning-based Leaf Image Recognition Frameworks
• Massively Parallel Feature Selection based on Ensemble of Filters and Multiple Robust Consensus Functions for Cancer Gene Identification
• Relationship Discovery and Navigation in Big Graphs
• A Fuzzy System for Three-Factor, Non-textual Authentication
• Efficient Graph-Based Volumetric Segmentation
• A Hybrid Intelligent System in Cultural Intelligence
• Semantic-Based Recommender System with Humain Feeling Relevance Measure
• Alignment of Time Series for Subsequence-to-Subsequence Time Series Matching
• The Effects of Typing Demand on EmotionalStress, Mouse and Keystroke Behaviours
• Friend Recommendation in a Social Bookmarking System: Design and Architecture Guidelines
• Quantum Behaved Genetic Algorithm: Constraints-Handling and GPU Computing
• A Genetic Algorithm Approach for Optimizing a Single-Finger Arabic Keyboard Layout
• Dynamic Well Bottom-Hole Flowing Pressure Prediction Based on Radial Basis Neural Network
• Modeling Energy Consumption in A Educational Building: Comparative Study between Linear Regression, Fuzzy Set Theory and Neural Networks
• Delivering Faster Results Through Parallelisation and GPU Acceleration
• Probabilistic Roadmaps and Hierarchical Genetic Algorithms for Optimal Motion Planning
• Using Mouse and Keyboard Dynamics to Detect Cognitive Stress during Mental Arithmetic
• Towards Using Games Theory to Detect New U2R Attacks
• Development and Evaluation of Virtual Reality Medical Training System for Anatomy Education
• Compression of ECG signal using Hybrid technique
• Performance Analysis of MATLAB Parallel Computing Approaches to Implement Genetic Algorithm for Image Compression.-

The book Intelligent Systems in Science and Information 2014 is ℗ℓthe carefully edited collection of 25 extended chapters from selected papers in the field of Computational Intelligence that, which received highly recommended feedback during the Science and Information Conference (SAI) 2014 review process. All chapters have gone through substantial extension and consolidation and were subject to another round of rigorous review and additional modification and represent the state of the art of the cutting-edge research and technologies in the related areas.

• Set Oriented Numerics
• Computational Game Theory
• Machine Learning Applied to Networks
• Complex Networks and Landscape Analysis
• Local Search and Optimization
• Genetic Programming
• Evolutionary Multiobjective Optimization
• Practical Aspects of Evolutionary Algorithms.

This volume encloses research articles that were presented at the EVOLVE 2014 International Conference in Beijing, China, July 1-4, 2014. The book gathers contributions that emerged from the conference tracks, ranging from probability to set oriented numerics and evolutionary computation; all complemented by the bridging purpose of the conference, e.g. Complex Networks and Landscape Analysis, or by the more application oriented perspective. The novelty of the volume, when considering the EVOLVE series, comes from targeting also the practitioner's view. This is supported by the Machine Learning Applied to Networks and Practical Aspects of Evolutionary Algorithms tracks, providing surveys on new application areas, as in the networking area and useful insights in the development of evolutionary techniques, from a practitioner's perspective. Complementary to these directions, the conference tracks supporting the volume, follow on the individual advancements of the subareas constituting the scope of the conference, through the Computational Game Theory, Local Search and Optimization, Genetic Programming, Evolutionary Multi-objective optimization tracks.

• Visibility-Based Optimal Path and Motion Planning.- Mathematical Preliminaries.- Static Optimal Visibility Problems.- Visibility-based Optimal Path Planning.- Visibility-based Optimal Motion Planning.- Multiple Observer Cooperative & Non-Cooperative Optimal Motion Planning.
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This monograph deals with various visibility-based path and motion planning problems motivated by real-world applications such as exploration and mapping planetary surfaces, environmental surveillance using stationary or mobile robots, and imaging of global air/pollutant circulation. The formulation and solution of these problems call for concepts and methods from many areas of applied mathematics including computational geometry, set-covering, non-smooth optimization, combinatorial optimization and optimal control. Emphasis is placed on the formulation of new problems and methods of approach to these problems. Since geometry and visualization play important roles in the understanding of these problems, intuitive interpretations of the basic concepts are presented before detailed mathematical development. The development of a particular topic begins with simple cases illustrated by specific examples, and then progresses forward to more complex cases. The intended readers of this monograph are primarily students and researchers in engineering, computer science and applied mathematics. An understanding of the mathematical development of the main results requires only basic knowledge of mathematical analysis, control, and optimization theories. Some exercises with various degrees of difficulty are provided at the end of the main chapters. The material presented here may serve as a portion of an introductory course or seminar on visibility-based optimal path and motion planning problems with the objective of stimulating interest and further studies in this relatively new area.
(source: Nielsen Book Data)

• Preface
• Modeling, Simulation & Optimization of Resources Management in Hospital Emergency Departments Using the Agent-Based Approach
• Computational Modeling In Male Reproduction
• Consumer-Centric Agent-Based Marketing Models: A How-to Approach
• Lattice-Boltzmann Simulation of Transport Phenomena in Agroindustrial Biosystems
• Agent-Based Models in Cancer Prevention Research
• Reflections on Agent-Based Modeling: Simulating Web Networks
• Model-Based Approaches to Diagnosis of Multi-Agent Plans: A Survey
• Modeling Behavior of Web Users as Agents with Reason & Sentiment
• Index.
• (source: Nielsen Book Data)

Agent-based models (ABM) and computational models simulate the actions and interactions of autonomous agents (both individual or collective entities such as organisations or groups) with a view to assessing their effects on the system as a whole. They combine elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. In this book, the authors discuss the modelling, simulation and optimisation of resources management in hospital emergency departments using the agent-based approach; computational modelling in male reproduction; consumer-centric agent-based marketing models; Lattice-Boltzmann simulation of transport phenomena in agroindustrial biosystems; agent-based models in cancer prevention research; model-based approaches to diagnosis of multi-agent plans; and modelling behaviour of web users as agents with reason and sentiment.
(source: Nielsen Book Data)