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• A strong left Fractional Calculus for Banach space valued functions.- Strong Right Abstract Fractional Calculus.- Strong mixed and generalized Abstract Fractional Calculus.- Foundations of General Fractional Analysis for Banach space valued functions.- Vector abstract fractional Korovkin Approximation.- Basic Abstract Korovkin theory.- High Approximation for Banach space valued functions.- Vectorial abstract fractional approximation using linear operators.- Abstract fractional trigonometric Korovkin approximation.- Multivariate Abstract Approximation for Banach space valued functions.- Arctangent function based Abstract Neural Network approximation. .
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This brief book presents the strong fractional analysis of Banach space valued functions of a real domain. The book's results are abstract in nature: analytic inequalities, Korovkin approximation of functions and neural network approximation. The chapters are self-contained and can be read independently. This concise book is suitable for use in related graduate classes and many research projects. An extensive list of references is provided for each chapter. The book's results are relevant for many areas of pure and applied mathematics. As such, it offers a unique resource for researchers, and a valuable addition to all science and engineering libraries.
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• Page Rank vs. Katz: Is the centrality algorithm choice relevant to measure user influence in Twitter?.- Weighted means based filters for SAR Imagery.- On Combination of Wavelet Transformation and Stabilized KH Interpolation for Fuzzy Inferences Based on High Dimensional Sampled Functions.- Abductive Reasoning on Molecular Interaction Maps.- Efficient unfolding of fuzzy connectives for multi-adjoint logic programs.- On Generalizations of Concept Lattices.- Generating fuzzy attribute rules via Fuzzy Formal Concept Analysis.
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This book presents recent research in the field of interaction between computational intelligence and mathematics, ranging from theory to applications. Computational intelligence, or soft computing consists of various bio-inspired methods, especially fuzzy systems, artificial neural networks, evolutionary and memetic algorithms. These research areas were initiated by professionals in various applied fields, such as engineers, economists, and financial and medical experts. Although computational intelligence offered solutions (at least quasi-optimal solutions) for problems with high complexity, vague and undeterministic features, initially little attention was paid to the mathematical models and analysis of the methods successfully applied. A typical example is the extremely successful Mamdani-algorithm, and its modifications and extensions, applied since the mid-1970s, where the first analysis of the simplest cases, showing why this algorithm was so efficient and stable, was not given until the early 1990s. Since the mid-2000s, the authors have organized international conferences annually to focus on the mathematical methodological issues in connection with computational intelligence approaches. These conferences have attracted a large number of submissions with a wide scope of topics and quality. The editors selected several high-quality papers and approached the authors to submit an essentially extended and improved book chapter based on the lectures.This volume is the first contributed book on the subject.
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• Part I: Foundations.- Introduction.- Genetic Algorithms.- Parameters.- Part II: Solution Spaces.- Multimodality.- Constraints.- Multiple Objectives.- Part III: Advanced Concepts.- Theory.- Machine Learning.- Applications.- Part IV: Ending.- Summary and Outlook.- Index.- References.
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This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.
(source: Nielsen Book Data)

• Preface.-
• Chapter 1: Introduction.- Part I: Generalized Related Set Based Approach.-
• Chapter 2: Information System for Relational Data.-
• Chapter 3: Properties of Granular-Relational Data Mining Framework.-
• Chapter 4: Association Discovery and Classification Rule Mining.-
• Chapter 5: Rough-Granular Computing.- Part II: Description Language Based Approach.-
• Chapter 6: Compound Information Systems.-
• Chapter 7: From Granular-Data Mining Framework to its Relational Version.-
• Chapter 8: Relation-Based Granules.-
• Chapter 9: Compound Approximation Spaces.- Conclusions.- References.- Index.
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This book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case. Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing! This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining relational data. Lastly, the book offers all readers interested in computational intelligence in the broader sense the opportunity to deepen their understanding of the newly emerging field granular-relational data mining.
(source: Nielsen Book Data)

• Part I Keynote Addresses: Robust Estimation of Heckman Model.- Part II Fundamental Theory: Sequential Monte Carlo Sampling for State Space Models.- Robustness as a Criterion for Selecting a Probability Distribution Under Uncertainty.- Why Cannot We Have a Strongly Consistent Family of Skew Normal (and Higher Order) Distributions.- Econometric Models of Probabilistic Choice: Beyond McFadden's Formulas.- How to Explain Ubiquity of Constant Elasticity of Substitution (CES) Production and Utility Functions Without Explicitly Postulating CES.- How to Make Plausibility-Based Forecasting More Accurate.- Structural Breaks of CAPM-type Market Model with Heteroskedasticity and Quantile Regression.- Weighted Least Squares and Adaptive Least Squares: Further Empirical Evidence.- Prior-free probabilistic inference for econometricians.- Robustness in Forecasting Future Liabilities in Insurance.- On Conditioning in Multidimensional Probabilistic Models.- New Estimation Method for Mixture of Normal Distributions.- EM Estimation for Multivariate Skew Slash Distribution.- Constructions of multivariate copulas.- Plausibility regions on the skewness parameter of skew normal distributions based on inferential models.- International Yield Curve Prediction with Common Functional Principal Component Analysis.- An alternative to p-values in hypothesis testing with applications in model selection of stock price data.- Confidence Intervals for the Common Mean of Several Normal Populations.- A generalized information theoretical approach to Non-linear time series model.- Predictive recursion maximum likelihood of Threshold Autoregressive model.- A multivariate generalized FGM copulas and its application to multiple regression.- Part III Applications: Key Economic Sectors and Their Transitions: Analysis of World Input-Output Network.- Natural Resources, Financial Development and Sectoral Value Added in a Resource Based Economy.- Can bagging improve the forecasting performance of tourism demand models?.- The Role of Asian Credit Default Swap Index in Portfolio Risk Management.- Chinese outbound tourism demand to Singapore, Malaysia and Thailand destinations: A study of political events and holiday impacts.- Forecasting Asian Credit Default Swap spreads: A comparison of multi-regime models.- Forecasting Asian Credit Default Swap spreads: A comparison of multi-regime models.- Effect of Helmet Use on Severity of Head Injuries Using Doubly Robust Estimators.- Forecasting cash holding with cash deposit using time series approaches.- Forecasting GDP Growth in Thailand with Different Leading Indicators using MIDAS regression models.- Testing the Validity of Economic Growth Theories Using Copula-based Seemingly Unrelated Quantile Kink Regression.- Analysis of Global Competitiveness Using Copula-based Stochastic Frontier Kink Model.- Gravity model of trade with Linear Quantile Mixed Models approach.- Stochastic Frontier Model in Financial Econometrics: A Copula-based Approach.- Quantile Forecasting of PM10 Data in Korea based on Time Series Models.- Do We Have Robust GARCH Models under Different Mean Equations: Evidence from Exchange Rates of Thailand?.- Joint Determinants of Foreign Direct Investment (FDI) Inflow in Cambodia: A Panel Co-integration Approach.- The Visitors' Attitudes and Perceived Value toward Rural Regeneration Community Development of Taiwan.- Analyzing the contribution of ASEAN stock markets to systemic risk.- Estimating Efficiency of Stock Return with Interval Data.- The impact of extreme events on portfolio in financial risk management.- Foreign Direct Investment, Exports and Economic Growth in ASEAN Region: Empirical Analysis from Panel Data.- Author Index.
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This book presents recent research on robustness in econometrics. Robust data processing techniques - i.e., techniques that yield results minimally affected by outliers - and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations.
(source: Nielsen Book Data)

These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the semantic Web, social networks, and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies, such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This twenty-sixth issue is a special issue with selected papers from the First International KEYSTONE Conference 2015 (IKC 2015), part of the keystone COST Action IC1302.
(source: Nielsen Book Data)

• Fundamentals of Sentiment Analysis and Its Applications
• Fundamentals of Sentiment Analysis: Concepts and Methodology
• The Comprehension of Figurative Language: What is the Influence of Irony and Sarcasm on NLP Techniques?
• Probabilistic Approaches for Sentiment Analysis: Latent Dirichlet Allocation for Ontology Building and Sentiment Extraction
• Description Logic Class Expression Learning Applied to Sentiment Analysis
• Capturing Digest Emotions by Means of Fuzzy Linguistic Aggregation
• Hyperelastic-based Adaptive Dynamics Methodology in Knowledge Acquisition for Computational Intelligence on Ontology Engineering of Evolving Folksonomy Driven Environment
• Sentiment-Oriented Information Retrieval: Affective Analysis of Documents Based on the SenticNet Framework
• Interpretability of Computational Models for Sentiment Analysis
• Chinese Micro-blog Emotion Classification by Exploiting Linguistic Features and SVMperf
• Social Media and News Sentiment Analysis for Advanced Investment Strategies
• Context Aware Customer Experience Management: A Development Framework Based on Ontologies and Computational Intelligence
• An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief
• Big Data Sentiment Analysis for Brand Monitoring in Social Media Streams by Cloud Computing
• Neuro-Fuzzy Sentiment Analysis for Customer Review Rating Prediction
• OntoLSA: An Integrated Text Mining System for Ontology Learning and Sentiment Analysis
• Knowledge-based Tweet Classification for Disease Sentiment Monitoring.

This edited volume provides the reader with a fully updated, in-depth treatise on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of models of sentiment analysis and ontology?oriented engineering. The volume involves studies devoted to key issues of sentiment analysis, sentiment models, and ontology engineering. The book is structured into three main parts. The first part offers a comprehensive and prudently structured exposure to the fundamentals of sentiment analysis and natural language processing. The second part consists of studies devoted to the concepts, methodologies, and algorithmic developments elaborating on fuzzy linguistic aggregation to emotion analysis, carrying out interpretability of computational sentiment models, emotion classification, sentiment-oriented information retrieval, a methodology of adaptive dynamics in knowledge acquisition. The third part includes a plethora of applications showing how sentiment analysis and ontologies becomes successfully applied to investment strategies, customer experience management, disaster relief, monitoring in social media, customer review rating prediction, and ontology learning. This book is aimed at a broad audience of researchers and practitioners. Readers involved in intelligent systems, data analysis, Internet engineering, Computational Intelligence, and knowledge-based systems will benefit from the exposure to the subject matter. The book may also serve as a highly useful reference material for graduate students and senior undergraduate students.

• Neurocognitive Robot Assistant for Robust Fall Detection .-Smart Robot Control via Novel Computational Intelligence Methods for Ambient Assisted Living
• Valorization of Assistive Technologies for Cognition: Lessons & Practices
• Safe and Automatic Addition of Fault Tolerance for Smart Homes Dedicated to People with Disabilities
• Smart Homes in the Era of Big Data
• An Investigation of The Use of Innovative Biology-based Computational Intelligence in Ubiquitous Robotics Systems: Data Mining Perspective
• Ambient Stupidity
• Security Implementations in Smart Sensor Networks
• Automatic Music Composition from a Self- Learning Algorithm.

This book demonstrates the success of Ambient Intelligence in providing possible solutions for the daily needs of humans. The book addresses implications of ambient intelligence in areas of domestic living, elderly care, robotics, communication, philosophy and others. The objective of this edited volume is to show that Ambient Intelligence is a boon to humanity with conceptual, philosophical, methodical and applicative understanding. The book also aims to schematically demonstrate developments in the direction of augmented sensors, embedded systems and behavioral intelligence towards Ambient Intelligent Networks or Smart Living Technology. It contains chapters in the field of Ambient Intelligent Networks, which received highly positive feedback during the review process. The book contains research work, with in-depth state of the art from augmented sensors, embedded technology and artificial intelligence along with cutting-edge research and development of technologies and applications of Ambient Intelligent Networks. This book is intended to introduce ideas, methods, technologies of the future development of humanity, Science and Technology.

• Intro; Contents; Contributors; Introduction; Part I Search and Optimization; A Metaheuristic for Optimizing the Performance and the Fairness in Job Scheduling Systems; 1 Introduction; 2 Problem Description; 2.1 Machines; 2.2 Jobs; 2.3 Optimization Criteria; 3 Overview of Existing Scheduling Approaches; 3.1 Standard Scheduling Algorithms; 3.2 Scheduling with Job Runtime Estimates; 3.3 User-to-User Fairness and Fair-Share; 3.4 Advanced Optimization Methods; 3.5 Summary; 4 Metaheuristic Scheduler; 4.1 Linear Schedule Compression; 4.2 Schedule Evaluation.

• 4.3 Metaheuristic for Schedule Optimization5 Experiments; 5.1 Simulation Setup; 5.2 Experimental Results; 5.3 Summary; 6 Conclusion and Future Work; References; Hybrid ACO and Tabu Search for Large Scale Information Retrieval; 1 Introduction; 2 Information Retrieval Background; 3 Lex as a Tool for Documents Indexing; 4 AC-IR Algorithm; 4.1 Solutions Encoding; 4.2 Pheromone Table and Probabilistic Decision Rules; 4.3 Updating the Pheromone; 4.4 Building and Improving a Solution; 5 ACS-IR Algorithm; 6 The Overall Algorithm; 7 Experimental Results; 7.1 Benchmarks; 7.2 Setting the Parameters.

• 7.3 Comparison of AS-IR, ACS-IR and CL-IR Algorithms8 Conclusion; References; Hosting Clients in Clustered and Virtualized Environment: A Combinatorial Optimization Approach; 1 Introduction; 1.1 Hardware Virtualization Technology; 1.2 Cluster Computing Technology; 1.3 Clients Hosting Problem; 2 Resource Allocation Problem; 3 Helpful Optimization Problems and Tools; 3.1 2-Dimensional Bin-Packing Problem; 3.2 The Max-Min Problem; 3.3 Data-Set and Solving Tool; 3.4 Branch-and-Bound Search; 4 Proposed Approach; 5 Integer Programming Models; 5.1 Minimizing the Number of Clusters.

• 5.2 Heavy Clients Distribution5.3 Balancing the Use of Resources; 6 Discussions; 7 Conclusion; References; Part II Machine Learning; On the Application of Artificial Intelligence Techniques to Create Network Intelligence; 1 Introduction; 1.1 AI for Internet of Things; 1.2 AI for Telecommunication Networks; 2 Graph Theory for Virus Epidemic Prediction; 2.1 State of the Art; 2.2 Architecture and Implementation; 3 Machine Learning for Smart Building Energy Management; 3.1 State of the Art; 3.2 Architecture and Implementation; 4 Intelligent Middleware for Cloud Robotics; 4.1 State of the Art.

• 4.2 Architecture and Implementation5 Multiple Neural Networks for Client Profiling on Telecommunication Networks; 5.1 State of the Art; 5.2 Architecture and Implementation; 6 Alarm Prediction on Telecommunication Networks; 6.1 State of the Art; 6.2 Architecture and Implementation; 7 Conclusions and Further Applications of AI in ICT; 7.1 Future Work on the Reported Solutions; 7.2 Further Applications of AI on ICT; References; A Statistical Framework for Mental Targets Search Using Mixture Models; 1 Introduction; 2 Related Work; 3 The Framework Structure; 4 Data Model; 5 Update Model.

This book presents various recent applications of Artificial Intelligence in Information and Communication Technologies such as Search and Optimization methods, Machine Learning, Data Representation and Ontologies, and Multi-agent Systems. The main aim of this book is to help Information and Communication Technologies (ICT) practitioners in managing efficiently their platforms using AI tools and methods and to provide them with sufficient Artificial Intelligence background to deal with real-life problems.

• Introduction to the subject of an agent in computer science.- Agent versus decomposition of an algorithm.- M-agent.- The agent system for balancing the distribution of resources.- The examples of applications of the agent systems.- Conclusion. Introduction to the subject of an agent in computer science.- Agent versus decomposition of an algorithm.- M-agent.- The agent system for balancing the distribution of resources.- The examples of applications of the agent systems.- Conclusion.
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This monograph presents the concept of agents and agent systems. It starts with a formal approach and then presents examples of practical applications. In order to form the principles of construction of autonomous agents, a model of the agent is introduced. Subsequent parts of the monograph include several examples of applications of the term agent. Descriptions of different examples of applications of agent systems in such fields as evolution systems, mobile robot systems, artificial intelligence systems are given. The book constitutes an outline of methodology of the design and realization of agent systems based on the M-agent architecture oriented on different areas of applications.
(source: Nielsen Book Data)

• 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.
(source: Nielsen Book Data)

• 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.
(source: Nielsen Book Data)

• 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.
• (source: Nielsen Book Data)

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)