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• Translator's Foreword: Why I Translate So Intelligently: Translation Metis in the Era of Google Translate Acknowledgments Preface Introduction
• 1. g: Intelligence and Genetic Fate
• 2. The "Blue Brain"
• 3. Like a Pollock Painting Conclusion Postscript to the English Translation. Artificial Intelligence: The Fourth Blow to Our Narcissism Notes Index.
• (source: Nielsen Book Data)

What is intelligence? The concept crosses and blurs the boundaries between natural and artificial, bridging the human brain and the cybernetic world of AI. In this book, the acclaimed philosopher Catherine Malabou ventures a new approach that emphasizes the intertwined, networked relationships among the biological, the technological, and the symbolic. Malabou traces the modern metamorphoses of intelligence, seeking to understand how neurobiological and neurotechnological advances have transformed our view. She considers three crucial developments: the notion of intelligence as an empirical, genetically based quality measurable by standardized tests; the shift to the epigenetic paradigm, with its emphasis on neural plasticity; and the dawn of artificial intelligence, with its potential to simulate, replicate, and ultimately surpass the workings of the brain. Malabou concludes that a dialogue between human and cybernetic intelligence offers the best if not the only means to build a democratic future. A strikingly original exploration of our changing notions of intelligence and the human and their far-reaching philosophical and political implications, Morphing Intelligence is an essential analysis of the porous border between symbolic and biological life at a time when once-clear distinctions between mind and machine have become uncertain.
(source: Nielsen Book Data)

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

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

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

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

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

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

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

"A collection of critical essays dealing with the social and ethical impacts of AI including issues of trust, reliability, and bias"-- Provided by publisher.

"The first book that provides a systematic and rigorous logical theory of causality"-- Provided by publisher.

• Chap. 1 Rethinking the Human-Agent Relationship: Which Social Cues Do Interactive Agents Really Need to Have?.- Chap. 2 Believability Through Psychosocial Behaviour: Creating Bots That Are More Engaging and Entertaining.- Chap. 3 Actor Bots.- Chap. 4 Embodied Conversational Agent Avatars in Virtual Worlds.- Chap. 5 Human-Like Combat Behaviour via Multiobjective Neuroevolution.- Chap. 6 Believable Bot Navigation via Playback of Human Traces.- Chap. 7 A Machine Consciousness Approach to the Design of Human-Like Bots.- Chap. 8 ConsScale FPS: Cognitive Integration for Improved Believability in Computer Game Bots.- Chap. 9 Assessing Believability.- Chap. 10 Making Diplomacy Bots Individual.- Chap. 11 Towards Imitation of Human Driving Style in Car Racing Games.
• (source: Nielsen Book Data)

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

"This book examines artistic practices that use machine learning and computational technologies through historical perspectives surrounding adaptive systems from the 1950s onwards"-- Provided by publisher.

• Chapter 1 Introduction
• Chapter 2 Analysis Process
• Chapter 3 How it Works
• Chapter 4 Goals
• Chapter 5 Visitor Analysis
• Chapter 6 Traffic Analysis
• Chapter 7 How People Use Content
• Chapter 8 Clickpath Analysis
• Chapter 9 Segmentation
• Chapter 10 Putting it Together
• Chapter 11 Testing
• Chapter 12 Measuring Behavior within Pages
• Chapter 13 AB Testing
• Chapter 14 Profiles
• Chapter 15 Culture
• Chapter 16 Conclusion.
• (source: Nielsen Book Data)

Practical Web Analytics for User Experience teaches you how to use web analytics to help answer the complicated questions facing UX professionals. Within this book, you'll find a quantitative approach for measuring a website's effectiveness and the methods for posing and answering specific questions about how users navigate a website. The book is organized according to the concerns UX practitioners face. Chapters are devoted to traffic, clickpath, and content use analysis, measuring the effectiveness of design changes, including A/B testing, building user profiles based on search habits, supporting usability test findings with reporting, and more. This is the must-have resource you need to start capitalizing on web analytics and analyze websites effectively.
(source: Nielsen Book Data)

"Academic, industry, and government experts provide a global survey of the financial services sector's transformation by AI, data science, and blockchain"-- Provided by publisher.

• Introduction - Ethical and Sustainable Human and Artificial AI Artificial Intelligence and Robotics Applying The Benefits of AI and Identifying Challenges and Risks Starting AI - How to Build A Machine Learning Toolbox Algorithms The Management, Roles and Responsibilities of Humans and Machines AI in Use in Industry - Reimagining Everything in the Fourth Industrial Revolution AI Case Studies .
• (source: Nielsen Book Data)

In alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed.
(source: Nielsen Book Data)

• Preface xv Acknowledgments xix About the Author xxi 1 Introduction to Reinforcement and Systemic Machine Learning 1 1
• .1. Introduction 1 1
• .2. Supervised, Unsupervised, and Semisupervised Machine Learning 2 1
• .3. Traditional Learning Methods and History of Machine Learning 4 1
• .4. What Is Machine Learning? 7 1
• .5. Machine-Learning Problem 8 1
• .6. Learning Paradigms 9 1
• .7. Machine-Learning Techniques and Paradigms 12 1
• .8. What Is Reinforcement Learning? 14 1
• .9. Reinforcement Function and Environment Function 16 1
• .10. Need of Reinforcement Learning 17 1
• .11. Reinforcement Learning and Machine Intelligence 17 1
• .12. What Is Systemic Learning? 18 1
• .13. What Is Systemic Machine Learning? 18 1
• .14. Challenges in Systemic Machine Learning 19 1
• .15. Reinforcement Machine Learning and Systemic Machine Learning 19 1
• .16. Case Study Problem Detection in a Vehicle 20 1
• .17. Summary 20 2 Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning 23 2
• .1. Introduction 23 2
• .2. What Is Systemic Machine Learning? 27 2
• .3. Generalized Systemic Machine-Learning Framework 30 2
• .4. Multiperspective Decision Making and Multiperspective Learning 33 2
• .5. Dynamic and Interactive Decision Making 43 2
• .6. The Systemic Learning Framework 47 2
• .7. System Analysis 52 2
• .8. Case Study: Need of Systemic Learning in the Hospitality Industry 54 2
• .9. Summary 55 3 Reinforcement Learning 57 3
• .1. Introduction 57 3
• .2. Learning Agents 60 3
• .3. Returns and Reward Calculations 62 3
• .4. Reinforcement Learning and Adaptive Control 63 3
• .5. Dynamic Systems 66 3
• .6. Reinforcement Learning and Control 68 3
• .7. Markov Property and Markov Decision Process 68 3
• .8. Value Functions 69 3.8
• .1. Action and Value 70 3
• .9. Learning an Optimal Policy (Model-Based and Model-Free Methods) 70 3
• .10. Dynamic Programming 71 3
• .11. Adaptive Dynamic Programming 71 3
• .12. Example: Reinforcement Learning for Boxing Trainer 75 3
• .13. Summary 75 4 Systemic Machine Learning and Model 77 4
• .1. Introduction 77 4
• .2. A Framework for Systemic Learning 78 4
• .3. Capturing the Systemic View 86 4
• .4. Mathematical Representation of System Interactions 89 4
• .5. Impact Function 91 4
• .6. Decision-Impact Analysis 91 4
• .7. Summary 97 5 Inference and Information Integration 99 5
• .1. Introduction 99 5
• .2. Inference Mechanisms and Need 101 5
• .3. Integration of Context and Inference 107 5
• .4. Statistical Inference and Induction 111 5
• .5. Pure Likelihood Approach 112 5
• .6. Bayesian Paradigm and Inference 113 5
• .7. Time-Based Inference 114 5
• .8. Inference to Build a System View 114 5
• .9. Summary 118 6 Adaptive Learning 119 6
• .1. Introduction 119 6
• .2. Adaptive Learning and Adaptive Systems 119 6
• .3. What Is Adaptive Machine Learning? 123 6
• .4. Adaptation and Learning Method Selection Based on Scenario 124 6
• .5. Systemic Learning and Adaptive Learning 127 6
• .6. Competitive Learning and Adaptive Learning 140 6
• .7. Examples 146 6
• .8. Summary 149 7 Multiperspective and Whole-System Learning 151 7
• .1. Introduction 151 7
• .2. Multiperspective Context Building 152 7
• .3. Multiperspective Decision Making and Multiperspective Learning 154 7
• .4. Whole-System Learning and Multiperspective Approaches 164 7
• .5. Case Study Based on Multiperspective Approach 167 7
• .6. Limitations to a Multiperspective Approach 174 7
• .7. Summary 174 8 Incremental Learning and Knowledge Representation 177 8
• .1. Introduction 177 8
• .2. Why Incremental Learning? 178 8
• .3. Learning from What Is Already Learned... 180 8
• .4. Supervised Incremental Learning 191 8
• .5. Incremental Unsupervised Learning and Incremental Clustering 191 8
• .6. Semisupervised Incremental Learning 196 8
• .7. Incremental and Systemic Learning 199 8
• .8. Incremental Closeness Value and Learning Method 200 8
• .9. Learning and Decision-Making Model 205 8
• .10. Incremental Classification Techniques 206 8
• .11. Case Study: Incremental Document Classification 207 8
• .12. Summary 208 9 Knowledge Augmentation: A Machine Learning Perspective 209 9
• .1. Introduction 209 9
• .2. Brief History and Related Work 211 9
• .3. Knowledge Augmentation and Knowledge Elicitation 215 9
• .4. Life Cycle of Knowledge 217 9
• .5. Incremental Knowledge Representation 222 9
• .6. Case-Based Learning and Learning with Reference to Knowledge Loss 224 9
• .7. Knowledge Augmentation: Techniques and Methods 224 9
• .8. Heuristic Learning 228 9
• .9. Systemic Machine Learning and Knowledge Augmentation 229 9
• .10. Knowledge Augmentation in Complex Learning Scenarios 232 9
• .11. Case Studies 232 9
• .12. Summary 235 10 Building a Learning System 237 10
• .1. Introduction 237 10
• .2. Systemic Learning System 237 10
• .3. Algorithm Selection 242 10
• .4. Knowledge Representation 244 10
• .5. Designing a Learning System 245 10
• .6. Making System to Behave Intelligently 246 10
• .7. Example-Based Learning 246 10
• .8. Holistic Knowledge Framework and Use of Reinforcement Learning 246 10
• .9. Intelligent Agents--Deployment and Knowledge Acquisition and Reuse 250 10
• .10. Case-Based Learning: Human Emotion-Detection System 251 10
• .11. Holistic View in Complex Decision Problem 253 10
• .12. Knowledge Representation and Data Discovery 255 10
• .13. Components 258 10
• .14. Future of Learning Systems and Intelligent Systems 259 10
• .15. Summary 259 Appendix A: Statistical Learning Methods 261 Appendix B: Markov Processes 271 Index 281.
• (source: Nielsen Book Data)

Reinforcement and Systemic Machine Learning for Decision Making explores a newer and growing avenue of machine learning algorithm in the area of computational intelligence. This book focuses on reinforcement and systemic learning to build a new learning paradigm, which makes effective use of these learning methodologies to increase machine intelligence and help us in building the advance machine learning applications. Illuminating case studies reflecting the authors' industrial experiences and pragmatic downloadable tutorials are available for researchers and professionals.
(source: Nielsen Book Data)

• The Lovelace Test
• Three types of creativity
• Ready steady go
• Algorithms, the secret to modern life
• From top-down to bottom-up
• Algorithmic evolution
• Painting by numbers
• Learning from the masters
• The art of mathematics
• The mathematician's telescope
• Music: the process of sounding mathematics
• The song-writing formula
• Deepmathematics
• Language games
• Let AI tell you a story
• Why we create: a meeting of minds.

Most books on AI focus on the future of work. But now that algorithms can learn and adapt, does the future of creativity also belong to well-programmed machines? To answer this question, Marcus du Sautoy takes us to the forefront of creative new technologies and offers a more positive and unexpected vision of our future cohabitation with machines.-- Provided by publisher.

• A realistic Overview of Intelligent Systems in Industry
• Prediction of the Attention Area in Ambient Intelligence Tasks Using Adaptive Neuro-Fuzzy Inference System (ANFIS)
• Chapter III. Integration of Knowledge Components in Hybrid Intelligent Control Systems
• Learning Intelligent Controls in High Speed Networks: Synergies of Computational Intelligence with Control and Q-Learning Theories
• Logical Operations and Inference in the Complex S-logic
• Generalized Nets as Tools for Modelling of Data Mining Processes
• Induction of Modular Classification Rules by Information Entropy Based Rule Generation
• Proposals for Knowledge Driven and Data Driven Applications in Security Systems
• On intuitionistic Fuzzy Operators from Modal Types
• Uncertain Switched Fuzzy Systems: A Robust Output Feedback Control Design
• Multistep Modeling for Approximation and Classification by Use of RBF Network Models.

This book presents a broad variety of different contemporary IT methods and applications in Intelligent Systems is displayed. Every book chapter represents a detailed, specific, far reaching and original re-search in a respective scientific and practical field. However, all of the chapters share the common point of strong similarity in a sense of being innovative, applicable and mutually compatible with each other. In other words, the methods from the different chapters can be viewed as bricks for building the next generation "thinking machines" as well as for other futuristic logical applications that are rapidly changing our world nowadays

• Modern Approaches for Grain Quality Analysis and Assessment
• Intelligent Technical Fault Condition Diagnostics of Mill Fan
• Abstraction of State-Action Space Utilizing Properties of the Body and Environment
• Trajectory Control of Manipulators Using an Adaptive Parametric Type-2 Fuzzy CMAC Friction and Disturbance Compensator
• An Evaluation of OWL-based Semantic Applications
• On Heading Change Measurement: Improvements for Any Angle Path-Planning
• CxK-Nearest Neighbor Classification with Ordered Weighted Averaging Distance
• ARTOD: Autonomous Real Time Objects Detection by a Moving Camera using Recursive Density Estimation
• Improved Genetic Algorithm for Downlink Carrier Allocation in an OFDMA System
• Structure-oriented Techniques for XML Document Partitioning
• Security Applications Using Puzzle and Other Intelligent Methods
• Semiautomatic Telecontrol by Multi-link Manipulators Using Mobile Telecameras
• Vision-Based Hybrid Map-Building and Robot Localization in Unstructured and Moderately Dynamic Environments
• Innovative Fuzzy-Neural Model Predictive Control Synthesis for Pusher Reheating Furnace
• Exactus Expert
• Search and Analytical Engine for Research and Development Support.-Acoustic and Device Feature Fusion for Load Recognition.

In this carefully edited book some selected results of theoretical and applied research in the field of broadly perceived intelligent systems are presented. The problems vary from industrial to web and problem independent applications. All this is united under the slogan: "Intelligent systems conquer the world". The book brings together innovation projects with analytical research, invention, retrieval and processing of knowledge and logical applications in technology. This book is aiming to a wide circle of readers and particularly to the young generation of IT/ICT experts who will build the next generations of intelligent systems.

• Low-Level Image Processing Based on Interval-Valued Fuzzy Sets and Scale-Space Smoothing.- Generalized Net Representation of Dataflow Process Networks.- Wireless Sensor Positioning ACO Algorithm.- Time Accounting Artificial Neural Networks for Biochemical Process Models.- Periodic Time-varying Observer-based Learning Control of A/F Ratio in Multi-cylinder IC Engines.- Fuzzy T-S Model Based Design of Min-Max Control for Uncertain Nonlinear Systems.- Modeling Parallel Optimization of the Early Stopping Method of Multilayer Perceptron.- Intelligent Controls for Switched Fuzzy Systems: Synthesis via Non-standard Lyapunov Functions.- A New Architecture for an Adaptive Switching Controller Based on Hybrid Multiple T-S Models.- Optimization of Linear Objective Function under min-Probabilistic Sum Fuzzy Linear Equations Constraint.- Intuitionistic Fuzzy Logic Implementation to Assess Purposeful Model Parameters Genesis.- Dynamic Representation and Interpretation in a Multiagent 3D Tutoring System.- Generalized Net Model of the Scapulohumeral Rhythm.- Method for Interpretation of Functions of Propositional Logic by Specific Binary Markov Processes.- Generalized Net Models of Academic Promotion and Doctoral Candidature.- Modeling Telehealth Services with Generalized Nets.- State-Space Fuzzy-Neural Predictive Control.- Free Search and Particle Swarm Optimisation Applied to Global Optimisation Numerical Tests From Two to Hundred Dimensions.-Intuitionistic Fuzzy Sets Generated by Archimedean Metrics and Ultrametrics.-Production Rule and Network Structure Models for Knowledge Extraction from Complex Processes Under Uncertainty.
• (source: Nielsen Book Data)

This volume is a brief, yet comprehensive account of new development, tools, techniques and solutions in the broadly perceived "intelligent systems". New concepts and ideas concern the development of effective and efficient models which would make it possible to effectively and efficiently describe and solve processes in various areas of science and technology. Special emphasis is on the dealing with uncertainty and imprecision that permeates virtually all real world processes and phenomena, and has to properly be modeled by formal and algorithmic tools and techniques so that they be adequate and useful. The papers in this volume concern a wide array of possible techniques exemplified by, on the one hand, logic, probabilistic, fuzzy, intuitionistic fuzzy, neuro-fuzzy, etc. approaches. On the other hand, they represent the use of such systems modeling tools as generalized nets, optimization and control models, systems analytic models, etc. They concerns a variety of approaches, from pattern recognition, image analysis, education system modeling, biological and medical systems modeling, etc.
(source: Nielsen Book Data)

• Advanced Classification of Remote Sensing High Resolution Imagery.- An Application for the Management of Natural Resources.- Analyzing the Impact of Strategic Performance Management Systems and Role Ambiguity on Performance: A Qualitative Approach.- Advanced Radial Approach to Resource Location Problems.- Verbal Decision Analysis Applied to the Prioritization of Influencing Factors in Distributed Software Development.- Agile Documentation Tool Concept.- Pervasive Business Intelligence: A Key Success Factor for Business.- Annotated Documents and Expanded CIDOC-CRM Ontology in the Automatic Construction of a Virtual Museum.
• (source: Nielsen Book Data)

This book primarily addresses Intelligent Information Systems (IIS) and the integration of artificial intelligence, intelligent systems and technologies, database technologies and information systems methodologies to create the next generation of information systems. It includes original and state-of-the-art research on theoretical and practical advances in IIS, system architectures, tools and techniques, as well as "success stories" in intelligent information systems. Intended as an interdisciplinary forum in which scientists and professionals could share their research results and report on new developments and advances in intelligent information systems, technologies and related areas - as well as their applications - , it offers a valuable resource for researchers and practitioners alike.
(source: Nielsen Book Data)

• Chapter 1: what a fuzzy set is and what it is not?.-
• Chapter 2: Fuzzy random variables a la Kruse & Meyer and a la Puri & Ralescu: key differences and coincidences.-
• Chapter 3: Statistical Inference for Incomplete Ranking Data: A Comparison of two Likelihood-Based Estimators.-
• Chapter 4: Interval Type-2 Defuzzification Using Uncertainty Weights.-
• Chapter 5: Exploring time-resolved data for patterns and validating single clusters.-
• Chapter 6: Interpreting Cluster Structure in Waveform Data with Visual Assessment and Dunn's Index.-
• Chapter 7: A shared encoder DNN for integrated recognition and segmentation of traffic scenes.-
• Chapter 8: Fuzzy ontology support for knowledge mobilisation.
• (source: Nielsen Book Data)

This book is a collection of several contributions which show the state of the art in specific areas of Computational Intelligence. This carefully edited book honors the 65th birthday of Rudolf Kruse. The main focus of these contributions lies on treating vague data as well as uncertain and imprecise information with automated procedures, which use techniques from statistics, control theory, clustering, neural networks etc. to extract useful and employable knowledge. .
(source: Nielsen Book Data)

• Chapter 1: Requirements for Digital Preservation.-
• Chapter 2: Metadata in Long-term Digital Preservation.-
• Chapter 3: The CREDO Project.-
• Chapter 4: CREDO Repository Architecture.-
• Chapter 5: Information Processing in CREDO Long-term Archive.-
• Chapter 6: Metadata in CREDO Long-term Archive.-
• Chapter 7: Persistence Management in Long-term Digital Archive.-
• Chapter 8: Power Efficiency and Scheduling Access to the Archive.-
• Chapter 9: Information Management in Federated Digital Archives.
• (source: Nielsen Book Data)

This book addresses the process of maintaining digital objects through time to ensure continued access, an aspect that has become a crucial issue in recent years. It offers a concise yet comprehensive discussion of key concepts and requirements for long-term digital preservation, and presents a pioneering framework for digital repositories that enables the long-term archiving and metadata management for large volumes of digital resources based on a system that has already been completely designed and launched. In the framework, the reliability of information readouts is ensured by the repository with two-level data recording replication and monitoring mechanisms in the repository management system (RMS) and the file systems, and by the RMS's distributed nature. The advanced RMS allows operations on the archival storage to be scheduled, while also taking into account low energy consumption requirements. After presenting the framework in detail, the book assesses and demonstrates the approach's viability in terms of delivering accessibility, authenticity and usability. As such, the book offers a valuable resource for information technology (IT) researchers and practitioners, as well as archivists and librarians.
(source: Nielsen Book Data)

• Part I. Introduction;
• Chapter 1. Semantic Web; 1.1 Description Logics and Semantic Web; 1.2 The Current Vision of the Semantic Web;
• Chapter 2. Probability; 2.1 Probabilistic Inference; 2.2 Probabilistic Learning;
• Chapter 3. Aims of the Thesis;
• Chapter 4. Structure of the Thesis; Part II. Description Logics;
• Chapter 5. Foundations of Description Logics;
• Chapter 6. Description Logics' Characteristics; 6.1 Concept and Role Constructors; 6.2 Family of DLs; 6.3 Knowledge Base; 6.3.1 TBox; 6.3.2 RBox; 6.3.3 ABox; 6.4 Semantics.

• Chapter 7. Significant Examples of Description Logics;
• Chapter 8. OWL: the Web Ontology Language;
• Chapter 9. Inference in Description Logics; 9.1 Approaches to Compute Explanations; 9.1.1 Solving min-a-enum: The Standard Definition; 9.1.2 Resolving min-a-enum: Pinpointing Formula; Part III. A Probabilistic Semantics for Description Logics;
• Chapter 10. Distribution Semantics; 10.1 Formal Definition; 10.2 PLP Languages under the Distribution Semantics; 10.2.1 Logic Programming; 10.2.2 LPAD; 10.2.3 ProbLog; 10.3 Inference in Probabilistic Logic Programming; 10.3.1 ProbLog Inference System.

• 10.3.2 PITA; 10.4 Learning in Probabilistic Logic Programming;
• Chapter 11. DISPONTE;
• Chapter 12. Probabilistic Description Logics; Part IV. Inference in Probabilistic DLs;
• Chapter 13. Inference; 13.1 Splitting Algorithm; 13.2 Binary Decision Diagrams;
• Chapter 14. BUNDLE;
• Chapter 15. TRILL; 15.1 TRILL on SWISH;
• Chapter 16. TRILL P;
• Chapter 17. Complexity of Inference;
• Chapter 18. Related Inference Systems;
• Chapter 19. Experiments; 19.1 BUNDLE: Comparison with PRONTO; 19.2 BUNDLE: Not Entailed Queries; 19.3 BUNDLE: Inference with Limited Number of Explanations; 19.4 BUNDLE: Scalability.

• 19.5 TRILL, TRILL P & BUNDLE: Comparing Different Approaches; 19.6 Discussion; Part V. Learning in Probabilistic DLs;
• Chapter 20. Learning;
• Chapter 21. EDGE: Parameter Learning; 21.1 Expectation Maximization Algorithm; 21.2 EDGE;
• Chapter 22. LEAP: Structure Learning; 22.1 CELOE; 22.2 LEAP;
• Chapter 23. Distributed Learning; 23.1 Map Reduce Approach; 23.2 The Message Passing Interface Standard; 23.3 EDGE MR; 23.4 LEAP MR;
• Chapter 24. Related Learning Systems;
• Chapter 25. Experiments; 25.1 EDGE: Comparison with Association Rules; 25.2 LEAP & EDGE: a Comparison Between Different Learning Problems.

• 25.3 EDGE MR: Parallelization Speedup; 25.4 EDGE MR: Memory Consumption; 25.5 LEAP MR: Parallelization Speedup; 25.6 Discussion; Part VI. Summary and Future Work;
• Chapter 26. Conclusion;
• Chapter 27. Future Work.

• Machine generated contents note: Survey on Applying Machine Learning Techniques for Behavioral Awareness / Wouter Joosen
• Modelling Spatial and Temporal Context to Support Activity Recognition / Stephen Marsland
• Affect Aware Ambient Intelligence: Current and Future Directions / Anne James
• Behavioral Biometrics and Ambient Intelligence: New Opportunities for Context-Aware Applications / Paulo Novais
• Energy and Environmental Long-Term Monitoring System for Inhabitants' Weil-Being / Filippo Palumbo
• Behavioural Patterns from Cellular Data Streams and Outdoor Lighting as Strong Allies for Smart Urban Ecosystems / Adam Sedziwy
• Learning Daily Routines in Smart Office Environments / Stefano Ferilli
• EKRUCAmI Architecture -- Applications in Healthcare Domain / Hyun Yoe
• Qualitative Image Descriptor QIDL+N to Obtain Logics and Narratives Applied to Ambient Intelligent Systems / Zoe Falomir.

Annotation We are moving towards a future where environments respond to human preferences and needs. In this world, smart devices equipped with intelligent features and the capability to sense, communicate with and support humans in daily activities will be unremarkable. We already expect our cars to warn us of hazards, track our location and provide timely route advice, and in future we will speak to simple machines and hold conversations with more complex systems, such as intelligent homes, which will help us to monitor conditions, track routine tasks, and program the heating, lighting, garden watering and entertainment centre. But questions have been raised in recent years as to how intelligent these so called smart systems or ambient intelligence environments really are. This book, State of the Art in AI Applied to Ambient Intelligence, part of the outcome of the Workshop on Artificial Intelligence Techniques for Ambient Intelligence (AITAmI) which has now run for 10 consecutive editions, aims to provide a clear picture of what has been achieved after a decade of discussion. It is representative of the diversity of approaches and issues which are currently being considered, and also indicates those avenues which are the most promising for exploration in the next decade. The book provides all those working in the field with an up-to-date reference where they will find inspiration to create better systems for the society of tomorrow.

• Part 1: Historical Review of Professor Zdzislaw Pawlak and Rough Set.- The Born and Growing of Chinese Rough Set Community with Help of Professor Zdzislaw Pawlak.- Zdzislaw Pawlak as I Saw Him and Remember Him Now.- Recent Development of Rough Computing: A Scientometrics View.-
• Part 2: Review of Rough Set Research.- Rough Sets, Rough Mereology and Uncertainty.- Rough Sets in Machine Learning: A Review.- Application of Tolerance Rough Sets in Structured and Unstructured Text Categorization: A Survey.- Medical Diagnosis: Rough Set View.- Rough Set Analysis of Imprecise Classes.- Pawlak's Many Valued Information System, Non-deterministic Information System, and a Proposal of New Topics on Information Incompleteness Toward the Actual Application.-
• Part 3: Rough Set Theory.- From Information Systems to Interactive Information Systems.- Back to the Beginnings: Pawlak's Definitions of the Terms Information System and Rough Set.- Knowledge and Consequence in AC Semantics for General Rough Sets.- Measuring Soft Roughness of Soft Rough Sets Induced by Covering.- Rough search of vague knowledge.- Vagueness and Uncertainty: An F-Rough Set Perspective.- Directions of Use of the Pawlak's Approach to Conflict Analysis.- Lattice Structure of Variable Precision Rough Sets.-
• Part 4: Rough Set Based Data Mining.- Mining for Actionable Knowledge in Tinnitus Datasets.- Rough-Granular Computing for Relational Data.- The Boosting and Bootstrap Ensembles for the Pair Classifier Based on the Dual Indiscernibility Matrix.
• (source: Nielsen Book Data)

This special book is dedicated to the memory of Professor Zdzislaw Pawlak, the father of rough set theory, in order to commemorate both the 10th anniversary of his passing and 35 years of rough set theory. The book consists of 20 chapters distributed into four sections, which focus in turn on a historical review of Professor Zdzislaw Pawlak and rough set theory; a review of the theory of rough sets; the state of the art of rough set theory; and major developments in rough set based data mining approaches. Apart from Professor Pawlak's contributions to rough set theory, other areas he was interested in are also included. Moreover, recent theoretical studies and advances in applications are also presented. The book will offer a useful guide for researchers in Knowledge Engineering and Data Mining by suggesting new approaches to solving the problems they encounter.
(source: Nielsen Book Data)

• Introduction
• A Swarm Global Optimization Algorithm Inspired in the Behavior of the Social-spider.-A States of Matter Algorithm for Global Optimization
• An Algorithm for Global Optimization Inspired by Collective Animal Behavior
• A Bio-inspired Evolutionary Algorithm: Allostatic Optimization
• Optimization Based on the Behavior of Locust Swarms.

The goal of this book is to present advances that discuss alternative Evolutionary Computation (EC) developments and non-conventional operators which have proved to be effective in the solution of several complex problems. The book has been structured so that each chapter can be read independently from the others. The book contains nine chapters with the following themes: 1) Introduction, 2) the Social Spider Optimization (SSO), 3) the States of Matter Search (SMS), 4) the collective animal behavior (CAB) algorithm, 5) the Allostatic Optimization (AO) method, 6) the Locust Search (LS) algorithm, 7) the Adaptive Population with Reduced Evaluations (APRE) method, 8) the multimodal CAB, 9) the constrained SSO method.

• Introduction and Related Work
• Optimal State Change Attempts
• Different Kinds of Effect Estimators
• A Comparison with Planning under Uncertainty
• Experimental Evaluation
• Conclusions.

This Springer Brief presents a basic algorithm that provides a correct solution to finding an optimal state change attempt, as well as an enhanced algorithm that is built on top of the well-known trie data structure. It explores correctness and algorithmic complexity results for both algorithms and experiments comparing their performance on both real-world and synthetic data. Topics addressed include optimal state change attempts, state change effectiveness, different kind of effect estimators, planning under uncertainty and experimental evaluation. These topics will help researchers analyze tabular data, even if the data contains states (of the world) and events (taken by an agent) whose effects are not well understood. Event DBs are omnipresent in the social sciences and may include diverse scenarios from political events and the state of a country to education-related actions and their effects on a school system. With a wide range of applications in computer science and the social sciences, the information in this Springer Brief is valuable for professionals and researchers dealing with tabular data, artificial intelligence and data mining. The applications are also useful for advanced-level students of computer science.

• Title Page
• Foreword
• Contents
• List of Contributors
• Part I. Software Engineering Meets Semantic Web: Concepts and Theories
• Motivation and Introduction
• Close Encounters of the Semantic Web and MDA Kinds
• Generating Model Transformations Using Ontology Engineering Space
• Part II. Realize Software Engineering by Semantic Web Technologies
• Towards a Consistent Feature Model using OWL
• Using Semantic Web Technologies for Management Application Integration
• Semantic Web Enabled Software Analysis
• Semantically Enabling Web Service Repositories

• ABC: A methodology for Semantic Web Application DevelopmentModel-driven Design Frameworks for Semantic Web Applications
• Part III. Design Ontologies for Software Engineering
• A Software Process Ontology and Its Application
• Enriching SE Ontologies with Bug Quality
• Learning Ontologies from Software Artifacts: Exploring and Combining Multiple Choices
• References

Over the last decade, ontology has become an important modeling component in software engineering. Semantic Web Enabled Software Engineering presents some critical findings on opening a new direction of the research of Software Engineering, by exploiting Semantic Web technologies. Most of these findings are from selected papers from the Semantic Web Enabled Software Engineering (SWESE) series of workshops starting from 2005. Edited by two leading researchers, this advanced text presents a unifying and contemporary perspective on the field. The book integrates in one volume a unified perspectiv.

• Making the Computer 'Brained'.- The Biology of Brain:An insight into the Human Brain.- Evolutionary Computation.- Artificial Neural Network (ANNs).- Structure and Operation of Cartesian Genetic Programming Developmental Network (CGPDN) Model.- Wumpus World.- Checkers.- Concluding Remarks and Future Directions.
• (source: Nielsen Book Data)

This book presents recent research on the evolution of artificial neural development, and searches for learning genes. It is fascinating to see how all biological cells share virtually the same traits, but humans have a decided edge over other species when it comes to intelligence. Although DNA decides the form each particular species takes, does it also account for intelligent behaviour in living beings? The authors explore the factors that are perceived as intelligent behaviour in living beings and the incorporation of these factors in machines using genetic programming, which ultimately provides a platform for exploring the possibility of machines that can learn by themselves, i.e. that can "learn how to learn". The book will be of interest not only to the specialized scientific community pursuing machine intelligence, but also general readers who would like to know more about the incorporation of intelligent behaviour in machines, inspired by the human brain.
(source: Nielsen Book Data)

• RFID-based Multilevel Sensing Network for Industrial Internet of Things.- Convey Intelligence to Edge Aggregation Analytics.- Publish-Subscribe Based Monitoring Model For Wireless Sensor Networks.- Towards Data governance in the Internet of Things.- Advancing Cognitive Cities with the Web of Things.- Transferring Wireless High Update Rate Supermedia Streams Over IoT.- Smart Connectivity for Internet of Things (IoT) Applications.- A Blockchain-based Storage System for Data Analytics in the Internet of Things.- Ensuring Quality of Service in the Internet of Things.- Wireless Sensor Network-IoT Infrastructure.
• (source: Nielsen Book Data)

This book presents a selection of highly relevant works concerning the Internet of Things, including: IoT Architectures, Standardization, Smart Cities, Smart Health, the communication of Smart Things, and outstanding IoT use cases. Some of these works present important future lines for the evolution of IoT systems. Thanks to its unique structure, the book allows authors to describe their works at an unprecedented level of detail. The special chapters contain extended versions of prominent and/or high-impact IoT projects that have laid the foundations of many subsequent IoT systems due to their level of innovation, and reflect highly relevant and innovative trends in the development of a new generation of IoT Systems.
(source: Nielsen Book Data)

• Learning automata theory.- Cellular learning automata.- Learning automata for wireless sensor networks.- Learning automata for cognitive Peer-to-peer networks.- Learning automata for Complex Social Networks.- Adaptive petri net based on learning automata.- Summary and future directions.
• (source: Nielsen Book Data)

This book collects recent theoretical advances and concrete applications of learning automata (LAs) in various areas of computer science, presenting a broad treatment of the computer science field in a survey style. Learning automata (LAs) have proven to be effective decision-making agents, especially within unknown stochastic environments. The book starts with a brief explanation of LAs and their baseline variations. It subsequently introduces readers to a number of recently developed, complex structures used to supplement LAs, and describes their steady-state behaviors. These complex structures have been developed because, by design, LAs are simple units used to perform simple tasks; their full potential can only be tapped when several interconnected LAs cooperate to produce a group synergy. In turn, the next part of the book highlights a range of LA-based applications in diverse computer science domains, from wireless sensor networks, to peer-to-peer networks, to complex social networks, and finally to Petri nets. The book accompanies the reader on a comprehensive journey, starting from basic concepts, continuing to recent theoretical findings, and ending in the applications of LAs in problems from numerous research domains. As such, the book offers a valuable resource for all computer engineers, scientists, and students, especially those whose work involves the reinforcement learning and artificial intelligence domains.
(source: Nielsen Book Data)

• Part I: From Machine Learning to Data Mining.- Analyzing Accident Prone Regions by Clustering.- Analyzing Life Insurance Data with Different Classification Techniques for Customers' Behavior Analysis.- Classification of Product Rating Using Data Mining Techniques.- MASS: a Semi-Supervised Multi-Label Classification Algorithm with Specific Feature.- Parallel Self-organizing Map Using Shared Virtual Memory Buffers.- Parametric Optimization of the Selected Classifiers in Binary Classification.- Prediction of Academic Performance During Adolescence Based on Socioeconomic, Psychological and Academic Factors.- A Super-Vector Deep Learning Coprocessor with High Performance-Power Ratio.- Part II: Big Data and Collaborative Decision Support Systems.- Bi-temporal Database Model for Legal Merger Transactions and Late-arriving Information Problem: the Case of Polish Merger Market.- Breathing Movement Analysis for Adjustment of Radiotherapy Planning.- Measuring Improvement in Access to Complete Data in Healthcare Collaborative Dabase Systems.- Rough Set Theory for Supporting Decision Making on Relevance in Browsing Multilingual Digital Resources.- The Social Influence on the Behavioral Intention to Use Mobile Electronic Medical Records.- Stock Prices Growth Pattern by the Emergency Demand After the Great East-Japan Earthquake.- Structure-Based Virtual Screening for Novel Modulators of Human Orexin 2 Receptor with Cloud Systems and Supercomputers.- Supply Chains of Cross-Border E-Commerce.- Towards Personalized Virtual Customer Experience.- Towards Big Management.- Part III: Computer Vision Analysis, Detection, Tracking and Recognition.- Combination of Collision Detection and Visibility Algorithms in Simulation of Effective Placement of Anti-Air Elements.- A Combination of Deep Learning and Hand-Designed Feature for Plant Identification based on Leaf and Flower Images.- An Efficient Defect Classification Algorithm for Ceramic Tiles.- Parking Assistant - Prediction of an Empty Parking Space in Time.- Recent Advances in the Field of Foreground Detection: An Overview.- Tracking of Bone Reparation Process with Using of Periosteal Callus Extraction based on Fuzzy C-Means Algorithm.- Part IV: Data-Intensive Text Processing.- Automatic Post-Editing of Kazakh Sentences Machine Translated from English.- Complex Technology of Machine Translation Resources Extension for the Kazakh Language.- Enhancing Latent Semantic Analysis by Embedding Tagging Algorithm in Retrieving Malay Text Documents.- Exploiting Distance Graph and Hidden Topic Models for Multi-label Text Classification.- Financial Reports and Financial News - An Information Content Gap Analysis.- The Great National Photocorpus of 20th-Century Vietnamese. Origins, assumptions and goals.- The Impact of User Sentiment Aroused by The-day-of-the-week on the Recommendation Effectiveness in Microblog.- Multi-Sentence Compression Using Word Graph and Integer Linear Programming.- A Performance Comparison of Feature Extraction Methods for Sentiment Analysis.- A Study of Plagiarism Checker System Based on Chinese Word Segmentation and SQL Intersection Operation Technique.- Text Summarization Based on Classification Using ANFIS.- Part V: Innovations in Web and Internet Technologies.- Analysis of Indoor Positioning Based on BLE.- A Fusion Technique of Schema and Syntax Rules for Validating Open Data.- Job Description Language for a Browser-Based Computing Platform - A Preliminary Report.- Measuring the Effectiveness of Knowledge Driven Web Applications.- Responsive Data Table Solution with New Scrolling Control Gesture for Better User Experience.- Part VI: New Methods and Applications in Software Engineering.- Adaptation of an ANN-Based Air Quality Forecasting Model to a New Application Area.- Checking Compliance of Program with SecureUML Model.- Generation of Test Data using Genetic Algorithm and Constraint Solver.- Investigating the Issues of using Agile Methods in Offshore Software Development in Sri Lanka.- A Method for Automated Test Cases Generation from UML Models with String ConstraintS.- Mobile Application for Calculation of Optimal Route Between Searched Points of Interest.- On Implementation of the Assumption Generation Method for Component-Based Software Verification.
• (source: Nielsen Book Data)

This book presents recent research in intelligent information and database systems. The carefully selected contributions were initially accepted for presentation as posters at the 9th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2017) held from to 5 April 2017 in Kanazawa, Japan. While the contributions are of an advanced scientific level, several are accessible for non-expert readers. The book brings together 47 chapters divided into six main parts: * Part I. From Machine Learning to Data Mining.* Part II. Big Data and Collaborative Decision Support Systems, * Part III. Computer Vision Analysis, Detection, Tracking and Recognition, * Part IV. Data-Intensive Text Processing, * Part V. Innovations in Web and Internet Technologies, and* Part VI. New Methods and Applications in Information and Software Engineering. The book is an excellent resource for researchers and those working in algorithmics, artificial and computational intelligence, collaborative systems, decision management and support systems, natural language processing, image and text processing, Internet technologies, and information and software engineering, as well as for students interested in such research areas.
(source: Nielsen Book Data)

• Data Mining and Machine Learning.- Planning and Combinatorial Optimization.- AI Applications.- Natural Language Processing.- Uncertainty and Preference Reasoning.- Agent Systems.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, held in Edmonton, AB, Canada, in May 2017. The 19 regular papers and 24 short papers presented together with 6 Graduate Student Symposium papers were carefully reviewed and selected from 62 submissions. The focus of the conference was on the following subjects: Data Mining and Machine Learning; Planning and Combinatorial Optimization; AI Applications; Natural Language Processing; Uncertainty and Preference Reasoning; and Agent Systems.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Intelligence computer data analysis techniques
• 3. The fundamentals of Cognitive Informatics
• 4. Cognitive information systems
• 5. The fundamentals of management sciences
• 6. Intelligent cognitive information systems in management application
• 7. Summary.
• (source: Nielsen Book Data)

Cognitive Information Systems in Management Sciences summarizes the body of work in this area, taking an analytical approach to interpreting the data, while also providing an approach that can be used for practical implementation in the fields of computing, economics, and engineering. Using numerous illustrative examples, and following both theoretical and practical results, Dr. Lidia Ogiela discusses the concepts and principles of cognitive information systems, the relationship between intelligent computer data analysis, and how to utilize computational intelligent approaches to enhance information retrieval. Real world implantation use cases round out the book, with valuable scenarios covering management science, computer science, and engineering. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS.
(source: Nielsen Book Data)

• Analysis and Control of a Novel 4-D Hyperchaotic System.- Analysis, Control and Synchronization of a Nine-Term Novel 3-D Chaotic System.- Backstepping Controller Design for the Global Chaos Synchronization of Sprott's Jerk Systems.- Multi-Scroll Chaotic Oscillator Based on a First-Order Delay Differential Equation.- Projective Synchronization Scheme Based on Fuzzy Controller for Uncertain Multivariable Chaotic Systems.- Deadbeat Control for for Multivariable Discrete Time Systems with Time Varying Delay.- Control of Smart Grid Residential Buildings with Demand Response.- Application of Some Modern Techniques in Load Frequency Control in Power Systems.- Investigating Metaheuristics Applications for Capacitated Location Allocation Problem in Logistics Networks.- Classification of Heart Disorders Based on Tunable-Q Wavelet Transform of Cardiac Sound Signals.- Reliability-Constrained Optimal Distribution System Reconfiguration.- Machine Learning aided Efficient Tools for Risk Evaluation and Operational Planning of Multiple Contingencies.- Goal Directed Synthesis of Serial Manipulators Based on Task Descriptions.- Intelligent Tracking Control System for Fast Image Scanning of Atomic Force Microscopes.- Fault Diagnosis Algorithms by Combining Structural Graphs and PCA Approaches for Chemical Processes.
• (source: Nielsen Book Data)

The development of computational intelligence (CI) systems was inspired by observable and imitable aspects of intelligent activity of human being and nature. The essence of the systems based on computational intelligence is to process and interpret data of various nature so that that CI is strictly connected with the increase of available data as well as capabilities of their processing, mutually supportive factors. Developed theories of computational intelligence were quickly applied in many fields of engineering, data analysis, forecasting, biomedicine and others. They are used in images and sounds processing and identifying, signals processing, multidimensional data visualization, steering of objects, analysis of lexicographic data, requesting systems in banking, diagnostic systems, expert systems and many other practical implementations. This book consists of 15 contributed chapters by subject experts who are specialized in the various topics addressed in this book. The special chapters have been brought out in the broad areas of Control Systems, Power Electronics, Computer Science, Information Technology, modeling and engineering applications. Special importance was given to chapters offering practical solutions and novel methods for the recent research problems in the main areas of this book, viz. Control Systems, Modeling, Computer Science, IT and engineering applications. This book will serve as a reference book for graduate students and researchers with a basic knowledge of control theory, computer science and soft-computing techniques. The resulting design procedures are emphasized using Matlab/Simulink software.
(source: Nielsen Book Data)

• Consequence
• Logics
• Classical Belief Revision
• AGM Contraction in Non-Classical Logics
• AGM Revision in Logics Without Negation
• Base Revision in Logics Without Negation
• Algorithms for Belief Bases
• Conclusion.

Since the advent of the Semantic Web, interest in the dynamics of ontologies (ontology evolution) has grown significantly. Belief revision presents a good theoretical framework for dealing with this problem; however, classical belief revision is not well suited for logics such as Description Logics. Belief Revision in Non-Classical Logics presents a framework which can be applied to a wide class of logics that include - besides most Description Logics such as the ones behind OWL - Horn Logic and Intuitionistic logic, amongst others. The author also presents algorithms for the most important constructions in belief bases. Researchers and practitioners in theoretical computing will find this an invaluable resource.
(source: Nielsen Book Data)

• Swarm and Evolutionary Computation.- Particle Swarm Optimization.- Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization.- Spider Monkey Optimization Algorithm.- Genetic Algorithm and Its Advances in Embracing Memetics.- Constrained Multi-Objective Evolutionary Algorithm.- Genetic Programming for Classification and Feature Selection.- Genetic Programming for Job Shop Scheduling.- Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems.
• (source: Nielsen Book Data)

This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike.
(source: Nielsen Book Data)

• Intercriteria Analysis and Arithmetic Functions.- Fuzzy Harmony Search Algorithm Using an Interval type- 2 Fuzzy Logic Applied to Benchmark Mathematical Functions.- Mixture Initialization Based on Prior Data Visual Analysis.- Spatiotemporal Parameter Estimation of Thermal Treatment Process via Initial Condition Reconstruction Using Neural Networks.- Interval type-2 Fuzzy Logic Dynamic Mutation and Cross-over Parameter Adaptation in a Fuzzy Differential Evolution Method.- Intuitionistic Fuzzy Evaluations for the Analysis of a Student's Knowledge in University e-Learning Courses.- S-logic with first and second imaginary states.- Generalized Net Model of the Processes in a Center of Transfusion Haematology.- Image to Sound Encryption Using a Self-Organizing Map Neural Network.- On Different Algorithms for InterCriteria Relations Calculation.- Defining Consonance Thresholds in InterCriteria Analysis: An Overview.- Design and Comparison of ECG Arrhythmias Classifiers Using Discrete Wavelet Transform, Neural Network and Principal Component Analysis.
• (source: Nielsen Book Data)

This book gathers extended versions of the best papers presented at the 8th IEEE conference on Intelligent Systems, held in Sofia, Bulgaria on September 4-6, 2016, which are mainly related to theoretical research in the area of intelligent systems. The main focus is on novel developments in fuzzy and intuitionistic fuzzy sets, the mathematical modelling tool of generalized nets and the newly defined method of intercriteria analysis. The papers reflect a broad and diverse team of authors, including many young researchers from Australia, Bulgaria, China, the Czech Republic, Iran, Mexico, Poland, Portugal, Slovakia, South Korea and the UK.
(source: Nielsen Book Data)

• PART 1, Data mining, machine learning, knowledge discovery, Tensor Networks for Dimensionality Reduction, Big Data and Deep Learning.- Local Data Characteristics in Learning Classifiers from Imbalanced Data.- Similarity dimensions of semantic ontologies.- Some interesting phenomenon occurring during self-learning process with its psychological interpretation.- PART 2, Neural networks and connectionist systems, On the interpretation and characterization of echo state networks dynamics: A complex systems perspective.- Optimization of Ensemble Neural Networks with Type-1 and Interval Type-2 Fuzzy Integration for Forecasting the Taiwan Stock Exchange.- Deep Neural Networks - A Brief History.- PART 3, Intelligent technologies in systems modeling, Techniques for Construction and Integration of Rule Bases.- New Aspects of Interpretability of Fuzzy Systems for Nonlinear Modeling.- On the intuitionistic fuzzy sets of n-th type.-
• PART 4. Intelligent technologies in decision making, optimization and control, MCTS/UCT in solving real-life problems.- Interactive cone contraction for evolutionary mutliple objective optimization.- A review of fuzzy and mathematic methods for dynamic parameter adaptation in the firefly algorithm.-
• PART 5. Applications of intelligent technologies Computational Intelligence Methods in Personalized Pharmacotherapy.- Embodying Intelligence in Autonomous and Robotic Systems with the Use of Cognitive Psychology and Motivation Theories.- Evolutionary Approach for Automatic Design of PID Controllers.- Fuzzy-genetic approach to identity verification using a handwritten signature.- A method of design and optimization for SiC-based grid-connected AC-DC converters.
• (source: Nielsen Book Data)

This book is a tribute to Professor Jacek Zurada, who is best known for his contributions to computational intelligence and knowledge-based neurocomputing. It is dedicated to Professor Jacek Zurada, Full Professor at the Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, J.B. Speed School of Engineering, University of Louisville, Kentucky, USA, as a token of appreciation for his scientific and scholarly achievements, and for his longstanding service to many communities, notably the computational intelligence community, in particular neural networks, machine learning, data analyses and data mining, but also the fuzzy logic and evolutionary computation communities, to name but a few. At the same time, the book recognizes and honors Professor Zurada's dedication and service to many scientific, scholarly and professional societies, especially the IEEE (Institute of Electrical and Electronics Engineers), the world's largest professional technical professional organization dedicated to advancing science and technology in a broad spectrum of areas and fields. The volume is divided into five major parts, the first of which addresses theoretic, algorithmic and implementation problems related to the intelligent use of data in the sense of how to derive practically useful information and knowledge from data. In turn, Part 2 is devoted to various aspects of neural networks and connectionist systems. Part 3 deals with essential tools and techniques for intelligent technologies in systems modeling and Part 4 focuses on intelligent technologies in decision-making, optimization and control, while Part 5 explores the applications of intelligent technologies.
(source: Nielsen Book Data)

• Introduction.- The metaheuristic algorithm of the social-spider.- Calibration of Fractional Fuzzy Controllers by using the Social-spider method.- The metaheuristic algorithm of the Locust-search.- Identification of fractional chaotic systems by using the Locust Search Algorithm.- The States of Matter Search (SMS).- Multimodal States of Matter search.- Metaheuristic algorithms based on Fuzzy Logic.
• (source: Nielsen Book Data)

This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.
(source: Nielsen Book Data)

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

This book presents the scientific outcome of the 4th ACIS International Conference on Computational Science/Intelligence & Applied Informatics (CSII 2017), which was held on July 9-13, 2017 in Hamamatsu, Japan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science, to share their experiences and to exchange new ideas and information in a meaningful way. The book includes research findings concerning all aspects (theory, applications and tools) of computer and information science, and discusses the practical challenges encountered and the solutions adopted to address them. The book features 16 of the conference's most promising papers, written by researchers who are expected to make significant contributions in the field of computer and information science.
(source: Nielsen Book Data)

• Testing, Prediction, and Cause in Econometric Models
• Information Criteria for Statistical Modeling in Data-Rich Era
• An invitation to quantum econometrics
• GL+ and GL- Regressions
• What If We Do Not Know Correlations?
• Markowitz Portfolio Theory Helps Decrease Medicines' Side Effect and Speed Up Machine Learning.

This book addresses both theoretical developments in and practical applications of econometric techniques to finance-related problems. It includes selected edited outcomes of the International Econometric Conference of Vietnam (ECONVN2018), held at Banking University, Ho Chi Minh City, Vietnam on January 15-16, 2018. 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. An extremely important part of economics is finances: a financial crisis can bring the whole economy to a standstill and, vice versa, a smart financial policy can dramatically boost economic development. It is therefore crucial to be able to apply mathematical techniques of econometrics to financial problems. Such applications are a growing field, with many interesting results? and an even larger number of challenges and open problems.

• 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. .
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• 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.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• 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.
• (source: Nielsen Book Data)

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)