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• 1: Formulas in Excel
• 2: Logical Functions
• 3: Text Functions
• 4: Date and Time Functions
• 5: Basic Statistical Functions
• 6: Mathematical Functions
• 7: Basic Financial Functions
• 8: Database Functions
• 9: Lookup and Reference Functions
• 10: Conditional Formatting with Formulas
• 11: Working with Array Formulas
• 12: Special Solutions with Formulas
• 13: User-defined Functions 14:Examples
• 15: Other Features Appendix: Excel Interface Guide Index.
• (source: Nielsen Book Data)

In this completely updated edition covering Excel 2019, previous versions, and Office 365, Microsoft Excel Functions Formulas 5/E demonstrates the secrets of Excel through the use of practical and useful examples in a quick reference format. Easy to use and equipped with a variety of functions, Microsoft Excel is the tool of choice when it comes to crunching numbers, building charts, and analyzing tables. The book's extensive examples and added video tutorials make it an excellent resource for all Excel users who want to understand, create, and apply formulas. Experienced users will also find Microsoft Excel Functions & Formulas 5/E an excellent reference for many of the program's advanced formulas and functions. The text is easy to understand with numerous step-by-step instructions and the actual, ready to use, Excel screenshots of the input and output from the formulas. The book includes companion files with video tutorials, over 250 worksheet files of examples for numerous functions, formulas, and all the figures from the text. FEATURES: Includes companion files with video tutorials, over 250 Excel worksheet examples, and all of the figures from the text (including 4-color) Completely updated to cover Microsoft Excel 2019, previous versions, and Office 365 Saves hundreds of hours with the latest Excel tips, worksheets, and shortcuts Written by a proven author with Microsoft Valued Professional(MVP) status The Companion Files are also available for downloading from the publisher by emailing proof of purchase to info@merclearning.com.
(source: Nielsen Book Data)

• 1: Formulas in Excel
• 2: Logical Functions
• 3: Text Functions
• 4: Date and Time Functions
• 5: Basic Statistical Functions
• 6: Mathematical Functions
• 7: Basic Financial Functions
• 8: Database Functions
• 9: Lookup and Reference Functions
• 10: Conditional Formatting with Formulas
• 11: Working with Array Formulas
• 12: Special Solutions with Formulas
• 13: User-defined Functions 14:Examples
• 15: Other Features Appendix: Excel Interface Guide Index.
• (source: Nielsen Book Data)

In this completely updated edition covering Excel 2019, previous versions, and Office 365, Microsoft Excel Functions Formulas 5/E demonstrates the secrets of Excel through the use of practical and useful examples in a quick reference format. Easy to use and equipped with a variety of functions, Microsoft Excel is the tool of choice when it comes to crunching numbers, building charts, and analyzing tables. The book's extensive examples and added video tutorials make it an excellent resource for all Excel users who want to understand, create, and apply formulas. Experienced users will also find Microsoft Excel Functions & Formulas 5/E an excellent reference for many of the program's advanced formulas and functions. The text is easy to understand with numerous step-by-step instructions and the actual, ready to use, Excel screenshots of the input and output from the formulas. The book includes companion files with video tutorials, over 250 worksheet files of examples for numerous functions, formulas, and all the figures from the text. FEATURES: Includes companion files with video tutorials, over 250 Excel worksheet examples, and all of the figures from the text (including 4-color) Completely updated to cover Microsoft Excel 2019, previous versions, and Office 365 Saves hundreds of hours with the latest Excel tips, worksheets, and shortcuts Written by a proven author with Microsoft Valued Professional(MVP) status The Companion Files are also available for downloading from the publisher by emailing proof of purchase to info@merclearning.com.
(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)

• 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)

• 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)

• Chapter 1: Project Initiation and Scoping
• Chapter 2: Introduction to General Ledger and Fusion Accounting Hub
• Chapter 3: Implementation Project plan using Functional Setup Manager
• Chapter 4: Enterprise Structure for Financials
• Chapter 5: Financials Reporting Structure: Charts of Accounts Structures, Calendars and Currencies
• Chapter 6: Implement Ledgers and Business Unit
• Chapter 7: Security & Audit in Fusion Applications
• Chapter 8: Journals, Budgets & Consolidation
• Chapter 9: Oracle Fusion Accounting Hub
• Chapter 10: Consolidation
• Chapter 11: Allocations Using Calculation Manager
• Chapter 12: Period Close
• Chapter 13: Building Financial Reports & Analysis Dashboards.
• (source: Nielsen Book Data)

Master Oracle Fusion General Ledger and Fusion Financials Accounting Hub Maintain a centralized, highly automated financial processing platform across your organization using Oracle Fusion Applications General Ledger and Financial Accounting Hub. This hands-on Oracle Press guide thoroughly explains the deployment process and illustrates every technique through a start-to-finish case study. Find out how to plan and carry out an end-to-end implementation, integrate data from disparate sources, build budgets and journals, handle ledger and subledger accounting, and distribute mission-critical information through reports and dashboards. Implementing Oracle Fusion Applications General Ledger & Financials Accounting Hub offers complete coverage of security, auditing, and consolidation. * Explore the capabilities of Oracle Fusion Applications General Ledger and Financials Accounting Hub * Configure, perform, and track a new implementation * Construct enterprise and financials reporting structures * Work with currencies, calendars, budgets, ledgers, and journals * Secure your system through authentication and authorization * Meet your enterprise's consolidation and elimination requirements * Transform and load data from external applications * Handle General Ledger revaluation, allocation, and period close * Build feature-rich financial reports and analysis dashboards.
(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)

• Foreword xi Preface xv Acknowledgments xvii
• Chapter 1: The Critical Role of Business Insight 1 The Disruptive Nature of Data 2 An Unconventional Look at Conventional Wisdom 3 Innovating at the Speed of Data 5 Weighing Risk and Bringing the Better Part of Gut Instinct Back into the Equation 6 People, Process, Technology, and Culture 8 Starting the Journey 10 Notes 12
• Chapter 2: The Journey: Taking the First Steps toward Transforming Your Organization 13 Different Approaches 13 Juggling Multiple Challenges 15 How to Deal with Challenges Effectively 17 Executive Sponsorship: Critical to Success 18 Understanding Current Capabilities 19 Aligning Capabilities with Business Objectives 20 Let's Start the Journey 22 Taking the First Steps to Transforming Your Organization 24 Note 24
• Chapter 3: Challenged Organizations: When Rugged Individualism and Department Silos Aren't Enough 25 Getting Along One Day at a Time: Organizations at the Individual Level 27 When "Have It Your Way" Isn't a Good Thing 32 Superhighways and Dirt Roads 33 Consolidated, but Not Cohesive: Organizations at the Departmental Level 37 Subject Matter Experts and Gatekeepers 38 Understanding the True Consequences of the Challenged Levels 46 Business Transformation Strategy Objectives for Challenged Organizations 46 Notes 47
• Chapter 4: Foundational Organizations: Making the Leap to an Enterprise-Wide Approach 49 The Possibilities That Come with Patience 51 Seeing the Value across the Enterprise 51 How an Enterprise Level Organization Functions 54 Big Data: The Big Opportunity for Enterprise Level Organizations 62 Don't Let Up 62 Continuous Improvement Required 64 Business Transformation Strategy Objectives for Foundational Organizations 64
• Chapter 5: Progressive Organizations: Harnessing the Power of Information to Achieve Market Advantage and Expand Their Business Offerings 67 Optimization: The Easiest Business Case of All 69 Toward Innovation and Beyond 79 Business Transformation Strategy Objectives for Progressive Organizations 87
• Chapter 6: Centers of Excellence: The Key to Accelerate Organizational Transformation 89 The 10,000-Foot View of Information 92 A Quick Look at the Key Responsibilities of a CoE 93 CoEs and the Levels of Maturity 95 How Should CoEs Be Organized? 96 Accelerating Maturity Not Creating Dependency 97 Finding the Right Spot in the Org Chart 99 Mapping the Mini-Units That a CoE Might Host 99 How the CoE Helps Secure the Organizational Pillars 104 Phased versus Big Bang Approach for Starting a CoE 109 Finding the Right Funding Mechanism 109 Selecting the Right Personalities 110 Ramping Up Your Change Agent 111 Note 112
• Chapter 7: Starting the Journey: Developing a Strategy and Roadmap to Guide Your Business Transformation 113 Knowing Where to Start 114 Riyad Bank's Enterprise Business Intelligence Competency Center 114 E.SUN Bank's Customer Risk Value Organization 118 Success Story Takeaways 120 Applying the Lessons from E.SUN and Riyad 121 The Most Important Characteristics of Successful Business Transformation Strategies 121 A Step-by-Step Look at the Key Components 122 Identifying a Starting Point 126 Summing It All Up 127 In the End, It's about Being a Leader 128 Appendix: Snapshot of the Information Evolution Model 131 The Individual Level: Getting Along One Day at a Time 131 Departmental Level: The Consolidated Organization 136 The Enterprise Organization: A Common Sense of Purpose 142 The Optimize Level Organization: Aligned and Ready 149 The Innovate Level: Spawning and Supporting New Ideas 156 Glossary 165 About the Author 173 Index 175.
• (source: Nielsen Book Data)

Effectively introduce and promote analytics within your enterprise All companies use information to set strategies and accomplish business objectives. But how many CEOs and CIOs would say they are satisfied that their companies get maximum value from information? Business Transformation reveals how SAS's Information Evolution Model (IEM) can be used together with analytics for groundbreaking results. Author Aiman Zeid provides the necessary information you need to introduce and promote the use of analytics and insight across your organization. Along with examples and best practices of global companies that have successfully been through this process, you'll learn how to identify the starting point and develop a road map for execution. * Reveals how to introduce and promote the use of analytics and insights across your organization * Written by a lead developer at SAS global Business Intelligence Competency Center program and services * Features global case studies and examples Practical and insightful, this reference provides businesses with an essential blueprint for creating improvements that optimize business returns and put the potential of data analytics to work.
(source: Nielsen Book Data)

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

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

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

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

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

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

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

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

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

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

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

• Cover; Copyright; Credits; Foreword; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface;
• Chapter 1: Getting Started with MicroStrategy; Introduction; Installing SQL Server 2012 Express LocalDB; Installing SQL Server Native Client 11.0; Installing SQL Server 2012 Command Line Utilities; Setting up the AdventureWorks DW sample database; Installing the .NET Framework 4.0 and the 4.0.2 update; Checking whether IIS is enabled and working; Installing MicroStrategy Suite; Registering the MicroStrategy License; Metadata and data warehouse.

• Creating ODBC DSN for metadata and data warehouseModifying the logon account for the Intelligence Server; Creating the metadata, and configuring the Intelligence Server; Opening the MicroStrategy Desktop application; Chapter 2: The First Steps in a MicroStrategy Project; Introduction; Creating an empty project; Setting up a data warehouse connection and selecting tables; Modifying a table structure; Using logical tables to create custom views; Generating constants with SELECT statements; Chapter 3: Schema Objects
• Attributes; Introduction; Attribute forms
• ID and DESC.

• Using functions in an attribute formParent-child relationship I; Parent-child relationship II; Other attribute forms; Selecting which forms are displayed; Building Data Explorer Hierarchies; Creating an attribute only report; Parent-child relationship in a report; Filters on attributes; Chapter 4: Objects
• Facts and Metrics; Introduction; Creating a simple counter fact and metric; Using SQL View to inspect SELECT statements; Understanding the GROUP BY clause; Adding more facts; Filters on metrics; Creating ranking metrics; Grouping at a different level (level metrics).

• Embedding filters inside metricsUsing Metric Join Type in reports; Creating a previous month metric (transformation); Chapter 5: Data Display and Manipulation
• Reports; Introduction; Going deeper into data with drill down; Manipulating grids
• Pivot and page-by; Dynamically adding and removing objects in reports; The bottom line
• customizing subtotals; Avoiding missteps
• NULL values in facts; How to sort data in grids; Emphasizing numbers with conditional formatting; Printing and exporting reports; Restricting results with view filters; Chapter 6: Data Analysis and Visualization
• Graphs.

• IntroductionDisplaying both grid and graph in the same view; Drag-and-drop objects using drop zones; Beautify your chart; Display multiple metrics with dual axis charts; Conditional formatting with thresholds; Chapter 7: Analysis on the Web
• Documents and Dashboards; Introduction; Setting up MicroStrategy Web; Creating your first ""Hello World"" document; Adding data to a document
• the dataset; Modifying the grouping in a document; Stepping up to dashboards; Using the editable mode to fine-tune the design; Adding interactivity with panels and selectors; Embedding images, HTML, and links.

Written in a cookbook style, this book will teach you through the use of recipes with examples and illustrations. Each recipe contains step-by-step instructions about everything necessary to execute a particular task.This book is intended for both BI and database developers who want to expand their knowledge of MicroStrategy. It is also useful for advanced data analysts who are evaluating different technologies. You do not need to be an SQL master to read this book, yet knowledge of some concepts like foreign keys and many-to-many relationships is assumed. Some knowledge of basic concepts such as dimensional modeling (fact tables, dimensions) will also help your comprehension of this book. No previous MicroStrategy knowledge is needed.
(source: Nielsen Book Data)

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

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

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

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

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

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

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

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

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