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"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)