Business Intelligence: Data Mining and Optimization for Decision Making (3版)

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【簡介】 Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide. 【目錄】 Preface. I Components of the decision-making process. 1 Business intelligence. 1.1 Effective and timely decisions. 1.2 Data, information and knowledge. 1.3 The role of mathematical models. 1.4 Business intelligence architectures. 1.5 Ethics and business intelligence. 1.6 Notes and readings. 2 Decision support systems. 2.1 Definition of system. 2.2 Representation of the decision-making process. 2.3 Evolution of information systems. 2.4 Definition of decision support system. 2.5 Development of a decision support system. 2.6 Notes and readings. 3 Data warehousing. 3.1 Definition of data warehouse. 3.2 Data warehouse architecture. 3.2.1 ETL tools. 3.3 Cubes and multidimensional analysis. 3.4 Notes and readings. II Mathematical models and methods. 4 Mathematical models for decision making. 4.1 Structure of mathematical models. 4.2 Development of a model. 4.3 Classes of models. 4.4 Notes and readings. 5 Data mining. 5.1 Definition of data mining. 5.2 Representation of input data. 5.3 Data mining process. 5.4 Analysis methodologies. 5.5 Notes and readings. 6 Data preparation. 6.1 Data validation. 6.2 Data transformation. 6.3 Data reduction. 7 Data exploration. 7.1 Univariate analysis. 7.2 Bivariate analysis. 7.3 Multivariate analysis. 7.4 Notes and readings. 8 Regression. 8.1 Structure of regression models. 8.2 Simple linear regression. 8.3 Multiple linear regression. 8.4 Validation of regression models. 8.5 Selection of predictive variables. 8.6 Notes and readings. 9 Time series. 9.1 Definition of time series. 9.2 Evaluating time series models. 9.3 Analysis of the components of time series. 9.4 Exponential smoothing models. 9.5 Autoregressive models. 9.6 Combination of predictive models. 9.7 The forecasting process. 9.8 Notes and readings. 10 Classification. 10.1 Classification problems. 10.2 Evaluation of classification models. 10.3 Classification trees. 10.4 Bayesian methods. 10.5 Logistic regression. 10.6 Neural networks. 10.7 Support vector machines. 10.8 Notes and readings. 11 Association rules. 11.1 Motivation and structure of association rules. 11.2 Single-dimension association rules. 11.3 Apriori algorithm. 11.4 General association rules. 11.5 Notes and readings. 12 Clustering. 12.1 Clustering methods. 12.2 Partition methods. 12.3 Hierarchical methods. 12.4 Evaluation of clustering models. 12.5 Notes and readings. III Business intelligence applications. 13 Marketing models. 13.1 Relational marketing. 13.2 Salesforce management. 13.3 Business case studies. 13.4 Notes and readings. 14 Logistic and production models. 14.1 Supply chain optimization. 14.2 Optimization models for logistics planning. 14.3 Revenue management systems. 14.4 Business case studies. 14.5 Notes and readings. 15 Data envelopment analysis. 15.1 Efficiency measures. 15.2 Efficient frontier. 15.3 The CCR model. 15.4 Identification of good operating practices. 15.5 Other models. 15.6 Notes and readings. Appendix A Software tools. Appendix B Dataset repositories. References. Index.

Artificial Intelligence：A Guide To Intelligent Systems (4版)

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【簡介】 What are the principles behind intelligent systems? How are they built? What are intelligent systems useful for? How do we choose the right tool for the job? These questions are answered by Michael Negnevitsky’s Artificial Intelligence : A Guide to Intelligent Systems. Unlike many books on computer intelligence, which use complex computer science terminology and are crowded with complex matrix algebra and differential equations, this text demonstrates that the ideas behind intelligent systems are simple and straightforward. This text assumes little or no programming experience as it tackles topics like expert systems, fuzzy systems, artificial neural networks, evolutionary computation, knowledge engineering, and data mining. 【目錄】 Introduction to Intelligent Systems 1.1 Intelligent Machines, or What Machines Can Do 1.2 The History of Artificial Intelligence, or From the ‘Dark Ages’ to Knowledge-based Systems 1.3 Generative AI 1.4 Summary Questions for Review References Expert Systems 2.1 Introduction, or Knowledge Representation Using Rules 2.2 The Main Players in the Expert System Development Team 2.3 Structure of a Rule-based Expert System 2.4 Fundamental characteristics of an expert system 2.5 Forward Chaining and Backward Chaining Inference Techniques 2.6 MEDIA ADVISOR : A Demonstration Rule-based Expert System 2.7 Conflict Resolution 2.8 Uncertainty Management in Rule-based Expert Systems 2.9 Advantages and Disadvantages of Rule-based Expert systems 2.10 Summary Questions for Review References Fuzzy Systems 3.1 Introduction, or What Is Fuzzy Thinking? 3.2 Fuzzy Sets 3.3 Linguistic Variables and Hedges 3.4 Operations of Fuzzy Sets 3.6 Fuzzy Inference 3.7 Building a Fuzzy Expert System 3.8 Summary Questions for Review References Frame-based Systems and Semantic Networks 4.1 Introduction, or What Is a Frame? 4.2 Frames as a Knowledge Representation Technique 4.3 Inheritance in Frame-based Systems 4.4 Methods and Demons 4.5 Interaction of Frames and Rules 4.6 Buy Smart : A Frame-based Expert System 4.7 The Web of Data 4.8 RDF – Resource Description Framework and RDF Triples 4.9 Turtle, RDF Schema and OWL 4.10 Querying the Semantic Web with SPARQL 4.11 Summary Questions for Review References Artificial Neural Networks 5.1 Introduction, or How the Brain Works 5.2 The Neuron as a Simple Computing Element 5.3 The Perceptron 5.4 Multilayer Neural Networks 5.5 Accelerated Learning in Multilayer Neural Networks 5.6 The Hopfield Network 5.7 Bidirectional Associative Memory 5.8 Self-organising Neural Networks 5.9 Reinforcement Learning 5.10 Summary Questions for Review References Deep Learning and Convolutional Neural Networks 6.1 Introduction, or How “Deep” Is a Deep Neural Network? 6.2 Image Recognition or How Machines See the World 6.3 Convolution in Machine Learning 6.4 Activation Functions in Deep Neural Networks 6.5 Convolutional Neural Networks 6.6 Back-propagation Learning in Convolutional Networks 6.7 Batch Normalisation 6.8 Summary Questions for Review References Evolutionary Computation 7.1 Introduction, or Can Evolution Be Intelligent? 7.2 Simulation of Natural Evolution 7.3 Genetic Algorithms 7.4 Why Genetic Algorithms Work 7.5 Maintenance Scheduling with Genetic Algorithms 7.6 Genetic Programming 7.7 Evolution Strategies 7.8 Ant Colony Optimisation 7.9 Particle Swarm Optimisation 7.10 Summary Questions for Review References Hybrid Intelligent Systems 8.1 Introduction, or How to Combine German Mechanics with Italian Love 8.2 Neural Expert Systems 8.3 Neuro-Fuzzy Systems 8.4 ANFIS : Adaptive Neuro-Fuzzy Inference System 8.5 Evolutionary Neural Networks 8.6 Fuzzy Evolutionary Systems 8.7 Summary Questions for Review References Knowledge Engineering 9.1 Introduction, or What Is Knowledge Engineering? 9.2 Will an Expert System Work for My Problem? 9.3 Will a Fuzzy Expert System Work for My Problem? 9.4 Will a Neural Network Work for My Problem? 9.5 Will a Deep Neural Network Work for My Problem? 9.6 Will Genetic Algorithms Work for My Problem? 9.7 Will Particle Swarm Optimisation Work for My Problem? 9.8 Will a Hybrid Intelligent System Work for My Problem? 9.9 Summary Questions for Review References Data Mining and Knowledge Discovery 10.1 Introduction, or What Is Data Mining? 10.2 Statistical Methods and Data Visualisation 10.3 Principal Components Analysis 10.4 Relational Databases and Database Queries 10.5 The Data Warehouse and Multidimensional Data Analysis 10.6 Decision Trees 10.7 Association Rules and Market Basket Analysis 10.8 Summary Questions for Review References Glossary Index

INTELLIGENT ANALYSIS OF FUNDUS IMAGES

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"This comprehensive compendium designs deep neural network models and systems for intelligent analysis of fundus imaging. In response to several blinding fundus diseases such as Retinopathy of Prematurity (ROP), Diabetic Retinopathy (DR) and Macular Edema (ME), different image acquisition devices and fundus image analysis tasks are elaborated. From the actual fundus disease analysis tasks, various deep neural network models and experimental results are constructed and analyzed. For each task, an actual system for clinical application is developed. This useful reference text provides theoretical and experimental reference basis for AI researchers, system engineers of intelligent medicine and ophthalmologists. Sample Chapter(s) Preface Chapter 1: Introduction Contents: Introduction Automated Analysis for Retinopathy of Prematurity by Deep Neural Networks DeepROP: An Automated ROP Screening System Diagnosis of Diabetic Retinopathy Using Deep Neural Networks Automated Identification and Grading System of Diabetic Retinopathy Using Deep Neural Networks Automated Segmentation of Macular Edema in OCT Using Deep Neural Networks DeepUWF: An Automated Ultrawide-field Fundus Screening System via Deep Learning DeepUWF-Plus: Automatic Fundus Identification and Diagnosis System Based on Ultrawide-field Fundus Imaging Readership: Researchers, professionals, academics and graduate students in neural networks and machine learning."