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Intelligent Data Analysis for Real-Life Applications

Book Description

With the recent and enormous increase in the amount of available data sets of all kinds, applying effective and efficient techniques for analyzing and extracting information from that data has become a crucial task. Intelligent Data Analysis for Real-Life Applications: Theory and Practice investigates the application of Intelligent Data Analysis (IDA) to these data sets through the design and development of algorithms and techniques to extract knowledge from databases. This pivotal reference explores practical applications of IDA, and it is essential for academic and research libraries as well as students, researchers, and educators in data analysis, application development, and database management.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  5. Foreword
  6. Preface
  7. Section 1: Machine Learning Methods Applied to Real-World Problems
    1. Chapter 1: A Discovery Method of Attractive Rules from the Tabular Structured Data
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. DISCOVERY METHOD OF FREQUENT PATTERNS
      5. TABULAR STRUCTURED DATA
      6. EVALUATION CRITERIA
      7. EXPERIMENTAL EVALUATION
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
    2. Chapter 2: Learning Different Concept Hierarchies and the Relations Between them from Classified Data
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. APPROACH
      5. EXPERIMENTS
      6. CONCLUSION
    3. Chapter 3: Individual Prediction Reliability Estimates in Classification and Regression
      1. ABSTRACT
      2. INTRODUCTION
      3. MOTIVATIONS FROM THE FIELD OF MODEL ANALYSIS
      4. RELIABILITY ESTIMATION
      5. EMPIRICAL EVALUATION OF ESTIMATORS
      6. APPLICATION ON A MEDICAL DOMAIN
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
    4. Chapter 4: Landmark Sliding for 3D Shape Correspondence
      1. ABSTRACT
      2. BACKGROUND
      3. LANDMARK SLIDING
      4. CONCLUSION
    5. Chapter 5: Supervised Classification with Bayesian Networks
      1. ABSTRACT
      2. INTRODUCTION
      3. SEMI-NAÏVE BAYESIAN NETWORK CLASSIFIERS
      4. MULTI-DIMENSIONAL BAYESIAN NETWORKS CLASSIFIERS
      5. OTHER NETWORKS-BASED PROBABILISTIC CLASSIFIERS
      6. HOW TO HANDLE NUMERIC VARIABLES
      7. APPLICATIONS
      8. CONCLUSION
  8. Section 2: Machine Learning Applications in Computer Vision
    1. Chapter 6: Decay Detection in Citrus Fruits Using Hyperspectral Computer Vision
      1. ABSTRACT
      2. INTRODUCTION
      3. OBJECTIVE
      4. HYPERSPECTRAL VISION SYSTEM
      5. VEGETAL MATERIAL
      6. LABELLED DATA SET
      7. ARTIFICIAL INTELLIGENCE ALGORITHMS: NEURAL NETWORKS
      8. SEGMENTATION MODELS BASED ON NEURAL NETWORKS
      9. SEGMENATION OF COMMON DAMAGE AND DECAY
      10. FRUIT CLASSIFICATION
      11. CONCLUSION
    2. Chapter 7: In-line Sorting of Processed Fruit Using Computer Vision
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. DESCRIPTION OF THE INSPECTION MACHINE
      5. CASE STUDY 1: POMEGRANATE ARILS
      6. CASE STUDY 2: SATSUMA SEGMENTS
      7. FUTURE TRENDS
      8. CONCLUSION
    3. Chapter 8: Detecting Impact Craters in Planetary Images Using Machine Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. APPROACHES TO AUTO-DETECTION OF CRATERS
      4. TOWARD ROBUST DETECTION OF CRATERS
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    4. Chapter 9: Integration of the Image and NL-text Analysis/Synthesis Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. APPLIED ONTOLOGY
      4. HYBRID SYSTEM OF IMAGES ANALYSIS
      5. SYSTEM OF THE CONCEPTUAL IMAGE SYNTHESIS
      6. SYSTEM OF THE NL-TEXT LINGUISTIC ANALYSIS
      7. SYSTEM OF THE NL-TEXT LINGUISTIC SYNTHESIS
      8. SYSTEM INTERACTION SCHEME
      9. MACHINE LEARNING IN NATURAL LANGUAGE PROCESSING
      10. CONCLUSION
  9. Section 3: Other Machine Learning Applications
    1. Chapter 10: Fault-Tolerant Control of Mechanical Systems Using Neural Networks
      1. ABSTRACT
      2. INTRODUCTION
      3. SYSTEM MODEL AND CONTROL OBJECTIVE
      4. NEURAL NETWORK FUNDAMENTALS
      5. MAIN FOCUS OF THE CHAPTER
      6. CASE STUDIES
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
    2. Chapter 11: Supervision of Industrial Processes using Self Organizing Maps
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. VISUALIZING PROCESS-RELATED KNOWLEDGE WITH THE SOM
      5. DEFINITION OF A PLANE
      6. VISUALIZATION OF PROCESS VARIABLES
      7. CLUSTER VISUALIZATION
      8. CASE BASED REASONING
      9. VISUALIZATION OF MODELS
      10. VISUALIZATION OF RULES
      11. CORRELATION DISCOVERY
      12. VISUALIZATION OF PROCESS DYNAMICS
      13. PROJECTION OF STATE TRAJECTORY
      14. ANALYSIS OF THE TRAJECTORY
      15. NOVELTY DETECTION AND VISUALIZATION
      16. DISSIMILARITY MAPS
      17. APPLICATION CASES
      18. SUPERVISION OF A COLD ROLLING MILL
      19. FUTURE RESEARCH DIRECTIONS
      20. CONCLUSION
    3. Chapter 12: Learning and Explaining the Impact of Enterprises’ Organizational Quality on their Economic Results
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. GENERAL EXPLANATION METHOD AND VIZUALIZATION
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    4. Chapter 13: Automatic Text Classification from Labeled and Unlabeled Data
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. A RBF SEMI-SUPERVISED CLASSIFICATION FRAMEWORK
      5. EXPERIMENTS
      6. CONCLUSION
    5. Chapter 14: Agent Based Systems to Implement Natural Interfaces for CAD Applications
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. AN AGENT-BASED ARCHITECTURE FOR SUPPORTING NATURAL INTERFACES
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    6. Chapter 15: Gaussian Process-based Manifold Learning for Human Motion Modeling
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. EXPERIMENTAL RESULTS AND DISCUSSIONS
      6. FUTURE RESEARCH ISSUES
      7. CONCLUSION
    7. Chapter 16: Probabilistic Graphical Models for Sports Video Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORKS
      4. PROBLEM FORMULATION
      5. FEATURE EXTRACTION: VISUAL OBSERVATIONS
      6. MID-LEVEL KEYWORDS DETECTION: GENERATIVE MODELS
      7. HIGH-LEVEL SEMANTICS DISCOVERY: DISCRIMINATIVE MODELS
      8. EXPERIMENTS AND DISCUSSION
      9. CONCLUSION AND FUTURE WORK
    8. Chapter 17: Static and Dynamic Multi-Robot Coverage with Grammatical Evolution Guided by Reinforcement and Semantic Rules
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. GRAMMATICAL EVOLUTION GUIDED BY REINFORCEMENT
      5. SEMANTIC RULES IN GRAMMATICAL EVOLUTION GUIDED BY REINFORCEMENT
      6. STATIC MULTI-ROBOT COVERAGE
      7. DYNAMIC MULTI-ROBOT COVERAGE
      8. RESULTS
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
    9. Chapter 18: Computer-Controlled Graphical Avatars and Reinforcement Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
  10. Compilation of References
  11. About the Contributors