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Soft Computing Applications for Database Technologies: Techniques and Issues

Book Description

Soft Computing Applications for Database Technologies: Techniques and Issues treats the new, emerging discipline of soft computing, which exploits the tolerance for imprecision and uncertainty to achieve solutions for complex problems. Soft computing methodologies include fuzzy sets, neural networks, genetic algorithms, Bayesian belief networks and rough sets, which are explored in detail through case studies and in-depth research. The advent of soft computing marks a significant paradigm shift in computing, with a wide range of applications and techniques which are presented and discussed in the chapters of this book.

Table of Contents

  1. Copyright
  2. List of Reviewers
  3. Preface
  4. 1. Fuzzy Database Modeling: An Overview and New Definitions
    1. ABSTRACT
    2. INTRODUCTION
    3. THE ZVIELI AND CHEN APPROACH (1986)
    4. PROPOSAL OF VAN GYSEGHEM AND DE CALUWE (1997)
    5. PROPOSAL BY YAZICI ET AL. (1996 AND 1999)
    6. THE CHEN AND KERRE APPROACH (1998 AND 2000)
    7. THE CHAUDHRY ET AL. APPROACH (1999)
    8. PROPOSAL OF MA ET AL. (2001, 2004 AND 2007)
    9. APPROACHES BY OTHER AUTHORS
    10. THE FUZZYEER MODEL
      1. Fuzzy Attributes
      2. Fuzzy Degrees
    11. DEFINITION OF FUZZY ATTRIBUTES IN THE FUZZYEER MODEL
      1. Fuzzy Values in Fuzzy Attributes
      2. Fuzzy Degree Associated to Each Value of an Attribute
      3. Fuzzy Degree Associated to Values of Some Attributes
      4. Fuzzy Degree with its Own Meaning
    12. FUZZY ENTITIES
    13. CONCLUSION
    14. ACKNOWLEDGMENT
    15. REFERENCES
    16. KEY TERMS AND DEFINITIONS
  5. 2. A Quick Presentation of Evolutionary Computation
    1. ABSTRACT
    2. INTRODUCTION AND HISTORY
    3. SHORT PRESENTATION OF THE EVOLUTIONARY COMPUTATION PARADIGM
    4. A UNIFIED EVOLUTIONARY ALGORITHM
      1. Representation of Individuals
      2. Evaluation (or Fitness) Function
      3. Individuals Initialisation
      4. A Generic Evolutionary Loop
    5. VARIATION OPERATORS
      1. Crossover: A (Usually) Binary Variation Operator
      2. Mutation
    6. SELECTION AND REPLACEMENT OPERATORS
    7. STOPPING CRITERIA
    8. PARAMETERS
    9. GENETIC PROGRAMMING
    10. CONCLUSION
    11. REFERENCES
    12. ENDNOTE
  6. 3. Evolutionary Algorithms in Supervision of Error-Free Control
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
      1. Advantages of the Evolutionary Algorithm Applied to Discredibility Detection
      2. Problem Statement
    4. THE STANDARD GENETIC ALGORITHM AND THE SIMULATED ANNEALING ALGORITHM IN DESCREDIBILITY DETECTION
      1. The Standard Genetic Algorithm
      2. Simulated Annealing Algorithm
      3. Comparison of Usability of the Algorithms for Discredibility Detection
      4. Testing Model-based Sensor Discredibility Detection Method
      5. Results and Findings from the Tests
    5. APPLICATION OF EVOLUTIONDY ALGORITHMS
    6. CONCLUSION
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
  7. 4. Soft Computing Techniques in Spatial Databases
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
      1. Crisp Spatial Data Types and Operations
      2. A Classification of Models for Vague Spatial Objects
    4. VASA: AN ALGEBRA FOR VAGUE SPATIAL DATA IN DATABASES
      1. What are Vague Spatial Objects?
      2. A Generic Definition of Vague Spatial Data Types
      3. What are Vague Spatial Operations?
      4. A Generic Definition of Vague Spatial Operations
    5. FUSA: AN ALGEBRA FOR FUZZY SPATIAL DATA IN DATABASES
      1. What are Fuzzy Spatial Objects?
      2. A Formal Definition of Fuzzy Spatial Data Types
      3. Fuzzy Spatial Set Operations
      4. Querying with VASA and FUSA
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
      1. Glossary
    11. ENDNOTE
  8. 5. Type-2 Fuzzy Interface for Artificial Neural Network
    1. ABSTRACT
    2. INTRODUCTION
    3. FUZZY LOGIC AND FUZZY INTERFACE SYSTEM
      1. Fuzzy Logic
      2. Fuzzy Inference System
      3. Type-2 Fuzzy Systems
    4. INTERFACING ANN SYSTEM
      1. Artificial Neural Network
      2. Structure of Multi-Layer ANN
        1. Phase 1: Creation of a Structure
        2. Phase 2: Initialization
        3. Phase 3: Training (Forward Pass and Backward Pass)
        4. Phase 4: Utilization
      3. Advantages Offered by ANN
    5. NEURO-FUZZY MODELING
    6. FRAMEWORK OF THE SYSTEM
    7. AN EXPERIMENT
      1. An Advisory for Course Selection
      2. Objectives of the Proposed System
        1. Review of Research and Development in the Area
      3. Location of the Experiment
      4. Parameters of the Case
      5. Design of the Base ANN
      6. Sample Input/Output and Process Design
    8. CONCLUSION
    9. ACKNOWLEDGMENT
    10. REFERENCES
    11. ADDITIONAL READING
    12. KEY TERMS AND DEFINITIONS
  9. 6. A Combined GA-Fuzzy Classification System for Mining Gene Expression Databases
    1. ABSTRACT
    2. INTRODUCTION
    3. FUZZY RULE-BASED CLASSIFICATION
      1. Fuzzy Rule Generation
      2. Fuzzy Reasoning
    4. GA-FUZZY CLASSIFICATION
      1. Genetic Operations
      2. Algorithm Summary
      3. Improving Classification Performance
    5. EXPERIMENTAL RESULTS
    6. CONCLUSION
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
  10. 7. Fuzzy Decision Rule Construction Using Fuzzy Decision Trees: Application to E-Learning Database
    1. ABSTRACT
    2. INTRODUCTION
    3. FUZZY SET THEORY
    4. DESCRIPTION OF A FUZZY DECISION TREE METHOD (YUAN & SHAW, 1995)
    5. FUZZY DECISION TREE ANALYSES OF EXAMPLE DATA SET
      1. Analysis with Two MF Based Fuzzification of Condition Attributes
      2. Analysis with Three MF Based Fuzzication of Condition Attributes
    6. APPLICATION OF FUZZY DECISION TREES TO E-LEARNING DATA SET
      1. E-Learning
      2. FDT Analysis E-Learning Data Set Using Two MFs for Each Condition Attribute
      3. FDT Analysis E-Learning Data Set Using Three MFs for Each Condition Attribute
    7. FUTURE RESEARCH DIRECTIONS
    8. CONCLUSION
    9. REFERENCES
    10. ADDITIONAL READING
      1. KEY TERMS AND DEFINITIONS
    11. ENDNOTE
  11. 8. A Bayesian Belief Network Methodology for Modeling Social Systems in Virtual Communities: Opportunities for Database Technologies
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
      1. Modeling Process
      2. Related Research and Building Bayesian Models
    4. FUNDAMENTAL COMPONENTS OF SOCIAL CAPITAL
      1. Computing Conditional Probabilities
      2. Querying the Model
    5. CASE SCENARIOS
      1. Case 1: A Virtual Learning Community of Graduate Students
      2. Case 2: A distributed Community of Practice of Software Engineers
      3. Case 3: A Distributed Community of Practice of Programmers
      4. Case 4: A Distributed Community of Practice of Biomedical and Clinicians
    6. UPDATING THE MODEL
      1. Community A
      2. Community B
      3. Community C
      4. Community D
    7. OPPORTUNITIES AND CHALLENGES
      1. Limitation of Bayesian Belief Network
      2. Model Validation and Sensitivity Analysis (SA)
    8. DISCUSSIONS AND CONCLUSIONS
    9. ACKNOWLEDGMENT
    10. REFERENCES
    11. ENDNOTE
  12. A. APPENDIX A: RESULTS OF THE SENSITIVITY ANALYSIS
  13. 9. Integrity Constraints Checking in a Distributed Database
    1. ABSTRACT
    2. INTRODUCTION
    3. PRELIMINARIES
    4. BACKGROUND
    5. STRATEGIES FOR CHECKING INTEGRITY CONSTRAINTS IN A DISTRIBUTED DATABASE
    6. FUTURE TRENDS
    7. CONCLUSION
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  14. 10. Soft Computing Techniques in Content-Based Multimedia Information Retrieval
    1. ABSTRACT
    2. INTRODUCTION
    3. CONTENT BASED-MULTIMEDIA INFORMATION RETRIEVAL
      1. Feature Extraction and Representation
      2. Matching
      3. Indexing and Retrieval
      4. Relevance Feedback
    4. SC FOR CB-MIR
      1. Artificial Neural Networks for CB-MIR Tasks
      2. Fuzzy Logic for CB-MIR Tasks
      3. Rough Sets for CB-MIR Tasks
      4. Genetic Algorithms for CB-MIR Tasks
      5. Hybrid SC Techniques for CB-MIR Tasks
    5. A CONTENT-BASED IMAGE RETRIEVAL SYSTEM USING SC TECHNIQUES
      1. Shape detection
      2. Shape matching
      3. Application Example
    6. CONCLUSION
    7. REFERENCES
  15. 11. An Exposition of Feature Selection and Variable Precision Rough Set Analysis: Application to Financial Data
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. INTRODUCTION TO FIBR DATA, AND SOFTWARE PRE-PROCESSING AND FEATURE SELECTION RESULTS
      1. FIBR Data Set
      2. Data Discretisation Results
      3. Feature Selection
    5. VPRS ANALYSIS OF THE FIBR DATA SET
      1. Technical Description of VPRS
      2. VPRS Analysis of FIBR Data Set
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. REFERENCES
    9. ADDITIONAL READING
    10. KEY TERMS AND DEFINITIONS
  16. 12. Interconnecting a Class of Machine Learning Algorithms with Logical Commonsense Reasoning Operations
    1. ABSTRACT
    2. INTRODUCTION
      1. Background
    3. THE LOGICAL REASONING RULES
      1. The Rules of the First Type
      2. Deductive Reasoning Rules of the Second Type
      3. Inductive Reasoning Rules of the Second Type
    4. THE CONCEPT OF A GOOD DIAGNOSTIC TEST
    5. GENERATION OF GOOD DIAGNOSTIC TESTS AS DUAL ELEMENTS OF INTERCONNECTED LATTICES
      1. Inductive Rules for Constructing Elements of a Dual Lattice
        1. The Generalization Rule
        2. The Specification Rule
        3. The Boundary Induction Transitions
        4. The Inductive Diagnostic Rule: The Concept of Essential Value
        5. The Dual Inductive Diagnostic Rule: The Concept of Essential Object
      2. Reducing the Rules of Inductive Transitions to the Deductive and Inductive Rules of the Second Type
        1. Realization of the Generalization Rule for Inferring GMRTs
        2. Realization of the Specification Rule for Inferring GIRTs
    6. THE DECOMPOSITION OF INFERRING GOOD DIAGNOSTIC TESTS INTO SUBTASKS
    7. THEOREM 1
    8. AN ALGORITHM FOR INFERRING GMRTS WITH THE USE OF THE SUBTASK OF THE FIRST KIND
    9. AN APPROACH TO INCREMENTAL INFERRING GOOD DIAGNOSTIC TESTS
    10. FUTURE RESEARCH DIRECTIONS
    11. CONCLUSION
    12. ACKNOWLEDGMENT
    13. REFERENCES
    14. ADDITIONAL READING
    15. KEY TERMS AND DEFINITIONS
  17. B. APPENDIX A: AN EXAMPLE OF USING ALGORITHM DIAGARA
  18. 13. A Human-Machine Interface Design to Control an Intelligent Rehabilitation Robot System
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. REHABILITATION ROBOT SYSTEM
    5. HUMAN-MACHINE INTERFACE IMPLEMENTATION
    6. HUMAN-MACHINE INTERFACE EXERCISE MODES
    7. GRAPHICAL USER INTERFACE
    8. EXPERIMENTAL RESULTS
    9. CONCLUSION AND FUTURE WORK
    10. ACKNOWLEDGMENT
    11. REFERENCES
    12. KEY TERMS AND DEFINITIONS
  19. C. APPENDIX: RULES OF HMI
  20. 14. Congestion Control Using Soft Computing
    1. ABSTRACT
    2. INTRODUCTION
    3. IMPACT OF CONGESTION
      1. Causes and Effect of Congestion
      2. Fairness
    4. DIFFERENT TYPES OF CONGESTION CONTROL
      1. Congestion Control Algorithms of TCP
      2. Congestion Window
      3. Slow Start and Congestion Avoidance
      4. Fast Recovery
    5. CONGESTION AVOIDANCE
      1. Active Queue Management (AQM)
      2. Random Early Detection (RED)
        1. Stabilized RED (SRED)
        2. Explicit Congestion Notification (ECN)
        3. Random Early Marking (REM)
    6. BLUE
    7. ADAPTIVE VIRTUAL QUEUE (AVQ)
    8. FUZZY LOGIC
      1. Fuzzy Enabled AQM (F-AQM)
      2. Congestion Control of AQM using Fuzzy logic
      3. Construction of FRM
    9. SIMULATION SETUP
    10. RESULTS AND OBSERVATIONS
    11. FUTURE RESEARCH DIRECTIONS
    12. CONCLUSION
    13. REFERENCES
    14. KEY TERMS AND DEFINITIONS
  21. Compilation of References
  22. About the Contributors