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Multidisciplinary Computational Intelligence Techniques

Book Description

The amount of digital data available in the world doubles almost every year. Managed well, this data can be used to extract new sources of knowledge for daily business use. Multidisciplinary Computational Intelligence Techniques: Applications in Business, Engineering, and Medicine explores the complex world of computational intelligence, which utilizes computational methodologies such as fuzzy logic systems, neural networks, and evolutionary computation for the purpose of managing and using data effectively and addressing complicated real-world problems. This publication brings together various segments of computational intelligence and its applications in the worlds of business, engineering, and medicine.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
  5. Preface
  6. Acknowledgment
  7. Chapter 1: Which Fundamental Factors Proxy for Share Returns?
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. PROPOSED METHODOLOGY: THE MULTI SELF-ORGANISING MAPS (SOM) BASED CLUSTERING APPROACH
    4. 3. DATA
    5. 4. MODELLING RESULTS
    6. 5. FURTHER RESEARCH
    7. 6. CONCLUSION
    8. REFERENCES
  8. Chapter 2: Efficient Pronunciation Assessment of Taiwanese-Accented English Based on Unsupervised Model Adaptation and Dynamic Sentence Selection
    1. ABSTRACT
    2. INTRODUCTION
    3. MISPRONUNCIATION DETECTION
    4. DYNAMIC SENTENCE SELECTION FOR EFFICIENT ASSESSMENT
    5. EXPERIMENTS
    6. DISCUSSION
    7. FUTURE RESEARCH DIRECTIONS
    8. CONCLUSION
    9. REFERENCES
  9. Chapter 3: A Bees Life Algorithm for Cloud Computing Services Selection
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. BACKGROUND
    4. 3. BEES LIFE ALGORITHM
    5. 4. EXPERIMENTAL STUDY
    6. 5. FUTURE RESEARCH DIRECTIONS
    7. 6. CONCLUSION
    8. REFERENCES
  10. Chapter 4: Cloud-Based Intelligent DSS Design for Emergency Professionals
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. EMERGENCY PROBLEM SIGNIFICANCE
    5. COMPUTATIONAL INTELLIGENCE METHODS
    6. CLOUD COMPUTING APPLICATIONS
    7. DSS FOR DISASTER MANAGEMENT
    8. MAIN FOCUS OF THE CHAPTER
    9. DISCUSSION AND RECOMMENDATION
    10. FUTURE RESEARCH DIRECTIONS
    11. CONCLUSION
    12. REFERENCES
    13. ENDNOTE
  11. Chapter 5: Occlusion Handling in Object Detection
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. OCCLUSION – RELATED WORKS
    4. 3. PROPOSED ALGORITHM
    5. 4. IMPLEMENTATION
    6. 5. EXPERIMENTAL EVALUATION
    7. 6. CONCLUSION AND FUTURE WORKS
    8. REFERENCES
  12. Chapter 6: Application of Multi-Objective Evolutionary Algorithms to Antenna and Microwave Design Problems
    1. ABSTRACT
    2. INTRODUCTION
    3. MULTI-OBJECTIVE OPTIMIZATION ALGORITHMS
    4. NUMERICAL EXAMPLES
    5. FUTURE RESEARCH DIRECTIONS
    6. CONCLUSION
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
  13. Chapter 7: Using Computational Intelligence for Improved Teleoperation over Wireless Network
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND AND PREVIOUS WORK
    4. INTELLIGENT CONTROL AND DECISION FOR NETWORKED TELEOPERATION
    5. FUTURE RESEARCH DIRECTIONS
    6. CONCLUSION
    7. REFERENCES
  14. Chapter 8: Bangla Music Genre Classification
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORK
    4. 3. THEORETICAL FRAMEWORK
    5. 4. CLASSIFICATION FRAMEWORK
    6. 5. RESULTS AND DISCUSSION
    7. 6. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
    8. ACKNOWLEDGMENT
    9. REFERENCES
  15. Chapter 9: Automatic Recognition and Localization of Saudi License Plates
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. SAUDI LICENSE PLATES
    5. CHARACTER RECOGNITION
    6. PLATE LOCALIZATION
    7. FUTURE RESEARCH DIRECTIONS
    8. CONCLUSION
    9. REFERENCES
    10. ADDITIONAL READING
  16. Chapter 10: Numerical Integration Using Swarm Intelligence Techniques
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. THE PROPOSED METHOD
    5. OVERALL DESCRIPTION STRATEGY OF PARTICLE SWARM OPTIMIZATION
    6. EXAMPLE
    7. FUTURE AND EMERGING TRENDS
    8. CONCLUSION
    9. ACKNOWLEDGMENT
    10. REFERENCES
    11. ADDITIONAL READING
  17. Chapter 11: A Multilevel Thresholding Method Based on Multiobjective Optimization for Non-Supervised Image Segmentation
    1. ABSTRACT
    2. INTRODUCTION
    3. 2. MULTILEVEL THRESHOLDING PROBLEM
    4. 3. FORMULATION OF MULTI-LEVEL IMAGE THRESHOLDING AS A M.O. PROBLEM
    5. 4. MULTIOBJECTIVE OPTIMIZATION
    6. 5. MO IN IMAGE SEGMENTATION WITH THRESHOLDING TECHNIQUES
    7. 6. FUTURE RESEARCH DIRECTIONS
    8. 7. CONCLUSION
    9. ACKNOWLEDGMENT
    10. REFERENCES
    11. ADDITIONAL READING
  18. Chapter 12: On the Use of Particle Swarm Optimization Techniques for Channel Assignments in Cognitive Radio Networks
    1. ABSTRACT
    2. INTRODUCTION
    3. SYSTEM MODEL AND PROBLEM STATEMENT
    4. OPTIMIZATION ALGORITHMS FOR CHANNEL ASSIGNMENT
    5. SIMULATION RESULTS AND ANALYSIS
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  19. Chapter 13: Optimisation of Radiation Beam in Linear Nodes Array of Wireless Sensor Network for Improved Performance
    1. ABSTRACT
    2. INTRODUCTION
    3. BEAMFORMING IN WIRELESS SENSOR NETWORKS
    4. THE RADIATION PATTERN OF THE ANTENNA ARRAY
    5. SIMULATION PROCESS OF THE RADIATION BEAM OPTIMISATION
    6. RESULTS AND DISCUSSION
    7. FUTURE RESEARCH DIRECTIONS
    8. CONCLUSION
    9. ACKNOWLEDGMENT
    10. REFERENCES
    11. KEY TERMS AND DEFINITIONS
  20. Chapter 14: ACPSO
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MAIN FOCUS OF THE CHAPTER
    5. FUTURE WORK
    6. CONCLUSION
    7. REFERENCES
  21. Chapter 15: A Survey of Semantic Web Based Architectures for Adaptive Intelligent Tutoring System
    1. ABSTRACT
    2. INTRODUCTION
    3. SEMANTIC WEB TECHNOLOGIES
    4. SEMANTIC WEB AND EDUCATION
    5. VIRTUAL LEARNING ENVIRONMENT
    6. ONTOLOGY AND LEARNING OBJECT REPOSITORIES
    7. APPLYING SEMANTIC WEB TO E-LEARNING
    8. CASE STUDIES: SEMANTIC WEB BASED ADAPTIVE INTELLIGENT TUTORING SYSTEMS
    9. SUMMARY OF ANALYSIS
    10. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
    11. REFERENCES
  22. Chapter 16: Outlier Detection in Logistic Regression
    1. ABSTRACT
    2. INTRODUCTION
    3. LOGISTIC REGRESSION MODEL FORMULATION
    4. LOGISTIC REGRESSION DIAGNOSTICS
    5. EXAMPLES
    6. FUTURE RESEARCH DIRECTIONS AND POTENTIAL USE IN OTHER FIELDS
    7. CONCLUSION
    8. REFERENCES
  23. Chapter 17: Quantum Computing Approach for Alignment-Free Sequence Search and Classification
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MAIN FOCUS: SEQUENCE ANALYSIS
    5. SOLUTIONS AND RECOMMENDATIONS
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. REFERENCES
    9. APPENDIX: COMPLEXITY ANALYSIS
    10. APPENDIX: SYNTHESIS: TRANSFER FUNCTIONS
  24. Chapter 18: Improving the Efficiency of Large-Scale Agent-Based Models Using Compression Techniques
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. METHODS
    5. CONCLUSION
    6. FUTURE DIRECTIONS
    7. REFERENCES
  25. Chapter 19: Embedded System for Heart Disease Recognition using Fuzzy Clustering and Correlation
    1. ABSTRACT
    2. INTRODUCTION
    3. 1. INITIAL CONSIDERATIONS
    4. 2. CONSIDERATIONS ON ELECTROCARDIOGRAPHIC LEADS
    5. 3. PRE-PROCESSING AND FILTERING SYSTEM
    6. 4. FUZZY MEMBERSHIP FUNCTION E FUZZY CLUSTERING
    7. 5. CORRELATION
    8. 6. EMBEDDED SYSTEMS, FPGAS, AND MICROBLAZE
    9. 7. SOFTWARE AND HARDWARE DEVELOPMENT
    10. RESULTS
    11. CONCLUSION
    12. REFERENCES
  26. Chapter 20: Parallel Evolutionary Computation in R
    1. ABSTRACT
    2. INTRODUCTION
    3. DIFFERENTIAL EVOLUTION
    4. DIFFERENTIAL EVOLUTION IN R
    5. PARALLEL EVOLUTIONARY COMPUTATION
    6. PACKAGES FOR EVOLUTIONARY COMPUTATION IN R
    7. CONCLUSION AND FUTURE DIRECTIONS
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. APPENDIX 1: GETTING STARTED WITH R
    11. APPENDIX 2: A PARALLEL DE FUNCTION IN R
  27. Chapter 21: Fuzzy Image Segmentation for Mass Detection in Digital Mammography
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MEDICAL IMAGING IN DIGITAL MAMMOGRAPHY
    5. FUZZY IMAGE SEGMENTATION FOR MASS DETECTION
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. REFERENCES
  28. Compilation of References
  29. About the Contributors