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Computational Modeling and Simulation of Intellect

Book Description

With recent progress in information generation, users are experiencing increasing difficulties in processing the available amounts of high-dimensional data, extracting information from it, and eventually finding a meaning in the structured data. Computational Modeling and Simulation of Intellect: Current State and Future Perspectives confronts the problem of meaning by fusing together methods specific to different fields and exploring the computational efficiency and scalability of these methods. Researchers, instructors, designers of information and management systems, users of these systems, and graduate students will acquire the fundamental knowledge needed to be at the forefront of the research and to use it in the applications. The topic is of great importance for information and management science and technology, both currently and in future.

Table of Contents

  1. Foreword
  2. Foreword
  3. Cover
  4. Title Page
  5. Copyright Page
  6. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  7. Preface
    1. ORGANIZATION OF THE BOOK
    2. REFERENCES
  8. Acknowledgment
  9. Section 1: Application of AI and CI Methods to Image and Signal Processing, Robotics, and Control
    1. Chapter 1: Image Processing for Localization and Parameterization of the Glandular Ducts of Colon in Inflammatory Bowel Diseases
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORKS
      4. THE PROPOSED AUTOMATIC SOLUTION FOR DUCT EXTRACTION
      5. THE AUTOMATIC SYSTEM FOR CLASSIFICATION OF THE DEVELOPMENT STAGE OF IBD
      6. RESULTS OF EXPERIMENTS
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. ADDITIONAL READING
    2. Chapter 2: The Kolmogorov Spline Network for Image Processing
      1. Abstract
      2. INTRODUCTION
      3. Background
      4. Kolmogorov Spline Network for Image compression
      5. KOLMOGOROV SPLINE NETWORK FOR PROGRESSIVE IMAGE TRANSMISSION
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
    3. Chapter 3: Intelligent Information Description and Recognition in Biomedical Image Databases
      1. Abstract
      2. Introduction
      3. RELATED WORKS
      4. SICKLE CELLS DISEASE APPLICATION
      5. Multicriteria Classification of Sickle Cells Disease
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. References
      9. ADDITIONAL READING
      10. KEY TERMS and DEFINITIONS
    4. Chapter 4: Machine Learning for Visual Navigation of Unmanned Ground Vehicles
      1. ABSTRACT
      2. INTRODUCTION
      3. THE TRAINING DATA PREPROCESSING
      4. MACHINE LEARNING ALGORITHMS
      5. EXPERIMENTS AND DISCUSSION
      6. CONCLUSION
      7. References
      8. KEY TERMS AND DEFINITIONS
    5. Chapter 5: Adaptive Dynamic Programming Applied to a 6DoF Quadrotor
      1. Abstract
      2. INTRODUCTION
      3. Efficient Learning:
      4. FUTURE RESEARCH DIRECTIONS
      5. CONCLUSION
      6. REFERENCES
    6. Chapter 6: Cognitive Based Distributed Sensing, Processing, and Communication
      1. Abstract
      2. INTRODUCTION
      3. NEURAL MODELING FIELDS
      4. Applications
      5. FUTURE RESEARCH DIRECTIONS
      6. Conclusion
      7. REFERENCES
      8. ADDITIONAL READING
    7. Chapter 7: Biogeography-Based Optimization for Robot Controller Tuning
      1. ABSTRACT
      2. 1. Introduction
      3. 2. Biogeography-Based Optimization (BBO)
      4. 3. Hardware
      5. 4. Robot Controller
      6. 5. BBO Robotics Experiments
      7. 6. Conclusion
      8. REFERENCES
      9. ADDITIONAL READING
      10. Key Terms and Definitions
  10. Section 2: Application of AI and CI Methods to Medicine and Environment Monitoring and Protection
    1. Chapter 8: An Exposition of Uncertain Reasoning Based Analysis
      1. Abstract
      2. Introduction
      3. CONCLUSION
      4. References
      5. Additional Reading
      6. Key terms and Definitions
    2. Chapter 9: Sigma Tuning of Gaussian Kernels Detection of Ischemia from Magnetocardiograms
      1. Abstract
      2. BACKGROUND OF SIGMA TUNING
      3. LITERATURE OVERVIEW
      4. PERFORMANCE METRICS
      5. SIGMA TUNING ALGORITHM
      6. Variable Selection
      7. EXPERIMENTAL RESULTS
      8. CONCLUSION
      9. FUTURE RESEARCH DIRECTION
      10. REFERENCES
      11. ADDITIONAL READING
      12. Key Terms and Definitions
    3. Chapter 10: Artificial Neural Network Modelling for Waste
      1. ABSTRACT
      2. INTRODUCTION
      3. ARTIFICIAL NEURAL NETWORKS (ANNS)
      4. BIOLOGICAL WASTE GAS TREATMENT SYSTEMS
      5. BIOLOGICAL WASTEWATER TREATMENT SYSTEMS
      6. TYPICAL DESIGN EQUATIONS AND MODELS FOR WASTE GAS TREATMENT SYSTEMS: ATTACHED GROWTH PROCESSES
      7. TYPICAL DESIGN EQUATIONS FOR WASTEWATER TREATMENT SYSTEMS: ACTIVATED SLUDGE PROCESS
      8. ANN BASED MODELLING FOR WASTE GAS TREATMENT SYSTEMS
      9. ANN BASED MODELLING FOR WASTEWATER TREATMENT SYSTEMS
      10. Model for ASP
      11. OTHER APPLICATIONS
      12. CONCLUSION
      13. ACKNOWLEDGMENT
      14. REFERENCES
      15. Additional READING
      16. KEY TERMS and DEFINITIONS
  11. Section 3: Concepts
    1. Chapter 11: Motivated Learning for Computational Intelligence
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND FOR MOTIVATED LEARNING
      4. NEED FOR MOTIVATED LEARNING
      5. MOTIVATED LEARNING METHOD
      6. Simulation experiments
      7. Case I
      8. Case II
      9. Comparing Motivated Learning and Reinforcement Learning
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. Key Terms and Definitions
    2. Chapter 12: Topological Coding in the Hippocampus
      1. ABSTRACT
      2. BACKGROUND
      3. NEURONAL SPATIAL MAPS
      4. SPACE RECONSTRUCTION EXPERIMENT
      5. STABILITY WITH RESPECT TO GRADUAL CHANGES
      6. TOPOLOGICAL ANALYSIS OF SPACE CODING
      7. GENERAL TOPOLOGY OF THE REPRESENTATIVE SPACE
      8. REPRESENTATIVE SPACE AND SPATIAL INFORMATION
      9. QUALITATIVE SPACE REPRESENTATION: RCC
      10. CONCLUSION
      11. ACKNOWLEDGMENT
      12. REFERENCES
      13. Endnotes
    3. Chapter 13: Knowledge Representation as a Tool for Intelligence Augmentation
      1. ABSTRACT
      2. INTRODUCTION
      3. TOWARDS A MODEL OF CONCEPTUALIZATION
      4. FIRST EXPERIMENT: PROBLEM SOLVING
      5. SECOND EXPERIMENT: PROBLEM ELICITATION
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS and DEFINITIONS
      11. Endnotes
    4. Chapter 14: Data Discovery Approaches for Vague Spatial Data
      1. Abstract
      2. INTRODUCTION
      3. Background
      4. Vague Querying for Spatial Data Mining
      5. Association Rules
      6. Spatial Data Mining Example
      7. Conclusion and Future Directions
      8. Acknowledgment
      9. REFERENCES
      10. Key Terms and Definitions
    5. Chapter 15: Feature Selection and Ranking
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE CEA METHOD
      5. CEA APPLICATION FOR FEATURE SELECTION AND RANKING
      6. CONCLUSION
      7. FUTURE RESEARCH DIRECTIONS
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS & DEFINITIONS
      11. ENDNOTE
      12. APPENDIX: DERIVATION OF EQUATION (14)
    6. Chapter 16: Brain-Like System for Audiovisual Person Authentication Based on Time-to-First Spike Coding
      1. Abstract
      2. INTRODUCTION
      3. Background
      4. Spike-Based Audiovisual System for Person Authentication
      5. Future Research Directions
      6. Conclusion
      7. REFERENCES
      8. ADDITIONAL READING
      9. Key Terms and Definitions
    7. Chapter 17: Modeling the Intellect from a Coordination Perspective
      1. Abstract
      2. INTRODUCTION
      3. THE ACTIVITY MODALITIES
      4. THE UNITY OF MIND AND ACTIVITY
      5. OUTLINING A RESEARCH PROGRAM
      6. A MODEL OF THE INTELLECT
      7. THE SOCIAL REALM
      8. THE CONCEPTUAL REALM
      9. THE NEURAL REALM
      10. INTEGRATION
      11. RESEARCH QUESTIONS
      12. CONCLUSION
      13. REFERENCES
      14. Endnotes
    8. Chapter 18: Information Processing by Chemical Reaction-Diffusion Media
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. FUTURE RESEARCH DIRECTIONS
      6. Conclusion
      7. References
      8. Endnote
  12. Section 4: Application of AI and CI Methods to Learning
    1. Chapter 19: MLVQ
      1. Abstract
      2. 1 Introduction
      3. 2 Clustering Algorithms
      4. 3 Experiments on an Artificial Experimental Data Set
      5. 4 Modified LVQ
      6. 5 Application Experiments
      7. 6 Conclusion
      8. 7 References
    2. Chapter 20: Outlier Detection in Linear Regression
      1. ABSTRACT
      2. Introduction
      3. Regression Analysis and Least Squares Estimation
      4. Unusual Observations: Outliers, High-Leverage Points and Influential Observations
      5. REGRESSION DIAGNOSTICS
      6. Robust Regression
      7. Findings and Future Issues
      8. References
      9. Key Terms and Definitions
    3. Chapter 21: Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks
      1. ABSTRACT
      2. INTRODUCTION
      3. CLASSIFICATION TASKS WITH UNBALANCED DATASETS
      4. Techniques and Algorithms for Coping with Unbalanced Datasets
      5. CASE STUDIES
      6. CONCLUSION
      7. REFERENCES
      8. Key Terms and Definitions
    4. Chapter 22: Ability of the 1-n-1 Complex-Valued Neural Network to Learn Transformations
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. LEARNING OF TRANSFORMATIONS ON THE STEINER CIRCLES
      5. RELATIONSHIP BETWEEN LEARNING PATTERNS AND WEIGHT VALUES
      6. DISCUSSION
      7. APPLICATION TO ASSOCIATIVE MEMORY
      8. CONCLUSION
      9. FUTURE RESEARCH DIRECTIONS
      10. ACKNOWLEDGMENT
      11. REFERENCES
      12. ADDITIONAL READING
      13. Key Terms and Definitions
  13. Compilation of References
  14. About the Contributors