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Efficiency and Scalability Methods for Computational Intellect

Book Description

Computational modeling and simulation has developed and expanded into a diverse range of fields such as digital signal processing, image processing, robotics, systems biology, and many more; enhancing the need for a diversifying problem solving applications in this area. Efficiency and Scalability Methods for Computational Intellect presents various theories and methods for approaching the problem of modeling and simulating intellect in order to target computation efficiency and scalability of proposed methods. Researchers, instructors, and graduate students will benefit from this current research and will in turn be able to apply the knowledge in an effective manner to gain an understanding of how to improve this field.

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
    1. ORGANIZATION OF THE BOOK
  7. Section 1: Efficient and Scalable Methods in Machine Learning, Data Mining, and Medicine
    1. Chapter 1: Up-to-Date Feature Selection Methods for Scalable and Efficient Machine Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. FEATURE SELECTION METHODS
      4. APPLICATIONS
      5. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
    2. Chapter 2: Online Machine Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. ONLINE REGRESSION ALGORITHMS
      4. ONLINE CLASSIFICATION ALGORITHMS
      5. ONLINE UNSUPERVISED LEARNING
      6. APPLICATION AREAS
      7. CONCLUSION
    3. Chapter 3: Uncertainty in Concept Hierarchies for Generalization in Data Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. GENERALIZATION DATA MINING
      5. GENERALIZATION IN TERMS OF PARTITIONS
      6. GENERALIZATION WITH UNCERTAIN HIERARCHIES
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
    4. Chapter 4: Efficiency and Scalability Methods in Cancer Detection Problems
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. STABLE FEATURE SELECTION
      5. EVALUATION OF THE STABLE FEATURE SELECTION
      6. COMPRESSIVE SENSING BASED FEATURE SELECTION
      7. EVALUATION OF CS FEATURE SELECTION
      8. CONCLUSION
  8. Section 2: Efficient and Scalable Methods in Image Processing, Robotics, Control, Computer Networks Defense, Human Identification, and Combinatorial Optimization
    1. Chapter 5: The Kolmogorov Spline Network for Authentication Data Embedding in Images
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE KOLMOGOROV SPLINE NETWORK FOR SECURED PROGRESSIVE TRANSMISSION
      5. APPLICATION
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
    2. Chapter 6: Real-Time Fuzzy Logic-based Hybrid Robot Path-Planning Strategies for a Dynamic Environment
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. CASCADE OF FUZZY SYSTEMS
      5. SAMPLE EMPIRICAL RESULT: THE HYBRID FUZZY A* ALGORITHM USING A FIXED GRID
      6. SAMPLE EMPIRICAL RESULT: THE HYBRID FUZZY A* UTILISING THE VORONOI DIAGRAM: NEAR-OPTIMAL PATH PLANNING (SHORTEST SAFEST PATH)
      7. SYSTEM CALIBRATION
      8. CONCLUSION
      9. FUTURE RESEARCH DIRECTIONS
    3. Chapter 7: Evolutionary Optimization of Artificial Neural Networks for Prosthetic Knee Control
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ARTIFICIAL NEURAL NETWORK CLOSED-LOOP CONTROL
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    4. Chapter 8: Techniques to Model and Derive a Cyber-Attacker’s Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. FUTURE RESEARCH DIRECTIONS
      4. CONCLUSION
    5. Chapter 9: A Scalable Approach to Network Traffic Classification for Computer Network Defense using Parallel Neural Network Classifier Architectures
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. CLASSIFICATION
      5. EXPERIMENTAL EVALUATION
      6. RESULTS AND DISCUSSIONS
      7. CONCLUSION AND FUTURE WORK
    6. Chapter 10: Biogeography-Based Optimization for Large Scale Combinatorial Problems
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. BBO SOLUTION FOR COMBINATORIAL PROBLEMS
      5. SOLUTION FOR LARGE SCALE PROBLEMS
      6. BBO/DBTSP
      7. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
      8. FUTURE DIRECTIONS
  9. Section 3: Concepts
    1. Chapter 11: Kolmogorov Superpositions
      1. ABSTRACT
      2. INTRODUCTION
      3. THE ANATOMY OF DIMENSION REDUCTION
      4. A NEW ALGORITHM
      5. CONCLUSION
    2. Chapter 12: Evaluating Scalability of Neural Configurations in Combined Classifier and Attention Models
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. IMPLICATIONS
      5. DISCUSSION AND CONCLUSION
      6. FUTURE RESEARCH DIRECTIONS
    3. Chapter 13: Numerical Version of the Non-Uniform Method for Finding Point Estimates of Uncertain Scaling Constants
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. REPRESENTATION OF THE NON-UNIFORM METHOD
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. APPENDIX: UNCERTAINTY DESCRIPTIONS OF THE SCALING CONSTANTS
    4. Chapter 14: Widely Linear Estimation with Geometric Algebra
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. COMPLEX-VALUED WIDELY LINEAR ESTIMATION
      5. QUATERNION WIDELY LINEAR ESTIMATION
      6. CLIFFORD WIDELY LINEAR ESTIMATION
      7. APPLICATION EXAMPLES
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
  10. Compilation of References
  11. About the Contributors