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Global Trends in Intelligent Computing Research and Development

Book Description

As the amount of accumulated data across a variety of fields becomes harder to maintain, it is essential for a new generation of computational theories and tools to assist humans in extracting knowledge from this rapidly growing digital data. Global Trends in Intelligent Computing Research and Development brings together recent advances and in depth knowledge in the fields of knowledge representation and computational intelligence. Highlighting the theoretical advances and their applications to real life problems, this book is an essential tool for researchers, lecturers, professors, students, and developers who have seek insight into knowledge representation and real life applications.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  6. Dedication
  7. Preface
  8. Acknowledgment
  9. Section 1: Classification and Clustering
    1. Chapter 1: Clustering-Based Stability and Seasonality Analysis for Optimal Inventory Prediction
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORKS
      4. 3. DOES GENERIC MODEL FIT BEST?
      5. 4. PREPROCESSING
      6. 5. STABILITY ANALYSIS
      7. 6. SEASONALITY ANALYSIS
      8. 7. PROGRESSION ANALYSIS
      9. 8. CONCLUSION
    2. Chapter 2: Efficient Color Image Segmentation by a Parallel Optimized (ParaOptiMUSIG) Activation Function
      1. ABSTRACT
      2. INTRODUCTION
      3. MATHEMATICAL PREREQUISITES
      4. PARALLEL SELF ORGANIZING NEURAL NETWORK (PSONN) ARCHITECTURE
      5. PARALLEL OPTIMIZED MULTILEVEL SIGMOIDAL (PARAOPTIMUSIG) ACTIVATION FUNCTION
      6. PARAOPTIMUSIG ACTIVATION FUNCTION BASED COLOR IMAGE SEGMENTATION SCHEME
      7. EXPERIMENTAL RESULTS
      8. CONCLUSION
      9. APPENDIX
    3. Chapter 3: Learning Using Soft Computing Techniques
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. GENETIC ALGORITHMS FOR TIME SERIES CLASSIFICATION
      4. 3. USE OF PSO FOR CLASSIFICATION AND CLUSTERING
      5. 4. SUMMARY
      6. 5. SCOPE FOR FUTURE WORK
    4. Chapter 4: Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding
      1. ABSTRACT
      2. INTRODUCTION
      3. 2. QUANTUM COMPUTING RUDIMENTS
      4. 3. NP-COMPLETE PROBLEMS AND QUANTUM COMPUTING
      5. 4. QUANTUM EVOLUTIONARY ALGORITHM
      6. 5. IMAGE THRESHOLDING
      7. 6. EVALUATION METRICS
      8. 7. QUANTUM INSPIRED GENETIC ALGORITHM
      9. 8. EXPERIMENTAL RESULTS
      10. 9. DISCUSSIONS AND CONCLUSION
    5. Chapter 5: Feature Selection Algorithms for Classification and Clustering in Bioinformatics
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. FEATURE SELECTION TECHNIQUES
      4. 3. SELECTION ALGORITHMS
      5. 4. ESTIMATING CLASSIFIER PERFORMANCE: PREDICTION ERROR
      6. 5. UNSUPERVISED CLASSIFICATION METHODS APPLIED TO GENE EXPRESSION DATA
      7. 6. FUTURE SCOPE
      8. 7. CONCLUDING REMARKS
    6. Chapter 6: Soft Subspace Clustering for Cancer Microarray Data Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. SOFT SUBSPACE CLUSTERINGS
      4. EMPIRICAL STUDY ON MICROARRAY DATA
      5. DISCUSSION AND FUTURE RESEARCH
      6. CONCLUSION
  10. Section 2: Foundations of Knowledge Representation
    1. Chapter 7: Multi-Objective Genetic and Fuzzy Approaches in Rule Mining Problem of Knowledge Discovery in Databases
      1. ABSTRACT
      2. INTRODUCTION
      3. MULTI-OBJECTIVE OPTIMIZATION PROBLEM
      4. RULE MINING PROBLEM OF KDD
      5. MULTI-OBJECTIVE GENETIC ALGORITHMS
      6. MULTI-OBJECTIVE GENETIC FUZZY RULE MINING
      7. APPLICATION AREAS OF MULTI-OBJECTIVE FUZZY-GENETIC APPROACHES
      8. FUTURE RESEARCH DIRECTION
      9. CONCLUSION
    2. Chapter 8: Rough Sets and Approximate Reasoning
      1. ABSTRACT
      2. INTRODUCTION
      3. BASIC NOTIONS AND DEFINITIONS
      4. SOME PROPOSALS FOR FUTURE WORK
      5. CONCLUSION
    3. Chapter 9: Case-Based Reasoning and Some Typical Applications
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND AND MOTIVATION
      4. SOME INTERESTING APPLICATIONS OF CBR
      5. HISTORY OF CBR
      6. FUNDAMENTALS OF CBR METHODS
      7. SOME TYPICAL CBR APPLICATIONS: KNOWLEDGE MANAGEMENT WITH CASE BASED REASONING APPLIED ON FIRE EMERGENCY HANDLING
      8. FEVER-TYPE DETECTION SYSTEM: A COMBINATION OF CBR AND BOTTOM-UP APPROACH
      9. APPLICATION OF CBR FOR SHIP TURNING EMERGENCY TO PREVENT COLLISION
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
    4. Chapter 10: Hybrid Genetic Algorithm
      1. ABSTRACT
      2. INTRODUCTION
      3. GENETIC ALGORITHM
      4. OPERATORS IN GA
      5. HYBRID GA
      6. GA IN NETWORKING OPTIMIZATION
      7. CONCLUSION
      8. APPENDIX
    5. Chapter 11: Knowledge Representation Using Formal Concept Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. BORDAT ALGORITHM
      6. NEXT NEIGHBOR ALGORITHM
      7. OBJECT INTERSCETION ALGORITHM
      8. NEXT CLOSURE ALGORITHM
      9. DISCUSSIONS AND CONCLUSION
  11. Section 3: Foundations of Computational Intelligence
    1. Chapter 12: Heterogeneous Data Structure “r-Atrain”
      1. ABSTRACT
      2. INTRODUCTION
      3. HOMOGENEOUS DATA STRUCTURE “r-TRAIN” (TRAIN)
      4. FUNDAMENTAL OPERATIONS ON THE DATA STRUCTURE ‘R-TRAIN’
      5. r-ATRAIN (ATRAIN): A POWERFUL HETEROGENEOUS DATA STRUCTURE
      6. FUNDAMENTAL OPERATIONS ON THE DATA STRUCTURE ‘r-ATRAIN’
      7. EMPIRICAL ANALYSIS
      8. CONCLUSION
    2. Chapter 13: Comparative Analysis of Hybrid Soft Set Methods
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERARY BACKGROUND
      4. SOFT SET AND PROPERTIES
      5. SOFT SET AND ROUGH SOFT SET
      6. DEPENDENCY
      7. CONCLUSION
    3. Chapter 14: Diagram Drawing Using Braille Text
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. DRAWING IN BRAILLE
      5. DESIGN OPTIMIZATION
      6. EVALUATION OF BRAILLE DIAGRAMS
      7. COMPUTATIONAL INTELLIGENCE IN DRAWING OF BRAILLE DIAGRAMS
      8. USER EVALUATION OF THE INTEGRATED SYSTEM
      9. FUTURE SCOPE
      10. CONCLUSION
    4. Chapter 15: Application of Functional Approach to Lists for Development of Relational Model Databases and Petri Net Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. DEFINITIONS AND PROPERTIES
      4. SOME THEOREMS
      5. THE ‘FILTER’ OPERATOR AND ITS PROPERTIES
      6. LIST THEORETIC RELATIONAL MODEL
      7. UNARY RELATIONAL OPERATIONS
      8. BINARY RELATIONAL OPERATIONS
      9. THE IMPLEMENTATION ISSUE
      10. FORMALIZATION OF PETRINETS USING LIST THEORETIC RELATIONAL ALGEBRA (LRA)
      11. CONCLUSION
      12. SCOPE FOR FUTURE WORK
      13. APPENDIX
    5. Chapter 16: Soft Sets
      1. ABSTRACT
      2. INTRODUCTION
      3. THEORY OF SOFT SETS
      4. GENERALIZATIONS OF SOFT SETS
      5. SOFT MAPPING
      6. SOFT GROUPS
      7. SOFT TOPOLOGY
      8. SOFT ENTROPY
      9. APPLICATIONS
      10. CONCLUSION
  12. Section 4: Information Science and Neural Network
    1. Chapter 17: Adaptive and Neural pH Neutralization for Strong Acid-Strong Base System
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE REVIEW
      4. THE pH PROCESS: PROBLEM DESCRIPTION
      5. STRONG ACID: STRONG BASE SYSTEM MODELING
      6. CONCLUSION
    2. Chapter 18: A Shannon-Like Solution for the Fundamental Equation of Information Science
      1. ABSTRACT
      2. INTRODUCTION
      3. THEORETICAL BACKGROUND
      4. A QUANTITATVE TREATMENT FOR
      5. CONCLUSION
  13. Compilation of References
  14. About the Contributors