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Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications

Book Description

Conventional computational methods, and even the latest soft computing paradigms, often fall short in their ability to offer solutions to many real-world problems due to uncertainty, imprecision, and circumstantial data. Hybrid intelligent computing is a paradigm that addresses these issues to a considerable extent. The Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications highlights the latest research on various issues relating to the hybridization of artificial intelligence, practical applications, and best methods for implementation. Focusing on key interdisciplinary computational intelligence research dealing with soft computing techniques, pattern mining, data analysis, and computer vision, this book is relevant to the research needs of academics, IT specialists, and graduate-level students.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Dedication
  6. Editorial Advisory Board
  7. Preface
  8. Section 1: Hybrid Intelligent Techniques: Concepts and Fundamentals
    1. Chapter 1: A Study of Different Color Segmentation Techniques for Crop Bunch in Arecanut
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. NEED FOR MACHINE VISION IN HARVESTING
      5. DATASET CREATION
      6. EXPLORATION OF DIFFERENT SEGMENTATION METHODS
      7. DISCUSSION AND CONCLUSION
      8. FUTURE RESEARCH
      9. REFERENCES
    2. Chapter 2: An Impact of Gaussian Mixtures in Image Retrieval System
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. SOLUTIONS AND RECOMMENDATIONS
      6. EXPERIMENTAL RESULTS AND PERFORMANCE ANALYSIS
      7. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
      8. REFERENCES
    3. Chapter 3: Cryptographic Techniques Based on Bio-Inspired Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. LITERATURE REVIEW
      5. RECONFIGURABLE HARDWARE AND CA
      6. BIO-INSPIRED BASED ALGORITHM FOR CRYPTOGRAPHY
      7. TESTING AND EXPERIMENTAL RESULTS
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. REFERENCES
    4. Chapter 4: Detailed Analysis of Ultra Low Power Column Compression WALLACE and DADDA Multiplier in Sub-Threshold Regime
      1. ABSTRACT
      2. INTRODUCTION
      3. BASICS OF MULTIPLIERS
      4. REVERSE BODY BIASING (RBB) EFFECTS ON LOGIC GATES: AN OBSERVATION
      5. LITERATURE SURVEY
      6. PROPOSED STANDARD CELL LIBRARY: SUB-THRESHOLD REGIME
      7. PROPOSED DADDA AND WALLACE MULTIPLIERS
      8. ASIC IMPLEMENTATION AND SIMULATION RESULTS
      9. CONCLUSION AND FUTURE WORK
      10. REFERENCES
      11. APPENDIX A: CADENCE RTL COMPILER
    5. Chapter 5: Utilization of Classification Techniques for the Determination of Liquefaction Susceptibility of Soils
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. DETAILS OF RVM
      5. DETAILS OF LSSVM
      6. SOLUTIONS AND RECOMMENDATIONS
      7. CONCLUSION
      8. ACKNOWLEGMENT
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    6. Chapter 6: Soft-Computational Techniques and Spectro-Temporal Features for Telephonic Speech Recognition
      1. ABSTRACT
      2. INTRODUCTION
      3. BASIC CONSIDERATIONS
      4. LITERATURE SURVEY OF VARIOUS WORKS DONE RELATED TO OUR TOPIC OF DISCUSSION
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    7. Chapter 7: Evolutionary Algorithms
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. EVOLUTIONARY ALGORITHMS: CONCEPTS AND DESIGNS
      5. APPLICATIONS TO BIOINFORMATICS
      6. CONCLUSION
      7. REFERENCES
    8. Chapter 8: Discovering Behavioural Patterns within Customer Population by using Temporal Data Subsets
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. CHAPTER ORGANIZATION
      5. 4. VEZA COMPANY: GOALS OF THE PROJECT
      6. 5. PROPOSED SOLUTION FROM THE PERSPECTIVE OF DATA MINING METHODS
      7. 6. DATA FOUNDATION
      8. 7. MODELING SOLUTION FOR PROSPECTIVE CUSTOMER VALUE CALCULATION
      9. 7. CHURN PREDICTION MODELLING
      10. 9. REVEALED KNOWLEDGE AS A BASE FOR THE CHURN MITIGATION STRATEGIES AND THE NEW PRODUCTS DEVELOPMENT
      11. 10. DISCUSSION
      12. 11. FUTURE RESEARCH
      13. 12. CONCLUSION
      14. REFERENCES
      15. ADDITIONAL READING
      16. KEY TERMS AND DEFINITIONS
      17. APPENDIX: PREDICTIVE CHURN MODEL DEVELOPMENT USING LOGISTIC REGRESSION
    9. Chapter 9: Hybrid Intelligence for Smarter Networking Operations
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND AND RELATED WORK
      4. 3. HYBRID INTELLIGENCE TECHNIQUE-BASED REASONER FOR SMARTER NETWORKING
      5. 4. PERFORMANCE ANALYSIS
      6. 5. FUTURE RESEARCH DIRECTIONS
      7. 6. CONCLUSION
      8. REFERENCES
    10. Chapter 10: Epidemic Estimation over Social Networks using Large Scale Biosensors
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND ON SENSING ARCHITECTURES
      4. BACKGROUND ON COMPUTATIONAL EPIDEMIOLOGY
      5. BACKGROUND ON SOCIAL NETWORK ANALYSIS
      6. SOLUTIONS
      7. EXPERIMENTAL EVALUATION
      8. RECOMMENDATIONS
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
  9. Section 2: Hybrid Intelligent Techniques: Applications
    1. Chapter 11: Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function
      1. ABSTRACT
      2. INTRODUCTION
      3. MULTIOBJECTIVE OPTIMIZATION
      4. MULTILAYER SELF-ORGANIZING NEURAL NETWORK (MLSONN) ARCHITECTURE
      5. NSGA II BASED OPTIMIZED MULTILEVEL SIGMOIDAL (OPTIMUSIG) ACTIVATION FUNCTION
      6. NSGA II BASED PARALLEL OPTIMIZED MULTILEVEL SIGMOIDAL (PARAOPTIMUSIG) ACTIVATION FUNCTION
      7. WELL KNOWN IMAGE SEGMENTATION QUALITY EVALUATION METRICS
      8. PROPOSED METHODOLOGY
      9. RESULT ANALYSIS
      10. CONCLUSION
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    2. Chapter 12: Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm
      1. ABSTRACT
      2. INTRODUCTION
      3. BASICS OF QUANTUM COMPUTING
      4. QUANTUM EVOLUTIONARY ALGORITHM
      5. IMAGE THRESHOLDING OPTIMIZATION
      6. IMAGE THRESHOLDING EVALUATION METRICS
      7. RANDOM INTERFERENCE BASED QUANTUM INSPIRED GENETIC ALGORITHM
      8. EXPERIMENTAL RESULTS
      9. DISCUSSIONS AND CONCLUSION
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    3. Chapter 13: The Application of Rough Set Theory and Near Set Theory to Face Recognition Problem
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. FACE RECOGNITION PROBLEMS
      5. EXPERIMENTAL RESULTS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
    4. Chapter 14: Automation in Sputum Microscopy
      1. ABSTRACT
      2. INTRODUCTION
      3. THE DIAGNOSIS OF TB
      4. DRUGS AND TREATMENT PROCEDURE
      5. REVIEW ON EXISTING DIAGNOSTIC SYSTEMS
      6. OVERVIEW AND DESIGN PRINCIPAL
      7. RESULTS AND DISCUSSION
      8. FUTURE SCOPE
      9. CONCLUSION
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
      12. APPENDIX 1
      13. APPENDIX 2
    5. Chapter 15: Application of Meta-Models (MPMR and ELM) for Determining OMC, MDD and Soaked CBR Value of Soil
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. CONCLUSION
      6. REFERENCES
    6. Chapter 16: An Intelligent Approach for Virtual Chemistry Laboratory
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK
      4. 3. FRAMEWORK FOR INTELLIGENT VIRTUAL CHEMISTRY LABORATORY (IVCL)
      5. 4. EXPERIMENTS AND RESULTS
      6. 5. CONCLUSION
      7. REFERENCES
    7. Chapter 17: A Novel Fuzzy Anomaly Detection Algorithm Based on Hybrid PSO-Kmeans in Content-Centric Networking
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. CONTENT-CENTRIC NETWORKS
      5. PARTICLE SWARM OPTIMIZATION (PSO)
      6. K-MEANS CLUSTERING ALGORITHM
      7. CLUSTERING PROBLEM
      8. FUZZY SETS (FS)
      9. PROPOSED FUZZY ANOMALY DETECTION SYSTEM
      10. EXPERIMENTAL RESULTS AND DISCUSSION
      11. CONCLUSION
      12. REFERENCES
    8. Chapter 18: Cluster Based Medical Image Registration Using Optimized Neural Network
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. FEATURE EXTRACTION USING GLCM
      6. PROPOSED METHOD
      7. SOLUTIONS AND RECOMMENDATIONS
      8. CONCLUSION
      9. REFERENCES
  10. Compilation of References
  11. About the Contributors