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Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications

Book Description

As technology continues to become more sophisticated, mimicking natural processes and phenomena also becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for man-made computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications takes an interdisciplinary approach to the topic of natural computing, including emerging technologies being developed for the purpose of simulating natural phenomena, applications across industries, and the future outlook of biologically and nature-inspired technologies. Emphasizing critical research in a comprehensive multi-volume set, this publication is designed for use by IT professionals, researchers, and graduate students studying intelligent computing.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editor-in-Chief
  5. Associate Editors
  6. Editorial Advisory Board
  7. Preface
  8. Section 1: Fundamental Concepts and Theories
    1. Chapter 1: A Theoretical Framework for Parallel Implementation of Deep Higher Order Neural Networks
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND AND LITERATURE REVIEW
      4. THEORETICAL FRAMEWORK
      5. SUMMARY AND FUTURE RESEARCH DIRECTIONS
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    2. Chapter 2: An Exploration of Backpropagation Numerical Algorithms in Modeling US Exchange Rates
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. METHODS
      4. 3. DATA AND RESULTS
      5. 4. CONCLUSION
      6. ACKNOWLEDGMENT
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    3. Chapter 3: Image Representation, Filtering, and Natural Computing in a Multivalued Quantum System
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. QUANTUM IMAGE PROCESSING
      5. QUANTUM NATURAL COMPUTING
      6. QUANTUM IMAGE FILTERATION
      7. CHRESTENSON GATES
      8. S-GATE
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    4. Chapter 4: Some Properties on the Capability of Associative Memory for Higher Order Neural Networks
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. HIGHER ORDER NEURONS
      4. 3. ASSOCIATIVE MEMORY FOR THE CONVENTIONAL NEURAL NETWORKS
      5. 4. ASSOCIATIVE CAPABILITIES FOR HONNs
      6. 5. HOMOGENEOUS NEURAL NETWORKS
      7. 6. CONCLUDING REMARKS AND FUTURE TREND
      8. REFERENCES
      9. APPENDIX
    5. Chapter 5: Swarm Intelligence in Solving Bio-Inspired Computing Problems
      1. ABSTRACT
      2. INTRODUCTION
      3. INFORMATION SYSTEM
      4. FOUNDATIONS OF ROUGH COMPUTING
      5. THE CONCEPT OF CORE AND REDUCT
      6. OVERVIEW OF SWARM INTELLIGENCE MODELS
      7. SWARM INTELLIGENCE IN MICROARRAY CLASSIFICATION
      8. SWARM INTELLIGENCE IN GENE EXPRESSION
      9. SWARM INTELLIGENCE IN HEALTHCARE
      10. SWARM INTELLIGENCE IN MEDICAL DECISION SUPPORT SYSTEM
      11. FUTURE RESEARCH DIRECTIONS
      12. CONCLUSION
      13. REFERENCES
      14. KEY TERMS AND DEFINITIONS
    6. Chapter 6: Evolutionary Algorithms
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. EVOLUTIONARY ALGORITHMS: CONCEPTS AND DESIGNS
      5. APPLICATIONS TO BIOINFORMATICS
      6. CONCLUSION
      7. REFERENCES
    7. Chapter 7: Artificial Neural Network and Its Application in Steel Industry
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. SOFT-COMPUTING TECHNIQUES
      4. 3. ARTIFICIAL NEURAL NETWORK
      5. 4. PROS AND CONS OF ARTIFICIAL NEURAL NETWORK
      6. 5. APPLICATION IN STEEL INDUSTRY
      7. 6. CONCLUSION
      8. REFERENCES
    8. Chapter 8: Parallel Multi-Criterion Genetic Algorithms
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. MULTI-OBJECTIVES GENETIC ALGORITHMS
      4. 3. PARALLEL MULTI-OBJECTIVES GENETIC ALGORITHMS
      5. 4. EXPERIMENTAL STUDY
      6. 5. APPLICATIONS
      7. 6. CONCLUSION
      8. REFERENCES
    9. Chapter 9: Evolutionary Computing to Examine Variation in Proteins with Evolution
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ISSUES, CONTROVERSIES AND PROBLEMS
      5. RECOMMENDATIONS AND SOLUTIONS
      6. SUMMARIZING DIFFERENT ALGORITHMS IN BRIEF
      7. FUTURE PROSPECTS
      8. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    10. Chapter 10: Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORKS
      4. 3. METHODS
      5. 4. EXPERIMENTAL RESULTS
      6. 5. CONCLUSION AND FUTURE WORK
      7. REFERENCES
    11. Chapter 11: On Mutual Relations amongst Evolutionary Algorithm Dynamics and Its Hidden Complex Network Structures
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. EVOLUTIONARY ALGORITHMS, THEIR DYNAMICS, AND USE
      4. 3. CONVERSION AND VISUALIZATION
      5. 4. CASE STUDIES
      6. 5. CONCLUSION AND FUTURE DIRECTIONS
      7. ACKNOWLEDGMENT
      8. REFERENCES
  9. Section 2: Development and Design Methodologies
    1. Chapter 12: Artificial Higher Order Neural Network Models
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. HIGHER ORDER NEURAL NETWORK ARCHITECTURE AND MODELS
      5. GENERAL LEARNING ALGORITHM AND WEIGHT UPDATE FORMULAE
      6. 24 HONN MODELS LEARNING ALGORITHM AND WEIGHT UPDATE FORMULAE
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
    2. Chapter 13: An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic
      1. ABSTRACT
      2. INTRODUCTION
      3. CLASSIFICATION OF COMPUTATIONAL APPROACHES
      4. THE ROLE OF SOFT COMPUTING FOR DESIGNING INTELLIGENT SYSTEM
      5. EVOLUTIONARY COMPUTING IN SEARCH AND OPTIMIZATION
      6. EVOLUTIONARY ALGORITHMS
      7. BASIC STEPS OF EVOLUTION CYCLE
      8. COMPARISON OF CLASSICAL OPTIMIZATION METHODS VS. GENETIC ALGORITHMS
      9. GENETIC ALGORITHM-A PRIME TYPE OF EA
      10. TYPES OF APPLICATIONS DEVELOPED USING GENETIC ALGORITHM
      11. WEAKNESS OF GENETIC ALGORITHM
      12. LINGUISTIC KNOWLEDGE REPRESENTATION WITH FUZZY LOGIC
      13. DIFFERENCE BETWEEN BOOLEAN LOGIC AND FUZZY LOGIC
      14. MEMBERSHIP FUNCTIONS
      15. FUZZY LOGIC BASED SYSTEMS
      16. FUZZY RULE BASED SYSTEM
      17. APPLICATION OF HEART DISEASE DIAGNOSIS
      18. DEVELOPMENT OF INTELLIGENT PROCESS FOR DIAGNOSIS OF HEART DISEASE
      19. RESULTS AND DISCUSSION
      20. SIMILAR KIND OF APPLICATIONS
      21. CONCLUSION
      22. REFERENCES
      23. ADDITIONAL READING
      24. KEY TERMS AND DEFINITIONS
      25. APPENDIX
    3. Chapter 14: Developmental Swarm Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. DEVELOPMENTAL SWARM INTELLIGENCE
      5. DISCUSSIONS AND FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
    4. Chapter 15: Neural Network Models
      1. ABSTRACT
      2. INTRODUCTION
      3. WHAT IS EMERGENCE?
      4. HOW TO DETECT EMERGENCE: MULTIPLE ANALYSES
      5. HOW TO DETECT EMERGENCE: SINGLE ANALYSES
      6. THE EMERGENCE PHENOMENA WITHIN AANS
      7. CONCLUSION
      8. REFERENCES
    5. Chapter 16: Derivation and Simulation of an Efficient QoS Scheme in MANET through Optimised Messaging Based on ABCO Using QualNet
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. GENERALITIES AND CHALLENGES IN AD HOC NETWORK
      4. 3. QUALITY OF SERVICES IN MANET
      5. 4. SWARM INTELLIGENCE
      6. 5. ALGORITHM PROLOGUE AND OUR ALGORITHM
      7. 6. SIMULATION RESULTS AND ANALYSIS
      8. 7. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    6. Chapter 17: A Method for Classification Using Data Mining Technique for Diabetes
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK
      4. 3. METHOD
      5. 4. EXPERIMENTAL RESULTS
      6. 4. CONCLUSION AND FUTURE WORK
      7. REFERENCES
    7. Chapter 18: Hookes-Jeeves-Based Variant of Memetic Algorithm
      1. ABSTRACT
      2. INTRODUCTION
      3. MATHEMATICAL FORM OF OPTIMIZATION PROBLEMS
      4. LOCAL AND GLOBAL OPTIMAL SOLUTIONS
      5. METHODS FOR GLOBAL OPTIMIZATION
      6. GENETIC ALGORITHMS
      7. HYBRIDIZED GENETIC ALGORITHMS
      8. LOCAL SEARCH HOOKE-JEEVES METHOD
      9. METHODOLOGY OF THE PROPOSED APPROACH
      10. SALIENT FEATURES OF MA
      11. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. KEY TERMS AND DEFINITIONS
      15. APPENDIX
    8. Chapter 19: An Artificial Neural Network Model as the Decision Support System of Ports
      1. ABSTRACT
      2. INTRODUCTION
      3. DECISION SUPPORT MODEL
      4. CASE STUDY IN TURKEY
      5. DISCUSSION OF RESULTS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
      9. APPENDIX
    9. Chapter 20: Innovative Hierarchical Fuzzy Logic for Modelling Using Evolutionary Algorithms
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. VARIABLE SELECTION AND RULE BASE DECOMPOSITION
      4. 3. RULE BASE IDENTIFICATION
      5. 4 CO-EVOLUTIONARY LEARNING
      6. 5. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
    10. Chapter 21: A Black-Box Model for Estimation of the Induction Machine Parameters Based on Stochastic Algorithms
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORKS
      4. 3. THE BLACK-BOX MODELING SYSTEM
      5. 4. APPLICATION OF THE PROPOSED APPROACH
      6. 5. DISCUSSIONS
      7. 6. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
    11. Chapter 22: Adaptive Hybrid Higher Order Neural Networks for Prediction of Stock Market Behavior
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED STUDIES TO HONN
      4. 3. ADAPTIVE HONN BASED FORECASTING MODELS
      5. 4. SIMULATION RESULTS AND ANALYSIS
      6. 5. CONCLUSION AND FURTHER RESEARCH
      7. REFERENCES
    12. Chapter 23: Genetic Algorithms for Small Enterprises Default Prediction
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. HYPOTHESES
      5. METHODOLOGY
      6. RESULTS
      7. DISCUSSION
      8. MANAGERIAL IMPLICATION
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSIONS
      11. REFERENCES
      12. ADDITIONAL READING SECTION
      13. KEY TERMS AND DEFINITIONS
      14. APPENDIX: NOMENCLATURE
  10. Section 3: Tools and Technologies
    1. Chapter 24: Quantum Computing Based Technique for Cancer Disease Detection System
      1. ABSTRACT
      2. INTRODUCTION
      3. CANCER DISEASE
      4. QUANTUM COMPUTING
      5. QUANTUM COMPUTING ALGORITHMS
      6. PROPOSED SYSTEM
      7. PROPOSED ALGORITHM
      8. EXPERIMENTAL SETUP
      9. RESULTS AND ANALYSIS
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
      12. REFERENCES
      13. KEY TERMS AND DEFINITIONS
    2. Chapter 25: MEMS Microrobot with Pulse-Type Hardware Neural Networks Integrated Circuit
      1. ABSTRACT
      2. INTRODUCTION
      3. MEMS MICROROBOT
      4. LOCOMOTION MECHANISMS OF THE MEMS MICROROBOT
      5. PULSE-TYPE HARDWARE NEURAL NETWORKS INTEGRATED CIRCUIT
      6. RESULTS
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    3. Chapter 26: Ultra High Frequency Polynomial and Trigonometric Higher Order Neural Networks for Control Signal Generator
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. UPT-HONN MODELS
      5. LEARNING ALGORITHM OF UPT-HONN MODELS
      6. UPT-HONN TESTING
      7. FUTHER RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
    4. Chapter 27: Ultra High Frequency Sigmoid and Trigonometric Higher Order Neural Networks for Data Pattern Recognition
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. UGT-HONN MODELS
      5. LEARNING ALGORITHM OF UGT-HONN MODELS
      6. UGT-HONN TESTING
      7. CONCLUSION
      8. REFERENCES
    5. Chapter 28: Artificial Sine and Cosine Trigonometric Higher Order Neural Networks for Financial Data Prediction
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. SIN-HONN AND COS-HONN MODELS
      5. LEARNING ALGORITHM OF SIN-HONN AND COS-HONN MODELS
      6. FINANCIAL DATA PREDICTION USING HONN MODELS
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
    6. Chapter 29: Cosine and Sigmoid Higher Order Neural Networks for Data Simulations
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MODELS OF CS-HONN
      5. CS-HONN TIME SERIES ANALYSIS SYSTEM
      6. LEARNING ALGORITHM OF CS-HONN
      7. TIME SERIES DATA TEST USING CS-HONN
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. REFERENCES
    7. Chapter 30: Higher Order Neural Network for Financial Modeling and Simulation
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. PRELIMINARIES
      4. 3. PSO-FLANN AS HONN FOR FINANCIAL MODELING
      5. 4. EXPERIMENTAL DETAILS
      6. 5. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
      7. REFERENCES
    8. Chapter 31: Ultra High Frequency SINC and Trigonometric Higher Order Neural Networks for Data Classification
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. UNT-HONN MODELS
      5. LEARNING ALGORITHM OF UPT-HONN MODELS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
    9. Chapter 32: Swarm Intelligence for Biometric Feature Optimization
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BIOMETRIC SYSTEM
      4. 3. FACE AS BIOMETRIC SYSTEM
      5. 4. FEATURE-BASED METHODS
      6. 5. FEATURE SELECTION (FS)
      7. 6. PROPOSED FACE RECOGNITION METHODOLOGY USING SWARM INTELLIGENCE
      8. 7. CONCLUSION AND FUTURE DIRECTION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    10. Chapter 33: Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. IMPORTANCE OF CUSTOMER PROFILING
      5. PARTICLE SWARM OPTIMISATION ALGORITHM AND PROFILING FROM PREDICTIVE MODELS
      6. FUTURE RESEARCH DIRECTIONS
      7. DISCUSSION
      8. CONCLUSION
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    11. Chapter 34: Quantum Behaved Swarm Intelligent Techniques for Image Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. THRESHOLDING EVALUATION METRICS
      4. SWARMS
      5. PRINCIPLES OF SWARM INTELLIGENCE
      6. OVERVIEW OF META-HEURISTIC ALGORITHMS
      7. QUANTUM COMPUTING FUNDAMENTALS
      8. QUANTUM BASED ALGORITHMS
      9. CONCLUSION
      10. REFERENCES
      11. ADDITIONAL READING
      12. KEY TERMS AND DEFINITIONS
    12. Chapter 35: Navigation Control of a Mobile Robot under Time Constraint using Genetic Algorithms, CSP Techniques, and Fuzzy Logic
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK
      4. 3. PROBLEM STATEMENT
      5. 4. PROPOSED FUZZY LOGIC APPROACH CONTROL
      6. 5. NEW GENETIC ALGORITHM
      7. 6. RESULTS
      8. 7. DISCUSSIONS
      9. 8. CONCLUSION
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
  11. Section 4: Utilization and Application
    1. Chapter 36: A Survey on Swarm Robotics
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. CHARACTERISTICS AND APPLICATION SCOPES OF SWARM ROBOTICS
      5. MODELING SWARM ROBOTICS
      6. ENTITY PROJECTS AND SIMULATIONS
      7. COOPERATIVE ALGORITHMS
      8. SWARM ROBOTICS SEARCHING ALGORITHMS
      9. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    2. Chapter 37: Using a Hybrid System Composed of Neural Networks and Genetic Algorithms for Financial Forecasting
      1. ABSTRACT
      2. INTRODUCTION
      3. NEURAL NETWORK
      4. MODELS EVALUATION
      5. GENETIC ALGORITHMS IN BUILDING ARTIFICIAL NEURAL NETWORKS
      6. NEURAL NETWORK OPTIMIZATION WITH GENETIC ALGORITHMS
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    3. Chapter 38: Analytics on Fireworks Algorithm Solving Problems with Shifts in the Decision Space and Objective Space
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. FIREWORKS ALGORITHM
      4. 3. MAPPING STRATEGIES IN FIREWORKS ALGORITHM
      5. 4. POPULATION DIVERSITY
      6. 5. EXPERIMENTAL STUDY
      7. 6. POPULATION DIVERSITY DISCUSSION
      8. 7. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
    4. Chapter 39: Studies of Computational Intelligence Based on the Behaviour of Cockroaches
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. APPLICATIONS OF COCKROACH SWARM INTELLIGENCE
      5. 4. THEORETICAL STUDY COCKROACH SWARM OPTIMIZATION AND AN EXPERIMENTAL RESULTS OF INTEGRATION OF ROBOT TO COCKROACH FOR COLLECTIVE DECISION MAKING
      6. 5. PROPOSED ALGORITHM FOR TRAVERSING SHORTEST DISTANCE CITY WAREHOUSE USING COCKROACH SWARM OPTIMIZATION
      7. 6. DISCUSSION
      8. 7. LIMITATIONS OF COCKROACH SWARM OPTIMIZATION ALGORITHM FOR CITY WAREHOUSE TRAVERSING PROBLEM
      9. 8. FUTURE PROSPECT
      10. 9. COMPARATIVE STATISTICAL RESULTS OF THE PROGRESS OF THE WORK ON COCKROACH
      11. 10. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. KEY TERMS AND DEFINITIONS
      15. APPENDIX
    5. Chapter 40: Application of Genetic Algorithms in Inventory Control
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS
      5. FUTURE AND CURRENT TRENDS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    6. Chapter 41: Utilizing Feature Selection on Higher Order Neural Networks
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND OF HIGHER ORDER NEURAL NETWORK
      4. FEATURE SELECTION
      5. EXPERIMENTS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    7. Chapter 42: On the Accelerated Convergence of Genetic Algorithm Using GPU Parallel Operations
      1. ABSTRACT
      2. INTRODUCTION
      3. 1. BACKGROUND
      4. 2. METHOD
      5. 3. RESULT AND DISCUSSION
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
    8. Chapter 43: Multi-Objective Generation Scheduling Using Genetic-Based Fuzzy Mathematical Programming Technique
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND OF FUZZY SETS IN OPF
      4. GENETIC ALGORITHM IN FUZZY OPTIMAL POWER FLOW
      5. OPF PROBLEM STATEMENT
      6. FUZZY OPF PROBLEM
      7. FUZZY MULTI-OBJECTIVE OPF PROBLEM SOLUTION
      8. STUDY CASE
      9. DISCUSSION AND COMPARISON
      10. CONCLUSION
      11. FUTURE RESEARCH DIRECTIONS
      12. ACKNOWLEDGMENT
      13. REFERENCES
      14. ADDITIONAL READING
      15. KEY TERMS AND DEFINITIONS
      16. APPENDIX 1: CASE 1 DATA
      17. APPENDIX 2: CASE 2 DATA
    9. Chapter 44: Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE REVIEW
      4. FORECASTING ALGORITHMS
      5. SHORT-TERM LOAD FORECASTING WITH PROPOSED ALGORITHMS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    10. Chapter 45: Evaluation of Genetic Algorithm as Learning System in Rigid Space Interpretation
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. LITERATURE REVIEW
      5. STOCHASTIC APPROXIMATION PROCEDURE
      6. MATHEMATICAL MODEL OF LEARNING
      7. FUNDAMENTAL THEOREM OF GENETIC ALGORITHM
      8. DISCUSSION
      9. CONCLUSION
      10. FURTHER WORK
      11. CRITICAL ANALYSIS ON THE ADVANTAGES AND DISADVANTAGES
      12. ACKNOWLEDGEMENT
      13. REFRENCES
      14. ADDITIONAL READING
      15. KEY TERMS AND DEFINITIONS
      16. APPENDIX
    11. Chapter 46: Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. FUZZY LOGIC SYSTEM
      5. GENETIC ALGORITHMS
      6. ANT COLONY OPTIMIZATION
      7. PARTICLE SWARM OPTIMIZATION
      8. ARTIFICIAL IMMUNE SYSTEM
      9. CULTURE ALGORITHM
      10. MOTIVATION
      11. DATA SOURCE
      12. PROBLEM STATEMENT
      13. GENERAL METHODOLOGY
      14. CASE STUDY
      15. CONCLUSION
      16. FUTURE RESEARCH WORK
      17. REFERENCES
      18. ADDITIONAL READING
      19. KEY TERMS AND DEFINITIONS
    12. Chapter 47: Application of Biologically Inspired Techniques for Industrial and Environmental Research via Air Quality Monitoring Network
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MOTIVATION
      5. DATA SOURCE
      6. PROBLEM STATEMENT
      7. GENERAL METHODOLOGY
      8. CASE STUDY
      9. CONCLUSION
      10. FUTURE RESEARCH DIRECTIONS
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    13. Chapter 48: Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORKS
      4. MATERIALS AND METHODS
      5. RESULTS AND DISCUSSION
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
    14. Chapter 49: Using Self Organizing Maps for Banking Oversight
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. LITERATURE REVIEW
      4. 3. EMPIRICAL DESIGN OF THE MODEL
      5. 4. RESULTS
      6. 5. CONCLUDING COMMENTS
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
      9. ENDNOTES
    15. Chapter 50: Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. USING HYBRID OF GA- BP ANN FOR CLUSTER CLASSIFICATION IN PERSONALIZED WEB SEARCH BASED ON CLUSTERED QUERY SESSIONS
      5. 4. EXPERIMENTAL STUDY
      6. 5. CONCLUSION AND FUTURE WORK
      7. REFERENCES
  12. Section 5: Issues and Challenges
    1. Chapter 51: Role of Consumer Engagement and Swarm Intelligence in Management of a Brand at Social Media
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. RESEARCH METHOD
      5. 4. ANALYSIS AND FINDINGS
      6. 5. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
      9. APPENDIX 1
      10. APPENDIX 2
    2. Chapter 52: Parameter Optimization of Photovoltaic Solar Cell and Panel Using Genetic Algorithms Strategy
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. OPTIMIZATION PARAMETERS SOLAR CELL: THEORY AND FORMULATION
      5. SOLAR CELL PARAMETERS EXTRACTION
      6. RESULTS AND DISCUSSION
      7. CONCLUSION
      8. FUTURE RESEARCH DIRECTIONS
      9. REFERENCES
      10. ADDITIONAL REFERENCES
      11. KEY TERMS AND DEFINITIONS
    3. Chapter 53: Customer Profiling in Complex Analytical Environments Using Swarm Intelligence Algorithms
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. IMPORTANCE OF CUSTOMER PROFILING
      5. PARTICLE SWARM OPTIMISATION ALGORITHM AND PROFILING FROM PREDICTIVE MODELS
      6. FUTURE RESEARCH DIRECTIONS
      7. DISCUSSION
      8. BIG DATA CHALENGES
      9. BAYESIAN NETWORKS AND PROFILING AS AN EXAMPLE OF COMPLEX PROFILING SOLUTION
      10. CONCLUSION
      11. REFERENCES
    4. Chapter 54: Stochastic Drought Forecasting Exploration for Water Resources Management in the Upper Tana River Basin, Kenya
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. TYPES OF DROUGHTS
      5. 4. DROUGHTS IN KENYA
      6. 5. DROUGHT ASSESSMENT INDICES
      7. 6. STOCHASTIC DROUGHT FORECASTING
      8. 7. CATEGORIES OF DROUGHT FORECASTING
      9. 7. RECOMMENDATIONS
      10. 8. CONCLUSION
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    5. Chapter 55: Theoretical Analyses of the Universal Approximation Capability of a Class of Higher Order Neural Networks Based on Approximate Identity
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. SOME NOTATIONS AND BASIC DEFINITIONS
      4. 3 THEORETICAL ANALYSES IN THE SPACE OF CONTINUOUS MULTIVARIATE FUNCTIONS
      5. 4. THEORETICAL ANALYSES IN THE SPACE OF LEBESGUE INTEGRABLE MULTIVARIATE FUNCTIONS
      6. 5. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
    6. Chapter 56: AI-Based Cyber Defense for More Secure Cyberspace
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ARTIFICIAL INTELLIGENCE TECHNIQUES IN CYBER DEFENSE
      5. ARTIFICIAL INTELLIGENCE -BASED CYBER DEFENSE MODEL FOR SUPPORT OF THE GOVERNMENTAL AND NON-GOVERNMENTAL ORGANIZATIONS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    7. Chapter 57: Hybrid BFO and PSO Swarm Intelligence Approach for Biometric Feature Optimization
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK
      4. 3. BIOMETRICS RECOGNITION SYSTEM
      5. 4. FEATURE EXTRACTION
      6. 5. FEATURE SELECTION (FS)
      7. 6. PROPOSED FACE RECOGNITION APPROACH
      8. 7. EXPERIMENTAL RESULTS AND DISCUSSION
      9. 8. CONCLUSION AND FUTURE DIRECTION
      10. REFERENCES
      11. APPENDIX
    8. Chapter 58: From Optimization to Clustering
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. PSO FOR OPTIMIZATION
      5. PSO FOR CLUSTERING
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
    9. Chapter 59: Potential Indicators Based Neural Networks for Cash Forecasting of an ATM
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK
      4. 3. BASIC MATERIALS
      5. 4. MLTP-FOR CASH FORECASTING
      6. 5. SIMULATION AND VALIDATION
      7. 6. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
      8. ACKNOWLEDGMENT
      9. REFERENCES
    10. Chapter 60: Swarm Intelligence for Dimensionality Reduction
      1. ABSTRACT
      2. INTRODUCTION
      3. LOW RANK APPROXIMATIONS
      4. SWARM INTELLIGENCE OPTIMIZATION
      5. IMPROVING NMF WITH SWARM INTELLIGENCE OPTIMIZATION
      6. SETUP
      7. EXPERIMENTAL EVALUATION
      8. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    11. Chapter 61: Determination of Bearing Capacity of Shallow Foundation Using Soft Computing
      1. ABSTRACT
      2. INTRODUCTION
      3. GAUSSIAN PROCESSES REGRESSION
      4. EXTREME LEARNING MACHINE
      5. MINIMAX PROBABILITY MACHINE REGRESSION
      6. DEVELOPMENT OF MODEL
      7. RESULTS AND DISCUSSIONS
      8. CONCLUSION
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY WORDS AND DEFINITIONS
  13. Section 6: Emerging Trends
    1. Chapter 62: Green Computing and Its Impact
      1. ABSTRACT
      2. 1. GREEN COMPUTING: THE NEW PARADIGM
      3. 2. WHY GREEN COMPUTING?
      4. 3. HOLISTIC APPROACH OF GREEN COMPUTING
      5. 4. GREEN HARDWARE AND SOFTWARE TECHNIQUES
      6. 5. COMPUTING TECHNIQUES FOR GREEN COMPUTING
      7. 6. CHARACTERISTICS
      8. 7. IMPACTS OF GREEN COMPUTING
      9. 8. GREEN AWARENESS
      10. 9. ISSUES AND CHALLENGES AHEAD
      11. CONCLUSION
      12. REFERENCES
      13. KEY TERMS AND DEFINITIONS
    2. Chapter 63: Schematic Classification Model of Green Computing Approaches
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. OVERVIEW OF GREEN COMPUTING
      4. 3. COMPUTER RESOURCE UTILIZATION AND CARBON EMISSION
      5. 4. NEED TO STUDY CLASSIFIED GREEN COMPUTING APPROACHES
      6. 5. COMPREHENSIVE CLASSIFICATION OF GREEN COMPUTING APPROACHES
      7. 6. CONCLUSION AND FUTURE DIRECTION
      8. REFERENCES
    3. Chapter 64: Prediction of International Stock Markets Based on Hybrid Intelligent Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. METHODS
      5. DATA AND EXPERIMENTAL RESULTS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
    4. Chapter 65: Green and Energy-Efficient Computing Architecture for E-Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. REVIEW OF EXISITNG WORKS
      5. COGALA ARCHITECTURE
      6. FUTURE RESEARCH DIRECTIONS
      7. REFERENCES
    5. Chapter 66: Adoption of Virtualization in Cloud Computing
      1. ABSTRACT
      2. INTRODUCTION
      3. DISCUSSION
      4. CLOUD COMPUTING
      5. VIRTUALIZATION
      6. GREEN COMPUTING
      7. APPLICATION OF VIRTUALIZATION IN CLOUD COMPUTING: A FOUNDATION STEP TOWARDS GREEN COMPUTING
      8. CONCLUSION
      9. REFERENCES
    6. Chapter 67: Toward a Sustainable Fishery Management Policy
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. METHOD
      5. 4. RESULTS
      6. 5. DISCUSSION
      7. REFERENCES
    7. Chapter 68: The Contribution of Teleworking towards a Green Computing Environment
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. A CONCEPTUAL MODEL OF GREEN TELEWORKING
      5. DISCUSSION OF THE USEFULNESS OF THE MODEL AND FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    8. Chapter 69: New Genetic Operator (Jump Crossover) for the Traveling Salesman Problem
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. GENETIC ALGORITHMS FOR THE TRAVELING SALESMAN PROBLEM
      4. 3. ADOPTED APPROCH: JUMP CROSSOVER
      5. 4. RESULTS AND DISCUSSIONS COMPARED WITH THE BENCHMARK
      6. 5. CONCLUSION
      7. REFERENCES
    9. Chapter 70: Improving Performance of Higher Order Neural Network using Artificial Chemical Reaction Optimization
      1. ABSTRACT
      2. INTRODUCTION
      3. 1. HIGHER ORDER NEURAL NETWORK (HONN)
      4. 2. ARTIFICIAL CHEMICAL REACTION OPTIMIZATION (ACRO)
      5. 3. STOCK MARKET FORECASTING
      6. 4. ACRO BASED FLN FOR STOCK MARKET FORECASTING
      7. 5. SIMULATION RESULTS AND PERFORMANCE ANALYSIS
      8. 6. CONCLUSION
      9. REFERENCES