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Machine Learning Algorithms for Problem Solving in Computational Applications

Book Description

Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.

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. Preface
  6. Acknowledgment
  7. Section 1: Machine Learning Applications
    1. Chapter 1: Machine Learning Approach for Content Based Image Retrieval
      1. ABSTRACT
      2. INTRODUCTION
      3. BRIEF REVIEW OF CONTENT BASED IMAGE RETRIEVAL TECHNIQUES
      4. FEATURE EXTRACTION AND FUZZY MAPPING
      5. NEURAL BASED FUSION OF CLASSES
      6. EXPERIMENTAL RESULTS
      7. CONCLUSION
    2. Chapter 2: Machine Learning Techniques in Handwriting Recognition
      1. ABSTRACT
      2. INTRODUCTION
      3. CONCLUSION
    3. Chapter 3: Semi Blind Source Separation for Application in Machine Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. SEMIBLIND ICA FOR SOURCE SEPARATION AND IDENTIFICATION
      4. CHALLENGES OF SOURCE SEPARATION IN BIO SIGNAL PROCESSING
      5. SEMIBLIND ICA FOR BIO SIGNAL APPLICATIONS
      6. SEMIBLIND ICA FOR AUDIO SIGNAL APPLICATIONS
      7. DISCUSSIONS AND CONCLUSION
      8. FUTURE RESEARCH DIRECTIONS
    4. Chapter 4: Machine Learning Algorithms for Analysis of DNA Data Sets
      1. ABSTRACT
      2. INTRODUCTION
      3. PRELIMINARIES AND BACKGROUND INFORMATION
      4. CLASSIFICATIONS OF A MACHINE LEARNING ALGORITHM BASED ON GRAPH PARTITIONING WITH ALIGNMENT SCORES
      5. CLASSIFICATIONS OF THE K-MEDOIDS MACHINE LEARNING ALGORITHM WITH ALIGNMENT SCORES
      6. CLASSIFICATIONS OF THE K-COMMITTEES MACHINE LEARNING ALGORITHM WITH ALIGNMENT SCORES
      7. CLASSIFICATIONS OF THE NEAREST NEIGHBOUR MACHINE LEARNING ALGORITHM WITH ALIGNMENT SCORES
      8. CLUSTERINGS OF A MACHINE LEARNING ALGORITHM BASED ON GRAPH PARTITIONS WITH ALIGNMENT SCORES
      9. CLUSTERINGS OF THE K-MEDOIDS MACHINE LEARNING ALGORITHM WITH ALIGNMENT SCORES
      10. CLUSTERINGS OF THE K-COMMITTEES MACHINE LEARNING ALGORITHM WITH ALIGNMENT SCORES
      11. CLUSTERINGS OF THE NEAREST NEIGHBOUR MACHINE LEARNING ALGORITHM WITH ALIGNMENT SCORES
      12. EXAMPLES OF SYNTHETIC DATA SETS
      13. CONCLUSION
    5. Chapter 5: Machine Learning Applications in Radiation Therapy
      1. ABSTRACT
      2. INTRODUCTION
      3. MODELING CLINICAL COMPLICATIONS USING MACHINE LEARNING TOOLS IN A MULTI-PLAN INTENSITY-MODULATED RADIATION THERAPY (IMRT) FRAMEWORK
      4. MACHINE LEARNING FOR INTRA-FRACTION TUMOR MOTION MODELLING WITH RESPIRATORY SURROGATES
      5. REVIEW OF RECENT ADVANCEMENT OF ML APPLICATIONS IN RADIATION THERAPY
      6. CONCLUSION
    6. Chapter 6: Insights from Jurisprudence for Machine Learning in Law
      1. ABSTRACT
      2. 1 INTRODUCTION
      3. 2 OVERVIEW OF MACHINE LEARNING IN LAW
      4. 3 JURISPRUDENCE CONCEPTS
      5. 4 LIMITATIONS OF KNOWLEDGE DISCOVERY FROM DATABASES
      6. 5 CONCLUSION
    7. Chapter 7: Machine Learning Applications in Computer Vision
      1. ABSTRACT
      2. INTRODUCTION
      3. 1- OBJECT DESCRIPTION AND REPRESENTATION IN COMPUTER VISION
      4. 2 REINFORCEMENT LEARNING AND COMPUTER VISION
      5. 3 TRANSDUCTIVE LEARNING
      6. 4 DISTANCES IN COMPUTER VISION AND DISTANCE METRIC LEARNING
    8. Chapter 8: Applications of Machine Learning for Linguistic Analysis of Texts
      1. ABSTRACT
      2. INTRODUCTION
      3. INTERNATIONAL CORPUS OF LEARNER ENGLISH
      4. LINGUISTIC INQUIRY AND WORD COUNT SOFTWARE
      5. PREPROCESSING
      6. INDEPENDENT INITIAL CLUSTERINGS
      7. CONSENSUS FUNCTIONS FOR ENSEMBLE CLUSTERINGS
      8. SUPERVISED CLASSIFICATION ALGORITHMS
      9. EXPERIMENTAL RESULTS
      10. CONCLUSION
    9. Chapter 9: An Automatic Machine Learning Method for the Study of Keyword Suggestion
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. METHOD
      5. EXPERIMENT
      6. CONCLUSION
  8. Section 2: Computational Intelligence Techniques and Applications
    1. Chapter 10: Emergence Phenomenon and Fuzzy Logic in Meaningful Image Segmentation and Retrieval
      1. ABSTRACT
      2. INTRODUCTION
      3. EMERGENCE PHENOMENON
      4. USE OF EMERGENCE PHENOMENON
      5. NEURAL NETWORKS FOR IMAGE CLASSIFICATION
      6. FUZZY LOGIC AS SIMILARITY MEASURE
      7. CONCLUSION
    2. Chapter 11: Predicting Adsorption Behavior in Engineered Floodplain Filtration System Using Backpropagation Neural Networks
      1. ABSTRACT
      2. INTRODUCTION
      3. ADSORPTION PROCESS
      4. EXPERIMENTAL
      5. PREDICTION OF ADSORPTION BEHAVIOUR IN EFF USING NEURAL NETWORKS
      6. CONCLUSION
    3. Chapter 12: Computational Intelligence Techniques for Pattern Recognition in Biomedical Image Processing Applications
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. AI BASED MR BRAIN IMAGE CLASSIFICATION
      5. EXPERIMENTAL RESULTS AND DISCUSSIONS
      6. CONCLUSION
      7. FUTURE WORK
    4. Chapter 13: A PSO-Based Framework for Designing Fuzzy Systems from Noisy Data Set
      1. ABSTRACT
      2. INTRODUCTION
      3. FUZZY SETS AND SYSTEMS
      4. PSO FRAMEWORK FOR EVOLVING FLS FROM NOISY DATA SET
      5. PSO MODEL AND STRATEGY PARAMETERS
      6. APPLICATION: DESIGNING FUZZY LOGIC SYSTEMS FROM NOISY DATA
      7. MACKEY-GLASS TIME SERIES FORECASTING
      8. SIMULATION RESULTS
      9. CONCLUSION
    5. Chapter 14: Neural Network Based Classifier Ensembles
      1. ABSTRACT
      2. INTRODUCTION
      3. CLASSIFIER ENSEMBLES
      4. EXPERIMENTAL DATASETS AND PARAMETERS
      5. CONCLUSION
    6. Chapter 15: Development of an Intelligent Neural Model to Predict and Analyze the VOC Removal Pattern in a Photocatalytic Reactor
      1. ABSTRACT
      2. INTRODUCTION
      3. PHOTOREACTOR FOR VOC REMOVAL AND WORKING MECHANISM
      4. BACKGROUND TO ARTIFICIAL NEURAL NETWORKS (ANNS)
      5. FEED-FORWARD NEURAL NETWORKS
      6. NEURAL MODELING PROCEDURE
      7. PHOTO OXIDATION EXPERIMENTS: GAS-PHASE TOLUENE REMOVAL
      8. ANN MODEL FOR A PHOTOCATALYTIC REACTOR
      9. CONCLUSION
  9. Section 3: Miscellaneous Techniques in Machine Learning
    1. Chapter 16: An Introduction to Pattern Classification
      1. ABSTRACT
      2. INTRODUCTION
      3. PATTERN SPACE
      4. CATEGORISATION OF CLASSIFICATION SYSTEMS
      5. DISTANCE-BASED CLASSIFICATION
      6. STATISTICAL CLASSIFICATION
      7. STATISTICAL PATTERN CLASSIFICATION FOR NORMAL DISTRIBUTIONS
      8. CONCLUSION
    2. Chapter 17: Nature-Inspired Toolbox to Design and Optimize Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. ADDING NI TOOLBOX IN THE MATLAB ENVIRONMENT
      4. PARTICLE SWARM OPTIMIZATION
      5. PSO MODELS AND TOPOLOGIES
      6. BENCHMARK TEST FUNCTIONS
      7. MATLAB-BASED NATURE INSPIRED TOOLBOX
      8. SIMULATION RESULTS FOR BENCHMARK OPTIMIZATION TEST FUNCTIONS
      9. CONCLUSION AND FUTURE SCOPE
    3. Chapter 18: Adaptive Intelligent Systems for Recognition of Cancerous Cervical Cells Based on 2D Cervical Cytological Digital Images
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED STUDIES
      4. MACHINE LEARNING
      5. ANALYTICAL FRAMEWORK
      6. SIMULATION AND DISCUSSION
      7. CONCLUSION
    4. Chapter 19: Ontology-Based Clustering of the Web Meta-Search Results
      1. ABSTRACT
      2. INTRODUCTION
      3. THE RESEARCH CONTEXT
      4. PURPOSE OF THE RESEARCH
      5. THE RESEARCH METHODOLOGY
      6. THE CLUSTERING ALGORITHM
      7. IMPLEMENTATION OF THE ONTOLOGY-BASED SEARCH CLUSTERING SOLUTION
      8. EXPERIMENTATION AND EVALUATION
      9. FUTURE DEVELOPMENT
      10. CONCLUSION
    5. Chapter 20: A Beam Search Based Decision Tree Induction Algorithm
      1. ABSTRACT
      2. INTRODUCTION
      3. CLASSIFICATION
      4. DECISION TREE INDUCTION METHODS
      5. THE PROPOSED BEAM CLASSIFIER
      6. EXPERIMENTS
      7. CONCLUSION AND FUTURE WORK
    6. Chapter 21: Learning with Querying and its Application in Network Security
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. APPLICATION IN NETWORK SECURITY
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
  10. Compilation of References
  11. About the Contributors