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Table of Contents
Intro; Foreword; Preface; Acknowledgements; Contents; About the Editors; Abbreviations; Knowledge Processing in Specific Domains; 1 Probabilistic Graphical Models for Medical Image Mining Challenges of New Generation; Abstract; 1 Introduction; 1.1 Image Mining Challenge; 1.2 Probabilistic Graphical Models; 2 Machine Learning Methods in a Medical Context; 2.1 Image Mining Challenges; 3 Medical Image Mining Applications; 3.1 Multiview Feature Representation with MR Imaging; 3.2 Learning and Estimating Respiratory Motion from 4D CT Lung Images.
3.3 Hierarchical Parsing in Medical Images Using Machine Learning Technologies [41]3.4 Anatomy Landmark Detection [41]; 3.5 Machine Learning in Brain Imaging [41]; 3.6 A Connectome-Based and Machine Learning Study [41]; 4 Limitations with Medical Images; 4.1 Future Guidelines; 5 Conclusion; References; 2 Pipeline Crack Detection Using Mathematical Morphological Operator; Abstract; 1 Introduction; 1.1 Image Analysis and Processing; 1.2 Image Coordinates; 2 Pipeline; 2.1 Pipeline Design; 2.2 Causes for Pipeline Damage; 2.3 Pipelines Monitoring-Smart Pigs.
2.4 Drawbacks of Existing Pipeline Models2.5 Image Analysis and Processing Model of a Pipeline; 2.6 Characteristics of Image Analysis Model; 2.7 Implementation of Digitized Camera with Fiber Optic Cable; 2.8 Pipeline Evaluation; 2.9 Fiber Optic Cable; 3 Image Detection Techniques; 3.1 Gate Turn-Off Thyristors; 3.2 High-Frequency Filter; 3.3 Image Analysis Using Bitmaps; 3.4 Methods to Identify Hidden Structures in an Image; 3.5 Filtration of an Image; 3.6 Characteristics of Cluster Algorithm Model; 3.7 K-Means Algorithm; 4 Morphological Image Processing System.
4.1 Implementation of the Operator Tool4.1.1 Design of the Algorithm; 4.2 Analysis with the Bitmap Set; 4.2.1 Data Gathering Procedure; 4.3 Mode of Analysis-Detection of the Crack with the Morphological Operator; 4.3.1 Types: Erosion and Corrosion; 4.3.2 Opening and Closing Techniques; 4.4 Proposed Method to Implement the Image Analysis Model; 5 Edge Detection of an Image; 5.1 Methods to Detect Edge Defects; 5.1.1 Smoothing of the Detected Image; 6 Conclusion; References; 3 Efficiency of Multi-instance Learning in Educational Data Mining; Abstract; 1 Introduction; 1.1 Educational Applications.
1.2 Baker's Taxonomy2 Problem Domain: Predicting Student Course Outcome at an Early Stage; 2.1 Description and Selection of Dataset; 2.2 Introduction to Classification Technique; 2.2.1 Types of Classification Techniques; 2.2.2 Solving a Classification Problem; 2.2.3 Classification Accuracy; 2.3 Instance-Based Learning; 3 Experimentation and Results; 3.1 Implementation of Single Instance Learning Classification Algorithm; 3.1.1 Rule-Based Algorithms; 3.1.2 Tree-Based Algorithms; 3.1.3 Naïve Bayes Algorithms; 3.1.4 Comparison of Single Instance Learning Algorithms.
3.3 Hierarchical Parsing in Medical Images Using Machine Learning Technologies [41]3.4 Anatomy Landmark Detection [41]; 3.5 Machine Learning in Brain Imaging [41]; 3.6 A Connectome-Based and Machine Learning Study [41]; 4 Limitations with Medical Images; 4.1 Future Guidelines; 5 Conclusion; References; 2 Pipeline Crack Detection Using Mathematical Morphological Operator; Abstract; 1 Introduction; 1.1 Image Analysis and Processing; 1.2 Image Coordinates; 2 Pipeline; 2.1 Pipeline Design; 2.2 Causes for Pipeline Damage; 2.3 Pipelines Monitoring-Smart Pigs.
2.4 Drawbacks of Existing Pipeline Models2.5 Image Analysis and Processing Model of a Pipeline; 2.6 Characteristics of Image Analysis Model; 2.7 Implementation of Digitized Camera with Fiber Optic Cable; 2.8 Pipeline Evaluation; 2.9 Fiber Optic Cable; 3 Image Detection Techniques; 3.1 Gate Turn-Off Thyristors; 3.2 High-Frequency Filter; 3.3 Image Analysis Using Bitmaps; 3.4 Methods to Identify Hidden Structures in an Image; 3.5 Filtration of an Image; 3.6 Characteristics of Cluster Algorithm Model; 3.7 K-Means Algorithm; 4 Morphological Image Processing System.
4.1 Implementation of the Operator Tool4.1.1 Design of the Algorithm; 4.2 Analysis with the Bitmap Set; 4.2.1 Data Gathering Procedure; 4.3 Mode of Analysis-Detection of the Crack with the Morphological Operator; 4.3.1 Types: Erosion and Corrosion; 4.3.2 Opening and Closing Techniques; 4.4 Proposed Method to Implement the Image Analysis Model; 5 Edge Detection of an Image; 5.1 Methods to Detect Edge Defects; 5.1.1 Smoothing of the Detected Image; 6 Conclusion; References; 3 Efficiency of Multi-instance Learning in Educational Data Mining; Abstract; 1 Introduction; 1.1 Educational Applications.
1.2 Baker's Taxonomy2 Problem Domain: Predicting Student Course Outcome at an Early Stage; 2.1 Description and Selection of Dataset; 2.2 Introduction to Classification Technique; 2.2.1 Types of Classification Techniques; 2.2.2 Solving a Classification Problem; 2.2.3 Classification Accuracy; 2.3 Instance-Based Learning; 3 Experimentation and Results; 3.1 Implementation of Single Instance Learning Classification Algorithm; 3.1.1 Rule-Based Algorithms; 3.1.2 Tree-Based Algorithms; 3.1.3 Naïve Bayes Algorithms; 3.1.4 Comparison of Single Instance Learning Algorithms.