Preface......Page 6
Contents......Page 8
About the Editors......Page 13
Abstract......Page 15
1.2 Purpose and Goal......Page 16
2.2.1 Available Feature Descriptors......Page 17
2.2.3 Features to Detect Fall......Page 19
3.2 Implementation of DE Algorithm......Page 20
3.5 The Fall Detection System......Page 21
4.1 BelgaLogos Image Dataset......Page 23
4.2 Object Detection......Page 24
4.3.1 Performance Metrics for Fall Detection......Page 26
4.4 Fall Detection with EC Framework......Page 27
5 Conclusions......Page 29
References......Page 30
Abstract......Page 33
1 Introduction......Page 34
2 Proposed 2-D Fractional-Order Optimal Unsharp Masking Framework for Image Enhancement......Page 36
3 Fitness Function Formulation......Page 39
4 Memetic Inclusions for Sine-Cosine Optimizer......Page 41
5 Memetic Inclusions in Cuckoo Search Optimizer......Page 44
6.1 Assessment Criterion......Page 48
6.3 Quantitative Assessments......Page 51
References......Page 56
1 Introduction......Page 60
2.1.1 Thresholding......Page 63
2.1.2 Edge-Based Segmentation......Page 64
2.1.3 Clustering-Based Segmentation......Page 65
2.1.4 Learning-Based Segmentation......Page 66
2.2 Artificial Bee Colony......Page 67
3.1 Thresholding......Page 69
3.2 Edge-Based Segmentation......Page 71
3.3 Clustering-Based Segmentation......Page 72
3.4 Learning-Based Segmentation......Page 73
4.1 ABC-Based Segmentation Methodology......Page 74
4.2 Experimental Results......Page 75
5 Conclusions......Page 77
References......Page 78
Abstract......Page 81
1 Introduction......Page 82
2.1 Optimization Problems......Page 83
2.4 MAs in Image Processing......Page 84
3 Building Blocks of CNN......Page 85
3.2 Structure of CNN......Page 86
3.3 Convolution Layer (CONV)......Page 87
3.5 Activation Function......Page 88
4 Object Detection Using CNN......Page 90
5.1 E-Net Architecture......Page 92
6 Automatic Car Damage Detection......Page 94
6.2 VGG Network......Page 95
7 Conclusion......Page 101
References......Page 102
1 Introduction......Page 104
2 Problem Definition......Page 105
4 Swarm-Based Methods Applied to Color Quantization......Page 106
4.1.1 The Ant-Tree for Color Quantization Method......Page 107
4.1.2 The Iterative ATCQ......Page 109
4.1.3 ATCQ Combined with Binary Splitting......Page 110
4.1.4 Other Color Quantization Methods that Use Artificial Ants......Page 112
4.2 Particle Swarm Optimization......Page 113
4.3 Artificial Bees......Page 114
4.3.1 Artificial Bee Colony Combined with K-Means......Page 115
4.3.2 Artificial Bee Colony Combined with ATCQ......Page 117
4.4 Firefly Algorithm......Page 119
4.5 The Shuffled Frog-Leaping Method......Page 121
4.6 A Brief Comparative Among Color Quantization Methods......Page 123
References......Page 126
Abstract......Page 130
1 Introduction......Page 131
2 Categorization of Computational Intelligence Techniques......Page 134
3 Taxonomy of CI Techniques for Geo-Spatial Feature Extraction......Page 137
4 Computational Intelligence Techniques for Feature Extraction......Page 139
4.2 Hybrid ACO2/PSO/BBO Optimization......Page 140
4.3 Hybrid FPAB/BBO Optimization......Page 143
4.4 BBO-GS......Page 145
4.5 ACO2/PSO/BBO-GS......Page 147
5.1 Classification Matrices......Page 149
5.2 Characteristic Comparison and Application Suitability of CI Techniques......Page 151
6 Conclusion......Page 159
References......Page 160
Abstract......Page 164
1 Introduction......Page 165
2 Problem Formulation......Page 166
2.2 Problems in the Parameter Value Selection......Page 167
3 Proposed Algorithm......Page 168
3.1 The Elemental Detection......Page 170
3.2.1 Parameters of Optimization......Page 171
4 Results and Discussion......Page 172
References......Page 176
Abstract......Page 178
2 Related Work......Page 179
3.2 Comparison of Hybrid of PBO SGD and Hybrid of PBO Adamโs......Page 181
4 Plate Tectonics Neighborhood-Based Classifier......Page 184
4.3 Plate Tectonics Neighborhood-Based Classifier Pseudo-Code (Fig.โฌ3)......Page 185
5.1 Results of PBO Classifier on Soybean Dataset......Page 187
5.2 Performance Analysis on Independently Collected Dataset on Crop Production in States with Historical Dataset......Page 188
5.3 Results on Crop Prediction Dataset......Page 190
References......Page 191
1 Introduction......Page 193
2.2 Associative Classification......Page 195
2.3 Weighted Associative Classification......Page 196
2.5 Memetic Algorithm......Page 197
3.2 Evolutionary Memetic Associative Classification......Page 198
4 Sample Computation......Page 199
5 Experiment Result......Page 205
References......Page 207