Machine Vision Inspection Systems, Machine Learning-Based Approaches. Группа авторов
target="_blank" rel="nofollow" href="#u393b0178-2671-417c-85d0-0b29ccb7fa28">10 Performance of Stepped Bar Plate-Coated Nanolayer of a Box Solar Cooker Control Based on Adaptive Tree Traversal Energy and OSELM 10.1 Introduction 10.2 Experimental Materials and Methodology 10.3 Results and Discussion 10.4 Conclusion References
15 11 Applications to Radiography and Thermography for Inspection 11.1 Imaging Technology and Recent Advances 11.2 Radiography and its Role 11.3 History and Discovery of X-Rays 11.4 Interaction of X-Rays With Matter 11.5 Radiographic Image Quality 11.6 Applications of Radiography References
16 12 Prediction and Classification of Breast Cancer Using Discriminative Learning Models and Techniques 12.1 Breast Cancer Diagnosis 12.2 Breast Cancer Feature Extraction 12.3 Machine Learning in Breast Cancer Classification 12.4 Image Techniques in Breast Cancer Detection 12.5 Dip-Based Breast Cancer Classification 12.6 RCNNs in Breast Cancer Prediction 12.7 Conclusion and Future Work References
17 13 Compressed Medical Image Retrieval Using Data Mining and Optimized Recurrent Neural Network Techniques 13.1 Introduction 13.2 Related Work 13.3 Methodology 13.4 Results and Discussion 13.5 Conclusion and Future Enhancement References
18 14 A Novel Discrete Firefly Algorithm for Constrained Multi-Objective Software Reliability Assessment of Digital Relay 14.1 Introduction 14.2 A Brief Review of the Digital Relay Software 14.3 Formulating the Constrained Multi-Objective Optimization of Software Redundancy Allocation Problem (CMOO-SRAP) 14.4 The Novel Discrete Firefly Algorithm for Constrained Multi-Objective Software Reliability Assessment of Digital Relay 14.5 Simulation Study and Results 14.6 Conclusion References
19 Index
List of Tables
1 Chapter 1Table 1.1 CA (in unit) of different classification techniques.
2 Chapter 2Table 2.1 Comparison of related studies.Table 2.2 Model validation accuracy.Table 2.3 Classification accuracies within individual alphabets.Table 2.4 Accuracies of different MNIST models.
3 Chapter 3Table 3.1 Categorization of output values.
4 Chapter 5Table 5.1 Accuracy rate comparison for various algorithms [8].
5 Chapter 6Table 6.1 Details of knot image dataset.Table 6.2 Details of augmentation required image dataset.Table 6.3 Confusion matrices for the training and testing sample.
6 Chapter 7Table 7.1 Usecase specification.
7 Chapter 8Table 8.1 Dataset attribute of WBCD.Table 8.2 Performance analysis of classification techniques.
8 Chapter 9Table 9.1 Related work.Table 9.2 People data for the participants to acquire dataset 1 using leap motio...Table 9.3 Random geometric transformations applied to the 3D points representing...
9 Chapter 10Table 10.1 Comparing online learning algorithm to use solar cooker.Table 10.2 SSBC analysis of cooking materials with furious SiO2/TiO2 performance...
10 Chapter 13Table 13.1 Summary of results.
11 Chapter 14Table 14.1 Software reliability and optimum component composition for different ...Table 14.2 Optimal combination and mean values of the 3 objectives obtained by t...Table 14.3 Optimal combination and mean values of the 3 objectives obtained by t...
List of Illustrations
1 Chapter 1Figure 1.1 Methodology.Figure 1.2 Ebola virus images (1–6) with sizes 331 × 152, 254 × 198, 203 × 248, ...Figure 1.3 Entero virus images (1–6) with sizes 225 × 225, 250 × 201, 225 × 225,...Figure 1.4 Lassa virus images (1–6) with sizes 251 × 201, 180 × 180, 259 × 194, ...Figure 1.5 SARS-CoV-2 virus images (1–6) with sizes 225 × 225, 256 × 197, 254 × ...Figure 1.6 Zika virus images (1-6) with sizes 225 225, 202 × 250, 225 × 225, 211...Figure 1.7 Classification result by applying LR technique.Figure 1.8 Classification result by applying NN technique.Figure