Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications
13.6 Gray scale orientation.Figure 13.7 Histogram of inclination.Figure 13.8 Pest spot identification.Figure 13.9 (a) Input image; (b) Filtered image; (c) Boundary detection; (d) Rem...Figure 13.10 Accuracy performance analysis.
14 Chapter 14Figure 14.1 Proposed framework using machine learning on the edge.Figure 14.2 Comparison of number of test cases.Figure 14.3 Comparison of testing time.
List of Tables
1 Chapter 1Table 1.1 Accuracy of classifiers.
2 Chapter 2Table 2.1 Existing studies using deep learning in edge.
3 Chapter 4Table 4.1 Protocols and its features.
4 Chapter 8Table 8.1 Performance of biometric in forensic investigation.Table 8.2 List of datasets for various biometric identity.
5 Chapter 9Table 9.1 Acronym used in the chapter.Table 9.2 Comparison of algorithms.
6 Chapter 10Table 10.1 Data type for attributes of dataset.Table 10.2 Statistical description of dataset.Table 10.3 Correlation between attributes in dataset.Table 10.4 Dataset sample.Table 10.5 Comparison of the evaluation results.
7 Chapter 11Table 11.1 Different architecture of deeper learning and its applications.
8 Chapter 12Table 12.1 Skin friction (τ).Table 12.2 Nusselt numeral (Nu).Table 12.3 Sherwood numeral (Sh).
9 Chapter 13Table 13.1 Sensors and their methodologies.Table 13.2 Pest of rice – sample dataset.Table 13.3 Gall midge – GLCM features.Table 13.4 Classification accuracy for paddy insect with SIFT features.
10 Chapter 14Table 14.1 Test cases generated for each of the scenarios.Table 14.2 Comparison of end-user application testing at the edge with ML and ot...
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