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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