Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications

Figure 5.8 ANN performance of the beam model [15].Figure 5.9 Arrangement of sensors in beam model [16].Figure 5.10 Methodology of Machine Learning [17].Figure 5.11 CFRP sandwich structure and PZT distribution [17].Figure 5.12 Result of four test [18].Figure 5.13 Sydney Harbour bridge [19].Figure 5.14 Sensor arrangements and joints of Harbour bridge [19].Figure 5.15 Top view of bridge [20].Figure 5.16 Observations [20].Figure 5.17 Event tree [21].Figure 5.18 Proposed method [22].Figure 5.19 Data sets [23].Figure 5.20 Test setup [25].Figure 5.21 Arrangement of sensors in panels [26].Figure 5.22 Similarity between system [27].Figure 5.23 ROC curve [28].Figure 5.24 Damages in flange [28].Figure 5.25 Pictures of mouth brooding fish (a) The way of protection. (b) Left ...Figure 5.26 Variable representation in cichlids [3].Figure 5.27 Inspiration of MBF algorithm [29].Figure 5.28 Application sectors of MBF system [29].Figure 5.29 Fitness curve of IGA system [31].Figure 5.30 Optimal domains [32].Figure 5.31 OSP algorithm for DABC [33].Figure 5.32 Convergence curves [1].Figure 5.33 Reinforced concrete one-way slab [35].Figure 5.34 Convergence curves [35].Figure 5.35 Expression tree [36].Figure 5.36 Comparison based on C1 and C3 parameter.Figure 5.37 Sensor contribution and coverage.

6 Chapter 6Figure 6.1 Technical problem design.Figure 6.2 Velocity curves with several qualities of α.Figure 6.3 Variation of “Gr” on velocity.Figure 6.4 Velocity curves with several qualities of M.Figure 6.5 Velocity curves with several qualities of Ko.Figure 6.6 Impact of “Gm” on velocity.Figure 6.7 Velocity curves with several qualities of Kr.Figure 6.8 Velocity curves with several qualities of Sc.Figure 6.9 Velocity curves with several qualities of S 0.Figure 6.10 Impact of “Q” on temperature.Figure 6.11 Impact of “ Pr ” on temperature.Figure 6.12 Effect of “Kr” on concentration profile.Figure 6.13 Concentration curves with several qualities of Sc.Figure 6.14 Concentration curves with several qualities of So.

7 Chapter 7Figure 7.1 M 0.Figure 7.2 M 1.

8 Chapter 8Figure 8.1 Basic structure of deep learning.Figure 8.2 Process involved in digital forensics.Figure 8.3 Cybernetics loop.Figure 8.4 Cybernetics relating method oriented and science oriented services [7...Figure 8.5 Data acquisition system in biometric identity.Figure 8.6 Deep learning process involved in fingerprint recognition.Figure 8.7 Deep learning framework for forensic data analysis.Figure 8.8 Digital forensic data reduction.Figure 8.9 Deep auto encoders.Figure 8.10 Restricted Boltzmann Machine.Figure 8.11 Federated learning architecture.Figure 8.12 IoT factors influencing computer forensics.

9 Chapter 9Figure 9.1 Structure of the heart.Figure 9.2 Cardiac image segmentation activities with various imaging types.Figure 9.3 Shows 3 concentric rings, in the inner rings with a focus sapling and...Figure 9.4 Shows three algorithms Weak edge actions (a) Circular scale image wit...Figure 9.5 Memory vs Computation time.

10 Chapter 10Figure 10.1 CNN architecture [16].Figure 10.2 LSTM unit [17].Figure 10.3 CNN-LSTM model used to classify DR.Figure 10.4 Hybrid CNN-LSTM architecture.Figure 10.5 Metrics of CNN-LSTM model.

11 Chapter 11Figure 11.1 Machine learning vs. deep learning.Figure 11.2 Block diagram of auto encoder.Figure 11.3 Architecture of convolution neural network.Figure 11.4 Various applications of deep learning.Figure 11.5 Different big data parameters.Figure 11.6 Machine learning in healthcare.

12 Chapter 12Figure 12.1 Concern physical configuration [1].Figure 12.2 Velocity outline for several Nemours of β. R=0.5, γ = α π/6, Pr=0.71...Figure 12.3 Velocity outline for several Nemours of α. R=0.5, β=5, γ = π/6, Pr=0...Figure 12.4 Velocity outlines for several Nemours of γ. R=0.5, α = π/6, β=5, Sc=...Figure 12.5 Velocity outline for several Nemours of Gr. R=0.5, Pr=0.71, Ko=1, γ=...Figure 12.6 Velocity outline for several Nemours of Gm. R=0.5, M=1, Sc=0.6, Pr=0...Figure 12.7 Velocity outline for several Nemours of M. R=0.5, Sc=0.6, Pr=0.71, K...Figure 12.8 Velocity outline for several Nemours of Ko. R=0.5, M=1, Sc=0.6, Pr=0...Figure 12.9 Temperature outline for several Nemours of Q. R=0.5, M=1, Sc=0.6, Pr...Figure 12.10 Temperature outline for several Nemours of Pr. R=0.5, M=1, Sc=0.6, ...Figure 12.11 Temperature outline for several Nemours of R., Q=0.5, M=1, Sc=0.6, ...Figure 12.12 Concentration outline for several Nemours of Kr.Figure 12.13 Concentration outline for several Nemours of Sc.

13 Chapter 13Figure 13.1 Paddy disease detection framework.Figure 13.2 RGB color image of Gall midge in paddy crop.Figure 13.3 Pre-processing of Gall midge insect.Figure 13.4 Deep CNN model.Figure 13.5 Deep CNN image denoising.Figure 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.

1 Chapter 1 Table 1.1 Accuracy of classifiers.

2 Chapter 2 Table 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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