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1 Chapter 2Figure 2.1 AlexNet architecture.Figure 2.2 Metastasis‐PET image.Figure 2.3 Lymph node‐fused PET‐CT image.Figure 2.4 Classification results of the primary tumor (T).Figure 2.5 Classification accuracy of the primary tumor (T).Figure 2.6 Loss function for the primary tumor (T).Figure 2.7 Confusion matrix for the primary tumor (T).Figure 2.8 Classification results of the metastasis (M).Figure 2.9 Classification accuracy of metastasis (M).Figure 2.10 Loss function for the metastasis (M).Figure 2.11 Confusion matrix for the metastasis (M).Figure 2.12 Classification results of the lymph node (N).Figure 2.13 Classification accuracy of the lymph node (N).Figure 2.14 Loss function for the lymph node (N).Figure 2.15 Confusion matrix for the lymph node (N).

2 Chapter 3Figure 3.1 The heart‐pacemaker system shows leads connected to the right atr...Figure 3.2 Timing diagram for a DDD mode pacemaker (adapted from Pinisetty e...Figure 3.3 Timing information of electrocardiogram signals.Figure 3.4 Model checking.Figure 3.5 Conformance testing with formal methods.Figure 3.6 Verification monitor.Figure 3.7 Enforcement mechanism.Figure 3.8 Overview of the RV monitoring approach (from Pinisetty et al. (20...Figure 3.9 Timed automaton defining property in 3.4.2 (from Pinisetty et a...Figure 3.10 Architecture of the RV monitor (from Pinisetty et al. (2018)).Figure 3.11 Pacemaker with runtime enforcer (from Pearce et al. (2019b)).Figure 3.12 Simplified DTA for policy , (from Pearce et al. (2019b)).Figure 3.13 System composition (from Pearce et al. (2019b)).Figure 3.14 Generalized enforcer hardware (from Pearce et al. (2019b)).

3 Chapter 4Figure 4.1 Schematic of autoencoder architecture. A representation of the phy...Figure 4.2 Integrative analysis of multiomics data through an autoencoder mo...Figure 4.3 Proposed capsule network architecture. The details of the multiple...Figure 4.4 The training and validation accuracy of the proposed model.Figure 4.5 Box plots of coupling coefficient values between primary‐ and typ...

4 Chapter 5Figure 5.1 Flowchart of chapter topics.Figure 5.2 Tools or methods for multi‐omics data analysis.

5 Chapter 6Figure 6.1 Super learner concept diagram (Kabir and Ludwig, 2019).Figure 6.2 Scatter plot of the five rules for breast cancer patients in the ...Figure 6.3 Scatter plot of five rules for individuals with breast cancer fro...Figure 6.4 Scatter plot of 23 rules for breast cancer patients in the non‐Hi...

6 Chapter 7Figure 7.1 Block diagram of the proposed method for the automatic identifica...Figure 7.2 An illustration of two class rough hypercuboids, considering the ...Figure 7.3 Comparative performance analysis of the proposed method and its v...Figure 7.4 Comparative performance analysis between the proposed method and ...Figure 7.5 Comparative performance analysis between the proposed method and ...Figure 7.6 Comparative performance analysis of the proposed and existing app...

7 Chapter 8Figure 8.1 Architecture of a simple neural network.Figure 8.2 Architecture of a convoluted neural network model.Figure 8.3 Performance of implemented networks with varying optimizers.Figure 8.4 Accuracy obtained by optimizers in a convolutional neural network...Figure 8.5 Number of epochs required for training by CNN optimizers with ear...

8 Chapter 9Figure 9.1 Anatomy of the human brain.Figure 9.2 MRI of a brain tumor.Figure 9.3 Support vector machine.Figure 9.4 Artificial neurons.Figure 9.5 Back‐propagation neural network.Figure 9.6 Random forest classifier.Figure 9.7 Flow chart of the proposed method.Figure 9.8 Original and resulting images with the bounding box.Figure 9.9 Splitting the whole image into LHS and RHS along the middle sagit...Figure 9.10 Stratified k‐fold cross validation.Figure 9.11 Structure of a confusion matrix.Figure 9.12 Accuracy of proposed and existing methods.