3.3 Virtualization.Figure 3.4 Phases of virtual machine migration.Figure 3.5 Pre-copy algorithm.Figure 3.6 Timeline for pre-copy.Figure 3.7 Improved pre-copy live migration.Figure 3.8 Iteration of time series–based pre-copy live migration.Figure 3.9 Structure and operations of MPLM.Figure 3.10 Structure of TPO.Figure 3.11 Structure of multiphase pre-copy live migration.Figure 3.12 Pre-copy vs. improved pre-copy.Figure 3.13 Low dirty and high dirty pages based on time series.Figure 3.14 Pre-copy vs. TPO.Figure 3.15 TPO vs. multiphase.4 Chapter 4Figure 4.1 Design flow of the proposed system.Figure 4.2 Comparative analysis of pre-trained networks for brain tumor classifi...
5 Chapter 5Figure 5.1 Block diagram.Figure 5.2 Pulse oximeter and heart rate sensor.Figure 5.3 Temperature sensor.Figure 5.4 Complete hardware of the coma patient monitoring system.Figure 5.5 Eye blink detection.Figure 5.6 Yawning detection.Figure 5.7 Alert message page.Figure 5.8 Subject testing.Figure 5.9 Results of heart rate and SpO2.Figure 5.10 EEG patterns in coma patient.
6 Chapter 6Figure 6.1 Basic structural representation of deep learning process.Figure 6.2 Simple architecture with hidden layers.Figure 6.3 Architectural models of deep learning.Figure 6.4 Architecture of recurrent neural networks.Figure 6.5 LSTM memory cell.Figure 6.6 GRU cell.Figure 6.7 Basic structural framework of convolutional neural network (CNN).Figure 6.8 Architectural framework for deep belief networks.Figure 6.9 Simple architecture of deep stacking networks.
7 Chapter 9Figure 9.1 IoMT healthcare system.Figure 9.2 Platform architecture.Figure 9.3 Communication protocol overview.Figure 9.4 The MQTT publish and subscribe model for IoT.Figure 9.5 CoAP message model.Figure 9.6 CoAP request/response model.Figure 9.7 AMQP interaction model with middleware.Figure 9.8 AMQP capabilities.Figure 9.9 AMQP for cloud connection.Figure 9.10 DDS protocol Architecture.
8 Chapter 10Figure 10.1 Architecture of wearable IoT-enabled rural health monitoring system.Figure 10.2 Body sensor node and its internal architecture.Figure 10.3 System framework of health monitoring center (HMC).Figure 10.4 Sequence diagrams of mesh peering and routing medical data.Figure 10.5 GUI alert when the patient’s blood pressure and sugar is critically ...Figure 10.6 Energy consumption in HMC.Figure 10.7 Survival rate.Figure 10.8 End-to-end delay.
9 Chapter 11Figure 11.1 Block diagram of the proposed system.Figure 11.2 Components of the noninvasive glucose monitoring system.Figure 11.3 Prototype of the glucose monitoring system.Figure 11.4 Output of glucose monitoring.
10 Chapter 13Figure 13.1 Three important V of big data.Figure 13.2 Applications of big data and IoT in healthcare.Figure 13.3 Basic details available in an electronic health record.Figure 13.4 Commonly used big data management tools.
11 Chapter 14Figure 14.1 General architecture and workflow of the proposed system [7].Figure 14.2 Remote patient monitoring [8].Figure 14.3 Blockchain architecture categories [7].Figure 14.4 Nodes in public vs. private Blockchain [8].Figure 14.5 Scenarios of using Blockchain in different healthcare situations [8]...Figure 14.6 Potential applications of the Blockchain [10].Figure 14.7 Characteristics of Blockchain.
12 Chapter 15Figure 15.1 Evolution of EHR.
List of Tables
1 Chapter 1Table 1.1 Physiochemical characters of EGFR, K-ras, and TP53 proteins as determi...Table 1.2 The number disulfide bonds were quantitated by Cys_Rec prediction prog...Table 1.3 Secondary structure of the EGFR, K-ras oncogene protein, and TP53.Table 1.4 Composition of α-helix EGFR, K-ras oncogene protein, and TP53.Table 1.5 Validation of the EGFR, K-ras oncogene protein, and TP53.Table 1.6 Predicted active sites of the EGFR, K-ras oncogene protein, and TP53.Table 1.7 Docking result of the EGFR, K-ras oncogene protein, and TP53.
2 Chapter 3Table 3.1 Type 1 VMM Approach.Table 3.2 Type 2 VMM Approach.
3 Chapter 4Table 4.1 Various symptoms of brain tumors.Table 4.2 Sample images used for classification purpose.Table 4.3 Performance of AlexNet pre-trained network.Table 4.4 Performance of GoogleNet pre-trained network.Table 4.5 Performance of ResNet101 pre-trained network.Table 4.6 Comparison of performance metrics between AlexNet, GoogleNet, and ResN...Table 4.7 Evaluation of accuracy and processing time of pre-trained networks.
4 Chapter 6Table 6.1 Applications of deep learning networks.
5 Chapter 10Table 10.1 Blood glucose classification.Table 10.2 Blood pressure classification.Table 10.3 Symptoms and signs of diabetic types.Table 10.4 DE parameter settings.
Guide
1 Cover
5 Preface
7 Index
Pages
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