Machine Learning Approaches for Convergence of IoT and Blockchain. Группа авторов
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3.8 AI integrated IoT home monitoring system.Figure 3.9 Smart home.Figure 3.10 Blockchain authentication.Figure 3.11 Smart traffic security.Figure 3.12 Number of cyber attacks.Figure 3.13 Cyber attacks across various countries.Figure 3.14 Number of data breaches.
4 Chapter 4Figure 4.1 Application of the IoT.Figure 4.2 Contribution of IoT in different sectors.Figure 4.3 Market forecast of IoMT.Figure 4.4 A deep neural networks with three hidden layers [19].Figure 4.5 Architecture of multilayer perceptron neural network [23].Figure 4.6 Applications of deep learning in the healthcare sector.Figure 4.7 Pillars of Blockchain technology [25].
5 Chapter 5Figure 5.1 Process of literature review.Figure 5.2 Research domains.Figure 5.3 Implementation of ML in smart buildings.Figure 5.4 Intelligence levels for smart cities [8].
6 Chapter 6Figure 6.1 ML development phases for healthcare.Figure 6.2 Sources of data for healthcare.Figure 6.3 Commonly used medical imaging modalities.Figure 6.4 IoT architecture for healthcare.
7 Chapter 7Figure 7.1 System design of blockchain.Figure 7.2 Architecture of intelligent vehicle [55].Figure 7.3 Blockchain technology.Figure 7.4 An ITS-oriented blockchain model [59].Figure 7.5 Expected potential attack surface [55].Figure 7.6 Issues emerging in the application of blockchain technology with tran...Figure 7.7 Basic mechanism of blockchain [94].
8 Chapter 8Figure 8.1 Flowchart of FCM clustering.Figure 8.2 Flowchart of crow search optimization algorithm.Figure 8.3 Flowchart of prediction-based lossless compression model.Figure 8.4 Image compression based on least square based prediction.Figure 8.5 Segmentation results corresponding to images from the brain web datab...Figure 8.6 Segmentation results correspond to real-time abdomen CT images. (a) I...Figure 8.7 Segmentation results correspond to real-time MR brain images. (a) Inp...Figure 8.8 Performance plot of partition coefficient and entropy.Figure 8.9 Performance plot of Xie and Beni Index.Figure 8.10 Performance plot of Fukuyama and Sugeno Index.Figure 8.11 Compression results corresponding to real-time abdomen CT images. (a...Figure 8.12 Compression results corresponding to real-time brain MR images. (a) ...Figure 8.13 Performance plot of compression ratio.Figure 8.14 Performance plot of FSSIM.Figure 8.15 Performance plot of PSNR.Figure 8.16 Performance plot of SC.Figure 8.17 Performance plot of NK.Figure 8.18 Performance plot of LMSE.Figure 8.19 Performance plot of NAE.Figure 8.20 Performance plot of compression quality.Figure 8.21 Hardware implementation of the image processing algorithms. (a) Fron...Figure 8.22 GUI interface for the loading of the DICOM image.Figure 8.23 Options available in the GUI interface.Figure 8.24 CROW-FCM segmentation result.Figure 8.25 Stages of GUI for feeding the compressed images into the cloud. (a) ...
9 Chapter 9Figure 9.1 Impact of IT on top urban populace.Figure 9.2 Smart cities overview.Figure 9.3 Traffic management.Figure 9.4 Smart parking.Figure 9.5 Smart policing.Figure 9.6 Shrewd lighting.Figure 9.7 Smart power.Figure 9.8 Google maps.Figure 9.9 Innovation in urban communities.Figure 9.10 Smart cities for all.Figure 9.11 Smart cities to prevent road accidents.Figure 9.12 Applications of IoT.
List of Tables
1 Chapter 3Table 3.1 Difference between public, private, and consortium blockchain.
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