user interface.Figure 6.4 Mean & standard deviation for urea and glucose (training data).Figure 6.5 Mean & standard deviation for urea and glucose (test data).Figure 6.6 Mean & standard deviation for creatinine (training & test data).Figure 6.7 Comparative analysis of accuracy & error of different classifiers cla...6 Chapter 7Figure 7.1 FlowchartFigure 7.2 Filtration and CLAHE performed in the image.Figure 7.3 Skull Stripping.Figure 7.4 Otsu’s segmentation.Figure 7.5 Multi-class SVM training results.Figure 7.6 Multi-class SVM testing results.
7 Chapter 8Figure 8.1 CNN.Figure 8.2 Comparison of RCNN and SPPnet.Figure 8.3 Pooling.
8 Chapter 9Figure 9.1 Cloud computing.Figure 9.2 Service models.Figure 9.3 Advantages of cloud computing.Figure 9.4 Disadvantages of cloud computing.Figure 9.5 Experiments.Figure 9.6 Cat experiment.
9 Chapter 10Figure 10.1 Cloud load balancing.Figure 10.2 Working of load balancer.Figure 10.3 Load balancing system based on cloud computing.Figure 10.4 Impact of load balancing.Figure 10.5 Quantum isochronous parallel computing model.Figure 10.6 Phase isochronous parallel model.Figure 10.7 Dynamic isochronous coordinate strategies.Figure 10.8 Weak threshold procedures in DIC model.Figure 10.9 DIC model staleness portal dynamic adjustment procedure.Figure 10.10 A-DIC training block diagram.
10 Chapter 11Figure 11.1 Biometric identification.Figure 11.2 Biometric modalities.Figure 11.3 Biometric modalities measurement.Figure 11.4 Biometric identification flow chart.Figure 11.5 Biometric architecture of the cloud-based biometric identification s...Figure 11.6 (a) Set of features of dataset values. (b) Prediction of class label...Figure 11.7 (a) Set of features having no predefined labels. (b) Cluster formati...Figure 11.8 Deep learning architecture layout.
11 Chapter 12Figure 12.1 Deep learning strategies.Figure 12.2 Auto-encoders.Figure 12.3 Data deep feature maps.Figure 12.4 Speech recognition system.Figure 12.5 Feature extraction.Figure 12.6 Drug discovery.Figure 12.7 Image recognition.
12 Chapter 13Figure 13.1 Deep learning model [13].Figure 13.2 Intrusion in cloud.Figure 13.3 Real time network anomaly detection system.Figure 13.4 Deep learning based intrusion detection system.Figure 13.5 Architecture of IDS in cloud.Figure 13.6 Model of RNN [16].
13 Chapter 14Figure 14.1 Deep autoencoder.Figure 14.2 Denoising autoencoder.Figure 14.3 Deep autoencoder and denoising autoencoder.Figure 14.4 Restricted boltzmann machine.Figure 14.5 Recurrent neural networks.Figure 14.6 CNN.Figure 14.7 Generative adversarial networks.Figure 14.8 Recursive neural networks.Figure 14.9 Security in cloud computing.Figure 14.10 Firewall.Figure 14.11 WAF.Figure 14.12 Working of WAF.Figure 14.13 Cloud WAF.
List of Tables
1 Chapter 3Table 3.1 Feature extraction techniques [8].Table 3.2 Classification techniques.
2 Chapter 6Table 6.1 Classified results of classifiers.Table 6.2 Random forest classification report.Table 6.3 Naïve bayes classification report.Table 6.4 Support vector machine classification report.
3 Chapter 7Table 7.1 Confusion matrix - hold out method.Table 7.2 Confusion matrix - K-fold method.Table 7.3 Evaluation variables.
Guide
1 Cover
5 Foreword
6 Preface
9 Index
Pages
1 v
2 ii
3 iii
4 iv
5 xv
6 xvi
7 xvii
8 xviii
9 xix
10 xx
11 1
12 2
13 3
14 4
15 5
16 6
17 7
18 8
19 9
20 10
