Security Issues and Privacy Concerns in Industry 4.0 Applications. Группа авторов

9.6 Prediction in Mammals 9.7 Prediction in Weather 9.8 Discussion 9.9 Proposed Framework 9.10 Implementation 9.11 Conclusion References

      14  10 Dog Breed Classification Using CNN 10.1 Introduction 10.2 Related Work 10.3 Methodology 10.4 Results and Discussions 10.5 Conclusions References

      15  11 Methodology for Load Balancing in Multi-Agent System Using SPE Approach 11.1 Introduction 11.2 Methodology for Load Balancing 11.3 Results and Discussion 11.4 Algorithms Used 11.5 Results and Discussion 11.6 Summary References

      16  12 The Impact of Cyber Culture on New Media Consumers 12.1 Introduction 12.2 The Rise of the Term of Cyber Culture 12.3 The Birth and Outcome of New Media Applications 12.4 Result References

      17  About the Editors

      18  Index

      19  End User License Agreement

      Guide

      1  Cover

      2  Table of Contents

      3  Title page

      4  Copyright

      5  Preface

      6  Begin Reading

      7  About the Editors

      8  Index

      9  End User License Agreement

      List of Illustrations

      1 Chapter 1Figure 1.1 Comparison between the estimated world population and the projected n...Figure 1.2 Characterization of the technologies in IoT-enabled smart cities.Figure 1.3 Smart city architecture.Figure 1.4 IoT with Smart-Aqua sensors via cloud.Figure 1.5 Working of OCB in agriculture.

      2 Chapter 2Figure 2.1 Network forensics process model for cloud investigation.Figure 2.2 OpenNebula: a community-based cloud management system that manages re...Figure 2.3 NetworkMiner analysis tool cloud-based forensics services.Figure 2.4 Measurement of the performance of network forensics while running clo...

      3 Chapter 3Figure 3.1 Technical framework of industrial wearable system.Figure 3.2 Proposed human–physical interaction systems.Figure 3.3 Automatic speech recognition framework.Figure 3.4 LPC framework.Figure 3.5 MFCC framework.

      4 Chapter 4Figure 4.1 Histogram analysis.Figure 4.2 Graph on correlation analysis.Figure 4.3 Graph of above table.Figure 4.4 Graph of above table.

      5 Chapter 5Figure 5.1 Architecture of fake profile detection.Figure 5.2 Schema for identifying and understanding the fraudulent profiles.Figure 5.3 Working procedure for proposed system.Figure 5.4 Dimensionality reduction using PCA.Figure 5.5 A classification model.Figure 5.6 SVM classification for 2-Dimensional data.Figure 5.7 Random forest classifier.Figure 5.8 10-fold cross-validation for a dataset.Figure 5.9 Efficiency vs. the number of profiles belonging to the training data ...Figure 5.10 Efficiency vs. the number of attributes selected from the profile.Figure 5.11 FP (False Positive) versus the number of profiles belonging to train...Figure 5.12 FN (False Negative) versus the number of profiles belonging to train...Figure 5.13 Performance analysis of different classifiers.Figure 5.14 Evaluation metrics (Precision, Recall and F-Score) of Random Forest,...

      6 Chapter 6Figure 6.1 Arrangement of sensors and Gateways in IoT-based system.Figure 6.2 Plant monitoring using IoT.Figure 6.3 ThinkSpeak dashboard.Figure 6.4 Sensor’s Reading in ThinkSpeak.Figure 6.5 Number of reached packets vs. accuracy (PDR:10%).Figure 6.6 Number of reached packets vs accuracy (PDR:20%).Figure 6.7 Number of reached packets vs. accuracy (PDR: 30%).Figure 6.8 Number of received packets vs. FN (PDR:10%).Figure 6.9 Number of received packets vs. FN (PDR:20%).Figure 6.10 Number of received packets vs. FN (PDR: 30%).Figure 6.11 Accuracy vs no. of received packets (M.N:30%) [PDR: 30%].Figure 6.12 Accuracy vs. no. of received packets (M.N:20%) [PDR: 30%].Figure 6.13 Accuracy vs no. of received packets (M.N:30%) [PDR: 20%].Figure 6.14 Accuracy vs. no. of received packets (M.N:20%) [PDR:20%].Figure 6.15 Accuracy vs. no. of received packets (M.N:30%) [PDR: 10%].Figure 6.16 Accuracy vs. no. of received packets (M.N:20%) [PDR: 10%].Figure 6.17 False alarm rate vs. no. of received packets (M.N:30%)