Intro
Advisory Committee
Technical Programme Committee
Scientific Committee
Preface
Contents
About the Editors
1 Breast MRI Registration Using Gorilla Troops Optimization
1.1 Introduction
1.1.1 Transformation
1.1.2 Normalized Mutual Information
1.2 Related Work
1.2.1 Registration of Medical Images
1.2.2 Medical Images Registration Approach
1.3 Proposed Methodology
1.3.1 GTO Algorithm
1.4 Result and Discussion
1.4.1 Dataset
1.4.2 Statistical Significance with NMI
1.5 Conclusion
1.6 Future Scope
References

2 Forward and Backward Key Secrecy Preservation Scheme for Medical Internet of Things
2.1 Introduction
2.2 Related Work
2.3 The Proposed Scheme
2.3.1 Initialization Phase
2.3.2 Registration Phase
2.3.3 Login Phase
2.3.4 Authentication and Key Negotiation Phase
2.4 Security Analysis and Performance Evaluation
2.4.1 Security Evaluation
2.4.2 Performance Evaluation
2.5 Conclusion and Future Work
References
3 Systematic Study of Detection Mechanism for Network Intrusion in Cloud, Fog, and Internet of Things Using Deep Learning
3.1 Introduction

3.2 NIDS Using Deep Learning in Internet of Things (IoT) Network
3.2.1 NIDS Using Deep Belief Network (DBN) in IoT
3.2.2 NIDS Using Convolutional Neural Network (CNN) in IoT
3.2.3 NIDS Using Autoencoder (AE) in IoT
3.2.4 Integration of Long Short-Term Memory (LSTM) and CNN in IoT
3.3 NIDS Using Deep Learning in Cloud Network
3.3.1 NIDS Using AE in Cloud Network
3.3.2 NIDS Using Restricted Boltzmann Machine (RBM) in Cloud Network
3.3.3 Integration of LSTM, Recurrent Neural Network (RNN), Deep Neural Network (DNN), and DBN in Cloud Network

3.3.4 NIDS Using Deep Learning (DL) and Machine Learning (ML) Techniques in Cloud Network
3.4 NIDS Using Deep Learning (DL) in Fog Network
3.4.1 NIDS Using Convolutional Neural Network (CNN) in Fog Network
3.4.2 NIDS Using AE in Fog Network
3.4.3 NIDS Using RNN in Fog Network
3.4.4 NIDS Using LSTM in Fog Network
3.4.5 NIDS Using Deep Learning (DL) and Machine Learning (ML) Techniques in Fog Network
3.5 NIDS Using Deep Learning in Edge Network
3.5.1 NIDS Using CNN in Edge Network
3.5.2 NIDS Using DBN in Edge Network
3.6 Conclusion
References

4 Creation and Statistical Analysis of a Corpus for Indian Ankylosing Spondylitis Patients with Focus on COVID-19
4.1 Introduction
4.2 Questionnaire Creation
4.3 Statistical Analysis
4.3.1 Discussions
4.4 Conclusion
References
5 Workload Prediction of Virtual Machines Using Integrated Deep Learning Approaches Over Cloud Data Centers
5.1 Introduction
5.2 Background of the Research
5.3 Forecasting Workload with a Deep Learning Model
5.3.1 Convolutional Neural Network Model
5.3.2 Long Short-Term Memory Based Model