Foreword xv Preface xvii 1 Biometric Identification Using Deep Learning for Advance Cloud Security 1 Navani Siroya and Manju Mandot 1.1 Introduction 2 1.2 Techniques of Biometric Identification 3 1.2.1 Fingerprint Identification 3 1.2.2 Iris Recognition 4 1.2.

3 Facial Recognition 4 1.2.4 Voice Recognition 5 1.3 Approaches 6 1.3.1 Feature Selection 6 1.3.2 Feature Extraction 6 1.

3.3 Face Marking 7 1.3.4 Nearest Neighbor Approach 8 1.4 Related Work, A Review 9 1.5 Proposed Work 10 1.6 Future Scope 12 1.7 Conclusion 12 References 12 2 Privacy in Multi-Tenancy Cloud Using Deep Learning 15 Shweta Solanki and Prafull Narooka 2.

1 Introduction 15 2.2 Basic Structure 16 2.2.1 Basic Structure of Cloud Computing 17 2.2.2 Concept of Multi-Tenancy 18 2.2.3 Concept of Multi-Tenancy with Cloud Computing 19 2.

3 Privacy in Cloud Environment Using Deep Learning 21 2.4 Privacy in Multi-Tenancy with Deep Learning Concept 22 2.5 Related Work 23 2.6 Conclusion 24 References 25 3 Emotional Classification Using EEG Signals and Facial Expression: A Survey 27 S J Savitha, Dr. M Paulraj and K Saranya 3.1 Introduction 27 3.2 Related Works 29 3.3 Methods 32 3.

3.1 EEG Signal Pre-Processing 32 3.3.1.1 Discrete Fourier Transform (DFT) 32 3.3.1.2 Least Mean Square (LMS) Algorithm 32 3.

3.1.3 Discrete Cosine Transform (DCT) 33 3.3.2 Feature Extraction Techniques 33 3.3.3 Classification Techniques 33 3.4 BCI Applications 34 3.

4.1 Possible BCI Uses 36 3.4.2 Communication 36 3.4.3 Movement Control 36 3.4.4 Environment Control 37 3.

4.5 Locomotion 38 3.5 Cloud-Based EEG Overview 38 3.5.1 Data Backup and Restoration 39 3.6 Conclusion 40 References 40 4 Effective and Efficient Wind Power Generation Using Bifarious Solar PV System 43 R. Amirtha Katesa Sai Raj, M. Arun Kumar, S.

Dinesh, U. Harisudhan and Dr. R. Uthirasamy 4.1 Introduction 44 4.2 Study of Bi-Facial Solar Panel 45 4.3 Proposed System 46 4.3.

1 Block Diagram 46 4.3.2 DC Motor Mechanism 47 4.3.3 Battery Bank 48 4.3.4 System Management Using IoT 48 4.3.

5 Structure of Proposed System 50 4.3.6 Spoiler Design 51 4.3.7 Working Principle of Proposed System 52 4.3.8 Design and Analysis 53 4.4 Applications of IoT in Renewable Energy Resources 53 4.

4.1 Wind Turbine Reliability Using IoT 54 4.4.2 Siting of Wind Resource Using IoT 55 4.4.3 Application of Renewable Energy in Medical Industries 56 4.4.4 Data Analysis Using Deep Learning 57 4.

5 Conclusion 59 References 59 5 Background Mosaicing Model for Wide Area Surveillance System 63 Dr. E. Komagal 5.1 Introduction 64 5.2 Related Work 64 5.3 Methodology 65 5.3.1 Feature Extraction 66 5.

3.2 Background Deep Learning Model Based on Mosaic 67 5.3.3 Foreground Segmentation 70 5.4 Results and Discussion 70 5.5 Conclusion 72 References 72 6 Prediction of CKD Stage 1 Using Three Different Classifiers 75 Thamizharasan, K., Yamini, P., Shimola, A.

and Sudha, S. 6.1 Introduction 75 6.2 Materials and Methods 78 6.3 Results and Discussion 84 6.4 Conclusions and Future Scope 89 References 89 7 Classification of MRI Images to Aid in Diagnosis of Neurological Disorder Using SVM 93 Phavithra Selvaraj, Sruthi, M.S., Sridaran, M.

and Dr. Jobin Christ M.C. 7.1 Introduction 93 7.2 Methodology 95 7.2.1 Data Acquisition 95 7.

2.2 Image Preprocessing 96 7.2.3 Segmentation 97 7.2.4 Feature Extraction 98 7.2.5 Classification 99 7.

3 Results and Discussions 100 7.3.1 Preprocessing 100 7.3.2 Classification 103 7.3.3 Validation 104 7.4 Conclusion 106 References 106 8 Convolutional Networks 109 Simran Kaur and Rashmi Agrawal 8.

1 Introduction 110 8.2 Convolution Operation 110 8.3 CNN 110 8.4 Practical Applications 112 8.4.1 Audio Data 112 8.4.2 Image Data 112 8.

4.3 Text Data 113 8.5 Challenges of Profound Models 113 8.6 Deep Learning In Object Detection 114 8.7 CNN Architectures 114 8.8 Challenges of Item Location 118 8.8.1 Scale Variation Problem 118 8.

8.2 Occlusion Problem 119 8.8.3 Deformation Problem 120 References 121 9 Categorization of Cloud Computing & Deep Learning 123 Disha Shrmali 9.1 Introduction to Cloud Computing 123 9.1.1 Cloud Computing 123 9.1.

2 Cloud Computing: History and Evolution 124 9.1.3 Working of Cloud 125 9.1.4 Characteristics of Cloud Computing 127 9.1.5 Different Types of Cloud Computing Service Models 128 9.1.

5.1 Infrastructure as A Service (IAAS) 128 9.1.5.2 Platform as a Service (PAAS) 129 9.1.5.3 Software as a Service (SAAS) 129 9.

1.6 Cloud Computing Advantages and Disadvantages 130 9.1.6.1 Advantages of Cloud Computing 130 9.1.6.2 Disadvantages of Cloud Computing 132 9.

2 Introduction to Deep Learning 133 9.2.1 History and Revolution of Deep Learning 134 9.2.1.1 Development of Deep Learning Algorithms 134 9.2.1.

2 The FORTRAN Code for Back Propagation 135 9.2.1.3 Deep Learning from the 2000s and Beyond 135 9.2.1.4 The Cat Experiment 136 9.2.

2 Neural Networks 137 9.2.2.1 Artificial Neural Networks 137 9.2.2.2 Deep Neural Networks 138 9.2.

3 Applications of Deep Learning 138 9.2.3.1 Automatic Speech Recognition 138 9.2.3.2 Electromyography (EMG) Recognition 139 9.2.

3.3 Image Recognition 139 9.2.3.4 Visual Art Processing 140 9.2.3.5 Natural Language Processing 140 9.

2.3.6 Drug Discovery and Toxicology 140 9.2.3.7 Customer Relationship Management 141 9.2.3.

8 Recommendation Systems 141 9.2.3.9 Bioinformatics 141 9.2.3.10 Medical Image Analysis 141 9.2.

3.11 Mobile Advertising 141 9.2.3.12 Image Restoration 142 9.2.3.13 Financial Fraud Detection 142 9.

2.3.14 Military 142 9.3 Conclusion 142 References 143 10 Smart Load Balancing in Cloud Using Deep Learning 145 Astha Parihar and Shweta Sharma 10.1 Introduction 146 10.2 Load Balancing 147 10.2.1 Static Algorithm 148 10.

2.2 Dynamic (Run-Time) Algorithms 148 10.3 Load Adjusting in Distributing Computing 149 10.3.1 Working of Load Balancing 151 10.4 Cloud Load Balancing Criteria (Measures) 152 10.5 Load Balancing Proposed for Cloud Computing 153 10.5.

1 Calculation of Load Balancing in the Whole System 154 10.6 Load Balancing in Next Generation Cloud Computing 155 10.7 Dispersed AI Load Adjusting Methodology in Distributed Computing Administrations 157 10.7.1 Quantum Isochronous Parallel 158 10.7.2 Phase Isochronous Parallel 159 10.7.

3 Dynamic Isochronous Coordinate Strategy 161 10.8 Adaptive-Dynamic Synchronous Coordinate Strategy 161 10.8.1 Adaptive Quick Reassignment (AdaptQR) 162 10.8.2 A-DIC (Adaptive-Dynamic Synchronous Parallel) 163 10.9 Conclusion 164 References 165 11 Biometric Identification for Advanced Cloud Security 167 Yojna khandelwal and Kapil Chauhan 11.1 Introduction 168 11.

1.1 Biometric Identification 168 11.1.2 Biometric Characteristic 169 11.1.3 Types of Biometric Data 169 11.1.3.

1 Face Recognition 169 11.1.3.2 Hand Vein 170 11.1.3.3 Signature Verification 170 11.1.

3.4 Iris Recognition 170 11.1.3.5 Voice Recognition 170 11.1.3.6 Fingerprints 171 11.

2 Literature Survey 172 11.3 Biometric Identification in Cloud Computing 174 11.3.1 How Biometric Authentication is Being Used on the Cloud Platform 176 11.4 Models and Design Goals 177 11.4.1 Models 177 11.4.

1.1 System Model 177 11.4.1.2 Threat Model 177 11.4.2 Design Goals 178 11.5 Face Recognition Method as a Biometric Authentication 179 11.

6 Deep Learning Techniques for Big Data in Biometrics 180 11.6.1 Issues and Challenges 181 11.6.2 Deep Learning Strategies For Biometric Identification 182 11.7 Conclusion 185 References 185 12 Application of Deep Learning in Cloud Security 189 Jaya Jain 12.1 Introduction 190 12.2 Literature Review 191 12.

3 Deep Learning 192 12.4 The Uses of Fields in Deep Learning 195 12.5 Conclusion 202 References 203 13 Real Time Cloud Based Intrusion Detection 207 Ekta Bafna 13.1 Introduction 207 13.2 Literature Review 209 13.3 Incursion In Cloud 211 13.3.1 Denial of Service (DoS) Attack 212 13.

3.2 Insider Attack 212 13.3.3 User To Root (U2R) Attack 213 13.3.4 Port Scanning 213 13.4 Intrusion Detection System 213 13.4.

1 Signature-Based Intrusion Detection System (SIDS) 213 13.4.2 Anomaly-Based Intrusion Detection System (AIDS) 214 13.4.3 Intrusion Detection System Using Deep Learning 215 13.5 Types of IDS in Cloud 216 13.5.1 Host Intrusion Detection System 216 13.

5.2 Network Based Intrusion Detection System 217 13.5.3 Distributed Based Intrusion Detection System.