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Rashmi Agrawal, Rashmi Deverajan Agrawal, Ganesh Gopal Deverajan, Vishal Dutt, Pramod Singh Rathore, Pramod Singh (Aryabhatta Engineering Coll Rathore...
Deep Learning Approaches to Cloud Security - Deep Learning Approaches for Cloud Security
English · Hardback
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Description
DEEP LEARNING APPROACHES TO CLOUD SECURITY
Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts.
Deep learning is the fastest growing field in computer science. Deep learning algorithms and techniques are found to be useful in different areas like automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delay in children. However, applying deep learning techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. This book provides state of the art approaches of deep learning in these areas, including areas of detection and prediction, as well as future framework development, building service systems and analytical aspects. In all these topics, deep learning approaches, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. This book is intended for dealing with modeling and performance prediction of the efficient cloud security systems, thereby bringing a newer dimension to this rapidly evolving field.
This groundbreaking new volume presents these topics and trends of deep learning, bridging the research gap, and presenting solutions to the challenges facing the engineer or scientist every day in this area. Whether for the veteran engineer or the student, this is a must-have for any library.
Deep Learning Approaches to Cloud Security:
* Is the first volume of its kind to go in-depth on the newest trends and innovations in cloud security through the use of deep learning approaches
* Covers these important new innovations, such as AI, data mining, and other evolving computing technologies in relation to cloud security
* Is a useful reference for the veteran computer scientist or engineer working in this area or an engineer new to the area, or a student in this area
* Discusses not just the practical applications of these technologies, but also the broader concepts and theory behind how these deep learning tools are vital not just to cloud security, but society as a whole
Audience: Computer scientists, scientists and engineers working with information technology, design, network security, and manufacturing, researchers in computers, electronics, and electrical and network security, integrated domain, and data analytics, and students in these areas
List of contents
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 Re
About the author
Pramod Singh Rathore, PhD, is an assistant professor in the computer science and engineering department at the Aryabhatta Engineering College and Research Centre, Ajmer, Rajasthan, India and is also visiting faculty at the Government University, MDS Ajmer. He has over eight years of teaching experience and more than 45 publications in peer-reviewed journals, books, and conferences. He has also co-authored and edited numerous books with a variety of global publishers, such as the imprint, Wiley-Scrivener.
Vishal Dutt, PhD, received his doctorate in computer science from the University of Madras, and he is an assistant professor in the computer science and engineering department at the Aryabhatta Engineering College in Ajmer, as well as visiting faculty at Maharshi Dayanand Saraswati University in Ajmer. He has four years of teaching experience and has more than 22 publications in peer-reviewed scientific and technical journals. He has also been working as a freelance writer for more than six years in the fields of data analytics, Java, Assembly Programmer, Desktop Designer, and Android Developer. Rashmi Agrawal, PhD, is a professor in the Department of Computer Applications at Manav Rachna International Institute of Research and Studies in Faridabad, India. She has over 18 years of experience in teaching and research and is a book series editor for a series on big data and machine learning. She has authored or coauthored numerous research papers in peer-reviewed scientific and technical journals and conferences and has also edited or authored books with a number of large book publishers, in imprints such as Wiley-Scrivener. She is also an active reviewer and editorial board member in various journals. Satya Murthy Sasubilli is a solutions architect with the Huntington National Bank, having received his masters in computer applications from the University of Madras, India. He has more than 15 years of experience in cloud-based technologies like big data solutions, cloud infrastructure, digital analytics delivery, data warehousing, and many others. He has worked with many Fortune 500 organizations, such as Infosys, Capgemini, and others and is an active reviewer for several scientific and technical journals. Srinivasa Rao Swarna is a program manager and senior data architect at Tata Consultancy Services in the USA. He received his BTech in chemical engineering from Jawaharlal Nehru Technological University, Hyderabad, India and completed his internship at Volkswagen AG, Wolfsburg, Germany in 2004. He has over 16 years of experience in this area, having worked with many Fortune 500 companies, and he is a frequent reviewer for several scientific and technical journals.
Summary
Deep learning is the fastest growing field in computer science. Deep learning algorithms and techniques are found to be useful in different areas like automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delay in children . However, applying deep learning techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. This book provides state of the art approaches of deep learning in these areas, including areas of detection and prediction, as well as future framework development, building service systems and analytical aspects. In all these topics, deep learning approaches, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. This book is intended for dealing with modeling and performance prediction of the efficient cloud security systems, thereby bringing a newer dimension to this rapidly evolving field.
This groundbreaking new volume presents these topics and trends of deep learning, bridging the research gap, and presenting solutions to the challenges facing the engineer or scientist every day in this area. Whether for the veteran engineer or the student, this is a must-have for any library.
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