Machine Learning Approach for Cloud Data Analytics in IoT. Группа авторов

      

Table of Contents

      1  Cover

      2  Title page

      3  Copyright

      4  Preface

      5  Acknowledgment

      6  1 Machine Learning–Based Data Analysis 1.1 Introduction 1.2 Machine Learning for the Internet of Things Using Data Analysis 1.3 Machine Learning Applied to Data Analysis 1.4 Practical Issues in Machine Learning 1.5 Data Acquisition 1.6 Understanding the Data Formats Used in Data Analysis Applications 1.7 Data Cleaning 1.8 Data Visualization 1.9 Understanding the Data Analysis Problem-Solving Approach 1.10 Visualizing Data to Enhance Understanding and Using Neural Networks in Data Analysis 1.11 Statistical Data Analysis Techniques 1.12 Text Analysis and Visual and Audio Analysis 1.13 Mathematical and Parallel Techniques for Data Analysis 1.14 Conclusion References

      7  2 Machine Learning for Cyber-Immune IoT Applications 2.1 Introduction 2.2 Some Associated Impactful Terms 2.3 Cloud Rationality Representation 2.4 Integration of IoT With Cloud 2.5 The Concepts That Rules Over 2.6 Related Work 2.7 Methodology 2.8 Discussions and Implications 2.9 Conclusion References

      8  3 Employing Machine Learning Approaches for Predictive Data Analytics in Retail Industry 3.1 Introduction 3.2 Related Work 3.3 Predictive Data Analytics in Retail 3.4 Proposed Model 3.5 Conclusion and Future Scope References

      9  4 Emerging Cloud Computing Trends for Business Transformation 4.1 Introduction 4.2 History of Cloud Computing 4.3 Core Attributes of Cloud Computing 4.4 Cloud Computing Models 4.5 Core Components of Cloud Computing Architecture: Hardware and Software 4.6 Factors Need to Consider for Cloud Adoption 4.7 Transforming Business Through Cloud 4.8 Key Emerging Trends in Cloud Computing 4.9 Case Study: Moving Data Warehouse to Cloud Boosts Performance for Johnson & Johnson 4.10 Conclusion References

      10  5 Security of Sensitive Data in Cloud Computing 5.1 Introduction 5.2 Data in Cloud 5.3 Security Challenges in Cloud Computing for Data 5.4 Cross-Cutting Issues Related to Network in Cloud 5.5 Protection of Data 5.6 Tighter IAM Controls 5.7 Conclusion and Future Scope References

      11  6 Cloud Cryptography for Cloud Data Analytics in IoT 6.1 Introduction 6.2 Cloud Computing Software Security Fundamentals 6.3 Security Management 6.4 Cryptography Algorithms 6.5 Secure Communications 6.6 Identity Management and Access Control 6.7 Autonomic Security 6.8 Conclusion