دانلود کتاب Cloud-based Intelligent Informative Engineering for Society 5.0

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Cover Half Title Series Page Title Page Copyright Page Table of Contents Preface Editors Contributors 1. Managing Information System with the Help of Cloud Computing 1.1 Introduction 1.1.1 What Is Cloud Computing? 1.1.2 History of Cloud Computing 1.1.3 Basics of Cloud Computing 1.1.4 Deployment Models 1.1.4.1 Service Models 1.2 What Is Cloud Computing in the Management Information System? 1.3 Need for MIS 1.3.1 Cloud Storage 1.3.2 Why Use Cloud Storage 1.3.3 Working of Cloud Storage 1.3.4 Review on Management Information System with the Help of Cloud Computing How Does Cloud Computing Change Management? 1.4 Data Management in Cloud Computing 1.5 Data Security in Cloud 1.6 Cloud-Based E-Learning Systems 1.6.1 Cloud-Based College-Enterprise Classroom Training Method 1.7 Cloud-Based Employee Management System 1.7.1 Employee Management System 1.7.2 Cloud-Based Human Resource Management System 1.8 Cloud-Based Health Management System 1.9 Supply Chain Management 1.9.1 Cloud Computing Paradigms 1.10 Conceptual Framework in Designing Cloud Computing Management Information System in Academic Area 1.11 Cloud Computing and Its Amalgamation with Information Science 1.11.1 Information Networks 1.11.2 Information System 1.11.3 Knowledge Lattice and Networks 1.11.4 Information Center and Data Center 1.11.5 Information Analysis Center 1.12 Cloud Computing: Challenges 1.12.1 Security 1.12.2 Data Possession 1.12.3 Standard Architecture 1.12.4 Need for Internet Connectivity 1.12.5 Compatibility 1.13 Cloud Computing Life Cycle 1.13.1 Methodology 1.14 Future Scope 1.15 Conclusion References 2. Wireless Networks Based in the Cloud That Support 5G 2.1 Introduction 2.1.1 The Emergence of Wireless Networking Technology 2.1.1.1 Capacity for Connectivity 2.1.1.2 Performance of the Network 2.1.1.3 Resource Optimization 2.1.2 Wireless Networks Capable of 5G 2.1.2.1 The Cost of Using the Internet (Energy Consumption by Existing Technologies) 2.1.2.2 Sufficient Speed and Capacity 2.1.2.3 Friendliness 2.1.2.4 Accessibility 2.1.2.5 Economy 2.1.2.6 Personality 2.1.3 5G and Mobile Cloud Computing 2.1.4 Mobile Cloud Computing Issues MCC Applications Encounter These Issues 2.1.4.1 Availability 2.1.4.2 Bandwidth 2.1.4.3 Heterogeneity 2.2 Networking That Are Hosted on the Cloud 2.2.1 The Virtualization of the Network Foundation 2.2.2 Radio Access Networks Hosted in the Cloud 2.2.3 Cloud Networking on Mobile Devices 2.2.4 MCN's Aims 2.3 Networking Platforms on the Cloud 2.3.1 OpenNebule 2.3.2 OpenStack 2.4 5G Wireless Mobile Network Adopts Deep Learning Architecture 2.4.1 Convolution Neural Network 2.5 Conclusion References 3. Implications of Cloud Computing for Health Care 3.1 Introduction 3.1.1 Definition of Cloud 3.1.2 What Is Cloud Computing? 3.2 Important Aspects of Cloud Computing 3.2.1 Benefits of Cloud Computing (CC) 3.2.2 Below Are the Working Models for CC 3.2.3 Public Cloud 3.2.4 Private Cloud 3.2.5 Hybrid Cloud 3.2.6 Community Cloud 3.3 Service Models 3.3.1 Infrastructure as a Service (IaaS) 3.3.2 Platform as a Service (PaaS) 3.3.3 Software as a Service (SaaS) 3.3.4 Advantages of Cloud Computing in Healthcare System 3.4 Collaboration 3.4.1 Security 3.4.2 Cost 3.4.3 Speed 3.4.4 Scalability and Flexibility 3.5 Applications of Cloud Computing in Health Care 3.5.1 Dynamic Scalability of Infrastructure 3.5.2 Information Sharing 3.5.3 Availability in CC 3.5.4 Benefits of Adopting CC for Healthcare Organizations 3.5.5 Impacts of Cloud Computing on Healthcare Sector 3.5.6 Ease of Interoperability 3.5.7 Access to Powerful Analytics 3.6 Ownership of Consumer (Patient) Information 3.6.1 Telemedicine Function 3.7 Barriers in Using CC in Healthcare Systems Sectors 3.7.1 Security Concerns 3.7.2 Complaisance with Safety Standards 3.7.3 System Downtime 3.7.4 World Market for CC in Health Sectors 3.7.5 Availability and Control 3.7.6 Security Threats 3.7.7 Legal and Compliance Risks 3.8 Conclusion References 4. Cloud Computing in Artificial Neural Network 4.1 Introduction 4.2 Characteristics of Cloud Computing 4.3 Scope of Cloud Computing in Artificial Neural Network 4.3.1 Basics of BNN 4.4 Basics of ANN 4.4.1 Services of Cloud Computing Inherited in Artificial Neural Network 4.4.2 Cloud Service as Software in ANN 4.4.3 ANN in Job Scheduling 4.4.4 ANN in Textiles 4.4.5 Cloud Service as Infrastructure in ANN 4.4.6 Supervised Learning 4.4.7 Unsupervised Learning 4.4.8 Cloud Service as Platform in ANN 4.4.9 How the Security Applies in Cloud Data by Using ANN 4.5 Reviews 4.6 Proposed Model 4.7 Conclusion 4.8 Future Scope References 5. Cloud Computing in Blockchain 5.1 Introduction 5.1.1 Blockchain Model Blocks Include 5.1.1.1 Blockchain 5.1.1.2 Blockchain Security 5.1.2 Ad Hoc Mobile Cloud Infrastructure 5.1.3 Bitcoin 5.1.4 Cloud Computing Authentication 5.1.5 Blockchain Specifications 5.1.5.1 E-Cash and Its Security 5.1.5.2 Access Control 5.1.5.3 Blockchain and Cloud Computing Security 5.2 Cloud Computing 5.2.1 Cloud Deployment Models 5.2.2 Community Cloud 5.2.3 Data Security 5.2.4 Restrictions 5.2.5 Reputation 5.2.6 No-Vendor Legal Liability 5.2.7 Cloud-Based Research 5.2.7.1 Reliability 5.2.7.2 Requirement 5.2.7.3 SLAs 5.2.7.4 Cloud Data Management 5.2.7.5 Data Encryption 5.2.7.6 Interoperability 5.3 Blockchain Technology 5.3.1 Emergence of Blockchain-Bitcoin 5.3.2 Differentials 5.3.2.1 Decentralisation 5.3.2.2 Persistence 5.3.2.3 Auditability 5.3.2.4 Anonymity 5.3.2.5 Autonomous 5.3.2.6 Immunity 5.3.2.7 Transparency 5.3.2.8 Traceability 5.3.3 Blockchain Types 5.3.3.1 Public Blockchain 5.3.3.2 Public Blockchain 5.3.3.3 Consortium Blockchain 5.3.4 Blockchain Phases 5.3.4.1 First-Generation Blockchain 5.3.4.2 Second-Generation Blockchain 5.3.4.3 Third-Generation Blockchain 5.3.4.4 Mining 5.3.4.5 Blockchain Nodes 5.3.4.6 Blockchain Layers 5.3.4.7 Hashing 5.3.4.8 Smart Contracts 5.3.5 Digital Signatures 5.3.6 Blockchain Performance Analysis 5.3.6.1 Bitcoin and Ethereum Performance Comparison 5.3.6.2 Hyperledger and Ethereum Comparison 5.3.7 Blockchain Applications 5.3.7.1 Financial Blockchain 5.3.7.2 Healthcare Blockchain 5.3.7.3 Blockchain in Data Provenance 5.3.7.4 5G Blockchain 5.3.7.5 Aviation Blockchain 5.3.7.6 Supply Chain Blockchain 5.3.7.7 Blockchain in Smart Homes 5.3.7.8 Blockchain in Smart Property 5.3.7.9 Blockchain Elsewhere 5.3.8 Blockchain Architecture 5.3.8.1 Blockchain's Workings 5.3.8.2 Consensus Algorithms 5.3.8.3 Proof of Work 5.3.8.4 Proof of Stake 5.3.8.5 Practical Byzantine Fault Tolerance (PBFT) 5.3.8.6 Delegated Stake Proof 5.3.8.7 Ripple 5.3.8.8 Tendermint 5.3.8.9 Node Identity Management 5.3.8.10 Energy Saving 5.3.8.11 Tolerated Adversary Power 5.3.9 Blockchain Challenges 5.3.9.1 Scalability 5.3.9.2 Privacy Leak 5.3.9.3 Laws 5.3.9.4 Governing 5.4 Support Blockchain for Cloud Computing 5.4.1 Interoperability 5.4.2 Data Encryption 5.4.3 SLAs 5.4.4 Cloud Data Management 5.4.5 Blockchain–Cloud Analysis 5.5 Conclusion References 6. Cloud Computing for Machine Learning and Cognitive Application 6.1 Introduction 6.1.1 Cloud Computing 6.1.2 Software as a Service 6.1.3 Platform as a Service 6.1.4 Infrastructure as a Service 6.2 Machine Learning 6.2.1 Supervised Learning 6.2.2 Unsupervised Learning 6.3 Literature Review 6.3.1 Cloud Computing 6.3.2 Multitenancy 6.3.3 Huge Scalability 6.3.4 Elasticity 6.3.5 Pay-as-You-Go 6.3.6 Self-Provision of Resources 6.4 The SPI Framework for Cloud Computing 6.4.1 The Cloud Services Delivery Model 6.4.1.1 The Software as a Service Model 6.4.1.2 The Platform as a Service Model 6.4.1.3 The Infrastructure as a Service Model 6.4.1.4 Cloud Deployment Model 6.4.2 Public Clouds 6.4.3 Private Clouds 6.4.4 Hybrid Clouds 6.4.5 The Impact of Cloud Computing on Users 6.4.6 Individual Business 6.4.7 Individual Customers 6.4.8 Start-Ups 6.4.9 Small- and Medium–Sized Business 6.4.10 Enterprise Businesses 6.5 Conclusions 6.6 Future Scope References 7. Edge Cloud Computing-Based Model for IoT 7.1 Introduction 7.1.1 Cloud Computing 7.1.2 Software-as-a-Service (SaaS) 7.1.3 Platform-as-a-Service (PaaS) 7.1.4 Infrastructure-as-a-Service (IaaS) 7.1.5 Cloud Computing at the Edge Offers Many Benefits for LSD-IoT 7.1.5.1 Scalable 7.1.5.2 Performance 7.1.5.3 Data Size 7.1.5.4 Availability 7.1.5.5 Effectiveness 7.2 Edge Computing: Why You Need It 7.2.1 Push From the Cloud Services 7.2.2 Push From the IoT 7.2.2.1 Go From Data Consumer to Data Creator 7.3 Related Work 7.3.1 Edge Computing Architecture 7.3.2 Cloudlet Computing 7.3.3 Fog Computing 7.3.4 Virtualization 7.4 Models of IoT Communication 7.4.1 Device to Device Communication (D2D) 7.4.2 Device to Cloud Communication (D2C) 7.4.3 Device to Gateway Communication (D2G) 7.5 Edge Computing Architecture 7.5.1 Far End 7.5.2 Near End 7.6 Cloud Architecture Based on IoT 7.6.1 IoT Applications in Detail 7.6.1.1 Smart Cities 7.6.1.2 Smart Security 7.6.1.3 Smart Medical Field 7.6.1.4 Intelligent Agriculture 7.6.1.5 Smart Industrial Control 7.6.1.6 Smart Entertainment and Media 7.6.1.7 Smart Legal System 7.7 Benefits of the Internet of Things 7.7.1 Communication 7.7.2 Storage 7.7.3 Processing Capabilities 7.7.4 New Abilities 7.8 Advantages of IoT and Cloud Computing Integration 7.8.1 Analysis 7.8.2 Scalability 7.8.3 Visualization 7.8.4 Flexibility 7.8.5 Fast Reaction Time 7.8.6 Automation 7.8.7 Multitenancy 7.9 Future Work 7.10 Conclusion References 8. Cloud-Based License Plate Recognition for Smart City Using Deep Learning 8.1 Introduction 8.1.1 Related Technologies 8.1.1.1 Deep Learning 8.1.1.2 Cloud Computing 8.1.2 Literature Review 8.2 Proposed Model 8.2.1 Image Acquisition 8.2.2 Horizontal Flipping 8.2.3 Color Augmentation 8.2.3.1 Brightness 8.2.3.2 Contrast 8.2.3.3 Saturation 8.2.3.4 Hue 8.2.4 Cropping 8.2.5 Data Pre-Processing 8.2.5.1 Smoothing 8.2.5.2 Scaling 8.2.5.3 Data Cleaning 8.3 Segmentation 8.3.1 Segmentation Approaches 8.3.2 Segmenting Images 8.3.3 Segmentation Based on Thresholds 8.3.4 Segmentation Based on Location 8.3.5 Clustering by Merging 8.3.6 Divisive Splitting or Clustering by Division 8.4 Segmentation Using an Artificial Neuronal Network 8.5 Optical Character Recognition 8.6 Convolutional Neural Networks 8.7 Evaluation Parameters for the Proposed Model 8.8 Conclusion 8.9 Future Work References 9. Sentimental Analysis Using Cloud Dictionary and Machine Learning Approach 9.1 Introduction 9.2 Literature Review 9.2.1 Machine Learning Approach 9.2.2 Supervised Learning 9.2.3 Decision Tree Classifier 9.2.4 Linear Classification 9.2.5 Support Vector Machine (SVM) 9.3 Lexicon-Based Approach 9.4 Methodology 9.4.1 Dictionary Based Approach 9.4.1.1 Text Data From Snscrape (SNS) 9.4.2 Data Pre-Processing 9.4.2.1 Tokenization 9.4.2.2 Stop Words Removal 9.4.2.3 Case Normalization 9.4.3 Data Polarization 9.5 Machine Learning Based Approach 9.5.1 Dataset: Contains 9.5.2 Data Pre-Processing and Cleaning 9.6 Binary Classifier Using LSTM 9.6.1 Class Prediction 9.7 Result and Discussion 9.8 Conclusion References 10. Impact of Cloud Computing on Entrepreneurship, Cost, and Security 10.1 Introduction 10.1.1 Theoretical Background 10.1.2 Cloud Computing 10.2 The Technical Part of the Cloud 10.2.1 SAAS (SaaS) 10.2.2 PAAS (PaaS) 10.2.3 IAAS (IaaS) 10.2.4 Public Cloud 10.2.5 Hybrid Cloud 10.3 Case Studies Abroad 10.3.1 Google.com 10.3.2 Amazon.com 10.3.3 Microsoft 10.3.4 Apple 10.3.5 Adoption of Cloud Computing in Europe 10.3.6 Potential Benefits of Cloud Computing 10.4 Concerns and Challenges 10.4.1 Cost Benefits 10.4.2 Cost Impact 10.5 Security Risks 10.5.1 Security Impact 10.6 Data Collection 10.7 Cloud Computing on Investments 10.8 Conclusions References 11. Green Cloud Computing 11.1 Introduction 11.1.1 Infrastructure as a Service (IaaS) 11.2 Amazon Web Services 11.2.1 AWS Storage Services 11.2.2 Amazon Glacier 11.2.3 Elastic Block Storage (EBS) 11.2.4 AWS Computing Service 11.3 Platforms as a Service (PaaS) 11.3.1 Public Cloud 11.3.2 Private Cloud 11.3.2.1 Security 11.3.2.2 Long-Term Savings 11.3.2.3 Regulatory Governance 11.3.3 Community Cloud 11.3.4 Hybrid Cloud 11.4 Literature Review 11.5 Existing Approaches 11.5.1 Advantages and Disadvantages 11.6 Conclusions and Future Work References 12. Study of Issues with Cloud Security 12.1 Introduction 12.1.1 Cloud Computing 12.1.2 The Cloud Model Consists of Five Key Features 12.2 Literature Survey 12.3 Cloud Models and Their Security Issues 12.3.1 Service Models 12.3.2 Deployment Models 12.4 Cloud Security Issues 12.4.1 Deployment Models Security Issues 12.4.2 Service Models Security Issues 12.5 Countermeasures 12.6 Conclusion References Index