دانلود کتاب Cloud Computing Technologies for Smart Agriculture and Healthcare (Chapman & Hall/CRC Cloud Computing for Society 5.0)

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Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
Editors
Contributors
Section I: Cloud Management
1. Virtualization Technology for Cloud-Based Services
1.1 Cloud Computing Overview
1.1.1 Features of Cloud Computing
1.1.2 Impact of Cloud Computing on Business and Its Ecosystems
1.1.3 Deployment Models for Cloud Based Agriculture Services
1.2 Virtualization Technology
1.2.1 Advantages of Virtualization
1.2.2 Benefits of Virtualization
1.2.3 Components Associated for Implementation of Virtualization
1.2.4 Benefits of Virtualization to Cloud Data Centers
1.2.5 Role of Virtualization in Cloud Resource Management
1.3 Virtual Machine Migration
1.3.1 Types of VM Migration
1.3.2 Cost of VM Live Migration
1.3.3 Applications of VM Live Migration
1.4 Applications of Cloud-Based Services in Agriculture Sector
1.5 Conclusion
References
2. Hybrid Cloud Architecture for Better Cloud Interoperability
2.1 Introduction
2.2 Super Five Technologies of Cloud
2.2.1 Standardization Technology
2.2.2 Virtualization Technology
2.2.3 Intercloud Technology
2.2.4 Fault Tolerance
2.2.5 Energy Efficiency
2.3 Architecture of Interoperable Clouds
2.4 Hybrid Cloud Interoperability Methodology
2.5 Modelling of Hybrid Interoperability Cloud Methodology
2.6 Tools Used to Design Hybrid Interoperability Methodology
2.7 Proposed Framework for Hybrid Cloud Interoperability
2.8 Working Philosophy of Hybrid Cloud Framework
2.9 Simulation of Hybrid Cloud in Cloudsim
2.10 Conclusions
References
3. Autoscaling Techniques for Web Applications in the Cloud
3.1 Introduction
3.1.1 Service Models
3.1.1.1 IaaS
3.1.1.2 PaaS
3.1.1.3 SaaS
3.2 Deployment Models
3.2.1 Public Cloud
3.2.2 Private Cloud
3.2.3 Hybrid Cloud
3.2.4 Community Cloud
3.3 Pricing Models
3.3.1 On-Demand Instances
3.3.2 Reserved Instances
3.3.3 Spot Instances
3.4 Scaling in the Cloud
3.4.1 Vertical Scaling
3.4.2 Horizontal Scaling
4.5 Auto Scaling Techniques
4.5.1 Reactive Approach: Threshold-based
3.5.2 Proactive Approach
3.5.3 Time Series Analysis
3.5.3.1 Linear Regression, Neural Network, SVM
3.5.3.2 Autoregressive Models (ARs)
3.5.3.3 Signal Prediction
3.5.4 Control Theory
3.5.5 Reinforcement Learning
3.5.6 Queuing Theory
3.6 Proactive Auto Scaling Technique Using SVM: A Case Study
3.6.1 Solving Approach
3.6.2 Experimental Setup
3.6.3 Client Infrastructure
3.6.4 Architecture
3.6.4 Algorithm
3.6.6 Implementation
3.6.6.1 Data Collection
3.6.6.2 Data Preprocessing
3.6.6.3 SVM Training
3.6.6.4 Process for Scaling
3.6.6.5 Results
3.7 Discussion
References
4. Community Cloud Service Model for People with Special Needs
4.1 Introduction
4.2 Deployment Models of Cloud
4.3 Main Objectives to Develop Community Service Model
4.4 Community Cloud Service Model
4.4.1 Sign Language Dictionary
4.4.2 Sign Language Learning Applications
4.4.3 Tools for Translation of Sign Language into Spoken Language and Vice Versa
4.4.3.1 Distance Learning Education System
4.4.4 Telemedicine and Healthcare Service for the Deaf and Mute
4.4.5 Employment Opportunities for Disabled
4.5 Benefit to Society
4.6 Conclusion and Future Scope
References
Section II: Cloud for Agriculture
5. Sensor Applications in Agriculture - A Review
5.1 Introduction
5.2 IOT in Agriculture
5.3 Major Applications in Agriculture
5.3.1 Benefits of Smart Agriculture Solutions
5.3.2 Soil Moisture Sensing
5.3.3 Land/Seedbed Preparation
5.3.4 Spray Drift Evaluation
5.3.5 Weeding Robot
5.4 Cloud Based Air Quality Monitoring: Case Study
5.4.1 Role of IoT
5.4.2 Role of Cloud Computing
5.4.3 Applications of Cloud Computing in Agriculture
5.4.4 Overview of Air Quality Monitoring System
The Real-Time Dataset
5.5 Future Advancements in Farm Management
5.6 Conclusion
References
6. Crop Biophysical Parameters Estimation Using SAR Imagery for Precision Agriculture Applications
6.1 Introduction
6.1.1 Morphological Characterization Sensors
6.1.2 Physiological Assessment Sensors for Vegetation
6.2 Motivation
6.3 Literature Survey
6.4 Proposed Systems
6.5 Case Studies for Precision Agriculture
6.5.1 Case Study 1: Classification of Crop Diseases Using IoT and Machine Learning in the Cloud Environment
6.5.2 Case Study 2: IoT-Based Smart System to Support Agricultural Parameters
6.5.3 Case Study 3: Climate Monitoring
6.5.4 Case Study 4: Crop Management
6.5.5 Case Study 5: Greenhouse Automation
6.6 Conclusion
References
7. Importance of Cloud Computing Technique in Agriculture Field Using Different Methodologies
7.1 Introduction
7.2 Methods and Values of Agriculture Entry to the Field of Cloud Computing
7.2.1 Agriculture and Cloud Computing
7.2.2 Cloud Computing Mechanisms to Support Agricultural Operations
7.3 Farmer\'s Attraction with the Cloud Computing Technology
7.4 Proposed Cloud Computing Platform for the Farmers
7.5 Cloud Computing is Helping the Agricultural Sector to Grow
7.6 Responsibilities of Cloud Computing in Agriculture Domain (Rural and Hills)
7.7 Advantages of Cloud Computing Technology in Agriculture
7.8 Challenges of Cloud Computing Technology in Agriculture
7.9 Applications of Cloud Computing Technology in the Field of Agriculture
7.10 Conclusion
References
8. Optimal Clustering Scheme for Cloud Operations Management Over Mobile Ad Hoc Network of Crop Systems
8.1 Introduction
8.2 Background
8.3 Previous Work Done
8.4 Existing Methodologies
8.5 Proposed Methodology
8.6 Stimulation and Result
8.7 Result and Discussion
8.8 Conclusion
8.9 Future Scope
References
9. A Novel Hybrid Method for Cloud Security Using Efficient IDS for Agricultural Weather Forecasting Systems
9.1 Introduction
9.2 Background
9.3 Previous Work Done
9.4 Existing Methodologies
9.5 Analysis of Methods
9.6 Proposed Methodology
9.7 Stimulation and Result
9.8 Results and Discussion
9.9 Conclusion
9.10 Future Scope
References
Section III: Cloud for Healthcare
10. Cloud Model for Real-Time Healthcare Services
10.1 Introduction
10.1.1 Objectives of Research
10.1.2 Organization
10.2 Related Work
10.3 Different Cloud Computing Uses in Real-Time Healthcare Services
10.4 Cloud Computing in Healthcare Applications
10.4.1 Healthcare Data Management, Data Sharing, and Access in the Cloud
10.4.2 Preventive Medical Care Using Cloud Computing
10.5 Issues and Challenges in Using Cloud Computing in Healthcare
10.6 Real-Time Virtual Machine Scheduling Framework of the Cloud Environment
10.6.1 Real-Time Healthcare Sensing and Actuation in the Cloud Environment
10.6.2 Real-Time Patients and Physician Interactions
10.7 Case Study of Different Healthcare Cloud Providers
10.8 Conclusions
Acknowledgment
References
11. Cloud Computing-Based Smart Healthcare System
11.1 Introduction
11.1.1 Fractal Dimension
11.2 Materials and Methods
11.2.1 EEG
11.2.2 Data Set
11.2.3 Higuchi\'s Fractal Dimension Method
11.2.4 Katz\'s Fractal Dimension Method
11.2.5 Classifier
11.2.6 Cloud Platform
11.2.7 Data Access Interface
11.2.8 Client Development
11.3 Results
11.3.1 HFD Method
11.3.2 KFD Method
11.4 Discussion
11.5 Conclusion
11.6 Future Scope
References
12. Rehearsal of Cloud and IoT Devices in the Healthcare System
12.1 Introduction
12.2 Efficient Services Provided for Healthcare Systems
12.2.1 Microsoft Cloud Services
12.2.2 Information Security Management (SMS)
12.3 Need of Cloud Computing for Healthcare
12.4 Benefits of Cloud Computing for Healthcare
12.4.1 Security
12.4.2 Cost
12.4.3 Scalability
12.4.4 Data Storage
12.4.5 Artificial Intelligence and Machine Learning
12.4.5 Collaboration
12.5 Risks of Cloud Computing in Healthcare System
12.6 Benefit of Microsoft Cloud in the Healing Healthcare System
12.7 Healthcare\'s Future is in the Cloud
12.7.1 The Circumstances for the Cloud
12.7.2 The Circumstances for IoT Devices
12.8 Classification Techniques in the Cloud and IoT-Based Health Monitoring and Diagnosis Approach
12.8.1 DXC Technology
12.8.2 Flexible Multi-Level Architecture
12.8.3 Dynamic Cloud Platform for an eHealth System Based on a Cloud SOA Architecture (DCCSOA)
12.8.4 Cloud Based 8E-Prescription Management System for Healthcare Services Using IoT Devices
12.8.5 Android-Based Mobile Data Acquisition (DAQ)
12.8.6 WSN Architecture with IoT
12.8.7 Fog Computing
12.8.8 Hospital Information Systems (HIS)
12.8.9 E-Health Internet of Things (IoT)
12.8.10 Mobile Cloud Computing for Emergency Healthcare (MCCEH) Model
12.8.11 Cloud-Based Intelligent Healthcare Monitoring System (CIHMS)
12.8.12 Remote Healthcare Service
12.9 Analysisof Existing Techniques
12.10 Conclusion
12.11 Future Scope
References
13. Cloud-Based Diagnostic and Management Framework for Remote Health Monitoring
13.1 Introduction
13.2 Literature Review
13.3 Diverse Approaches for a Remote Healthcare Monitoring System
13.3.1 E-Health Monitoring System
13.3.2 Wearable Sensors-Based Remote Health Monitoring System
13.3.3 Secured Remote Health Monitoring System
13.3.4 Smart Technology for Healthcare Professionals - An Analysis
13.3.5 Disease Prediction as an Added Feature of an e-Healthcare Application
13.3.6 Cloud Technology Supported Hospital File Management System
13.4 Exemplary Design \"Smart Doctor-Patient Diagnostic and Management System\"
13.4.1 System at a Glance
13.4.2 Overview
13.4.3 System Modules
13.4.3.1 Disease Prediction
13.4.3.2 Finding a Doctor
13.4.3.3 Online Prescription
13.4.3.4 Text-to-Speech Conversion
13.4.3.5 Emergency Alert Button
13.4.3.6 Doctor Login with Registration Number
13.4.4 System Design
13.4.5 ER Diagram
13.4.6 Algorithm Details
13.4.7 Dashboard for Patient and for Doctors
13.4.8 Concluding Remarks
13.5 Conclusions and Future Work
Acknowledgments
References
14. Efficient Accessibility in Cloud Databases of Health Networks with Natural Neighbor Approach for RNN-DBSCAN
14.1 Introduction
14.2 Background
14.3 Previous Work Done
14.4 Existing Methodologies
14.4.1 Natural Neighbor to Identify the Density of Data Objects
14.4.2 RNN-DBSCAN Method
14.4.3 DPC-KNN Method
14.4.4 A-DPC Method
14.4.5 LP-SNG Algorithm
14.5 Analysis of Method
14.6 Proposed Methodology
14.7 Simulation and Results
14.8 Results and Discussion
14.9 Conclusion
14.10 Future Scope
References
15. Blood Oxygen Level and Pulse Rate Monitoring Using IoT and Cloud-Based Data Storage
15.1 Introduction
15.1.1 Overview
15.1.2 Problem Statement
15.1.3 Background
15.2 Literature Review
15.3 Problem with Existing System
15.3.1 Problems Faced by Doctors
15.3.2 Problems Faced by the Patient
15.4 Components and Sensors
15.4.1 Pulse Oximeter
15.4.1.1 MAXIM MAX30100 Sensor
15.4.1.2 Working of MAX30100 Sensor
15.4.2 Firebase Realtime Database
15.4.2.1 Structure of Firebase Realtime Database
15.4.2.2 Configuration of Firebase Realtime Database
15.4.2.3 Read and Write Data on Firebase Realtime Database
15.4.2.3.1 Write Data
15.4.2.3.2 Read Data
15.4.3 NodeMCU (ESP 8266)
15.5 System Architecture
15.6 Implementation
15.6.1 Patient Monitoring
15.6.1.1 Connection Between MAX30100 and ESP8266
15.6.1.2 Communication Between ESP8266 and Cloud Data Storage
15.6.1.3 Data Storage in the Cloud and Its Data Structure
15.6.2 User Interface
15.7 Data Analysis and Results
15.8 Future Scope
15.9 Conclusion
References
16. Parkinson Disease Prediction Model and Deployment on AWS Cloud
16.1 Introduction
16.2 Related Work
16.3 Description of Data Set
16.4 Feature Importance Analysis
16.5 Prediction Techniques
16.5.1 Logistic Regression
16.5.2 Decision Tree
16.5.3 SVM (Support Vector Machine)
16.5.4 KNN (K-Nearest Neighbor)
16.5.5 Random Forest
16.6 Deploying Model on AWS Cloud
16.7 Result
16.8 Conclusion
References
17. Federated Learning for Brain Tumor Segmentation on the Cloud
17.1 Introduction
17.2 Data Set and Preprocessing
17.2.1 Data Set
17.2.2 Data Set Preprocessing
17.3 Double Clustered Federated Learning System
17.3.1 U-Net Architecture
17.3.1.1 Downsampling Stage of U-Net
17.3.1.2 ResNet
17.3.1.3 Upsampling in U-Net Architecture
17.4 General Training
17.4.1 Pre-Training the U-Net
17.5 Federated Learning and Cloud Development
17.5.1 Federated Learning
17.5.1.1 FedProx
17.5.2 Federated Training
17.5.3 Cloud Deployment
17.6 Global Deployment
17.7 Conclusion
References
18. Smart System for COVID-19 Susceptibility Test and Prediction of Risk along with Validation of Guidelines Conformity Using the Cloud
18.1 Introduction
18.2 Proposed Solution
18.3 Methodology
18.3.1 SpO2 Level Measurement
18.3.2 COVID Risk Detection
18.3.3 Mask Detection
18.3.4 Social Distancing Using Bluetooth
18.4 Results
18.5 Conclusion
Acknowledgments
References
19. Designing a Policy Data Prediction Framework in Cloud for Trending COVID-19 Issues over Social Media
19.1 Introduction
19.2 Background
19.3 Previous Work Done
19.4 Existing Methodologies
19.5 Analysis of Methods
16.6 Proposed Methodology
19.7 Simulation and Results
19.8 Results and Discussion
19.9 Conclusion
19.10 Future Scope
References
Index