Cloud Computing Technologies for Smart Agriculture and Healthcare

Tramadol 50 Mg Online Uk 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 a 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