Synergistic Interaction of Big Data with Cloud Computing for Industry 4.0

Description

The goal of this book is to help aspiring readers and researchers understand the convergence of Big Data with the Cloud. This book presents the latest information on the adaptation and implementation of Big Data technologies in various cloud domains and Industry 4.0. Synergistic Interaction of Big Data with Cloud Computing for Industry 4.0 discusses how to develop adaptive, robust, scalable, and reliable applications used in solutions for day-to-day problems. It reviews the advantages and consequences of utilizing Cloud Computing to tackle Big Data issues within the manufacturing and production sector as part of Industry 4.0. Top Big Data experts throughout the world have contributed their expertise to this book, addressing the major challenges, issues, and advances in Big Data and Cloud Computing for Industry 4.0. By exploring the basic and high-level concepts, this book serves as a guide for those in the industry while also helping beginners and the more advanced in understanding both the basic and the advanced aspects of the synergistic Interaction of Big Data and Cloud Computing. Cover Half Title Series Information Title Page Copyright Page Table of Contents Preface Editors’ Biographies List of Contributors 1 Big Data Based On Fuzzy Time-Series Forecasting for Stock Index Prediction 1.1 Introduction 1.2 Discussion On Fuzzy Time-Series Prediction 1.3 Methodology 1.4 Results and Discussion 1.4.1 TAIEX Forecasting Graphical Representation of Actual and Forecasted Data From 2015 to 2020 1.4.2 BSE Forecasting Graphical Representation of Actual and Forecasted Data From 2015 to 2020 1.4.3 KOSPI Forecasting Graphical Representation of Actual and Forecasted Data From 2015 to 2020 1.5 Conclusion References 2 Big Data-Based Time-Series Forecasting Using FbProphet for the Stock Index 2.1 Introduction 2.2 Methodology 2.3 Methods 2.4 Materials 2.5 Experiment Results and Discussion Graphical Representation of Sensex Actual and Forecasted Data From 2011 to 2020 Graphical Representation of Actual and Forecasted Data From 2011 to 2020 Graphical Representation of Actual and Forecasted Data From 2011 to 2020 Graphical Representation of Actual and Forecasted Data From 2011 to 2020 Graphical Representation of Actual and Forecasted Data From 2011 to 2020 Graphical Representation of Actual and Forecasted Data From 2011 to 2020 2.6 Conclusion Acknowledgment References 3 The Impact of Artificial Intelligence and Big Data in the Postal Sector 3.1 Background 3.2 Purpose and Goals of the Study 3.3 Digitalization of Postal Services 3.3.1 The Impact of AI and Big Data in Postal Operation 3.4 The Emerging Technologies That Are Expected to Impact the Postal Service Through Big Data 3.4.1 Big Data Analytics Tools 3.4.2 Internet of Postal Things 3.4.3 Connected Vehicles 3.5 The Emerging Technologies That Are Expected to Impact the Postal Service Through Artificial Intelligence 3.5.1 Last Mile Logistics App 3.5.2 Autonomous Delivery 3.5.3 Optical Character Recognition (Ocr) Machines 3.5.4 Stamp Verification 3.5.5 Document Classification Automation 3.5.6 Address Changing and Validation Process 3.6 Conclusion References 4 Advances in Cloud Technologies and Future Trends 4.1 Introduction 4.2 Cloud Computing Models and Services 4.2.1 Taxonomy of the Cloud-Based On Services Provided 4.2.2 Cloud Architecture 4.3 Creation of Virtual Machines and Docker Containers 4.3.1 Virtualization 4.3.2 Full Virtualization 4.3.3 Para-Virtualization 4.3.4 Deployment Model Private Cloud Public Cloud Hybrid Cloud Community Cloud 4.4 KVM and Containers 4.4.1 CPU Performance 4.4.2 Memory Performance 4.4.3 Network Performance 4.4.4 Disk Performance 4.4.5 Application Performance 4.4.6 Application Performance – MySQL 4.5 Cloud Architecture and Resource Management 4.6 Conclusion Acknowledgment Conflict of Interest References 5 Reinforcement of the Multi-Cloud Infrastructure With Edge Computing 5.1 Introduction 5.2 Cloud Computing 5.3 Multi-Cloud Computing 5.4 Why Organizations Choose Multi-Cloud 5.5 Edge Computing 5.6 Cloud Edge Computing 5.7 Security 5.8 Openstack 5.9 Openstack Components 5.9.1 Compute (Nova) 5.9.2 Object Storage (Swift) 5.9.3 Block Storage (Cinder) 5.9.4 Networking (Neutron) 5.9.5 Dashboard (Horizon) 5.9.6 Identity (Keystone) 5.9.7 Image Service (Glance) 5.10 Reinforcement of Cloud-Edge Infrastructure With Openstack 5.10.1 Creation of Personal Private Multi-Cloud-Edge Infrastructure Using Openstack 5.10.2 User Authentication 5.10.3 Access Control 5.10.4 Data Loss Prevention (DLP) 5.10.5 Monitoring the User Accounts 5.10.6 Secure the Data in Storage as Well as Data-In-Transit 5.10.7 User Revocation 5.11 Results and Discussion 5.12 Conclusion and Future Work References 6 Study and Investigation of PKI-Based Blockchain Infrastructure 6.1 Background 6.1.1 PKI 6.1.2 Problems With PKI 6.1.3 What Is Blockchain? 6.1.4 PKI With Blockchain 6.2 Necessity of PKI With Blockchain 6.3 Literature Survey 6.4 Objective 6.5 Methodology 6.6 Results and Discussion 6.7 Conclusion and Future Work References 7 Stock Index Forecasting Using Stacked Long Short-Term Memory (LSTM): Deep Learning and Big Data 7.1 Introduction 7.2 Materials 7.3 Research Methodology 7.4 Result and Discussion Graphical Representation of TAIEX Graphical Representation of BSE Graphical Represntation of KOSPI 7.5 Conclusion References 8 A Comparative Study and Analysis of Time-Series and Deep Learning Algorithms for Bitcoin Price Prediction 8.1 Introduction 8.2 Literature Survey 8.2.1 Related Works 8.2.2 Blockchain Technology 8.3 Methodology 8.3.1 Machine Learning Models 8.3.1.1 ARIMA Model 8.3.1.2 LASSO Regression 8.3.1.3 Recurrent Neural Network (RNN) 8.3.1.4 Long Short-Term Memory (LSTM) 8.3.2 Dataset 8.4 Results and Discussions 8.4.1 ARIMA – Time-Series Model On Daily Data On Monthly Average Data 8.4.2 SARIMA On Daily Data On Monthly Data 8.4.3 LSTM 8.4.4 LASSO Regression 8.5 Future Work 8.6 Conclusion References 9 Machine Learning for Healthcare 9.1 Introduction 9.2 ML in Healthcare 9.2.1 Cancer 9.2.2 Cardiovascular Diseases 9.2.3 Diabetes 9.2.4 Obesity 9.3 Challenges and Opportunities FOR ML in Healthcare 9.4 Conclusion References 10 Transfer Learning and Fine-Tuning-Based Early Detection of Cotton Plant Disease 10.1 Introduction 10.2 Related Work 10.3 Methods 10.3.1 Data Preprocessing 10.3.2 Fine-Tuning 10.3.3 InceptionV3 10.3.4 EfficientNet 10.4 Implementation 10.4.1 Hardware and Software Setup 10.4.2 Image Acquisition 10.4.3 Data Preprocessing and Augmentation 10.4.4 Training 10.5 Experiments and Results 10.6 Conclusion and Future Scope References 11 Recognition of Facial Expressions in Infrared Images for Lie Detection With the Use of Support Vector Machines 11.1 Introduction 11.1.2 Different Available Machine Learning Algorithms 11.2 Background and Literature Review 11.3 Methodology 11.3.1 IR Image Acquisition 11.3.2 Face Detection 11.3.3 Feature Extraction 11.3.4 Classification 11.3.5 Libraries and Packages of SVM 11.4 Results and Discussions 11.5 Conclusion and Future Work References 12 Support Vector Machines for the Classification of Remote Sensing Images: A Review 12.1 Introduction 12.2 Motivation of the Review 12.3 Relevant Literature Review 12.4 Conclusion and Future Work References 13 A Study On Data Cleaning of Hydrocarbon Resources Under Deep Sea Water Using Imputation Technique-Based Data Science ... 13.1 Introduction 13.2 Literature Survey 13.3 Overview of Data Science 13.3.1 Data Science Life Cycle Phases 13.4 Data Cleaning Or Data Wrangling Processes 13.5 Methodology 13.5.1 Imputation-Based Algorithms 13.5.1.1 Imputation Using Mean 13.5.1.2 Imputation Using Median 13.5.1.3 Imputation Using KNN 13.5.1.4 Imputation Using Decision Tree 13.5.2 Root Mean Square Error (RMSE) 13.5.3 Correlation 13.5.4 K-Nearest Neighbor Algorithm Pseudo-Code for KNN Algorithm 13.6 Results and Discussions 13.7 Conclusion and Future Work References Index