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.
Author(s): Indranath Chatterjee, Rajanish K. Kamat, Sheetal S. Zalte-Gaikwad
Series: Innovations in Big Data and Machine Learning
Publisher: CRC Press
Year: 2022
Language: English
Pages: 216
City: Boca Raton
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
