Synergistic Interaction of Big Data with Cloud Computing for Industry 4.0 - Zalte-Gaikwad, Sheetal S.; Chatterjee, Indranath; Kamat, Rajanish K.; (ed.) - Prospero Internet Bookshop
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Synergistic Interaction of Big Data with Cloud Computing for Industry 4.0

 
Edition number: 1
Publisher: CRC Press
Date of Publication:
 
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Short 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.

Long description:

The idea behind this book is to simplify the journey of aspiring readers and researchers to 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 that can be used in solutions for day-to-day problems. It focuses on the two frontiers ? Big Data and Cloud Computing ? and 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. The book unites some of the top Big Data experts throughout the world who contribute their knowledge and expertise on the different aspects, approaches, and concepts related to new technologies and novel findings. Based on the latest technologies, the book offers case studies and covers 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 more advanced learners understand both basic and more complex aspects of the synergy between Big Data and Cloud Computing.


Table of Contents:

1. Big Data Based on Fuzzy Time-Series Forecasting for Stock Index Prediction.


2. Big Data-Based Time-Series Forecasting Using FbProphet for Stock Index.


3. The Impact Of Artificial Intelligence and Big Data in the Postal Sector.


4. Advances in Cloud Technologies and Future Trends.


5. Reinforcement of the Multi-Cloud Infrastructure with Edge Computing.


6. Study and Investigation of PKI-Based Blockchain Infrastructure.


7. Stock Index Forecasting Using Stacked Long Short-Term Memory (LSTM): Deep Learning and Big Data.


8. A Comparative Study and Analysis of Time-Series and Deep Learning Algorithms for Bitcoin Price Prediction.


9. Machine Learning for Healthcare.


10. Transfer Learning and Fine-Tuning-Based Early Detection of Cotton Plant Disease.


11. Recognition of Facial Expressions of Infrared Images for Lie Detection with the Use of Support Vector Machines.


12. Support Vector Machines for the Classification of Remote Sensing Images: A Review.


13. A Study on Data Cleaning of Hydrocarbon Resources under Deep Sea Water Using Imputation Technique-Based Data Science Approaches.