Danda B. Rawat - Convergence of Cloud with AI for Big Data Analytics : Foundations and Innovation

1. Chapter 1
2. Chapter 2
3. Chapter 3
4. c04
5. Chapter 5
6. Chapter 7
7. Chapter 8
8. Chapter 10
9. Chapter 11
10. Chapter 12
11. Chapter 13
12. Chapter 14
13. Chapter 15
14. Chapter 16
15. Chapter 17

1. Chapter 1
2. Chapter 2
3. Chapter 3
4. c04
5. Chapter 5
6. Chapter 6
7. Chapter 7
8. Chapter 8
9. Chapter 9
10. Chapter 10
11. Chapter 11
12. Chapter 12
13. Chapter 13
14. Chapter 14
15. Chapter 15
16. Chapter 16
17. Chapter 17

Edited by

Danda B. Rawat
Lalit K Awasthi
Valentina Emilia Balas
Mohit Kumar

and

Jitendra Kumar Samriya

This book was written to discuss the milestones in the development of three recent domains in computer science engineeringCloud Computing, Artificial Intelligence and Big Data Analyticsand to analyse the convergence of cloud computing with artificial intelligence for big data analytics. Despite the fact that all three domains work separately, they can be linked in interesting ways. However, even though AI and big data can be easily linked, because AI needs a huge amount of data to train the model, they still suffer from a data storage issue. This drawback can be addressed with the help of cloud computing, which makes it possible to provide on- demand services to the client in terms of computer resources, such as storage and computing power, without the need for user management. This book aims to provide the scope of research on the discussed technologies.

The 17 chapters of the book cover the intertwining concepts of three key levels that are of interest to the scientific community:

1. Artificial Intelligence
2. Big Data
3. Cloud Computing

A chapter-wise breakdown of the contents of the book follows:

• discusses the integration of artificial intelligence, big data and cloud computing with the internet of things (IoT).
• discusses cloud computing and virtualization.
• presents a time and cost-effective multi-objective scheduling technique for cloud computing environment.
• discusses cloud-based architecture for effective surveillance and diagnosis of COVID-19.
• presents smart agriculture applications using cloud and the IoT.
• presents applications of federated learning in computing technologies.
• analyzes the application of edge computing in smart healthcare.
• discusses a smart agriculture application using Fog-IoT.
• presents a systematic study of the global impact of COVID-19 on the IoT.
• discusses efficient solar energy management using IoT-enabled Arduino-based MPPT techniques.
• presents an axiomatic analysis of pre-processing methodologies using machine learning in text mining from the perspective of social media in the IoT.
• presents an app-based agriculture information system for rural farmers in India.
• provides a systematic survey on AI-enabled cyber-physical systems in healthcare.
• discusses an artificial neural network (ANN) aware methanol detection approach with CuO-doped SnO2 in gas sensor.
• describes how to detect heart arrhythmias using deep learning algorithms.
• presents an artificial intelligence approach for signature detection.
• compares various classification models using machine learning to predict the price range of mobile phones.

Writing this book has been a rewarding experience, which was enhanced by the tremendous effort of a team of very dedicated contributors. We would like to thank the authors for their respective chapters and also express our thanks to the list of editors who provided suggestions to improve content delivery. All feedback was considered, and there is no doubt that some of the content was influenced by their suggestions. We especially would like to thank the publisher, who believed in the content and provided a platform to reach the intended audience. Finally, we are thankful to our families for their continued support. Without them, the book would not have been possible.

The Editors

October 2022