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Computing for Data Analysis: Theory and Practices
Author
Title
Computing for Data Analysis: Theory and Practices [electronic resource] / by Sanjay Chakraborty, Lopamudra Dey.
ISBN
9789811980046
Edition
1st ed. 2023.
Publication
Singapore : Springer Nature Singapore : Imprint: Springer, 2023.
Physical Description
1 online resource (XVIII, 222 p.) 91 illus., 69 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
This book covers various cutting-edge computing technologies and their applications over data. It discusses in-depth knowledge on big data and cloud computing, quantum computing, cognitive computing, and computational biology with respect to different kinds of data analysis and applications. In this book, authors describe some interesting models in the cloud, quantum, cognitive, and computational biology domains that provide some useful impact on intelligent data (emotional, image, etc.) analysis. They also explain how these computing technologies based data analysis approaches used for various real-life applications. The book will be beneficial for readers working in this area.
Variant and related titles
Springer ENIN.
Other formats
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Format
Books / Online
Language
English
Added to Catalog
February 22, 2023
Series
Data-Intensive Research,
Contents
Part 1. Introduction
Chapter 1. Introduction
Part 2. Integration of Cloud, Internet of Things, Virtual Reality and Big Data Analytics
Chapter 2. Impact of Big Data and Cloud Computing on Data Analysis
Chapter 3. Edge Computing with Internet of Things (IoT) and Data Analysis
Chapter 4. Virtual and Augmented Reality with Embedded Systems
Part 3. Biological Applications of Data Analytics
Chapter 5. Computational Biology towards Data Analysis
Chapter 6. Data Classification through Cognitive Computing
Part 4. Quantum Computing for Data Analysis
Chapter 7. Quantum Computing in Machine Learning
Chapter 8. Quantum Computing in Image Processing
Part 5. Computations for Various Data Applications and Future
Chapter 9. Challenges and Future Research Directions on Data Computation .
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