Overview
Explains multimodality data analytics in big data environments
Important techniques applied to image and speech processing, multimodal information processing, data science, and artificial intelligence
Valuable for researchers, professionals and students in engineering, and computer science
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (15 chapters)
-
Introduction
-
Sentiment, Affect and Emotion Analysis for Big Multimodal Data
-
Unsupervised Learning Strategies for Big Multimodal Data
-
Supervised Learning Strategies for Big Multimodal Data
-
Multimodal Big Data Processing and Applications
Keywords
About this book
This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments. After an introduction, the editors organize the book into four main parts on sentiment, affect and emotion analytics for big multimodal data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data processing and applications.
The book will be of value to researchers, professionals and students in engineering and computer science, particularly those engaged with image and speech processing, multimodal information processing, data science, and artificial intelligence.
Editors and Affiliations
Bibliographic Information
Book Title: Multimodal Analytics for Next-Generation Big Data Technologies and Applications
Editors: Kah Phooi Seng, Li-minn Ang, Alan Wee-Chung Liew, Junbin Gao
DOI: https://doi.org/10.1007/978-3-319-97598-6
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-319-97597-9Published: 30 July 2019
eBook ISBN: 978-3-319-97598-6Published: 18 July 2019
Edition Number: 1
Number of Pages: XV, 391
Number of Illustrations: 41 b/w illustrations, 109 illustrations in colour
Topics: Artificial Intelligence