Multimodal Analytics for Next-Generation Big Data Technologies and Applications

Seng Kah Phooi; Li-Minn Ang; Alan Wee-Chung Liew; Junbin Gao

doi:10.1007/978-3-319-97598-6

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Edited book

Multimodal Analytics for Next-Generation Big Data Technologies and Applications

Seng Kah Phooi, Li-Minn Ang, Alan Wee-Chung Liew and Junbin Gao

Springer Nature

2019

DOI: https://doi.org/10.1007/978-3-319-97598-6

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Abstract

Machine learning

Artificial intelligence

Decision making

Video processing

Image processing

multimodal data

data analytics

big data

information processing

computational intelligence

speech processing

data science

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.

Details

Title: Multimodal Analytics for Next-Generation Big Data Technologies and Applications
Authors: Seng Kah Phooi (Editor) - UNSW Australia
Li-Minn Ang (Editor) - Griffith University
Alan Wee-Chung Liew (Editor) - Griffith University
Junbin Gao (Editor) - University of Sydney
Publication details: 391 pages
Publisher: Springer Nature
DOI: 10.1007/978-3-319-97598-6
ISBN: 9783319975979; 9783319975986
Organisation Unit: Engage Research Lab; School of Science, Technology and Engineering; University of the Sunshine Coast, Queensland
Language: English
Record Identifier: 99613608602621
Output Type: Edited book

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