Multimodal Analytics for Next-Generation Big Data Technologies and Applications

• Corporate Author: SpringerLink (Online service)
• Other Authors: Seng, Kah Phooi (Editor, http://id.loc.gov/vocabulary/relators/edt), Ang, Li-minn (Editor, http://id.loc.gov/vocabulary/relators/edt), Liew, Alan Wee-Chung (Editor, http://id.loc.gov/vocabulary/relators/edt), Gao, Junbin (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Subjects: Artificial intelligence.; Artificial Intelligence.
• Physical Description: XV, 391 p. 150 illus., 109 illus. in color. online resource.
• ISBN: 9783319975986
• DOI: 10.1007/978-3-319-97598-6

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.