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Multimodal Analytics for Next-Generation Big Data Technologies and Applications

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Table of Contents

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    Book Overview
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    Chapter 1 Multimodal Information Processing and Big Data Analytics in a Digital World
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    Chapter 2 Speaker-Independent Multimodal Sentiment Analysis for Big Data
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    Chapter 3 Multimodal Big Data Affective Analytics
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    Chapter 4 Hybrid Feature-Based Sentiment Strength Detection for Big Data Applications
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    Chapter 5 Multimodal Co-clustering Analysis of Big Data Based on Matrix and Tensor Decomposition
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    Chapter 6 Bi-clustering by Multi-objective Evolutionary Algorithm for Multimodal Analytics and Big Data
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    Chapter 7 Unsupervised Learning on Grassmann Manifolds for Big Data
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    Chapter 8 Multi-product Newsvendor Model in Multi-task Deep Neural Network with Norm Regularization for Big Data
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    Chapter 9 Recurrent Neural Networks for Multimodal Time Series Big Data Analytics
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    Chapter 10 Scalable Multimodal Factorization for Learning from Big Data
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    Chapter 11 Big Multimodal Visual Data Registration for Digital Media Production
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    Chapter 12 A Hybrid Fuzzy Football Scenes Classification System for Big Video Data
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    Chapter 13 Multimodal Big Data Fusion for Traffic Congestion Prediction
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    Chapter 14 Parallel and Distributed Computing for Processing Big Image and Video Data
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    Chapter 15 Multimodal Approaches in Analysing and Interpreting Big Social Media Data
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Title
Multimodal Analytics for Next-Generation Big Data Technologies and Applications
Published by
Springer International Publishing, December 2018
DOI 10.1007/978-3-319-97598-6
ISBNs
978-3-31-997597-9, 978-3-31-997598-6
Editors

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

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X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 11%
Student > Master 3 11%
Lecturer 2 7%
Student > Ph. D. Student 2 7%
Student > Postgraduate 2 7%
Other 3 11%
Unknown 13 46%
Readers by discipline Count As %
Computer Science 8 29%
Engineering 3 11%
Biochemistry, Genetics and Molecular Biology 1 4%
Unknown 16 57%