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Multimodal Analytics for Next-Generation Big Data Technologies and Applications

Overview of attention for book
Multimodal Analytics for Next-Generation Big Data Technologies and Applications
Springer International Publishing

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Multimodal Information Processing and Big Data Analytics in a Digital World
  3. Altmetric Badge
    Chapter 2 Speaker-Independent Multimodal Sentiment Analysis for Big Data
  4. Altmetric Badge
    Chapter 3 Multimodal Big Data Affective Analytics
  5. Altmetric Badge
    Chapter 4 Hybrid Feature-Based Sentiment Strength Detection for Big Data Applications
  6. Altmetric Badge
    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
  15. Altmetric Badge
    Chapter 14 Parallel and Distributed Computing for Processing Big Image and Video Data
  16. Altmetric Badge
    Chapter 15 Multimodal Approaches in Analysing and Interpreting Big Social Media Data
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27 Mendeley
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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

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 demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 11%
Student > Master 3 11%
Lecturer 2 7%
Researcher 2 7%
Student > Postgraduate 2 7%
Other 2 7%
Unknown 13 48%
Readers by discipline Count As %
Computer Science 8 30%
Engineering 2 7%
Biochemistry, Genetics and Molecular Biology 1 4%
Unknown 16 59%