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Multimodal Analytics for Next-Generation Big Data Technologies and Applications

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Cover of 'Multimodal Analytics for Next-Generation Big Data Technologies and Applications'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    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
Attention for Chapter 6: Bi-clustering by Multi-objective Evolutionary Algorithm for Multimodal Analytics and Big Data
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Citations

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Chapter title
Bi-clustering by Multi-objective Evolutionary Algorithm for Multimodal Analytics and Big Data
Chapter number 6
Book title
Multimodal Analytics for Next-Generation Big Data Technologies and Applications
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-319-97598-6_6
Book ISBNs
978-3-31-997597-9, 978-3-31-997598-6
Authors

Maryam Golchin, Alan Wee-Chung Liew, Golchin, Maryam, Liew, Alan Wee-Chung

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 50%
Student > Doctoral Student 2 50%
Readers by discipline Count As %
Computer Science 3 75%
Business, Management and Accounting 1 25%

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