Automatic MOOC Video Classification using Transcript Features and Convolutional Neural Networks-Reference-Cited by-同舟云学术

Automatic MOOC Video Classification using Transcript Features and Convolutional Neural Networks

Published:2017-10-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2017 ACM Workshop on Multimedia-based Educational and Knowledge Technologies for Personalized and Social Online Training
language:
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Author:

Chatbri Houssem¹,McGuinness Kevin¹,Little Suzanne¹,Zhou Jiang²,Kameyama Keisuke³,Kwan Paul⁴,O'Connor Noel E.¹

Affiliation:

1. Dublin City University, Dublin, Ireland

2. Dublin City University, Dublin, Japan

3. University of Tsukuba, Tsukuba, Japan

4. University of New England, Armidale, Australia

Funder

Irish Research Council for Science, Engineering and Technology

Science Foundation Ireland

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3132390.3132393

Reference29 articles.

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2. Olaoluwa Adigun and Bart Kosko. 2017. Using noise to speed up video classification with recurrent backpropagation International Joint Conference on Neural Networks (IJCNN). IEEE 108--115. Olaoluwa Adigun and Bart Kosko. 2017. Using noise to speed up video classification with recurrent backpropagation International Joint Conference on Neural Networks (IJCNN). IEEE 108--115.

3. Understanding the MOOCs continuance: The role of openness and reputation

4. Subhasree Basu Yi Yu and Roger Zimmermann. 2016. Fuzzy clustering of lecture videos based on topic modeling International Workshop on Content-Based Multimedia Indexing (CBMI). IEEE 1--6. Subhasree Basu Yi Yu and Roger Zimmermann. 2016. Fuzzy clustering of lecture videos based on topic modeling International Workshop on Content-Based Multimedia Indexing (CBMI). IEEE 1--6.

5. Automatic Video Classification: A Survey of the Literature

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