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Open Access

ARTICLE

OTT Messages Modeling and Classification Based on Recurrent Neural Networks

Guangyong Yang1, Jianqiu Zeng1, Mengke Yang2, *, Yifei Wei3, Xiangqing Wang3, Zulfiqar Hussain Pathan4
1 School of Economics & Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
2 School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
3 School of Electronic & Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
4 Mehran University of Engineering & Technology, Jamshoro, Pakistan.
* Corresponding Author: Mengke Yang. Email: .

Computers, Materials & Continua 2020, 63(2), 769-785. https://doi.org/10.32604/cmc.2020.07528

Received 31 May 2019; Accepted 02 August 2019; Issue published 01 May 2020

Abstract

A vast amount of information has been produced in recent years, which brings a huge challenge to information management. The better usage of big data is of important theoretical and practical significance for effectively addressing and managing messages. In this paper, we propose a nine-rectangle-grid information model according to the information value and privacy, and then present information use policies based on the rough set theory. Recurrent neural networks were employed to classify OTT messages. The content of user interest is effectively incorporated into the classification process during the annotation of OTT messages, ending with a reliable trained classification model. Experimental results showed that the proposed method yielded an accurate classification performance and hence can be used for effective distribution and control of OTT messages.

Keywords

OTT messages, information privacy, nine-rectangle-grid, hierarchical classification, recurrent neural networks.

Cite This Article

G. Yang, J. Zeng, M. Yang, Y. Wei, X. Wang et al., "Ott messages modeling and classification based on recurrent neural networks," Computers, Materials & Continua, vol. 63, no.2, pp. 769–785, 2020.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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