@Article{cmc.2020.07528,
AUTHOR = {Guangyong Yang, Jianqiu Zeng, Mengke Yang, Yifei Wei, Xiangqing Wang, Zulfiqar Hussain Pathan},
TITLE = {OTT Messages Modeling and Classification Based on Recurrent  Neural Networks},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {769--785},
URL = {http://www.techscience.com/cmc/v63n2/38543},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.07528}
}