@Article{cmc.2020.010082,
AUTHOR = {Weijin Jiang, Fang Ye, Wei Liu, Xiaoliang Liu, Guo Liang, Yuhui Xu, Lina Tan},
TITLE = {Research on Prediction Methods of Prevalence Perception under  Information Exposure},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2263--2275},
URL = {http://www.techscience.com/cmc/v65n3/40168},
ISSN = {1546-2226},
ABSTRACT = {With the rapid development of information technology, the explosive growth 
of data information has become a common challenge and opportunity. Social network 
services represented by WeChat, Weibo and Twitter, drive a large amount of information 
due to the continuous spread, evolution and emergence of users through these platforms. 
The dynamic modeling, analysis, and network information prediction, has very important 
research and application value, and plays a very important role in the discovery of
popular events, personalized information recommendation, and early warning of bad 
information. For these reasons, this paper proposes an adaptive prediction algorithm for 
network information transmission. A popularity prediction algorithm is designed to 
control the transmission trend based on the gray Verhulst model to analyze the law of 
development and capture popular trends. Experimental simulations show that the 
proposed perceptual prediction model in this paper has a better fitting effect than the 
existing models.},
DOI = {10.32604/cmc.2020.010082}
}