Generative and non-parametric model for real-time event detection in social networks based on textual analysis

被引:0
作者
Aziziansiadar, Masoumeh [1 ]
机构
[1] Tehran Univ Appl Sci, Sharif Acad Jahad, Tehran, Iran
来源
NEXO REVISTA CIENTIFICA | 2023年 / 36卷 / 03期
关键词
Event detection; social networks; generator; text analysis; non-parametric; real time; ANOMALY DETECTION; ABNORMALITY DETECTION; NEURAL-NETWORKS; LOCALIZATION;
D O I
10.5377/nexo.v36i03.16462
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
One of the things that is followed in monitoring systems is the detection of rare events in real time among the multitude of common events in social networks. Considering the lack of recognition and unavailability of rare events, their detection is considered a challenge. In this research, a new architecture and approach based on generative adversarial network infrastructure was presented to detect common and rare events in real time. In this research, the attempt is to provide a new approach to the performance of architectures based on deep generative adversarial networks, a way to solve various problems without supervision with a semi-supervisory approach and adversarial generative infrastructure. This architecture is based on the automatic extraction and use of video input data features. The results of the equal error rate in the UCSDped1 and UCSDped2 datasets were 2.0 and 17.0, respectively, in the performance characteristic curve.
引用
收藏
页码:404 / 421
页数:18
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