Efficient Detection of Large-Scale Multimedia Network Information Data Anomalies Based on the Rule-Extracting Matrix Algorithm

With the continuous development of multimedia social networks, online public opinion information is becoming more and more popular. The rule extraction matrix algorithm can effectively improve the probability of information data to be tested. The network information data abnormality detection is rea...

Full description

Saved in:
Bibliographic Details
Published in:Advances in multimedia 2021-11, Vol.2021, p.1-7
Main Author: Zhao, Jie
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Big Data
Conditional probability
Learning
Mathematical analysis
Multimedia
Online instruction
Social networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the continuous development of multimedia social networks, online public opinion information is becoming more and more popular. The rule extraction matrix algorithm can effectively improve the probability of information data to be tested. The network information data abnormality detection is realized through the probability calculation, and the prior probability is calculated, to realize the detection of abnormally high network data. Practical results show that the rule-extracting matrix algorithm can effectively control the false positive rate of sample data, the detection accuracy is improved, and it has efficient detection performance.
ISSN:1687-5680
1687-5699
DOI:10.1155/2021/3299891