Abstract
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.
Highlights
With the continuous development of multimedia social networks, online public opinion information has become increasingly popular. e dissemination of information has become a new trend with the rapid increase of hot topics among the general public, such as Baidu Hot Search and Weibo Hot Search [1,2,3]
For the collected multimedia network information data center, the filtered input vector is obtained through Fourier transformation, and the input vector is set as a stable vector input to achieve multimedia network interference filtering
If the data of the multimedia network information are constructed based on linearly correlated time series, interference suppression is achieved through the following filtering model, as shown in the following formula: xn a0 + a0xn−i + bj, (21)
Summary
With the continuous development of multimedia social networks, online public opinion information has become increasingly popular. e dissemination of information has become a new trend with the rapid increase of hot topics among the general public, such as Baidu Hot Search and Weibo Hot Search [1,2,3]. With the continuous development of multimedia social networks, online public opinion information has become increasingly popular. This kind of multimedia network is a double-edged sword. It can convey both positive and negative information. Erefore, how to effectively detect such abnormal network information has become an urgent problem to be solved. E detection of network format data is realized through filtering, and the detection of network information data anomaly is realized through probability calculation based on the rule-extracting matrix algorithm. E value of the prior probability is calculated in an attempt, and the network data abnormality is detected, aiming to explore the detection of abnormal network data performance
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
