Evidence map›Paper›PMID 36188706›Full record

ArticleComputational intelligence and neuroscience2022

Security Analysis of Social Network Topic Mining Using Big Data and Optimized Deep Convolutional Neural Network.

Kunzhi Tang, Chengang Zeng, Yuxi Fu, Gang Zhu

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kunzhi TangCollege of Engineering & Computer Science, The Australian National University, Canberra 2615, Australia.
Chengang ZengSchool of International Education, Zhejiang Normal University, Jinhua 321004, China.ORCID https://orcid.org/0000-0002-4179-1035
Yuxi FuDepartment of Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai 519087, China.
Gang ZhuChinese Academy of International Trade and Economic Cooperation, Beijing 100000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research aims to conduct topic mining and data analysis of social network security using social network big data. At present, the main problem is that users' behavior on social networks may reveal their private data. The main contribution lies in the establishment of a network security topic detection model combining Convolutional Neural Network (CNN) and social network big data technology. Deep Convolution Neural Network (DCNN) is utilized to complete the analysis and search of social network security issues. The Long Short-Term Memory (LSTM) algorithm is used for the extraction of Weibo topic information in the memory wisdom. Experimental results show that the recognition accuracy of the constructed model can reach 96.17% after 120 iterations, which is at least 5.4% higher than other models. Additionally, the accuracy, recall, and

Indexed as

Big DataNeural Networks, ComputerAlgorithmsSocial Networking

Identifiers

PMID36188706
PMCPMC9525195

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.