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- W2969878825 abstract "Recently the Online Social Networks(OSN) are effectively utilized by the people, organizations, and communities etc for both commercial and non- commercial choices. To gain some fun or monetary benefit spammers create fake users to manipulate the matter. So detection of spam users has critical challenging issue for research communities. To tackle with this issue, present methodologies depend on discrete features of the user attributes such as age Name, Age, Gender, Location etc,. Most of the time these techniques fail to detect fake users in OSNs.In the proposed methodology the algorithm, learns the behavior of the normal OSN user and trains such a way that to distinguish the fake/spam user using the various neural network strategies such as FFNN, FBNN etc, to identify fake user and yield the result. Normally, the user behaviours are specifically highlighted to distinguish the fake and normal user. In view of this Social Networks datasets are considered for the out comes on own network and inter network experiments state that our proposed strategy achieves more efficiency." @default.
- W2969878825 created "2019-08-29" @default.
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- W2969878825 date "2019-08-01" @default.
- W2969878825 modified "2023-10-16" @default.
- W2969878825 title "A Forged User Detection Technique For Online Social Networks Based On The Self Detailed Attribute Assessment Using Neural Networks" @default.
- W2969878825 hasPublicationYear "2019" @default.
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