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DC Field | Value | Language |
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dc.contributor.author | Tiwari, Dimple | - |
dc.date.accessioned | 2025-05-19T10:44:00Z | - |
dc.date.available | 2025-05-19T10:44:00Z | - |
dc.date.issued | 2025 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/abstract/document/10911051 | - |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/1965 | - |
dc.description.abstract | : Email spam detection has become a serious issue in contemporary communication systems as a result of the proliferation of unwanted emails. Traditional methods often fall short of the ever- evolving tactics employed by spammers. Deep learning methods provide promising avenues to raise the effectiveness and precision of spam identi- fication systems. This paper looks at the current advancements in the usage of deep learning algo- rithms for email spam identification. We provide experimental data, review relevant literature, dis- cuss techniques, and render conclusions about the efficacy of deep learning in reducing unsolicited emails | en_US |
dc.publisher | An analytical review of Email Spam Recognition Using Deep Learning | en_US |
dc.title | Advancement in Electronics & Communication Engineering | en_US |
dc.type | Article | en_US |
Appears in Collections: | VSE&T |
Files in This Item:
File | Description | Size | Format | |
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Dimple.docx | 119.01 kB | Microsoft Word XML | View/Open |
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