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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sharma, Yashvardhan | - |
dc.date.accessioned | 2024-11-15T09:23:06Z | - |
dc.date.available | 2024-11-15T09:23:06Z | - |
dc.date.issued | 2018-04 | - |
dc.identifier.uri | https://www.degruyter.com/document/doi/10.1515/jisys-2017-0501/html | - |
dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16394 | - |
dc.description.abstract | This paper presents a comprehensive analysis and comparison of various proposed sequential models based on different deep networks such as the convolutional neural network, long short-term memory, and recurrent neural network. The different sequential models are analyzed based on the number of layers, the number of output dimensions, order, and the combination of different deep network architectures. The proposed approach is compared to a baseline model based on traditional machine learning techniques. | en_US |
dc.language.iso | en | en_US |
dc.publisher | De Gruyter | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Spam detection | en_US |
dc.subject | Deep Learning (DL) | en_US |
dc.subject | Machine learning (ML) | en_US |
dc.subject | Fake reviews | en_US |
dc.title | Composite Sequential Modeling for Identifying Fake Reviews | en_US |
dc.type | Article | en_US |
Appears in Collections: | Department of Computer Science and Information Systems |
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