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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16394
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dc.contributor.authorSharma, Yashvardhan-
dc.date.accessioned2024-11-15T09:23:06Z-
dc.date.available2024-11-15T09:23:06Z-
dc.date.issued2018-04-
dc.identifier.urihttps://www.degruyter.com/document/doi/10.1515/jisys-2017-0501/html-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16394-
dc.description.abstractThis 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.isoenen_US
dc.publisherDe Gruyteren_US
dc.subjectComputer Scienceen_US
dc.subjectSpam detectionen_US
dc.subjectDeep Learning (DL)en_US
dc.subjectMachine learning (ML)en_US
dc.subjectFake reviewsen_US
dc.titleComposite Sequential Modeling for Identifying Fake Reviewsen_US
dc.typeArticleen_US
Appears in Collections:Department of Computer Science and Information Systems

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