DSpace logo

Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/8226
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSharma, Yashvardhan-
dc.date.accessioned2023-01-02T10:54:35Z-
dc.date.available2023-01-02T10:54:35Z-
dc.date.issued2018-04-
dc.identifier.urihttps://www.degruyter.com/document/doi/10.1515/jisys-2017-0501/html?lang=en-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8226-
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 Learningen_US
dc.subjectMachine Learningen_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

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.