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dc.contributor.advisorSadeque,Farig Yousuf
dc.contributor.authorBiswas, Trisha
dc.contributor.authorLamia, Tasmim Afroj
dc.contributor.authorShykat, Tarikul Islam
dc.contributor.authorRafi, Md. Arifin Ahmed
dc.date.accessioned2024-04-23T05:29:03Z
dc.date.available2024-04-23T05:29:03Z
dc.date.copyright©2023
dc.date.issued2023-05
dc.identifier.otherID: 20101628
dc.identifier.otherID: 19301190
dc.identifier.otherID: 19301008
dc.identifier.otherID: 19301009
dc.identifier.urihttp://hdl.handle.net/10361/22652
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 58-59).
dc.description.abstractDevelopment in information and technology has made the communication easier in the recent decades. Easy access of social media is creating restraints amid of differentiating fake and real news. In the recent period the problem has increased drastically and use of image is making the news more impactful. Even though news websites are publishing the news and provide the source of authentication still there are other portals and platform which intentionally spread fake news to exploit an event. In this paper we proposed a hybrid system where we are combining CNN and RNN to detect fake news . We applied two techniques to reduce the model complexity and increase accuracy based on text data and image. With this system, detecting fake news it’ll stop misleading people and creating an unstable situation as well as taking benefits of the situation.en_US
dc.description.statementofresponsibilityTrisha Biswas
dc.description.statementofresponsibilityTasmim Afroj Lamia
dc.description.statementofresponsibilityTarikul Islam Shykat
dc.description.statementofresponsibilityMd. Arifin Ahmed Rafi
dc.format.extent69 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectNatural language processingen_US
dc.subjectMultinomial naive bayesen_US
dc.subjectBERTen_US
dc.subjectBi-GRUen_US
dc.subjectBi-LSTMen_US
dc.subjectWord embeddingen_US
dc.subjectFake newsen_US
dc.subjectSupport vector machineen_US
dc.subject.lcshDeep learning (Machine learning)
dc.subject.lcshFake news--Prevention--Data processing.
dc.titleMultimodal fake news detection using text and imageen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB.Sc. in Computer Science


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