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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorGarcia Bernardo, Javier
dc.contributor.authorFranssen, Joep
dc.date.accessioned2022-09-09T01:04:53Z
dc.date.available2022-09-09T01:04:53Z
dc.date.issued2022
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/42493
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectIn the academic field, researchers are expected to publish papers on a frequent basis to stay relevant. This focus on ‘quantity production’ negatively affects the research quality and results in more erroneous studies. Published erroneous studies can have severe consequences as it could contribute to harmful changes in health policies or other sensitive domains. We aimed at tackling this problem by building a shallow and deep learning model that can detect erroneous research.
dc.titleFraud detection using deep learning models
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.courseuuApplied Data Science
dc.thesis.id9183


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