Fraud detection using deep learning models
dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Garcia Bernardo, Javier | |
dc.contributor.author | Franssen, Joep | |
dc.date.accessioned | 2022-09-09T01:04:53Z | |
dc.date.available | 2022-09-09T01:04:53Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/42493 | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | In 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.title | Fraud detection using deep learning models | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.courseuu | Applied Data Science | |
dc.thesis.id | 9183 |