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Machine learning technique for facial datasets to detect examination fraudulent activities in the online examination: a systematic review approach

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dc.contributor.author Nafrees, Abdul Cader Mohamed
dc.contributor.author Sasubilli, Durgaprasad
dc.contributor.author Bharathi, B.
dc.contributor.author Komma, Siva Saikumar Reddy
dc.date.accessioned 2023-04-12T06:29:29Z
dc.date.available 2023-04-12T06:29:29Z
dc.date.issued 2022-12
dc.identifier.citation International Conference on Current Development in Engineering and Technology, 2022, Sage University, India. en_US
dc.identifier.isbn 978-1-6654-5415-5
dc.identifier.uri http://ir.lib.seu.ac.lk/handle/123456789/6620
dc.description.abstract Online Examination (OE) is the most challenging part of E-Learning (EL) since there is no proper mechanism provided to reduce the OE's fraudulent activities by the students. All the previous research provided different methods to avoid this issue, but those techniques could not be applied due to a few drawbacks of those methods. Recent studies suggested using facial recognition with Machine learning (ML) applications to reduce OE malpractices. This systematic review confirmed that Convolutional Neural Networks (CNN) can be applied to identify students' facial recognition with the help of the CK+ dataset compared to other ML techniques and datasets. Furthermore, future research can be conducted to develop an automated OE proctoring system in real-time. It is noted that this study could not be included a few more recent study results due to no funding. Also, there are no studies found related to this study for comparison of ML techniques and datasets. en_US
dc.language.iso en_US en_US
dc.publisher SAGE University en_US
dc.subject CNN en_US
dc.subject Machine learning en_US
dc.subject Online examination en_US
dc.subject E-learning en_US
dc.subject Malpractices en_US
dc.title Machine learning technique for facial datasets to detect examination fraudulent activities in the online examination: a systematic review approach en_US
dc.type Article en_US


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    THESE ARE RESEARCH ARTICLES OF ACADEMIC STAFF, PUBLISHED IN JOURNALS AND PROCEEDINGS ELSWHERE

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