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- W4292572359 abstract "As a result of both pandemics and the advantage of a remote examination, job interviews have become popular and necessary. These systems are being used by the majority of companies and academic institutions for both recruitment and online exams. In this study, a new virtual assessment system uses deep learning to continuously monitor. Physical locations without the use of a live proctor, however, conducting exams in a safe environment are one of the main disadvantages of remote examination systems. In this work, there is a pipeline for an online interview and exam fraud analysis. Only a video of the candidate is required by the system, which is recorded during the exam. Face detection, recognition, object detection, and tracking algorithms are part of this work. The proposed work focus on techniques for identifying malpractices during online exam." @default.
- W4292572359 created "2022-08-22" @default.
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- W4292572359 date "2022-01-01" @default.
- W4292572359 modified "2023-09-26" @default.
- W4292572359 title "A Novel Framework for Malpractice Detection in Online Proctoring Environment" @default.
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- W4292572359 doi "https://doi.org/10.1007/978-981-19-2894-9_20" @default.
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