Academic Journal

AI-Based Cheating Detection System Using Computer Vision.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: AI-Based Cheating Detection System Using Computer Vision.
Συγγραφείς: Keerthi, Muppala Naga, sai, Anisetti
Πηγή: International Scientific Journal of Engineering & Management; Jun2026, Vol. 5 Issue 6, p1-9, 9p
Θεματικοί όροι: Computer vision, Artificial intelligence, Human activity recognition, Education ethics
Περίληψη: The AI-Based Cheating Detection System Using Computer Vision is a smart monitoring system designed to identify suspicious activities during online examinations.[1] The system uses a webcam and computer vision techniques to observe the student's face and movements in real time. It can detect actions such as looking away from the screen, multiple faces appearing in the camera, or the absence of a face from the video frame.[3] The captured video is processed using artificial intelligence algorithms to analyze student behavior and identify possible cheating attempts. When suspicious activity is detected, the system generates alerts and records the event for review. This project helps educational institutions conduct fair and secure online examinations with less human supervision. The system is easy to use, cost-effective, and improves the integrity of online assessments. [ABSTRACT FROM AUTHOR]
Copyright of International Scientific Journal of Engineering & Management is the property of International Scientific Journal of Engineering & Management and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: AI-Based Cheating Detection System Using Computer Vision.
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  Data: <searchLink fieldCode="AR" term="%22Keerthi%2C+Muppala+Naga%22">Keerthi, Muppala Naga</searchLink><br /><searchLink fieldCode="AR" term="%22sai%2C+Anisetti%22">sai, Anisetti</searchLink>
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  Data: International Scientific Journal of Engineering & Management; Jun2026, Vol. 5 Issue 6, p1-9, 9p
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  Data: <searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Human+activity+recognition%22">Human activity recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Education+ethics%22">Education ethics</searchLink>
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  Data: The AI-Based Cheating Detection System Using Computer Vision is a smart monitoring system designed to identify suspicious activities during online examinations.[1] The system uses a webcam and computer vision techniques to observe the student's face and movements in real time. It can detect actions such as looking away from the screen, multiple faces appearing in the camera, or the absence of a face from the video frame.[3] The captured video is processed using artificial intelligence algorithms to analyze student behavior and identify possible cheating attempts. When suspicious activity is detected, the system generates alerts and records the event for review. This project helps educational institutions conduct fair and secure online examinations with less human supervision. The system is easy to use, cost-effective, and improves the integrity of online assessments. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of International Scientific Journal of Engineering & Management is the property of International Scientific Journal of Engineering & Management and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.55041/ISJEM08053
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      – Code: eng
        Text: English
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      – SubjectFull: Human activity recognition
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              M: 06
              Text: Jun2026
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              Y: 2026
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