Pariksha Nirikshak: Anti Cheating System for examination Hall
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2025
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| author | Miss.Sonawane S.M, Patel Alisha, Ambale Yashswini, Shirole Suruchi |
| author_facet | Miss.Sonawane S.M, Patel Alisha, Ambale Yashswini, Shirole Suruchi |
| contents | <p>The educational system is facing a growing problem with the proliferation of exam cheating as a result of<br>new forms of electronic communication and enjoyment. Most students nowadays are too busy worrying<br>about getting a passing grade to put in the time and effort necessary to really prepare for the test. Classical<br>exam surveillance has become outdated as a result of the emergence of multiple cheating strategies. This<br>means that automated cheating case identification using cutting-edge tech is an absolute need. By utilizing<br>deep learning and computer vision techniques to analyze the student's posture in real-time, this research<br>presents an anti-cheating strategy that focuses on behavior analysis. To do this, we employ YOLO-Faciallandmark and media pipe models to extract domain information from video frames at a high level. The<br>next step in predicting cases of cheating is to employ a decision tree classification model. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15243289 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Pariksha Nirikshak: Anti Cheating System for examination Hall Miss.Sonawane S.M, Patel Alisha, Ambale Yashswini, Shirole Suruchi <p>The educational system is facing a growing problem with the proliferation of exam cheating as a result of<br>new forms of electronic communication and enjoyment. Most students nowadays are too busy worrying<br>about getting a passing grade to put in the time and effort necessary to really prepare for the test. Classical<br>exam surveillance has become outdated as a result of the emergence of multiple cheating strategies. This<br>means that automated cheating case identification using cutting-edge tech is an absolute need. By utilizing<br>deep learning and computer vision techniques to analyze the student's posture in real-time, this research<br>presents an anti-cheating strategy that focuses on behavior analysis. To do this, we employ YOLO-Faciallandmark and media pipe models to extract domain information from video frames at a high level. The<br>next step in predicting cases of cheating is to employ a decision tree classification model. </p> |
| title | Pariksha Nirikshak: Anti Cheating System for examination Hall |
| url | https://doi.org/10.5281/zenodo.15243289 |