Publication:
YOLOv5 Based Student Engagement and Emotional States Detection in E-Classes

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dc.citedby0
dc.contributor.authorWang S.en_US
dc.contributor.authorShibghatullah A.S.en_US
dc.contributor.authorKeoy K.H.en_US
dc.contributor.authorIqbal J.en_US
dc.contributor.authorid58984450100en_US
dc.contributor.authorid24067964300en_US
dc.contributor.authorid14054280900en_US
dc.contributor.authorid58563731500en_US
dc.date.accessioned2025-03-03T07:46:17Z
dc.date.available2025-03-03T07:46:17Z
dc.date.issued2024
dc.description.abstractThe rapid expansion of E-learning environments has highlighted the critical issue of cyberbullying within digital classrooms. This study introduces a novel approach for early detection of cyberbullying by analyzing student engagement and emotional states in real time. Our SER-YOLO model fuses an advanced You Only Look Once version 5 (YOLOv5) with a Student Emotion Recognition system, enriched by sophisticated methodological improvements. It features Soft NMS to refine the Non-Maximum Suppression (NMS) process, embeds the Channel Attention (CA) module to augment the network's backbone, and employs Enhanced Intersection over Union (EIOU) for bounding box regression. This method proactively detects changes in student engagement and emotional states, providing an effective mechanism for the early detection and management of cyberbullying in E-learning environments. ? 2022 The Author.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.57417/jrnal.10.4_357
dc.identifier.epage361
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85204723100
dc.identifier.spage357
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85204723100&doi=10.57417%2fjrnal.10.4_357&partnerID=40&md5=6cf1c2464ba3c661b3d7520fc710405b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36978
dc.identifier.volume10
dc.pagecount4
dc.publisherALife Robotics Corporation Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Robotics, Networking and Artificial Life
dc.titleYOLOv5 Based Student Engagement and Emotional States Detection in E-Classesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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