The rapid expansion of online education has heightened concerns about cyberbullying in virtual classrooms. This paper presents a comprehensive approach for detecting cyberbullying by analyzing students’ engagement and emotional responses in online classrooms. Due to the influence of camera resolution and surrounding light in online classrooms, students’ facial expressions are often blurry, and the changes in facial features may not be significant. Moreover, most current recognition algorithms utilize larger models, which may not be suitable for real-time detection in online environments. To address these challenges, this study introduces a student facial expression recognition (SFER) method based on an enhanced YOLOv5 (you only look once version 5) model, termed SFER-YOLOv5. Firstly, the improved soft-non-maximum suppression (NMS) is employed to replace the original NMS, effectively enhancing training efficiency. Then, the coordinate attention module is incorporated into the backbone network to improve detection accuracy, particularly in classroom settings with multiple students or when students are at a considerable distance from the camera. Next, the efficient intersection over union (EIoU) loss function is utilized. EIoU calculates width and height losses separately based on complete-IoU, replacing the aspect ratio. Finally, focal loss is introduced to address sample imbalance issues. The comparative results show that SFER-YOLOv5 achieves an mAP@0.5 of 78.4% on the facial expression recognition-2013 dataset, 98.1% on the CK+ dataset, and 88.9% on our self-constructed dataset student facial expressions in classroom (SFEC). These results underscore the effectiveness of SFER-YOLOv5 in enhancing the accuracy of SFER. The proposed method detects reduced engagement, offering a preventive strategy for mitigating cyberbullying in virtual learning environments.

Tongue diagnosis is an important foundation of the syndrome difference. The segmentation of the body of tongue is a premise to establishing a system of automatic diagnosis by the feature of tongue in Traditional Chinese Medicine (TCM), whose qualities affect on the performance of tongue diagnosis. Because of similar color between tongue body and background, it is difficult to segment tongue. In order to overcome two key difficulties with the initialization and boundary concavities (important to the analysis of tooth's marks), a novel method for tongue contour extraction based on improved level set curve evolution is proposed. We present an automatic initialization of contour by the feature of tongue in the HSV color space. Improved level set method takes not only color information but also tongue contour shape constraint represented by energy function between the evolving curve and parametric shape model. Applying our method to the large database of tongue images, we achieve promising experimental results.