A New Approach for Counting and Identification of Students Sentiments in Online Virtual Environments Using Convolutional Neural Networks

Author:

Hernández-Aguilar José AlbertoORCID,Hernández YasmínORCID,Rivera Cruz Lizmary,Bonilla Robles Juan Carlos

Publisher

Springer Nature Switzerland

Reference18 articles.

1. Almog, U.: Yolov3 Explained (2020). https://towardsdatascience.com/yolo-v3-explained-ff5b850390f. Accessed 14 Oct 2023

2. Liu, S., Zhao, Y., Xue, F., Chen, B., Chen, X.: DeepCount: Crowd counting with WiFi via deep learning. (2019). arXiv preprint arXiv:1903.05316 . Accessed 15 Oct 2023

3. Hardjono, B., Tjahyadi, H., Rhizma, M.G., Widjaja, A.E., Kondorura, R., Halim, A.M.: Vehicle counting quantitative comparison using background subtraction, viola jones and deep learning methods. In: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 556–562. IEEE (2018)

4. Mehta, D., Siddiqui, M.F.H., Javaid, A.Y.: Facial emotion recognition: a survey and real-world user experiences in mixed reality. Sensors 18(2), 416 (2018)

5. DeepFace: Deepface 0.0.79. (2023). https://pypi.org/project/deepface/. Accessed 16 Oct 2023

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