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- W4385726401 abstract "In a traditional classroom-based learning system, it is easy to observe students and their reactions to a certain topic being explained during a session through their facial expressions. This helps the instructor to react accordingly by re-explaining a certain point or addressing a specific student. However, this is not the case when it comes to online lecturing since the instructor is required to monitor several students’ video streams while managing all the controls needed to produce an illustrative lecture. Our proposed system tackles this by providing real-time feedback about the student’s emotions to the teacher, keeping the lecturer updated every specific period throughout an online class. This would help the lecturers adjust their teaching performance according to the state of the majority of the class or address the needs of a specific student. Our proposed methodology combines multiple stages of unsupervised and supervised machine learning. The proposed model is Convolutional Neural Network (CNN) based which leverages both accuracy and speed and is deployed in a real-time framework that enables fast and accurate real-time output. Experiments are carried out on the Acted Facial Expressions In The Wild (AFEW) and the Extended Cohn-Kanade (CK+) datasets to determine the recognition accuracy for the proposed FER system. The system reached an accuracy score of 97.96% on CK+ and 96.12% on the AFEW." @default.
- W4385726401 created "2023-08-11" @default.
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- W4385726401 date "2022-12-17" @default.
- W4385726401 modified "2023-09-30" @default.
- W4385726401 title "A Deep Learning-based Emotion Recognition System for Interactive E-Learning" @default.
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- W4385726401 doi "https://doi.org/10.1109/iccta58027.2022.10206184" @default.
- W4385726401 hasPublicationYear "2022" @default.
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