Analyzing the Impact of Personalized Emotion Recognition on Academic Success of University Students Using AVO and HO-MWNN Neural Models
DOI:
https://doi.org/10.31838/NJAP/08.02.12Keywords:
classification, emotional intelligence, emotion recognition, university students, physical activityAbstract
In education, emotions play pivotal role, comparable in significance to environmental factors and the language used for information transmission. Their impact is integral to cognitive processes governing learning and the assimilation of new knowledge. However, maintaining a grasp on students' emotions poses a challenge for educators due to issues such as disciplinary constraints or the inherent difficulty in tracking the subtle indicators of internal emotions among a diverse student body. It is important to understand the link between emotional intelligence and academic success and well-being. This research proposes a personalized emotion recognition model tailored for university students, aiming to comprehend their engagement levels and evaluate the effectiveness and utility of the implemented or prospective system. This research work utilized MobileNet to extracts facial features and relevant features were identified by African Vulture Optimization algorithm. High order multi wavelet neural network were used for classification to enhance model accuracy. The datasets CK+, FER-2013, and JAFFE experimented by AVO-HO-MWNN. This study aims to highligten the emotional intelligence of university students.
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