Edge Computing with Deep Learning and Internet of Things for Recognising and Predicting Students Emotions and Mental Health
Shaymaa Hussein Nowfal
Shaymaa Hussein Nowfal
Medical Physics Department, College of Science, University of Warith Al-Anbiyaa, Karbala, Iraq, Medical Physics Department, College of Applied Medical Sciences, University of Kerbala, Karbala, Iraq.
A person's Mental Health (MH) dramatically influences their complete evolution in life, including their cognitive, emotional, and psychomotor components. A person with good MH is content with life and can be creative, learn new things, and take risks to accomplish more significant objectives. Currently, college students are dealing with MH concerns for various causes, which affect their academic performance and significantly contribute to poor academic results. Therefore, encouraging MH in college students presents a significant problem for educators, parents, teacher educators, and governments. Adolescence is a crucial and delicate time characterized by considerable physical, emotional, social, and religious changes. The physical, social, and psychological facets of an individual's growth are laid out in this period, with mental health as a crucial factor in promoting these gains. Therefore, it becomes crucial for researchers to use Deep Learning (DL) algorithms to study the association between MH and vital psychological characteristics, such as emotional intelligence, personality traits, and intelligence. The personal aspects, namely personality, emotional intelligence, and MH are all related ideas that influence one another. Individuals must have mental well-being and emotional harmony to have a good personality. The current study uses DL techniques to investigate the relationship between college students' MH, emotional intelligence, and personality features. To perform a thorough study on emotion identification and Mental Health Prediction (MHP) among college students, this project investigates the integration of edge computing enabled by the Internet of Things (IoT) in the context of intelligent systems. Innovative treatments are urgently needed due to this population's rising prevalence of MH issues. This paper aims to continuously monitor and predict college students' MH using Edge Computing (EC) and IoT technology.
Keywords
Mental Health, Facial Expression, Higher Education, Emotion, Deep Learning, and Accuracy.
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Firas Tayseer Ayasrah
Firas Tayseer Ayasrah
College of Education, Humanities and Science, Al Ain University, Al Ain, Abu Dhabi, United Arab Emirates.
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Cite this article
Shaymaa Hussein Nowfal, Firas Tayseer Ayasrah, Vijaya Bhaskar Sadu, Jasmine Sowmya V, Subbalakshmi A V V S and Kamal Poon, “Edge Computing with Deep Learning and Internet of Things for Recognising and Predicting Students Emotions and Mental Health”, Journal of Machine and Computing, pp. 1092-1106, October 2024. doi:10.53759/7669/jmc202404101.
