Student Behavioral Analysis Using Computer Vision

Sharma, Aastha; Mohanty, Debasis; Ijaz, Muhammad Fazal; Rana, Abu ul Hassan S.

doi:10.1007/978-981-16-2911-2_28

Cited by 1 publication

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References 19 publications

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“…Schools can set up MHC courses and integrate MHC and subject teaching to create a healthy campus culture. Meanwhile, establishing an MHC room or an MHC network integrating society, family, and school encourages the healthy development of MHC to work for students under PSE [ 9 ]. MHC must be designed to improve both students' and teachers' mental health, establish students' mental health files, and build a comprehensive mental health network [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation

Liu

2022

Computational Intelligence and Neuroscience

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The purpose was to timely identify the mental disorders (MDs) of students receiving primary and secondary education (PSE) (PSE students) and improve their mental quality. Firstly, this work analyzes the research status of the mental health model (MHM) and the main contents of PSE student-oriented mental health quality cultivation under deep learning (DL). Secondly, an MHM is implemented based on big data technology (BDT) and the convolutional neural network (CNN). Simultaneously, the long short-term memory (LSTM) is introduced to optimize the proposed MHM. Finally, the performance of the MHM before and after optimization is evaluated, and the PSE student-oriented mental health quality training strategy based on the proposed MHM is offered. The results show that the accuracy curve is higher than the recall curve in all classification algorithms. The maximum recall rate is 0.58, and the minimum accuracy rate is 0.62. The decision tree (DT) algorithm has the best comprehensive performance among the five different classification algorithms, with accuracy of 0.68, recall rate of 0.58, and F1-measure of 0.69. Thus, the DT algorithm is selected as the classifier. The proposed MHM can identify 56% of students with MDs before optimization. After optimization, the accuracy is improved by 0.03. The recall rate is improved by 0.19, the F1-measure is improved by 0.05, and 75% of students with MDs can be identified. Diverse behavior data can improve the recognition effect of students’ MDs. Meanwhile, from the 60th iteration, the mode accuracy and loss tend to be stable. By comparison, batch_size has little influence on the experimental results. The number of convolution kernels of the first convolution layer has little influence. The proposed MHM based on DL and CNN will indirectly improve the mental health quality of PSE students. The research provides a reference for cultivating the mental health quality of PSE students.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Student Behavioral Analysis Using Computer Vision

Cited by 1 publication

References 19 publications

Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation

Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation

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