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- W4387068319 abstract "The process of teaching and learning is frequently seen as the most important one in a school. However, manually taking attendance and keeping track of the pupils in a class can be laborious tasks that have a negative impact on the teaching and learning process. Additionally, disobedience on the part of the students such as not paying attention, using a phone, wandering around, and sneaking out of class might exacerbate the problem. An automated system will help the teachers to focus on the main syllabus and students to concentrate on studies. The results that we obtain from the system can be used by the faculty to analyze what number of students are really interested in the subject. If teachers can understand the negative attitudes of their students, they will be able to change the learning environment for the students in more logical ways. To overcome this situation, our aim is to develop an automated system that will mark the attendance and monitor the attentiveness of students. The system records the whole session and identifies whether the student is concentrating in the classroom or not, then reports to the faculties. To check what amount of time students attended the lecture with full attention or without any misbehavior, eye aspect ratio is used." @default.
- W4387068319 created "2023-09-27" @default.
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- W4387068319 date "2023-01-01" @default.
- W4387068319 modified "2023-09-27" @default.
- W4387068319 title "Student Monitoring System Using Deep Learning" @default.
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- W4387068319 doi "https://doi.org/10.1007/978-981-99-2742-5_48" @default.
- W4387068319 hasPublicationYear "2023" @default.
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