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Intelligent Attendance System Using Artificial Neural Network Based on Students’ Background

机译:基于学生和rsquo的人工神经网络智能考勤系统;背景

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Determining the rate of student attendance is an important task in determining the completion of the courses. Despite the success of the technology, it is unfortunate that in many academic institutions, the current systems used to detect student absences. Furthermore, one of the crucial problems in the attendance system does not count student background for continuing in the courses. In this study, we propose an intelligent approach for calculating student attendance based on their Grade Point Average (GPA) and their activities, this approach uses Artificial Neural Network (ANN) for proposing an intelligent attendance system to calculate the attendance rating accurately, meaning the system provide a new rating for each student based on their background. The aim of this research is developing an attendance system for motivation students taking attendance or taking high grade in the class. The result of this approach helps the instructor to allow students who have more activities with more absents to continue in the courses, if not the students have low activity should taking high attendance. This system will more efficient for monitoring students in the classes and replacing absent to activity.
机译:确定学生出勤率是确定课程完成时的重要任务。尽管技术成功,但在许多学术机构中,仍有遗憾的是,目前用于检测学生缺席的目前的系统。此外,出勤系统中的一个关键问题之一不计数学生背景以继续课程。在这项研究中,我们提出了一种基于其成绩点平均值(GPA)及其活动来计算学生出席的智能方法,这种方法采用人工神经网络(ANN)来提出精确计算出勤率的考勤额定值,意味着系统为每个学生提供了基于其背景的新评级。本研究的目的正在开发出席学生参加或在课堂上获得高年级的主动学生的出勤制度。这种方法的结果有助于教师允许在课程中继续在课程中具有更多活动的学生,如果不是学生的活动很低,那么应该高举。该系统更有效地监控课程中的学生并缺席活动。

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