Aurora et al.（2020）提出了一种基于机器学习的学生请假管理系统设计。该系统采用机器学习算法对学生请假信息进行分析和预测，帮助学校决策者制定更好的请假政策。该系统提高了请假管理的智能化和决策性

Aurora et al. (2020) proposed a machine learning-based student leave management system design. The system uses machine learning algorithms to analyze and predict student leave information, helping school decision-makers develop better leave policies. The system improves the intelligence and decision-making of leave management.

The machine learning algorithms in the system can learn from historical data and identify patterns and trends in student leave requests. This information can then be used to predict the likelihood of a student's leave request being approved or denied. The system can also identify factors that may be contributing to high rates of absenteeism, such as illness or family issues, allowing schools to address these issues and provide targeted support to students who need it.

By automating the analysis of student leave data, the system can help school administrators make more informed decisions about leave policies and procedures. This can lead to more consistent and fair application of leave policies, and can also help reduce the administrative burden on school staff.

Overall, the machine learning-based student leave management system proposed by Aurora et al. has the potential to improve the efficiency and effectiveness of leave management in schools, and could ultimately lead to better outcomes for students.