基于集成分类和回归神经网络的组织单元角色任务评价

IF 1.7 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
M. Abbod, A. Alrashedi
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引用次数: 0

摘要

在本文中,我们研究了组织中的用户被赋予不同的角色是多么容易,以及使用预测分析工具确保任务顺利完成是多么重要。因此,采用分类回归树链接神经网络的集成来评价与组织单元关联的基于角色的任务的有效性。人力资源管理系统的设计和发展,以获得全面的信息,他们的员工的表现水平,以及确定他们的能力,技能,他们执行的任务,以及他们如何执行这些任务。从系统评估中提取数据集并用于机器学习评估。线性回归模型、决策树和遗传算法已被证明在所有情况下都能很好地预测。通过这种方式,研究结果强调了确保用户任务及时完成的必要性,以及提高组织分配个人职责的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Ensemble Classification and Regression Neural Network for Evaluating Role-based Tasks Associated with Organizational Unit
In this paper, we have looked at how easy it is for users in an organisation to be given different roles, as well as how important it is to make sure that the tasks are done well using predictive analytical tools. As a result, ensemble of classification and regression tree link Neural Network was adopted for evaluating the effectiveness of role-based tasks associated with organization unit. A Human Resource Manangement System was design and developed to obtain comprehensive information about their employees’ performance levels, as well as to ascertain their capabilities, skills, and the tasks they perform and how they perform them. Datasets were drawn from evaluation of the system and used for machine learning evaluation. Linear regression models, decision trees, and Genetic Algorithm have proven to be good at prediction in all cases. In this way, the research findings highlight the need of ensuring that users tasks are done in a timely way, as well as enhancing an organization’s ability to assign individual duties.
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
22
审稿时长
4 weeks
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