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首页> 外文会议>IEEE International Conference in MOOCs, Innovation and Technology in Education >A decision support system approach for accreditation quality assurance council at higher education institutions in Yemen
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A decision support system approach for accreditation quality assurance council at higher education institutions in Yemen

机译：也门高等教育机构认证与质量保证委员会的决策支持系统方法

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Classification is used for discovery of a predictive learning function that classifies data item into one of several predefined classes. e.g., classify universities based on students number or based on offered programs, or classify cars based on gas mileage, and Presentation it by decision-tree, classification rule, neural network, and genetic algorithms, etc. It is noted that, there are large amount of data obtained from the universities. We need to evaluate the accurate assessment of the performance of any institution. Currently the decision in ministry of higher education and scientific research is taken randomly, not based on logical analysis. Moreover, the education towards to universal, the ministry of higher education in Yemen has to activate the council of accreditation, and support it to start quickly and effectively. In this paper, we have used approach to assist the council for accreditation to start automation for accreditation operations and mechanisms, that's proposed by using machine learning techniques, also to help decision makers for taking accurate and swift decisions. Our study is used to classify institution that wants to take a license from council to three classes: grant license of accreditation, grand license provided to improvement has done, or not granting accreditation license. We have used the intelligent algorithms for calculate probability of grand accreditation license based on degree of council standards, which we used to predict through a model building to classification by using Naïve Bayes algorithm. The proposed method is typically for evaluation the new institution by depends on evaluation of existing institutions. We experiment the proposed framework by flexible parameters and attributes with private training dataset, that's carefully generated and tested using real life applications. In addition, we implemented our proposed approach as a program.
机译：分类用于发现预测学习功能，该功能将数据项分类为几个预定义类别之一。例如，根据学生人数或所提供的程序对大学进行分类，或者根据加油里程对汽车进行分类，并通过决策树，分类规则，神经网络和遗传算法进行显示。从大学获得的数据量。我们需要评估对任何机构绩效的准确评估。目前，高等教育和科学研究部的决定是随机作出的，而不是基于逻辑分析。此外，也门的高等教育部要普及教育，必须激活认证委员会，并支持其迅速有效地启动。在本文中，我们使用了机器学习技术来协助认证委员会启动认证操作和机制的自动化，还可以帮助决策者做出准确，迅速的决策。我们的研究用于将希望从理事会获得执照的机构分为三类：授予认证执照，为改善工作提供的盛大执照或不授予认证执照。我们已经使用了智能算法来根据理事会标准的程度来计算总体认可许可的概率，我们使用该算法通过建立模型，使用朴素贝叶斯算法进行分类来进行预测。所提出的方法通常用于评估新机构，具体取决于对现有机构的评估。我们使用私有训练数据集通过灵活的参数和属性对建议的框架进行了实验，该数据集是使用现实生活中的应用程序精心生成和测试的。此外，我们将建议的方法作为一个程序来实施。

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