利用数据挖掘和深度学习技术预测研究成果

Amber Urooj, H. Khan, Saqib Iqbal, Q. Althebyan
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引用次数: 1

摘要

科学计量学分析科学、技术和创新。它测量和分析科学文献。本研究的目的是预测研究人员的优秀性,并检验科学计量指标之间的关系。本文采用数据挖掘技术来研究研究成果。本研究使用的数据集由406名研究人员的数据组成,这些数据是从MathSciNet (MSN)数据库中提取的。数据挖掘分类算法,如朴素贝叶斯,决策树,随机森林,支持向量机,逻辑回归和深度学习应用于数据集,以预测研究的卓越性。并在一些性能指标的基础上对这些算法的性能进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Prediction of Research Excellence using Data Mining and Deep Learning Techniques
Scientometrics analyses the science, technology and innovation. It measures and analyses the scientific literature. The goal of our research is to predict excellence of the researchers and examine the relationship between scientometric indicators. Data Mining Techniques are used to study research excellence in this paper. A dataset used in this research study consisted of 406 researcher's data which is extracted from MathSciNet (MSN) databases. Data mining classification algorithms like Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and Deep Learning are applied on the dataset for the prediction of research excellence. The performance of these algorithms is also compared on the basis of some performance measures.
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