{"title":"利用数据挖掘和深度学习技术预测研究成果","authors":"Amber Urooj, H. Khan, Saqib Iqbal, Q. Althebyan","doi":"10.1109/SNAMS53716.2021.9732153","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"On Prediction of Research Excellence using Data Mining and Deep Learning Techniques\",\"authors\":\"Amber Urooj, H. Khan, Saqib Iqbal, Q. Althebyan\",\"doi\":\"10.1109/SNAMS53716.2021.9732153\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":387260,\"journal\":{\"name\":\"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SNAMS53716.2021.9732153\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNAMS53716.2021.9732153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.