A Computational Complexity-Based Method for Predicting Scholars’ Ages through Articles’ Information
Many scholars have conducted in-depth research on the evaluation and prediction of scholars’ scientific impact and meanwhile discovered various factors that affect the success of scholars. Among all these relevant factors, scholars’ ages have been universally acknowledged as one of the most importan...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6648863 |
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| _version_ | 1832550117453856768 |
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| author | Jun Zhang Xiaoyan Su Mingliang Hou Jing Ren |
| author_facet | Jun Zhang Xiaoyan Su Mingliang Hou Jing Ren |
| author_sort | Jun Zhang |
| collection | DOAJ |
| description | Many scholars have conducted in-depth research on the evaluation and prediction of scholars’ scientific impact and meanwhile discovered various factors that affect the success of scholars. Among all these relevant factors, scholars’ ages have been universally acknowledged as one of the most important factors for it can shed light on many practical issues, e.g., finding supervisors, discovering rising stars, and research funding or award applications. However, due to the inaccessibility or the privacy issues of acquiring scholars’ personal data, there is little research to explore the true ages of scholars currently. Alternatively, scholars’ publications’ information can be obtained through various digital libraries. Inspired by this fact, we propose a novel scholar’s age prediction method based on their articles’ information. Our method first classifies factors that affect scholars’ ages into intuitive and complex types according to their computational complexity and then apply machine learning algorithms to predict the ages of scholars based on these factors. The experimental results on the real dataset demonstrate that our method can effectively predict the true ages of scholars. Given that there is no completely accurate dataset because of the continuous publication of academic papers, we then apply our method on the incomplete dataset. Nevertheless, our method still has high prediction accuracy in such situations. |
| format | Article |
| id | doaj-art-0f3902ff0956467c9b235274dc722e55 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0f3902ff0956467c9b235274dc722e552025-02-03T06:07:36ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66488636648863A Computational Complexity-Based Method for Predicting Scholars’ Ages through Articles’ InformationJun Zhang0Xiaoyan Su1Mingliang Hou2Jing Ren3Graduate School of Education, Dalian University of Technology, Dalian 116024, ChinaSchool of Economics and Management, Dalian University of Technology, Dalian 116024, ChinaSchool of Software, Dalian University of Technology, Dalian 116620, ChinaSchool of Software, Dalian University of Technology, Dalian 116620, ChinaMany scholars have conducted in-depth research on the evaluation and prediction of scholars’ scientific impact and meanwhile discovered various factors that affect the success of scholars. Among all these relevant factors, scholars’ ages have been universally acknowledged as one of the most important factors for it can shed light on many practical issues, e.g., finding supervisors, discovering rising stars, and research funding or award applications. However, due to the inaccessibility or the privacy issues of acquiring scholars’ personal data, there is little research to explore the true ages of scholars currently. Alternatively, scholars’ publications’ information can be obtained through various digital libraries. Inspired by this fact, we propose a novel scholar’s age prediction method based on their articles’ information. Our method first classifies factors that affect scholars’ ages into intuitive and complex types according to their computational complexity and then apply machine learning algorithms to predict the ages of scholars based on these factors. The experimental results on the real dataset demonstrate that our method can effectively predict the true ages of scholars. Given that there is no completely accurate dataset because of the continuous publication of academic papers, we then apply our method on the incomplete dataset. Nevertheless, our method still has high prediction accuracy in such situations.http://dx.doi.org/10.1155/2021/6648863 |
| spellingShingle | Jun Zhang Xiaoyan Su Mingliang Hou Jing Ren A Computational Complexity-Based Method for Predicting Scholars’ Ages through Articles’ Information Complexity |
| title | A Computational Complexity-Based Method for Predicting Scholars’ Ages through Articles’ Information |
| title_full | A Computational Complexity-Based Method for Predicting Scholars’ Ages through Articles’ Information |
| title_fullStr | A Computational Complexity-Based Method for Predicting Scholars’ Ages through Articles’ Information |
| title_full_unstemmed | A Computational Complexity-Based Method for Predicting Scholars’ Ages through Articles’ Information |
| title_short | A Computational Complexity-Based Method for Predicting Scholars’ Ages through Articles’ Information |
| title_sort | computational complexity based method for predicting scholars ages through articles information |
| url | http://dx.doi.org/10.1155/2021/6648863 |
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