Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions

Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions...

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Main Authors: Xiaomei Bai, Fuli Zhang, Jie Hou, Feng Xia, Amr Tolba, Elsayed Elashkar
Format: Article
Language:English
Published: IEEE 2017-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8010278/
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author Xiaomei Bai
Fuli Zhang
Jie Hou
Feng Xia
Amr Tolba
Elsayed Elashkar
author_facet Xiaomei Bai
Fuli Zhang
Jie Hou
Feng Xia
Amr Tolba
Elsayed Elashkar
author_sort Xiaomei Bai
collection DOAJ
description Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous studies have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors' influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions.
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spelling doaj.art-b3f07856dc354dd190d0c67dff7d87812022-12-21T18:14:17ZengIEEEIEEE Access2169-35362017-01-015163721638210.1109/ACCESS.2017.27391798010278Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of InstitutionsXiaomei Bai0Fuli Zhang1Jie Hou2Feng Xia3https://orcid.org/0000-0002-8324-1859Amr Tolba4https://orcid.org/0000-0003-3439-6413Elsayed Elashkar5Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, ChinaLibrary, Anshan Normal University, Anshan, ChinaKey Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, ChinaKey Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, ChinaComputer Science Department, Community College, King Saud University, Riyadh, Saudi ArabiaAdministrative Sciences Department, Community College, King Saud University, Riyadh, Saudi ArabiaPredicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous studies have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors' influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions.https://ieeexplore.ieee.org/document/8010278/Scientific impactpredictionfeature selectionmachine learningscientometrics
spellingShingle Xiaomei Bai
Fuli Zhang
Jie Hou
Feng Xia
Amr Tolba
Elsayed Elashkar
Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
IEEE Access
Scientific impact
prediction
feature selection
machine learning
scientometrics
title Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
title_full Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
title_fullStr Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
title_full_unstemmed Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
title_short Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
title_sort implicit multi feature learning for dynamic time series prediction of the impact of institutions
topic Scientific impact
prediction
feature selection
machine learning
scientometrics
url https://ieeexplore.ieee.org/document/8010278/
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