Integrated prediction and control of mobility changes of young talents in the field of science and technology based on convolutional neural network
As the scientific and technological levels continue to rise, the dynamics of young talent within these fields are increasingly significant. Currently, there is a lack of comprehensive models for predicting the movement of young professionals in science and technology. To address this gap, this study...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024019819 |
| _version_ | 1797267738997030912 |
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| author | Lianfeng Xia Fanshuai Meng |
| author_facet | Lianfeng Xia Fanshuai Meng |
| author_sort | Lianfeng Xia |
| collection | DOAJ |
| description | As the scientific and technological levels continue to rise, the dynamics of young talent within these fields are increasingly significant. Currently, there is a lack of comprehensive models for predicting the movement of young professionals in science and technology. To address this gap, this study introduces an integrated approach to forecasting and managing the flow of these talents, leveraging the power of convolutional neural networks (CNNs). The performance test of the proposed method shows that the prediction accuracy of this method is 76.98%, which is superior to the two comparison methods. In addition, the results showed that the average error of the model was 0.0285 lower than that of the model based on the recurrent prediction error (RPE) algorithm learning algorithm, and the average time was 41.6 s lower than that of the model based on the backpropagation (BP) learning algorithm. In predicting the flow of young talent, the study uses flow characteristics including personal characteristics, occupational characteristics, organizational characteristics and network characteristics. Through the above results, the study found that convolutional neural network can effectively use these features to predict the flow of young talents, and its model is superior to other commonly used models in processing speed and accuracy. The above results indicate that the model can provide organizations and government agencies with useful information about the flow trend of young talents, and help them to formulate better talent management strategies. |
| first_indexed | 2024-03-07T22:01:09Z |
| format | Article |
| id | doaj.art-69c2f61bdb5740c287c875ad5153b217 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-25T01:21:22Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-69c2f61bdb5740c287c875ad5153b2172024-03-09T09:26:39ZengElsevierHeliyon2405-84402024-02-01104e25950Integrated prediction and control of mobility changes of young talents in the field of science and technology based on convolutional neural networkLianfeng Xia0Fanshuai Meng1Henan Polytechnic, Zhengzhou, 450046, China; Mongolian University of Life Sciences, Ulaanbaatar, 17024, Mongolia; Corresponding author.Henan Polytechnic, Zhengzhou, 450046, China; Mongolian University of Life Sciences, Ulaanbaatar, 17024, MongoliaAs the scientific and technological levels continue to rise, the dynamics of young talent within these fields are increasingly significant. Currently, there is a lack of comprehensive models for predicting the movement of young professionals in science and technology. To address this gap, this study introduces an integrated approach to forecasting and managing the flow of these talents, leveraging the power of convolutional neural networks (CNNs). The performance test of the proposed method shows that the prediction accuracy of this method is 76.98%, which is superior to the two comparison methods. In addition, the results showed that the average error of the model was 0.0285 lower than that of the model based on the recurrent prediction error (RPE) algorithm learning algorithm, and the average time was 41.6 s lower than that of the model based on the backpropagation (BP) learning algorithm. In predicting the flow of young talent, the study uses flow characteristics including personal characteristics, occupational characteristics, organizational characteristics and network characteristics. Through the above results, the study found that convolutional neural network can effectively use these features to predict the flow of young talents, and its model is superior to other commonly used models in processing speed and accuracy. The above results indicate that the model can provide organizations and government agencies with useful information about the flow trend of young talents, and help them to formulate better talent management strategies.http://www.sciencedirect.com/science/article/pii/S2405844024019819Convolutional neural networkScientific and technological personnelFlow changeCombination prediction methodControl measure |
| spellingShingle | Lianfeng Xia Fanshuai Meng Integrated prediction and control of mobility changes of young talents in the field of science and technology based on convolutional neural network Heliyon Convolutional neural network Scientific and technological personnel Flow change Combination prediction method Control measure |
| title | Integrated prediction and control of mobility changes of young talents in the field of science and technology based on convolutional neural network |
| title_full | Integrated prediction and control of mobility changes of young talents in the field of science and technology based on convolutional neural network |
| title_fullStr | Integrated prediction and control of mobility changes of young talents in the field of science and technology based on convolutional neural network |
| title_full_unstemmed | Integrated prediction and control of mobility changes of young talents in the field of science and technology based on convolutional neural network |
| title_short | Integrated prediction and control of mobility changes of young talents in the field of science and technology based on convolutional neural network |
| title_sort | integrated prediction and control of mobility changes of young talents in the field of science and technology based on convolutional neural network |
| topic | Convolutional neural network Scientific and technological personnel Flow change Combination prediction method Control measure |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024019819 |
| work_keys_str_mv | AT lianfengxia integratedpredictionandcontrolofmobilitychangesofyoungtalentsinthefieldofscienceandtechnologybasedonconvolutionalneuralnetwork AT fanshuaimeng integratedpredictionandcontrolofmobilitychangesofyoungtalentsinthefieldofscienceandtechnologybasedonconvolutionalneuralnetwork |