Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning

Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when re...
Ausführliche Beschreibung

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Autor*in:	Lixia Yang [verfasserIn] Jiao Liu [verfasserIn] Chaohong Liu [verfasserIn] Shaoqing Tian [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments.

Übergeordnetes Werk:	In: International Journal of Emerging Technologies in Learning (iJET) - Kassel University Press, 2017, 17(2022), 23
Übergeordnetes Werk:	volume:17 ; year:2022 ; number:23

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3991/ijet.v17i23.35935

Katalog-ID:	DOAJ085447226

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allfields	10.3991/ijet.v17i23.35935 doi (DE-627)DOAJ085447226 (DE-599)DOAJ23be72d077b949629445f13a03774507 DE-627 ger DE-627 rakwb eng T58.5-58.64 Lixia Yang verfasserin aut Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. Education L Information technology Jiao Liu verfasserin aut Chaohong Liu verfasserin aut Shaoqing Tian verfasserin aut In International Journal of Emerging Technologies in Learning (iJET) Kassel University Press, 2017 17(2022), 23 (DE-627)51597630X (DE-600)2244179-7 18630383 nnns volume:17 year:2022 number:23 https://doi.org/10.3991/ijet.v17i23.35935 kostenfrei https://doaj.org/article/23be72d077b949629445f13a03774507 kostenfrei https://online-journals.org/index.php/i-jet/article/view/35935 kostenfrei https://doaj.org/toc/1863-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2022 23
spelling	10.3991/ijet.v17i23.35935 doi (DE-627)DOAJ085447226 (DE-599)DOAJ23be72d077b949629445f13a03774507 DE-627 ger DE-627 rakwb eng T58.5-58.64 Lixia Yang verfasserin aut Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. Education L Information technology Jiao Liu verfasserin aut Chaohong Liu verfasserin aut Shaoqing Tian verfasserin aut In International Journal of Emerging Technologies in Learning (iJET) Kassel University Press, 2017 17(2022), 23 (DE-627)51597630X (DE-600)2244179-7 18630383 nnns volume:17 year:2022 number:23 https://doi.org/10.3991/ijet.v17i23.35935 kostenfrei https://doaj.org/article/23be72d077b949629445f13a03774507 kostenfrei https://online-journals.org/index.php/i-jet/article/view/35935 kostenfrei https://doaj.org/toc/1863-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2022 23
allfields_unstemmed	10.3991/ijet.v17i23.35935 doi (DE-627)DOAJ085447226 (DE-599)DOAJ23be72d077b949629445f13a03774507 DE-627 ger DE-627 rakwb eng T58.5-58.64 Lixia Yang verfasserin aut Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. Education L Information technology Jiao Liu verfasserin aut Chaohong Liu verfasserin aut Shaoqing Tian verfasserin aut In International Journal of Emerging Technologies in Learning (iJET) Kassel University Press, 2017 17(2022), 23 (DE-627)51597630X (DE-600)2244179-7 18630383 nnns volume:17 year:2022 number:23 https://doi.org/10.3991/ijet.v17i23.35935 kostenfrei https://doaj.org/article/23be72d077b949629445f13a03774507 kostenfrei https://online-journals.org/index.php/i-jet/article/view/35935 kostenfrei https://doaj.org/toc/1863-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2022 23
allfieldsGer	10.3991/ijet.v17i23.35935 doi (DE-627)DOAJ085447226 (DE-599)DOAJ23be72d077b949629445f13a03774507 DE-627 ger DE-627 rakwb eng T58.5-58.64 Lixia Yang verfasserin aut Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. Education L Information technology Jiao Liu verfasserin aut Chaohong Liu verfasserin aut Shaoqing Tian verfasserin aut In International Journal of Emerging Technologies in Learning (iJET) Kassel University Press, 2017 17(2022), 23 (DE-627)51597630X (DE-600)2244179-7 18630383 nnns volume:17 year:2022 number:23 https://doi.org/10.3991/ijet.v17i23.35935 kostenfrei https://doaj.org/article/23be72d077b949629445f13a03774507 kostenfrei https://online-journals.org/index.php/i-jet/article/view/35935 kostenfrei https://doaj.org/toc/1863-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2022 23
allfieldsSound	10.3991/ijet.v17i23.35935 doi (DE-627)DOAJ085447226 (DE-599)DOAJ23be72d077b949629445f13a03774507 DE-627 ger DE-627 rakwb eng T58.5-58.64 Lixia Yang verfasserin aut Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. Education L Information technology Jiao Liu verfasserin aut Chaohong Liu verfasserin aut Shaoqing Tian verfasserin aut In International Journal of Emerging Technologies in Learning (iJET) Kassel University Press, 2017 17(2022), 23 (DE-627)51597630X (DE-600)2244179-7 18630383 nnns volume:17 year:2022 number:23 https://doi.org/10.3991/ijet.v17i23.35935 kostenfrei https://doaj.org/article/23be72d077b949629445f13a03774507 kostenfrei https://online-journals.org/index.php/i-jet/article/view/35935 kostenfrei https://doaj.org/toc/1863-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2022 23
language	English
source	In International Journal of Emerging Technologies in Learning (iJET) 17(2022), 23 volume:17 year:2022 number:23
sourceStr	In International Journal of Emerging Technologies in Learning (iJET) 17(2022), 23 volume:17 year:2022 number:23
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topic_facet	Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. Education L Information technology
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container_title	International Journal of Emerging Technologies in Learning (iJET)
authorswithroles_txt_mv	Lixia Yang @@aut@@ Jiao Liu @@aut@@ Chaohong Liu @@aut@@ Shaoqing Tian @@aut@@
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callnumber-first	T - Technology
author	Lixia Yang
spellingShingle	Lixia Yang misc T58.5-58.64 misc Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. misc Education misc L misc Information technology Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning
authorStr	Lixia Yang
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topic_title	T58.5-58.64 Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments
topic	misc T58.5-58.64 misc Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. misc Education misc L misc Information technology
topic_unstemmed	misc T58.5-58.64 misc Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. misc Education misc L misc Information technology
topic_browse	misc T58.5-58.64 misc Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments. misc Education misc L misc Information technology
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title	Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning
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title_full	Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning
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title_sort	quality evaluation of order-based talent training in internationalized enterprises based on machine learning
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title_auth	Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning
abstract	Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments.
abstractGer	Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments.
abstract_unstemmed	Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments.
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title_short	Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning
url	https://doi.org/10.3991/ijet.v17i23.35935 https://doaj.org/article/23be72d077b949629445f13a03774507 https://online-journals.org/index.php/i-jet/article/view/35935 https://doaj.org/toc/1863-0383
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