Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network

The rationalization of human resource management is helpful for enterprises to efficiently train talents in the field, improve the management mode, and increase the overall resource utilization rate of enterprises. The current computational models applied in the field of human resources are usually...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Qi Feng, Zixuan Feng, Xingren Su
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/f12c0474f56f494baa27112d06472376
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:f12c0474f56f494baa27112d06472376
record_format	dspace
spelling	oai:doaj.org-article:f12c0474f56f494baa27112d064723762021-11-08T02:36:59ZDesign and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network1687-527310.1155/2021/7149631https://doaj.org/article/f12c0474f56f494baa27112d064723762021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7149631https://doaj.org/toc/1687-5273The rationalization of human resource management is helpful for enterprises to efficiently train talents in the field, improve the management mode, and increase the overall resource utilization rate of enterprises. The current computational models applied in the field of human resources are usually based on statistical computation, which can no longer meet the processing needs of massive data and do not take into account the hidden characteristics of data, which can easily lead to the problem of information scarcity. The paper combines recurrent convolutional neural network and traditional human resource allocation algorithm and designs a double recurrent neural network job matching recommendation algorithm applicable to the human resource field, which can improve the traditional algorithm data training quality problem. In the experimental part of the algorithm, the arithmetic F1 value in the paper is 0.823, which is 20.1% and 7.4% higher than the other two algorithms, respectively, indicating that the algorithm can improve the hidden layer features of the data and then improve the training quality of the data and improve the job matching and recommendation accuracy.Qi FengZixuan FengXingren SuHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Qi Feng Zixuan Feng Xingren Su Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network
description	The rationalization of human resource management is helpful for enterprises to efficiently train talents in the field, improve the management mode, and increase the overall resource utilization rate of enterprises. The current computational models applied in the field of human resources are usually based on statistical computation, which can no longer meet the processing needs of massive data and do not take into account the hidden characteristics of data, which can easily lead to the problem of information scarcity. The paper combines recurrent convolutional neural network and traditional human resource allocation algorithm and designs a double recurrent neural network job matching recommendation algorithm applicable to the human resource field, which can improve the traditional algorithm data training quality problem. In the experimental part of the algorithm, the arithmetic F1 value in the paper is 0.823, which is 20.1% and 7.4% higher than the other two algorithms, respectively, indicating that the algorithm can improve the hidden layer features of the data and then improve the training quality of the data and improve the job matching and recommendation accuracy.
format	article
author	Qi Feng Zixuan Feng Xingren Su
author_facet	Qi Feng Zixuan Feng Xingren Su
author_sort	Qi Feng
title	Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network
title_short	Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network
title_full	Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network
title_fullStr	Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network
title_full_unstemmed	Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network
title_sort	design and simulation of human resource allocation model based on double-cycle neural network
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/f12c0474f56f494baa27112d06472376
work_keys_str_mv	AT qifeng designandsimulationofhumanresourceallocationmodelbasedondoublecycleneuralnetwork AT zixuanfeng designandsimulationofhumanresourceallocationmodelbasedondoublecycleneuralnetwork AT xingrensu designandsimulationofhumanresourceallocationmodelbasedondoublecycleneuralnetwork
_version_	1718443150600568832

Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network

Ejemplares similares