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Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network
Business development is dependent on a well-structured human resources (HR) system that maximizes the efficiency of an organization's human resources input and output. It is tough to provide adequate instructions for HR's unique task. In a time when the domestic labor market is still matur...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440777/ https://www.ncbi.nlm.nih.gov/pubmed/36065368 http://dx.doi.org/10.1155/2022/5613407 |
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author | Rajagopal, Navaneetha Krishnan Saini, Mankeshva Huerta-Soto, Rosario Vílchez-Vásquez, Rosa Kumar, J. N. V. R. Swarup Gupta, Shashi Kant Perumal, Sasikumar |
author_facet | Rajagopal, Navaneetha Krishnan Saini, Mankeshva Huerta-Soto, Rosario Vílchez-Vásquez, Rosa Kumar, J. N. V. R. Swarup Gupta, Shashi Kant Perumal, Sasikumar |
author_sort | Rajagopal, Navaneetha Krishnan |
collection | PubMed |
description | Business development is dependent on a well-structured human resources (HR) system that maximizes the efficiency of an organization's human resources input and output. It is tough to provide adequate instructions for HR's unique task. In a time when the domestic labor market is still maturing, it is difficult for companies to make successful adjustments in HR structures to meet fluctuations in demand for human resources caused by shifting corporate strategies, operations, and size. Data on corporate human resources are often insufficient or inaccurate, which creates substantial nonlinearity and uncertainty when attempting to predict staffing needs, since human resource demand is influenced by numerous variables. The aim of this research is to predict the human resource demand using novel methods. Recurrent neural networks (RNNs) and grey wolf optimization (GWO) are used in this study to develop a new quantitative forecasting method for HR demand prediction. Initially, we collect the dataset and preprocess using normalization. The features are extracted using principal component analysis (PCA) and the proposed RNN with GWO effectively predicts the needs of HR. Moreover, organizations may be able to estimate personnel demand based on current circumstances, making forecasting more relevant and adaptive and enabling enterprises to accomplish their objectives via efficient human resource planning. |
format | Online Article Text |
id | pubmed-9440777 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-94407772022-09-04 Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network Rajagopal, Navaneetha Krishnan Saini, Mankeshva Huerta-Soto, Rosario Vílchez-Vásquez, Rosa Kumar, J. N. V. R. Swarup Gupta, Shashi Kant Perumal, Sasikumar Comput Intell Neurosci Research Article Business development is dependent on a well-structured human resources (HR) system that maximizes the efficiency of an organization's human resources input and output. It is tough to provide adequate instructions for HR's unique task. In a time when the domestic labor market is still maturing, it is difficult for companies to make successful adjustments in HR structures to meet fluctuations in demand for human resources caused by shifting corporate strategies, operations, and size. Data on corporate human resources are often insufficient or inaccurate, which creates substantial nonlinearity and uncertainty when attempting to predict staffing needs, since human resource demand is influenced by numerous variables. The aim of this research is to predict the human resource demand using novel methods. Recurrent neural networks (RNNs) and grey wolf optimization (GWO) are used in this study to develop a new quantitative forecasting method for HR demand prediction. Initially, we collect the dataset and preprocess using normalization. The features are extracted using principal component analysis (PCA) and the proposed RNN with GWO effectively predicts the needs of HR. Moreover, organizations may be able to estimate personnel demand based on current circumstances, making forecasting more relevant and adaptive and enabling enterprises to accomplish their objectives via efficient human resource planning. Hindawi 2022-08-27 /pmc/articles/PMC9440777/ /pubmed/36065368 http://dx.doi.org/10.1155/2022/5613407 Text en Copyright © 2022 Navaneetha Krishnan Rajagopal et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Rajagopal, Navaneetha Krishnan Saini, Mankeshva Huerta-Soto, Rosario Vílchez-Vásquez, Rosa Kumar, J. N. V. R. Swarup Gupta, Shashi Kant Perumal, Sasikumar Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network |
title | Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network |
title_full | Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network |
title_fullStr | Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network |
title_full_unstemmed | Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network |
title_short | Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network |
title_sort | human resource demand prediction and configuration model based on grey wolf optimization and recurrent neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440777/ https://www.ncbi.nlm.nih.gov/pubmed/36065368 http://dx.doi.org/10.1155/2022/5613407 |
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