TY - JOUR
T1 - Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network
AU - Rajagopal, Navaneetha Krishnan
AU - Saini, Mankeshva
AU - Huerta-Soto, Rosario
AU - Vílchez-Vásquez, Rosa
AU - Kumar, J. N.V.R.Swarup
AU - Gupta, Shashi Kant
AU - Perumal, Sasikumar
N1 - Publisher Copyright:
© 2022 Navaneetha Krishnan Rajagopal et al.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85137215970&partnerID=8YFLogxK
U2 - 10.1155/2022/5613407
DO - 10.1155/2022/5613407
M3 - Article
C2 - 36065368
AN - SCOPUS:85137215970
SN - 1687-5265
VL - 2022
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
M1 - 5613407
ER -