Next Article in Journal
Entropy of the Multi-Channel EEG Recordings Identifies the Distributed Signatures of Negative, Neutral and Positive Affect in Whole-Brain Variability
Previous Article in Journal
Entropy Generation and Heat Transfer in Drilling Nanoliquids with Clay Nanoparticles
Open AccessArticle

Convolutional Recurrent Neural Networks with a Self-Attention Mechanism for Personnel Performance Prediction

by Xia Xue 1,2, Jun Feng 1,3,*, Yi Gao 1, Meng Liu 1, Wenyu Zhang 4,5, Xia Sun 1,*, Aiqi Zhao 1 and Shouxi Guo 1
1
School of Information Science and Technology, Northwest University, Xi’an 710127, China
2
Maths and Information Technology School, Yuncheng University, Yuncheng 044000, China
3
State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Northwest University, Xi’an 710127, China
4
College of Economic and Management, Xi’an University of Posts and Telecommunications, Xi’an 710016, China
5
China Aerospace Academy of Systems Science and Engineering, Beijing 100048, China
*
Authors to whom correspondence should be addressed.
Entropy 2019, 21(12), 1227; https://doi.org/10.3390/e21121227
Received: 22 October 2019 / Revised: 5 December 2019 / Accepted: 13 December 2019 / Published: 16 December 2019
Personnel performance is important for the high-technology industry to ensure its core competitive advantages are present. Therefore, predicting personnel performance is an important research area in human resource management (HRM). In this paper, to improve prediction performance, we propose a novel framework for personnel performance prediction to help decision-makers to forecast future personnel performance and recruit the best suitable talents. Firstly, a hybrid convolutional recurrent neural network (CRNN) model based on self-attention mechanism is presented, which can automatically learn discriminative features and capture global contextual information from personnel performance data. Moreover, we treat the prediction problem as a classification task. Then, the k-nearest neighbor (KNN) classifier was used to predict personnel performance. The proposed framework is applied to a real case of personnel performance prediction. The experimental results demonstrate that the presented approach achieves significant performance improvement for personnel performance compared to existing methods. View Full-Text
Keywords: personnel performance prediction; self-attention mechanism; convolutional neural networks; long short-term memory; cross-entropy personnel performance prediction; self-attention mechanism; convolutional neural networks; long short-term memory; cross-entropy
Show Figures

Figure 1

MDPI and ACS Style

Xue, X.; Feng, J.; Gao, Y.; Liu, M.; Zhang, W.; Sun, X.; Zhao, A.; Guo, S. Convolutional Recurrent Neural Networks with a Self-Attention Mechanism for Personnel Performance Prediction. Entropy 2019, 21, 1227.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop