Convolutional Recurrent Neural Networks with a Self-Attention Mechanism for Personnel Performance Prediction Article Swipe
Xue Xia
,
Jun Feng
,
Yi Gao
,
Meng Liu
,
Wenyu Zhang
,
Xia Sun
,
Aiqi Zhao
,
Shouxi Guo
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/e21121227
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/e21121227
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.
Related Topics
Concepts
Computer science
Machine learning
Artificial intelligence
Convolutional neural network
Performance improvement
Classifier (UML)
Performance prediction
Artificial neural network
Task (project management)
Recurrent neural network
Mechanism (biology)
Simulation
Operations management
Economics
Management
Epistemology
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/e21121227
- https://www.mdpi.com/1099-4300/21/12/1227/pdf?version=1576483344
- OA Status
- gold
- Cited By
- 20
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2996255653
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2996255653Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/e21121227Digital Object Identifier
- Title
-
Convolutional Recurrent Neural Networks with a Self-Attention Mechanism for Personnel Performance PredictionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-12-16Full publication date if available
- Authors
-
Xue Xia, Jun Feng, Yi Gao, Meng Liu, Wenyu Zhang, Xia Sun, Aiqi Zhao, Shouxi GuoList of authors in order
- Landing page
-
https://doi.org/10.3390/e21121227Publisher landing page
- PDF URL
-
https://www.mdpi.com/1099-4300/21/12/1227/pdf?version=1576483344Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1099-4300/21/12/1227/pdf?version=1576483344Direct OA link when available
- Concepts
-
Computer science, Machine learning, Artificial intelligence, Convolutional neural network, Performance improvement, Classifier (UML), Performance prediction, Artificial neural network, Task (project management), Recurrent neural network, Mechanism (biology), Simulation, Operations management, Economics, Management, Epistemology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 4, 2023: 3, 2022: 5, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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