Design and Application of Intelligent Work Stress Assessment System Based on Machine Learning

Aiming at the problems such as inaccurate evaluation of teachers in the traditional job stress evaluation system, this paper uses deep neural network algorithm to design an intelligent job stress evaluation system based on machine learning. This paper first introduces the main module division of the...

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Veröffentlicht in:2024 Second International Conference on Data Science and Information System (ICDSIS) S. 1 - 4
Hauptverfasser: He, Haiping, Yang, Yi
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 17.05.2024
Schlagworte:
Accuracy
Artificial neural networks
Data models
Deep Neural Network Algorithm
Feature Extraction
Intelligent Work Stress Evaluation System
Machine learning
Machine learning algorithms
Prediction algorithms
Training
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Abstract Aiming at the problems such as inaccurate evaluation of teachers in the traditional job stress evaluation system, this paper uses deep neural network algorithm to design an intelligent job stress evaluation system based on machine learning. This paper first introduces the main module division of the system, and then describes the data preparation and the construction and training process of the deep neural network algorithm model. Finally, the performance of the system designed in this paper is explored through comparative experiments. The experimental results show that the average accuracy of the system is 95.92 \%, the recall rate is 90.48 \%, and the prediction is 85.36 \%, which is much higher than the performance of the traditional work stress evaluation system. Therefore, the system designed in this paper is helpful to solve the problems caused by teachers' work pressure.
AbstractList Aiming at the problems such as inaccurate evaluation of teachers in the traditional job stress evaluation system, this paper uses deep neural network algorithm to design an intelligent job stress evaluation system based on machine learning. This paper first introduces the main module division of the system, and then describes the data preparation and the construction and training process of the deep neural network algorithm model. Finally, the performance of the system designed in this paper is explored through comparative experiments. The experimental results show that the average accuracy of the system is 95.92 \%, the recall rate is 90.48 \%, and the prediction is 85.36 \%, which is much higher than the performance of the traditional work stress evaluation system. Therefore, the system designed in this paper is helpful to solve the problems caused by teachers' work pressure.
Author He, Haiping
Yang, Yi
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  givenname: Yi
  surname: Yang
  fullname: Yang, Yi
  email: 995132670@qq.com
  organization: SHNU,The College of Information Mechanical and Electrical Engineering,Shanghai,China
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Snippet Aiming at the problems such as inaccurate evaluation of teachers in the traditional job stress evaluation system, this paper uses deep neural network algorithm...
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SubjectTerms Accuracy
Artificial neural networks
Data models
Deep Neural Network Algorithm
Feature Extraction
Intelligent Work Stress Evaluation System
Machine learning
Machine learning algorithms
Prediction algorithms
Training
Title Design and Application of Intelligent Work Stress Assessment System Based on Machine Learning
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