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Summary

Artificial neural networks and genetic algorithms effectively predict college students' psychological pressure from learning, life, and personal events. This approach aids in timely intervention for student well-being.

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Area of Science:

  • Artificial Intelligence
  • Computational Psychology
  • Machine Learning

Background:

  • Artificial neural networks (ANNs) have gained prominence since the 1980s for their nonlinear parallel processing, learning, and flexibility.
  • Traditional Hopfield network models face limitations with their learning methods.
  • Understanding and predicting college student psychological pressure is crucial for timely intervention.

Purpose of the Study:

  • To identify, evaluate, and predict psychological pressure in college students.
  • To explore the application of ANNs combined with genetic algorithms and Hi-PLS regression for psychological assessment.
  • To improve upon the learning methods of Hopfield network models.

Main Methods:

  • Utilized genetic algorithms to optimize power values and triggers within the Hopfield network model.
  • Employed backpropagation (BP) through mobile human-computer interaction equipment.
  • Integrated hereditary algorithms and Hi-PLS regression with ANNs for predictive analysis.

Main Results:

  • Successfully identified, evaluated, and predicted psychological pressure across learning, life, and personal event dimensions.
  • Achieved predictive accuracy with test results below 1% error.
  • Demonstrated a superior approach compared to traditional Hopfield network learning methods.

Conclusions:

  • The integrated ANN, genetic algorithm, and Hi-PLS regression model offers a highly effective method for assessing student psychological pressure.
  • This approach enables timely understanding of students' mental states, facilitating anxiety and fear reduction.
  • The study highlights significant research value in applying advanced computational methods to student mental health.