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Deep Learning Based Emotion Recognition and Visualization of Figural Representation.

Xiaofeng Lu1

  • 1Department of Fine Arts, Shandong University of Arts, Jinan, China.

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Summary
This summary is machine-generated.

This study introduces an improved Convolutional Neural Network-Bi-directional Long Short-Term Memory (CNN-BiLSTM) algorithm for recognizing learner emotions in intelligent learning environments. The CNN-BiLSTM model achieved 98.75% accuracy, significantly outperforming other deep learning methods.

Keywords:
CNN-BiLSTMdeep learningemotion recognitiongraphic visualizationneural network

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Human-Computer Interaction

Background:

  • Learner emotion recognition is crucial for effective intelligent learning environments.
  • Current methods for analyzing learner expressions in digital settings require enhancement.
  • Visualizing learner emotions aids in understanding engagement and cognitive states.

Purpose of the Study:

  • To develop and validate an advanced algorithm for speech and expression-based emotion recognition.
  • To investigate the performance of deep learning models in intelligent learning contexts.
  • To enhance the graphic visualization of learner emotions for better pedagogical insights.

Main Methods:

  • Comparative analysis of several deep learning neural network algorithms.
  • Proposal and implementation of an improved Convolutional Neural Network-Bi-directional Long Short-Term Memory (CNN-BiLSTM) algorithm.
  • Simulation experiments to evaluate the proposed algorithm's effectiveness.

Main Results:

  • The proposed CNN-BiLSTM algorithm achieved an accuracy of 98.75%.
  • Accuracy was at least 3.15% higher than other compared algorithms.
  • Recall rates improved by at least 7.13%, with a consistent recognition rate above 90%.

Conclusions:

  • The improved CNN-BiLSTM algorithm demonstrates superior performance in learner emotion recognition.
  • This method offers a significant experimental reference for emotion recognition and expression visualization in intelligent learning.
  • Effective emotion recognition can lead to more responsive and adaptive intelligent learning systems.