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    This study presents a novel ensemble neural random forest method for accurate facial expression recognition (FER). The approach achieves a high 97.3% recognition rate, advancing healthcare applications.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Biomedical Engineering

    Background:

    • Facial expressions are indicators of various health conditions, necessitating reliable facial expression recognition (FER) systems in healthcare.
    • Accurate FER is challenging due to the subtle nuances of facial features.
    • Existing methods struggle with the complexity and variability of human expressions.

    Purpose of the Study:

    • To introduce an advanced ensemble neural random forest model for improved facial expression recognition.
    • To enhance feature extraction using convolutional neural network (CNN) architecture.
    • To optimize the random forest classifier for robust performance.

    Main Methods:

    • Utilized a CNN architecture with four convolutional layers, maxpooling, batch normalization, and dropout for feature extraction.
    • Implemented an optimized random forest classifier, tuning parameters like tree count, depth, and features.
    • Evaluated the model on six diverse, publicly available datasets.

    Main Results:

    • Achieved a high weighted average recognition rate of 97.3% across multiple datasets.
    • Demonstrated the model's effectiveness in capturing subtle facial features for accurate classification.
    • The ensemble approach successfully combined CNN feature extraction with optimized random forest classification.

    Conclusions:

    • The proposed ensemble neural random forest method offers a significant advancement in facial expression recognition.
    • This technique shows great promise for integration into healthcare frameworks for diagnostic and monitoring purposes.
    • The high accuracy validates the model's robustness and effectiveness on varied facial expression data.