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Hyperparameter tuning using Lévy flight and interactive crossover-based reptile search algorithm for eye movement

V Pradeep1, Ananda Babu Jayachandra2, S S Askar3

  • 1Department of Information Science and Engineering, Alva's Institute of Engineering and Technology, Mangaluru, India.

Frontiers in Physiology
|May 30, 2024
PubMed
Summary

This study introduces a novel method for detecting eye movement events using a Bidirectional Long Short-Term Memory (BILSTM) network optimized by the Reptile Search Algorithm (LICRSA). This approach significantly improves classification accuracy for assistive technologies.

Keywords:
F1-scoreLévy flight and interactive crossoveraccuracybidirectional long short-term memoryeye movement event classificationfuzzy data augmentationreptile search algorithm

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Eye movement analysis is crucial for human-machine interfaces and assistive technologies.
  • Developing accurate classifiers for eye movement events remains a significant challenge.

Purpose of the Study:

  • To propose an effective eye movement event classification method.
  • To enhance the performance of Bidirectional Long Short-Term Memory (BILSTM) networks through hyperparameter optimization.

Main Methods:

  • Utilized a Bidirectional Long Short-Term Memory (BILSTM) network for classification.
  • Employed the Lévy flight and interactive crossover-based reptile search algorithm (LICRSA) for hyperparameter optimization.
  • Incorporated fuzzy data augmentation (FDA) to mitigate overfitting and VGG-19 for feature extraction.

Main Results:

  • The proposed BILSTM-LICRSA model achieved high performance across four datasets (Lund2013, collected dataset, GazeBaseR, UTMultiView).
  • Demonstrated superior performance, with an F1-score of 98.99% on the GazeBaseR dataset, outperforming the Multi-Source Information-Embedded Approach (MSIEA).

Conclusions:

  • The BILSTM-LICRSA model offers a robust and accurate solution for eye movement event classification.
  • This method holds promise for advancing assistive technologies for individuals with paralysis.