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An artificial neural network approach for the language learning model.

Zulqurnain Sabir1, Salem Ben Said2, Qasem Al-Mdallal3

  • 1Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.

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This study introduces an artificial intelligence (AI) approach using a scale conjugate gradient neural network (SCJGNN) to solve language-based differential models. The AI method accurately models language learning stages with minimal error.

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Area of Science:

  • Computational Linguistics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Language-based differential models are crucial for understanding learning processes.
  • Developing accurate numerical solutions for these models is computationally challenging.
  • Existing methods may lack efficiency or precision in capturing learning dynamics.

Purpose of the Study:

  • To present numerical solutions for a language-based differential model using artificial intelligence.
  • To implement and validate a scale conjugate gradient neural network (SCJGNN) procedure.
  • To classify language learning into unknown, familiar, and mastered stages.

Main Methods:

  • Utilized an artificial intelligence (AI) procedure based on a scale conjugate gradient neural network (SCJGNN).
  • Employed the Adam scheme to minimize mean square error for dataset generalization.
  • Configured the SCJGNN with a log-sigmoid activation function, 12 neurons, and specific layer structures, processing data in training (75%), validation (13%), and testing (12%) ratios.

Main Results:

  • Achieved high accuracy in numerical solutions, with absolute errors ranging from 10-06 to 10-08 for all learning classes.
  • Demonstrated perfect model performance through regression analysis for each learning stage.
  • Validated the dependability of the SCJGNN approach using histogram and function fitness analyses.

Conclusions:

  • The AI-based SCJGNN provides a robust and accurate method for solving language-based differential models.
  • The model effectively distinguishes between unknown, familiar, and mastered language learning states.
  • The SCJGNN approach shows significant potential for applications in computational linguistics and educational technology.