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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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A framework for vehicle quality evaluation based on interpretable machine learning.

Mohammad Alwadi¹, Girija Chetty², Mohammad Yamin³

¹Arab Open University (AOU), Amman - Jordan, Jordan.

International Journal of Information Technology : an Official Journal of Bharati Vidyapeeth'S Institute of Computer Applications and Management

|December 5, 2022

Summary

This summary is machine-generated.

This study introduces a computational framework using interpretable machine learning for objective vehicle quality evaluation. The approach enhances understanding and provides deep insights into vehicle quality assessment.

Keywords:

Explainability Interpretability Machine learning Risk assessment Vehicle quality

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Area of Science:

Engineering
Computer Science
Data Science

Background:

Vehicle quality is crucial for customer satisfaction and longevity.
Objective scientific methods are needed for reliable vehicle quality assessment.
Current methods may lack interpretability and deep insight.

Purpose of the Study:

To present a computational framework for vehicle quality evaluation.
To utilize interpretable machine learning techniques for objective assessment.
To improve the interpretability and insight of vehicle quality evaluation models.

Main Methods:

Development of a computational framework for vehicle quality evaluation.
Application of interpretable machine learning techniques.
Validation using a publicly available vehicle quality dataset.
Employing post-hoc model interpretability enhancement techniques.

Main Results:

The proposed framework provides an objective, machine learning-based approach to vehicle quality evaluation.
The method significantly improves model interpretability.
Deep insights into vehicle quality factors were achieved.
Successful validation on a public dataset confirmed the framework's efficacy.

Conclusions:

The developed framework offers an objective and interpretable method for assessing vehicle quality.
Interpretable machine learning enhances the scientific rigor of vehicle quality evaluation.
This approach provides valuable insights for improving vehicle design and manufacturing.