International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 12, Issue 8 (August 2025), Pages: 1-15

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 Original Research Paper

Cognitively inspired sound-based automobile problem detection: A step toward explainable AI (XAI)

 Author(s): 

 Muhammad Fawad Nasim 1, Muhammad Anwar 2, Almuhannad S. Alorfi 3, Hamza Awad Ibrahim 4, Ali Ahmed 5, *, Arfan Jaffar 1, 6, Sheeraz Akram 1, 6, 7, Abubakar Siddique 8, Hafiz Muhammad Zeeshan 9

 Affiliation(s):

  1Faculty of Computer Science and Information Technology, The Superior University, Lahore, Pakistan
  2Department of Information Sciences, Division of Science and Technology, University of Education, Lahore, Pakistan
  3Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia
  4Department of Computer College of Engineering and Computers, Al-Gunfudah Umm-Al Qura University, Makkah, Saudi Arabia
  5Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia
  6Intelligent Data Visual Computing Research (IDVCR), Lahore, Pakistan
  7Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
  8Wellington Institute of Technology, Te Pukenga, Wellington, New Zealand
  9Department of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0002-8944-8922

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.08.001

 Abstract

Recently, there have been efforts to create automated systems for diagnosing engine problems using sound detection. However, most of these methods lack robustness and interpretability, functioning as "black boxes" that make it difficult to understand their decision-making processes. The Learning Classifier System (LCS), a machine learning approach that operates using a set of rules, has demonstrated potential for providing robust, interpretable, and generalizable solutions across different domains. This work aims to develop a new LCS-based system for automatically detecting engine problems, with a focus on making its decision-making process understandable, contributing to explainable artificial intelligence. The system's performance is evaluated using features from the time domain, frequency domain, and time-frequency domain. Its robustness is tested with noisy sound data gathered under various normal and abnormal conditions. Experimental results show that this new approach outperforms conventional state-of-the-art methods by 2.6%−6.0%, achieving a maximum performance accuracy of 98.6%.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Audio classification, Acoustic features classification, Engine sound classification, Learning classifier system, Rule-based systems

 Article history

 Received 13 June 2024, Received in revised form 10 January 2025, Accepted 1 July 2025

 Acknowledgment

This research work was funded by the Institutional Fund Project under grant no. (GPIP: 927-830-2024). The authors gratefully acknowledge technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Nasim MF, Anwar M, Alorfi AS, Ibrahim HA, Ahmed A, Jaffar A, Akram S, Siddique A, and Zeeshan HM (2025). Cognitively inspired sound-based automobile problem detection: A step toward explainable AI (XAI). International Journal of Advanced and Applied Sciences, 12(8): 1-15

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 Figures

  Fig. 1  Fig. 2  Fig. 3  Fig. 4  Fig. 5  Fig. 6  Fig. 7  Fig. 8  Fig. 9  Fig. 10  Fig. 11  Fig. 12  Fig. 13 

 Tables

  Table 1  Table 2  Table 3  Table 4  Table 5  Table 6  Table 7  Table 8 

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