Development of Excessive-Smoke-Emitting-Car Fault Diagnostic System (ESECFDS) based on Case-Base Reasoning (CBR) Methodology

Article type: Research Article

Authors: De, Sumana^* | Chakraborty, Baisakhi

Affiliations: Department of Computer Science and Engineering, National Institute of Technology Durgapur, India

Correspondence: [*] Corresponding author: Sumana De, Department of Computer Science and Engineering, National Institute of Technology Durgapur, India. E-mail: sumanade@gmail.com.

Abstract: This paper proposes a diagnostic system, ESECFDS, to solve the problem of excessive smoke emission from a faulty car, based on Case-Based Reasoning (CBR) Methodology that accepts a user query as a new case, compares it with several stored cases in a Case-Base (CB) based on a similarity score using Jaccard Similarity method and provides feedback result to the user. The CBR cycle R4 Model of Aamodt and Plaza has been modified to R5 Model to improve the time complexity of the proposed algorithm of the system over the popular R4 Model.

Keywords: Excessive-Smoke-Emitting-Car, Fault Diagnostic system, ESECFDS, CBR, Case, Decision Tree, Sub-Case-Base, Jaccard Similarity Method, User Query, Feedback

DOI: 10.3233/KES-210054

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 25, no. 1, pp. 75-95, 2021