The Temporal Robustness of Classification Algorithms: Investigating the Impact of Temporal Changes on Model Performance

Muhammad Sajid; Sadia Latif; Rana Muhammad Nadeem; Aafia Latif; Muhammad Hassnain Azhar

doi:10.71146/kjmr350

Authors

Muhammad Sajid Department of Computer Science, NCBA&E Multan, Pakistan Author
Sadia Latif Department of Computer Science , Bahauddin Zakaria University Multan. Author
Rana Muhammad Nadeem Department of Computer Science, Govt. Graduate College Burewala, Pakistan. Author
Aafia Latif Department of CS & IT, Govt.Graduate College Burewala, Pakistan Author
Muhammad Hassnain Azhar Department of Computer Science , Institute of Southern Punjab, Multan, Pakistan. Author

DOI:

https://doi.org/10.71146/kjmr350

Keywords:

Digital Image Processing (DIP), License Plate Recognition (LPR), Template Matching (TM), Artificial Neural Network (ANN), Support Vector Machine (SVM)

Abstract

Classifiers are the main source of processing of identification application task, So the performance of classifiers effect the work of any application. In this paper, author is working in the Digital Image Processing (DIP) domain, In License Plate Recognition (LPR) application of it. The purpose of this paper is, to introduce systemic literature review on why classification algorithms don’t work effectively after some period of time in some countries. Which decrease the performance of classifiers while processing License Plate Recognition (LPR) application or any identification application. Recognition of characters in any identification system take most important than other steps.