Citation

Al Hila, Turki Khaled and Wong, Wai Kit and Min, Thu Soe and Wong, Eng Kiong and S., Manikandan (2024) Malaysian Banknote Reader Featuring Counterfeit Detection Using Fuzzy Logic Weighted Specific (FLWS) Algorithm. Journal of Engineering Technology and Applied Physics, 6 (1). pp. 108-116. ISSN 26828383

Text View of Malaysian Banknote Reader Featuring Counterfeit Detection Using Fuzzy Logic Weighted Specific (FLWS) Algorithm.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

To identify fake Malaysian banknotes, this research suggested a revolutionary fuzzy logic weighted specific (FLWS) approach in image processing techniques. The FLWS Algorithm has the benefit of a more accurate model because it is a human guidance learning algorithm that demands training to obtain the precise weights for each security feature. The trial outcomes also demonstrated that, for the purpose of detecting counterfeit Malaysian banknotes, the FLWS model outperformed the parallel fuzzy logic weighted averaging (FLWA) algorithm, MobileNet model, and VGG16 model. Its adoption of well-known watermark features, with specific weights assigned, and well-known machine learning techniques to distinguish between genuine Malaysian banknotes and counterfeit Malaysian banknotes gives it a clear advantage over earlier or current banknote counterfeit detection techniques.

Item Type:	Article
Uncontrolled Keywords:	Image Processing, Fuzzy Logic, Malaysian Banknotes, Banknote Reader, Counterfeit Detection
Subjects:	H Social Sciences > HG Finance > HG1501-3550 Banking Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Mr. MUHAMMAD AZRUL MOSRI
Date Deposited:	03 Apr 2024 04:50
Last Modified:	03 Apr 2024 04:50
URII:	http://shdl.mmu.edu.my/id/eprint/12373

Downloads

Edit (login required)