Malaysian Banknote Reader Featuring Counterfeit Detection Using Fuzzy Logic Weighted Specific (FLWS) Algorithm Manuscript Received: 25 November 2023, Accepted: 27 December 2023, Published: 15 March 2024, ORCiD:0000-0003-1477-8449,

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Turki Khaled Al-Hila
Wai Kit Wong
Thu Soe Min
Eng Kiong Wong
S. Manikandan


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.

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