Open AccessThe Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting
Author(s) -
Rihab Salah Khairy,
Ameer Hussein,
Haider TH. Salim ALRikabi
Publication year - 2021
Publication title -
international journal of intelligent engineering and systems
Language(s) - English
Resource type - Journals
eISSN - 2185-310X
pISSN - 1882-708X
DOI - 10.22266/ijies2021.0228.31
Subject(s) - adaboost , counterfeit , voting , computer science , banknote , ensemble learning , artificial intelligence , machine learning , pattern recognition (psychology) , support vector machine , politics , political science , law
The movement of cash flow transactions by either electronic channels or physically created openings for the influx of counterfeit banknotes in financial markets. Aided by global economic integration and expanding international trade, attention must be geared at robust techniques for the recognition and detection of counterfeit banknotes. This paper presents ensemble learning algorithms for banknotes detection. The AdaBoost and voting ensemble are deployed in combination with machine learning algorithms. Improved detection accuracies are produced by the ensemble methods. Simulation results certify that the ensemble models of AdaBoost and voting provided accuracies of up to 100% for counterfeit banknotes.