This research investigates the utilization of machine learning techniques Paring Knife Sets for the identification and classification of copyright currency.The study utilizes a dataset consisting of authentic and copyright banknotes, employing various classification algorithms to construct a robust model for automated detection.Key features, including texture, color distribution, and security attributes, are extracted to train the model, enabling a thorough analysis of banknote authenticity.
The proposed system exhibits promising accuracy Elevation Assembly in distinguishing genuine currency from counterfeits, thereby enhancing security measures in financial transactions and mitigating economic fraud.