Machine learning based approach for detection of fake banknotes using support vector machine

Abstract

Currency counterfeiting is a significant offense that has an impact on a nation's finances. Due to the enormous progress in printing technology, it is now quite simple to create fake currency that resembles real currency in both appearance and texture, making it nearly difficult to manually tell them apart. The suggested approach will be helpful in identifying fake currency in financial systems. Because of the rise of fake currency in the market, numerous false note detecting techniques are available globally to address this issue, however the most of them rely on expensive technology. In this paper, we'll introduce a revolutionary way for separating fake banknotes from real ones using the support vector machine (SVM) approach. To categorize bank notes as authentic or counterfeit utilizing the data retrieved from the photos of the bank notes, SVM performs better overall and is more effective, particularly when it comes to pattern categorization. Finally, the results of our experiment will demonstrate that the suggested algorithm does really yield extremely good performance.

Detecting fake news spreaders on twitter

“…Fake news that is published on social media has gained a lot of attention recently. As a result, techniques for automatically recognizing fake news have drawn considerable interest using machine learning [1]. It is a simple binary classification issue to determine whether a news tweet is fake or not.…”