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In order to sabotage our country's currency, criminals have introduced counterfeit notes that look like the real thing into the financial market. In the wake of the country's demonetization, a lot of fake currency is circulating. In general, it is difficult for a human being to distinguish a forged note from a genuine one, as many features of a forged note are similar to those of the original one. It's difficult to tell the difference between a real bank note and a fake one. Because of this, there must be an automated system that can be found in banks or ATMs. It is necessary to design an efficient algorithm that can predict whether a banknote is genuine or forged in order to create such an automated system, as counterfeit banknotes are extremely precise. Bank currency authentication can be detected using six supervised machine learning algorithms that were tested on a dataset from the UCI machine learning repository. On the basis of various quantitative analysis parameters such as Precision and Accuracy as well as MCC and F1-Score, we have applied Support Vector Machine (SVM), Random Forest (Random Forest), Logistic Regression (Logistic Regression), Naive Bayes, Decision Tree (K-Nearest Neighbor), and K-Nearest Neighbor (KNN). [ABSTRACT FROM AUTHOR]

To put it simply, malware is posing a serious threat to global network security as the Internet era has progressed at an unprecedented rate. Using SIMGRU, we present an Android malware detection method that falls under the static detection category. Using the clustering similarity, which is widely used in static analysis of Android malware, we were able to improve the Gated Recurrent Unit (GRU) and produce three distinct structures of Sim GRU: Input Sim GRU, Hidden Sim GRU, and Input hidden SIMGNU There are two types of input hidden sim gru: input and hidden. Results show that the GRU model and other methods are outperformed by the Sim GRU, Hidden GRU, and Input Hidden GRU. [ABSTRACT FROM AUTHOR]