Biblio

Frauds are known to be dynamic and have no patterns, hence they are not easy to identify. Fraudsters use recent technological advancements to their advantage. They somehow bypass security checks, leading to the loss of millions of dollars. Analyzing and detecting unusual activities using data mining techniques is one way of tracing fraudulent transactions. transactions. This paper aims to benchmark multiple machine learning methods such as k-nearest neighbor (KNN), random forest and support vector machines (SVM), while the deep learning methods such as autoencoders, convolutional neural networks (CNN), restricted boltzmann machine (RBM) and deep belief networks (DBN). The datasets which will be used are the European (EU) Australian and German dataset. The Area Under the ROC Curve (AUC), Matthews Correlation Coefficient (MCC) and Cost of failure are the 3-evaluation metrics that would be used.

Accurate model is very important for the control of nonlinear system. The traditional identification method based on shallow BP network is easy to fall into local optimal solution. In this paper, a modeling method for nonlinear system based on improved Deep Belief Network (DBN) is proposed. Continuous Restricted Boltzmann Machine (CRBM) is used as the first layer of the DBN, so that the network can more effectively deal with the actual data collected from the real systems. Then, the unsupervised training and supervised tuning were combine to improve the accuracy of identification. The simulation results show that the proposed method has a higher identification accuracy. Finally, this improved algorithm is applied to identification of diameter model of silicon single crystal and the simulation results prove its excellent ability of parameters identification.