Fault detection is critical to ensure the safely routine operations in nuclear power plants (NPPs), requiring very high accuracy and efficiency. Meanwhile, the rapid development of modern information technologies have profoundly changed and promoted various sectors including nuclear industry. Inspired by the great progress and promising performance of deep learning based image classification recent years, a two-stage fault detection methodology in NPPs has been proposed in this paper. First the time-series data describing the operating status of NPPs have been transformed into two-dimensional images by four methods, preserving the time-series information in images and converting the fault detection problem into a supervised image classification task. Then four specific image classifying models based on three primary deep learning architectures have been separately experimented on the imaged time-series data, achieving excellent accuracies. Further the performances of different combinations of transforming means and classifying models have been compared and discussed with extensive experiments and detailed analysis of throughput for four transforming methods. This methodology proposed has obtained remarkable results by reshaping data format and structure, making image classifying models applicable, which not only efficiently detect and warn possible faults in NPPs but also enhances the capability for safety management in nuclear power systems.

Someone commits payment fraud when they obtain the payment information of another person and use it for unauthorized transactions or purchases. Owing to the ease and convenience of e-commerce, digital purchasing is becoming increasingly popular today and because of the convenience of online shopping, many individuals prefer to shop online. This has resulted in a substantial rise in credit card fraud. Detecting and preventing payment fraud is difficult because the standard rules-based anti-fraud systems deployed by banks cannot manage the high volume of online transactions. This creates unique difficulties for banks and a substantial increase in losses. Therefore, it is crucial to effectively identify and eliminate fraud. In our research, we use machine learning methods to construct models that can detect and analyze fraudulent payments. We primarily employ the Generalized Linear, Decision Tree, Gradient Boosting, and Naive Bayes Models, and determine that the Generalized Linear Model is the most effective.