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The research topic presented in this paper belongs to small training data problem in machine learning (especially in deep learning), it intends to help the work of those working in medicine by analyzing pathological X-ray recordings, using only very few images. This scenario is a particularly hot issue nowadays: how could a new disease for which only limited data are available be diagnosed using features of previous diseases? In this problem, so-called few-shot learning, the difficulty of the classification task is to learn the unique feature characteristics associated with the classes. Although there are solutions, but if the images come from different views, they will not handle these views well. We proposed an improved method, so-called Double-View Matching Network (DVMN based on the deep neural network), which solves the few-shot learning problem as well as the different views of the pathological recordings in the images. The main contribution of this is the convolutional neural network for feature extraction and handling the multi-view in image representation. Our method was tested in the classification of images showing unknown COVID-19 symptoms in an environment designed for learning a few samples, with prior meta-learning on images of other diseases only. The results show that DVMN reaches better accuracy on multi-view dataset than simple Matching Network without multi-view handling.

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This article is devoted to the initial phase of data analysis of failure data from process control systems. Failure data can be used for example to detect weak spots in a production process, but also for failure prediction. To achieve these goals data mining techniques can be used. In this article, we propose a method to prepare and transform failure data from process control systems for application of data mining algorithms, especially cluster analysis.