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Lobaneva E.

Postgraduate, National Research University MPEI

Algorithms for the formation of images of the states of objects for their analysis by deep neural networks

Algorithms of visualization of numerical data characterizing the state of objects and systems of various nature with the aim of finding hidden patterns in them using convolutional neural networks are presented. The algorithms used methods for obtaining images from numerical data on the basis of the discrete Fourier transform of time series fragments, as well as on the basis of the application of visualization using three-component system diagrams, if such a three-component representation of the system is possible. The software implementation of the proposed algorithms was performed in the Linux environment in the Python 3 language using the Keras open neural network library, which is a superstructure above the TensorFlow machine learning framework. For the learning process of the neural network, a Nvidia graphics processor was used that supports the technology of the CUDA parallel computing software and hardware architecture, which significantly reduced the learning time. The proposed approach is the recognition States of the objects according to their visualized data are based on the recognition of no boundaries or forms of the figures in the images and their textures. Also presented is a program that generates sets of images to implement the process of learning and testing convolutional neural networks in order to pre-tune them and assess the quality of the proposed algorithms.Keywords: Internet, Internet security, parental control applications, user security, information security, Internet threats.

Preliminary assessment of the pragmatic value of information in the classifiсation problem based on deep neural networks

A method is proposed for preliminary assessment of the pragmatic value of information in the problem of classifying the state of an object based on deep recurrent networks of long short-term memory. The purpose of the study is to develop a method for predicting the state of a controlled object while minimizing the number of used prognostic parameters through a preliminary assessment of the pragmatic value of information. This is an especially urgent task under conditions of processing big data, characterized not only by significant volumes of incoming information, but also by information rate and multiformatness. The generation of big data is now happening in almost all areas of activity due to the widespread introduction of the Internet of Things in them. The method is implemented by a two-level scheme for processing input information. At the first level, a Random Forest machine learning algorithm is used, which has significantly fewer adjustable parameters than a recurrent neural network used at the second level for the final and more accurate classification of the state of the controlled object or process. The choice of Random Forest is due to its ability to assess the importance of variables in regression and classification problems. This is used in determining the pragmatic value of the input information at the first level of the data processing scheme. For this purpose, a parameter is selected that reflects the specified value in some sense, and based on the ranking of the input variables by the level of importance, they are selected to form training datasets for the recurrent network. The algorithm of the proposed data processing method with a preliminary assessment of the pragmatic value of information is implemented in a program in the MatLAB language, and it has shown its efficiency in an experiment on model data. Read more...