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The question of a better understanding of the behavior of neural networks is quite relevant, especially in industries with a high level of risks. To solve this problem, the possibilities of the new DeepRED decomposition algorithm, capable of extracting decision-making rules by deep neural networks with several hidden layers, are explored in the paper. The study of the DeepRED algorithm was carried out on the example of extracting the rules of an experimental neural network during the classification of images of the MNIST database of handwritten digits, which made it possible to reveal a number of limitations of the DeepRED algorithm. [ABSTRACT FROM AUTHOR]

Successful identification of a person in a real-time video stream is a critical function in a wide range of relevant subject areas. On the other hand, such a task is complex and can be effectively solved only with the help of modern artificial intelligence approaches. In this work, the previously developed MTCNN approach is used as a key algorithm for face image recognition and vectorization, which uses a cascade of several neural networks to identify key points of the face of a recognizable personality. In addition to the main algorithm, an integrated system was implemented that provides the functionality of person identification available to the end user. Additionally, face identification modules were implemented based on vectors representing a person in a video frame, a subsystem for storing and replenishing the database of recognizable persons, and a convenient user interface. Modifications of the MTCNN architecture proposed in this paper made it possible to achieve real-time performance and acceptable identification quality at the level of 0.92 according to the F1-metric. [ABSTRACT FROM AUTHOR]

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