With the rapid development of technology and the widespread use of video surveillance, modeling the architecture of neural networks for human recognition in video is attracting increasing attention from researchers. This article presents a study of the use of neural networks (NN) as an interdisciplinary model for classifying objects in video, including solving the problem of face search. This highlights the versatility of neural networks in integrating trained data and accurately classifying objects, which is critical for ensuring security and efficiency of video surveillance. The study uses an analysis of various neural network architectures, as well as a study of their operating algorithms. Data obtained from a literature review and experimental results allow us to evaluate the effectiveness of solving the task of classifying objects in video using various architectures, without tying the study to a specific data set. The study confirms the possibility of using modern neural network architectures for human recognition in real-time video based on the experience of experts in the field of computer vision and machine learning. The active use of neural networks as a tool for video surveillance increases the safety of infrastructure facilities and the efficiency of security services. Ultimately, this article presents an analysis of neural network architectures for facial recognition in video streams, advocating their use as a key element in the development of modern video surveillance systems and ensuring public safety.

Keywords: neural networks, neural network architectures, video surveillance systems, real-time recognition, improving security, social well-being

In the context of rapid urbanization of society, modeling the processes of sustainable urban development has attracted considerable attention from scientists. This paper presents a study of fuzzy cognitive maps (FCMs) as an interdisciplinary model for simulating urban development processes. This highlights the versatility of FCM in integrating expertise and quantifying the impact of indicators that shape urban space, from infrastructure and housing to environmental sustainability and community well-being. The study uses a synthesis of an extensive literature review and expert opinions to create and refine a cognitive map tailored for municipal development. The methodology outlined formulates a systematic approach to selecting concepts, assigning weights, and validating the model. Through collaboration with cross-disciplinary experts, the study confirms the value of FCM for identifying cascading effects in the decision-making process when shaping urban development strategies. Recognizing the limitations of expert methods and the fuzzy nature of data, the article argues for the effectiveness of FCM in not only identifying but also addressing emerging urbanization problems. Ultimately, this article contributes a nuanced perspective to strategic planning discourse by advocating for the use of NCC as a management decision support tool that can assist policymakers in achieving a sustainable and equitable urban future.

Keywords: fuzzy cognitive maps, urban development, urban planning, sustainable urbanization, expert systems, social well-being

In today's world, facial recognition is becoming an increasingly important and relevant task. With the development of technology and the increasing amount of data, the need for reliable, accurate and efficient face recognition systems increases. Neural networks demonstrate high efficiency in solving computer vision problems and have great potential for improving existing mathematical models of face recognition. This paper is devoted to the study of methods for human face recognition, the Viola-Jones algorithm will be discussed in detail, which, which can be applied in the task of face recognition using neural networks. It will also analyse techniques for training deep learning models using libraries that also use the Viola-Jones algorithm and describe an algorithm for using the trained model in an API that can be used in desktop and mobile applications.

Keywords: biometric identification, human face recognition, mathematical models, face recognition methods, deep learning, convolutional neural networks, tensorflow