This article explores the introduction and implementation of neural network models in the field of agriculture, with an emphasis on their use in smart greenhouses. Smart greenhouses are innovative systems for controlling the microclimate and other factors affecting plant growth. Using neural networks trained on data on soil moisture, temperature, illumination and other parameters, it is possible to predict future indicators with high accuracy. The article discusses the stages of data collection and preparation, the learning process of neural networks, as well as the practical implementation of this approach. The results of the study highlight the prospects for the introduction of neural networks in the agricultural sector and their important role in optimizing plant growth processes and increasing the productivity of agricultural enterprises.

Keywords: neural network, predicting indicators, smart greenhouse, artificial intelligence, data modeling, microclimate

The article thoroughly explores cloud, fog, and edge computing, highlighting the distinctive features of each technology. Cloud computing provides flexibility and reliability with remote access capabilities, but encounters delays and high costs. Fog computing focuses on data processing at a low level of infrastructure, ensuring high speed and minimal delays. Edge computing shifts computations to the data source itself, eliminating delays and enhancing security. Applications of these technologies in various fields are analyzed, and their future development is predicted in the rapidly evolving world of information systems.

Keywords: cloud computing, fog computing, edge computing, cloud technologies, data processing infrastructure, scope of application, hybrid computing, Internet of Things, artificial intelligence, information systems development

This paper describes the issue of choosing a distributed registry platform when designing information systems in the financial sector of the economy. The relevance of these studies is due to the ever-increasing growth in demand for information systems of the financial sector of the economy formed using distributed registry technology. The growing interest in this technology is associated with the need to ensure reliable storage of information, the change of which will be monitored by the participants of this transaction. The purpose of this work is to determine the most suitable platform using the hierarchy analysis method. In the course of the work, the main platforms of the distributed registry were identified, as well as the key criteria for these frameworks were determined, taking into account the requirements of business process participants. These criteria were evaluated. For each alternative evaluation matrix, the indicators of the maximum eigenvalue vector were determined according to separate criteria, and the consistency of the judgment was proved, including the determination of the consistency index, the local priority index and the consistency ratio. A synthetic analysis of the criteria under consideration was carried out. Based on the data obtained during the synthetic analysis, the most promising platform was selected. Conclusions on the evaluated systems are formed.

Keywords: distributed registry, hierarchy analysis method, system analysis, information systems, computer science

This article discusses the problems of determining the road surface for automatic control of a vehicle using an artificial neural network. The current state of the industry is described, as well as the relevance of these studies. Describes the input data for determining the road surface. The idea of the applicability of the image segmentation method for determining the road surface is substantiated. The structure of an artificial neural network based on the U-NET architecture is being formed. In particular, the structure of the sequence of layers is described. Particular attention is paid to the mathematical modeling of the convolution process and the maximum pool. A mathematical model of the learning process of an artificial neural network, as well as activation functions: linear functions and sigmoids, is given. An algorithm for forming an artificial neural network model is proposed. The learning process of this function is visualized on the graph. The result of the training is presented.

Keywords: artificial neural networks, UNET, data analysis,, machine learning, deep lerning, convolutional neural networks, convolution, maximum pool, image segmentation, modeling