This article explores the opportunities and challenges of integrating cloud, fog, and edge computing in the context of digital transformation. The analysis reveals that the synergy of these technologies enables optimization of big data processing, enhances system adaptability, and ensures information security. Special attention is given to hybrid architectures that combine the advantages of centralized and decentralized approaches. Practical aspects are addressed, such as the use of the ENIGMA simulator for modeling scalable infrastructures and the EC-CC architecture for smart grids and IoT systems. The role of specialized frameworks in optimizing routing and improving infrastructure reliability is also highlighted. The integration of these technologies drives advancements in key industries, including energy, healthcare, and the Internet of Things, despite challenges related to data security.

Keywords: cloud computing, fog computing, edge computing, hybrid architectures, Internet of Things, digital transformation, big data, decentralized systems, computing integration, distributed computing, data security, resource optimization, data transfer speed

This article explores various architectures of neural networks in order to create models in the field of agriculture, with an emphasis on their use in livestock farms. The paper describes the architecture of Kolmogorov-Arnold networks, considers the stages of data collection and preliminary preparation, the learning process of neural networks, as well as their implementation. As a result, models were developed using Kolmogorov-Arnold networks and a multilayer perceptron. The study compared the effectiveness of the proposed architectures. The experiment demonstrates that Kolmogorov-Arnold networks have higher accuracy in predictions, which makes them a promising tool for forecasting. The developed model has been integrated into the livestock information system being developed to predict the growth, health and other indicators of animals, allowing for more accurate management of the growing process.

Keywords: precision animal husbandry, Kolmogorov-Arnold network, modeling, neural network, monitoring, cultivation, data modeling, forecasting

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