Computer vision and image processing techniques have been extensively used in various fields and a wide range of applications, as well as recently in surface treatment to determine the quality of metal processing. Accordingly, digital image evaluation and processing are carried out to perform image segmentation, identification, and classification to ensure the quality of metal surfaces. In this work, a novel method is developed to effectively determine the quality of metal surface processing using computer vision techniques in real time, according to the average size of irregularities and caverns of captured metal surface images. The presented literature review focuses on classifying images into treated and untreated areas. The high computation burden to process a given image frame makes it unsuitable for real-time system applications. In addition, the considered current methods do not provide a quantitative assessment of the properties of the treated surfaces. The markup, processed, and untreated surfaces are explored based on the entropy criterion of information showing the randomness disorder of an already treated surface. However, the absence of an explicit indication of the magnitude of the irregularities carries a dependence on the lighting conditions, not allowing to explicitly specify such characteristics in the system. Moreover, due to the requirement of the mandatory use of specific area data, regarding the size of the cavities, the work is challenging in evaluating the average frequency of these cavities. Therefore, an algorithm is developed for finding the period of determining the quality of metal surface treatment, taking into account the porous matrix, and the complexities of calculating the surface tensor. Experimentally, the results of this work make it possible to effectively evaluate the quality of the treated surface, according to the criterion of the size of the resulting irregularities, with a frame processing time of 20 ms, closely meeting the real-time requirements.

In order to determine the basic conditions for further research to identify threats to cloud technologies and measures for their counteraction, the article provides an extended analysis of cloud computing as a service. Among the above services, the basic services IaaS, SaaS, PaaS are selected, which are the basis for the existence of more unified services (CaaS, MCaS, DaaS, FaaS, IPaaS, MBaaS, NaaS, SeCaa, DBaaS, MaaS, DBaaS, MaaS, DBaaS, MaaS that increase the scope. Listed are possible service providers, including Ukrainian. It is determined that in order to transfer the work of systems of engineering calculations and computer-aided design (CAD) systems to a cloud platform, a rather new promising service CAEaaS (Cоmputеr Аidеd Еnginееrіng аs а Sеrvіcе) – computer engineering systems as a service. The most popular SAEs are used in the following industries: mechanical engineering and machine tools, defense and aerospace, energy, shipbuilding, semiconductor production, telecommunications, chemical, pharmaceutical and medical industry, construction, production of heating, air conditioning, ventilation. The success of a project decision depends on the awareness of responsible choice in the very first stage. Unfortunately, in support of the CAEaaS cloud service, Ukraine is only making the first steps and can provide the technological needs of enterprises, provided the involvement of foreign suppliers. According to the authors of the article, the future of the Ukrainian industry according to the cloud service SAEaS. Because CAE helps reduce the cost and time of product development, improve product quality and durability. Design decisions can be made based on their impact on performance. Designs can be evaluated and refined using computer modeling rather than physical prototype testing, saving money and time. This article is the basis for further research on the identification of threats to cloud technologies and their response.

Evolutionary processes, which primarily affected computer technologies, led to the appearance of several types of computer networks, representing a set of computer devices combined into one system. The main purpose of such a system is user access to shared resources and the ability to exchange data between subscribers during work. Such networks are called software-configurable networks - SDN. SDN networks have long been the basis for building carrier-class telecommunication networks. However, they have a certain number of shortcomings that must be eliminated. The object of the study is the process of evaluating and improving the user experience of subscribers in software-configured networks. The subject of the study is a method of evaluating and improving the user experience of subscribers in software-configured networks based on the use of machine learning. The purpose of the work is to develop a model and a corresponding method for assessing the quality of the user experience of subscribers of SDN networks. As a result of the research, for the first time, a method of evaluating and improving the user experience of subscribers of SDN networks was developed based on the use of machine learning. The method consists in sequentially conducting an automated survey of users, measuring indicators of subscriber service quality, selecting and building a regression model from a set of defined models, and managing the user experience according to the measured parameters of SDN subscriber service quality. The developed method, in contrast to the known ones, makes it possible to improve the quality of the user experience in real time. Conclusions. The research of existing mechanisms for managing the user experience of subscribers and the analysis of regression models for the possibility of using them to establish the relationship between network parameters and user experience allowed to develop a generalized model for evaluating and improving the user experience of subscribers of SDN networks, based on the use of machine learning, and to develop an algorithm works of the method. The developed method makes it possible to build accurate models of the relationship of QoE and QoS parameters and increases the quality of the user experience of subscribers of SDN networks by up to 10%.

Cybersecurity threats are constantly evolving, and cloud computing is no exception. Attackers are improving attack techniques aimed at identifying vulnerabilities in IaaS, PaaS, and SaaS. The work examines the following problematic issues: insufficient analytical tools; data privacy and security; financial and organizational costs to ensure cyber security of IaaS, PaaS and SaaS cloud technologies. The aim of the work is to investigate how the common models of cloud computing: infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS) need to properly implement adequate and appropriate protection measures to ensure cyber security. For this purpose, the paper examined models of cloud technologies, it was determined that cloud services include various models that allow users to access various types of resources via the Internet. It has been found that there are three generally accepted models of cloud services: infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS), and two main players: the cloud provider and the cloud subscriber. The set of levels over which each of these players has control depends on the model of the cloud service or environment. For each of these cloud services, its description was given, recommendations were given for access control, privacy assurance, terms of use were defined, advantages and disadvantages were given, and market trends of these services were considered. Proposed approaches to the formation of a secure application development environment in cloud services. Features such as wide network access, resource pooling, fast elasticity, service metering, and data sharing are also summarized. Guidelines for designing access control for IaaS, PaaS, and SaaS according to their different characteristics are proposed. In addition, the security policy rules for each cloud system are summarized. Technologies are proposed to protect security at each of the three main layers of cloud services: the application, middleware, and virtual machine layers, due to differences in the organization that controls each of these layers. It was found that for any level it is possible to provide more effective protection if the same subject controls the level below. Since the network, hardware, and resource abstraction layer in all cloud service models are controlled by the cloud provider, it has more effective protections at its disposal.