Glushchenko V.
Professor of the Department of SMART Technologies, Moscow Polytechnic University, Russia, Moscow

Abstract
The subject of the article is the methodological provisions of the general theory of transport systems; the object of the article is the transport system; the purpose of the article is to increase the efficiency of the processes of designing and restructuring transport systems; to achieve this goal, the article solves the following tasks: the theoretical provisions of the general theory of the development and functioning of transport systems (transportology) are developed; a comparative analysis of the organizational structures of innovative activities in transport engineering; the specifics of innovative entrepreneurship in the conditions of the global crisis are described; the provisions of the criterion approach in the theory of the firm are being developed; it is shown that the formation of clusters and technological platforms as new organizational forms of innovation development is associated with the need to decentralize decision-making and more fully take into account the interests of all participants in the innovation process, including individuals and small innovative firms; the research methods in the article are historical, logical and system analysis; synthesis; firm theory; theory of hierarchical systems; the scientific novelty of the article is connected with the formation of the methodology of design and restructuring of transport systems

Keywords: transport, science, analysis, innovation, structure, theory, firm, globalization, market, criterion, crisis, analysis, methodology

Aghayeva K.
Doctor of Philosophy, associate professor of Azerbaijan State Oil and Industry University
Mammadov T.
master’s student of Azerbaijan State Oil and Industry University

Abstract
This abstract preview an original and exploration that sets out to differentiate and evaluate the offerings of three dominant cloud service providers: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).  This study ventures beyond traditional assessments by not only scrutinizing performance, security, and cost-effectiveness but also delving into the increasingly important dimensions of environmental sustainability and developer-friendliness. Performance metrics encompass not just raw computing power but also focus on service reliability, scalability, and the potential for multi-cloud interoperability. By scrutinizing containerization solutions, serverless computing frameworks, and data storage options, the study aims to unravel the nuanced strengths and potential pitfalls of each platform. Security, an evergreen concern in the cloud landscape, is investigated from both technological and organizational standpoints. The research looks at end-to-end encryption, compliance standards, and the inherent security culture of each cloud provider to provide a holistic view of safety measures. Cost-effectiveness evaluation is not limited to pricing structures alone. The study unpacks the value-added features, budget management tools, and growth potential offered by each provider. It also aims to project the long-term cost implications, helping businesses make fiscally sound choices. Unique to this comparative analysis is an investigation into the environmental impact of cloud technologies. The synthesis of this multifaceted evaluation equips decision-makers with an uncommonly thorough understanding of cloud technologies. Businesses and IT professionals can leverage these findings to make nuanced and tailored selections that align with their strategic goals, ethical considerations, and technological visions in a cloud landscape marked by its dynamism and diversity.

 Keywords: Cloud service providers, Cloudscape Navigator, Cloud technologies

Shavkun V.
Candidate of Technical Sciences, Associate Professor of the Department of Electrical transport,
O.M. Beketov National University of Urban Economy in Kharkiv
Kharkiv, Ukraine
Мoroz M.
Candidate of Technical Sciences, Associate Professor of the Department of Occupational and Life Safety,
 O.M. Beketov National University of Urban Economy in Kharkiv
Kharkiv, Ukraine 

Abstract
The question of ensuring the appropriate level of electrical safety by controlling the technical condition of the insulation of traction motors is considered. Mathematical models of changing the parameters of electric machines and their resource on different routes of the corresponding trolleybus have been developed, which is confirmed experimentally, ie according to the operating data. The issues of the influence of the length of the haul on the temperature conditions of the power electrical equipment of trolleybuses, in particular traction electric motors, are considered.

 Keywords: trolleybus, electric motor, insulation, electrical safety, reliability, parameters, technical condition.    temperature conditions, loading factors, insulation.

Salimov V.
Assistant professor of Azerbaijan State Oil and Industry University
Sariyeva S.
Master’s student of Azerbaijan State Oil and Industry University

Abstract
The digital age has steered into a new period of commerce, transubstantiating traditional business models and functional strategies across the globe. Azerbaijan, a burgeoning hub for technological advancement, has not remained untouched by this digital revolution. In the realm of electronic commerce (e-commerce), the country substantiates a massive affluence of structured and unstructured data, inclusively known as big data. This data, gathered from client relations, seller conditioning, market trends, and the broader business ecosystem, presents unknown openings for e-commerce realities. This paper is devoted  to the application  of data mining (DM) methods  in the Azerbaijani e-commerce sector, focusing on three vital algorithms:, clustering, classification and association rules . By employing these algorithms, e-commerce companies can unlock perceptivity for wares planning, sales forecasting, basket analysis, customer relationship management, and market segmentation. The primary idea of this study is to explore the application of data mining in enhancing e-commerce through the methodical analysis of both structured and unstructured data acquired from different sources, including cloud computing services. The significance of data mining in streamlining operations and fostering competitive advantage is emphasized, highlighting its role in data-driven decision-making. Additionally, the paper addresses the challenges encountered in the data mining process, such as spider identification, data transformation, and the presentation of data models in a business-friendly manner. The issues of managing slowly changing dimensions and making the data transformation and model-building processes more accessible to business users are also scrutinized. Through this exploration, the paper aims to offer a comprehensive guide for e-commerce companies in Azerbaijan, equipped with vast data reserves, to effectively leverage data mining techniques for business optimization and competitive edge. This study not only underscores the transformative potential of data mining in the e-commerce landscape of Azerbaijan but also presents a pathway for businesses to navigate the complexities of big data, ensuring sustained growth and innovation in the digital marketplace.

 Keywords: Data Mining, Big Data, E-Commerce, Cloud Computing, Azerbaijan

Mamatov N.
Doctor of Technical Sciences, Professor, Head of the Department of Digital Technologies and Artificial Intelligence,
“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Tashkent, Uzbekistan
Samijonov A.
Assistant, Department of Digital Technologies and Artificial Intelligence,
“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Tashkent, Uzbekistan
Erejepov K.
Researcher, Department of Digital technologies and artificial intelligence,
“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Tashkent, Uzbekistan
Narzullayev I.
PhD student,
Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan
Samijonov B.
Student,
Sejong University, South Korea

Abstract
Face image identification and verification has been a popular research topic in computer vision for several years. Geometrical normalization of the face image is important in this, and it has a direct impact on the recognition accuracy. This article is devoted to face image geometric normalization algorithms, it describes a face image normalization algorithm with high recognition speed and real-time performance.

 Keywords: face image, normalization, algorithm, scale, brightness, recognition, warp matrix, threshold, segment.