MODERN STATE AND POST-WAR PROSPECTS OF FINANCIAL INCLUSION IN UKRAINE CONSIDERING THE EU EXPERIENCE

The purpose of the article is theoretical and methodological substantiation of the state and post-war prospects of financial inclusion in Ukraine, taking into account the experience of EU countries. The objectives of the study are as follows: to theoretically substantiate the content and features of financial inclusion, taking into account the experience of EU countries; to assess the current state of financial inclusion; and to outline post-war prospects for the development of financial inclusion in Ukraine. It is argued that the key parameters of financial inclusion are financial well-being, financial behaviour, financial knowledge, and the utilization of financial products, as well as physical access to financial services. Drawing on the experience of EU countries, it is substantiated that creating conditions for free access to financial services for all categories of the population and businesses, as well as their digitalization, contribute to the growth of citizens' income, the simplification of investment mechanisms, and thus the economic development of the country. An assessment of the state of financial inclusion in Ukraine is conducted through online surveys of various categories of citizens. It is determined that Ukrainians feel a strong need for additional sources of income, and the financial situation of the majority of them is extremely unstable. Based on the results of correlation-regression analysis, the priorities of financial inclusion are identified as the level of financial literacy and competency among the population. This will enable them to budget their income and expenses, promote the transformation of savings into investments, and have a positive impact on the post-war recovery of the country's economy

Метод автоматичного визначення порогу градієнтного фільтра для швидкої обробки об’єктів динамічних сцен

In this paper, we present a new method based on the use of information provided by the gradient methods for determining the geometric parameters of objects with high accuracy. The algorithm is based on the use of data obtained after image processing by gradient filters. Also, it reacts to the slightest change in the contours of objects of the dynamic image scenes. Repeated experiments using more than 5000 real images were processed to improve the theory. Given a high refresh rate of modern systems, a position of the Center Of Gravity (COG) in the dynamic images is changing gradually even for rapid motion. Using this feature, COG for each frame of a training sample is defined under various threshold values by means of an algorithm. A number of elements (frames) in the training sample are selected depending on the type of the dynamic object, a task set and on the initial conditions. The suggested method is recommended for further use by the expert system, in parallel with its own operation, with a goal to maintain a threshold value on the optimal level in case of dynamic perturbing factors. After the research, we found that the prediction accuracy increased that essentially improved results. A number of experiments demonstrated increasing the accuracy of determination of the center of blurred objects. Also, we have eliminated the human factor. All of the calculations are done automatically. These data are very useful and important for all areas of science where high accuracy of the results is necessary.Данная статья посвящена новому методу нахождения порогового значения градиентных фильтров, для решения задач определения геометрических параметров объектов с повышенной точностью. Алгоритм основан на использовании данных полученных после обработки изображения градиентными фильтрами, а так же реагирует на малейшие изменения контуров объектов изображений динамических сцен.Дана стаття присвячена новому методу знаходження порогового значення градієнтних фільтрів, для розв’язання задач визначення геометричних параметрів об’єктів з підвищеною точністю. Алгоритм заснований на використанні даних отриманих після обробки зображення градієнтними фільтрами, а також реагує на найменші зміни контурів об’єктів зображень динамічних сцен