The paper is devoted to the problem of automatic detection and recognition of license plates, the solution of which has many potential applications, from security to traffic management. The purpose of this work was to compare the methods of finding and recognizing car number plates, based on the application of deep learning algorithms, which takes into account different regional standards of car number plates, video quality, different speeds of vehicles, the location of the camera in relation to the vehicle license plate, defects of the car number plate (pollution , deformation), as well as changes in external lighting conditions. The advantages and disadvantages of localization and segmentation of car number plates on cars using image binarization, Viola–Jones and Harr methods are given. It was determined that adaptive approaches are better due to the possibility of compensating the impact of obstacles on different areas of the image, for example, the distribution of shadows due to the heterogeneity of illumination. It was determined that many methods in real algorithms rely directly or indirectly on the presence of number limits. Even if the limits are not used when the number is determined, they have the possibility to be used for further analysis. The methods of templates, image histograms, and contour analysis were compared to identify familiar features in the image (segmentation). It is shown that an effective approach for recognition of car license plates can be based on the application of the methods of Viola-Jones, Harr, the analysis of brightness histograms and the SVM method. Formulated conclusions on the effectiveness of the implementation of each of the procedures were confirmed as a result of conducting experiments with the developed software in the python 3 language using the cv2 computer vision library. The described approach makes it possible to obtain a fairly high recognition accuracy at different angles of rotation of the license plate relative to the camera. Keywords: automatic recognition, license plates, localization, normalization, segmentation, character recognition.
The article examines the task of assessing the cost of housing in the cities of Ukraine. The purpose of this work is to simplify the determination of the value of apartments on the real estate market using machine learning technologies. To solve this problem, it is proposed to use a program module in Python using the Sequential direct distribution model of the keras library. A program was created that estimates the value of apartments according to their parameters using a neural network. The importance of forecasting in the field of real estate is shown, because the housing market is a systemic part of the regional economy. The results of the software application, which consists of two parts, are presented. The first program collects the necessary data for training a neural network about apartments from the OLX site ads, their structuring and recording in a csv file. The second program provides tools for preliminary analysis of the collected data, after which they are cleaned, divided into training and test samples and trained on their basis by a multilayer neural network of direct propagation using a machine learning algorithm. The layers API of the keras library was used to design the neural network, which allows the user to create arbitrary layers. For regularization, the keras.regularizers tool, which is also in the layers API, is used. To configure model metrics, the compile method was used. Three hidden layers were defined, for each of which 512 neurons were introduced and the Relu activation function was chosen. Calculations of the correlation of prediction indicators and error curves of machine learning are given. As a result of testing the trained neural network on a test set of 652 examples, an average absolute error of 3570.88 was obtained, and the accuracy of the model was approximately 85%. Thus, the neural network has reached an acceptable level of accuracy for estimating the cost of apartments in the city of Kharkiv. Ways to reduce test errors and learning errors using cross-validation are proposed. Concepts of learning hyper-parameters and their regularization are considered Keywords: neural networks, deep learning, machine learning, regression, prediction, estimation, data analysis.
The article is devoted to the review of the current state, problems and directions of improving the activities of banking and microfinance organizations (MFIs) in the lending market. The methods of attracting customers to obtain loans are analyzed and the international experience of banking and non-banking organizations in the field of lending to the population is investigated. It was concluded that the economic development of the state not only implies, but also requires the development of market lending mechanisms that can not only provide banking institutions with a profit, but also ensure stable economic growth. In addition, it should be noted that there is a different focus on research in this area, as well as the lack of a unified approach to determining directions for improving lending competitiveness, and the mismatch between the chosen strategies for the real situation that has developed in Ukraine. It has been established that the largest share in the lending market belongs to consumer loans, classic loans to individuals, when borrowed funds are taken to pay for various necessities of life. At the same time, the presence of negative trends and factors affecting the stability and stability of the banking system has been identified, which makes it necessary to study and constantly monitor the status of consumer lending in order to identify potential problems. It is stated that at present, to expand the client market of credit institutions, it is necessary not only to expand the range of items provided under credit, but also to disseminate information and promote these services on the market. The main ways to attract customers to obtain loans and events that are actively used by credit institutions around the world are identified. The opinion of leading experts on the need to address a number of tasks to ensure the rights and legitimate interests of borrowers in the field of consumer lending is recorded. The principles of building a hierarchical system for working with loan applications are outlined. Keywords: lending, banking institutions, macro-financial organizations, borrower, money market, profitability, interest.
The current state and tendencies of development of hoarding investment by legal entities and the population of the country are considered in the article. It is especially important that these investments are available not only for legal entities, but also for the population, where there is a clear relationship between changes in the share of savings hoarded by private individuals and fluctuations in uncertainty, and growing investment and hoarding demand are the consequences of the financial crisis. inflation expectations, geopolitical instability and growing needs for diversification. On the basis of economic-theoretical analysis the essence, character of behavior, types and conditions of realization of hoarding investments (TI) in crisis economy are analyzed. The concept of "hoarding investments" has been clarified. The main subjects and objects of hoarding investments are identified. The objects of hoarding investments are bank metals (and coins from them) precious stones, jewelry, art objects and antiques. Available types, modern tendencies, methods and conditions of realization of hoarding investments are investigated. Coins issued by both Ukrainian and foreign banks were found to be numismatically valuable. However, foreign coins entering our market are usually issued in large numbers and, accordingly, have less numismatic value. In the United States, consumption of diamond jewelry is constantly growing due to the combination of domestic market unsaturation with well-established lending mechanisms, Europe is characterized by stagnation in the consumption of diamond jewelry, and for some countries, such as Germany, even a reduction. Hoarding investments in collectibles are specific in nature, due to their complexity, the relatively narrow market for each type of collection, the need for special knowledge and skills for proper investment. Keywords: hoarding investments, banking institutions, crisis economy, risk diversification, coins, precious stones, collectibles, profitability, interest.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.