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Документ Usage of Mask R-CNN for automatic license plate recognition(Національний технічний університет "Харківський політехнічний інститут", 2023) Podorozhniak, A. O.; Liubchenko, N. Yu.; Sobol, Maksym; Onishchenko, D. P.The subject of study is the creation process of an artificial intelligence system for automatic license plate detection. The goal is to achieve high license plate recognition accuracy on large camera angles with character extraction. The tasks are to study existing license plate recognition technics and to create an artificial intelligence system that works on big shooting camera angles with the help of modern machine learning solution – deep learning. As part of the research, both hardware and software-based solutions were studied and developed. For testing purposes, different datasets and competing systems were used. Main research methods are experiment, literature analysis and case study for hardware systems. As a result of analysis of modern methods, Mask R-CNN algorithm was chosen due to high accuracy. Conclusions. Problem statement was declared; solution methods were listed and characterized; main algorithm was chosen and mathematical background was presented. As part of the development procedure, accurate automatic license plate system was presented and implemented in different hardware environments. Comparison of the network with existing competitive systems was made. Different object detection characteristics, such as Recall, Precision and F1-Score, were calculated. The acquired results show that developed system on Mask R-CNN algorithm process images with high accuracy on large camera shooting angles.Документ A comparison of classifiers applied to the problem of biopsy images analysis(Національний технічний університет "Харківський політехнічний інститут", 2020) Hlavcheva, Daria; Yaloveha, Vladyslav; Podorozhniak, Andrii; Lukova-Chuiko, NataliiaThe purpose of the research is to compare classification algorithms for the histopathological images analyzing issue and to optimize the parameters for obtaining better classification accuracy. The following tasks are solved in the article: preprocessing of BreCaHAD dataset images, implementation and training of CNN, applying K-nearest neighbours, SVM, Random Forest,XGBoost, and perceptron algorithms for classifying features that were extracted by CNN, and results comparison. The object of the research is the process of classifying tumor cells in the microscopic biopsy images. The subject of the research is the processof using ML algorithms for classification of the features extracted by CNN from input biopsy image. The scientific novelty of the research is a comparative analysis of classifiers on the task of “tumor” and “healthy” cells images classification from processed BreCaHAD dataset. As a result it was obtained that from chosen classifiers SVM reached the highest accuracy on test data –0.972. This is the only algorithm that shows better accuracy than perceptron. Perceptron gets 0.966 classification accuracy. K-nearest neighbours, Random Forest, and XGBoost algorithms reached lower results. The algorithms' hyperparameters optimization was carried out. The results have been compared with related works. The following research methodsare used: the theory of deep learning, mathematical statistics, parameters optimization.