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Документ An Overview of Existing Automated Methods Definition of the Author of the Written Text Identification Characteristics(2020) Sliusarieva, Yuliia; Borysova, Natalia; Melnyk, KarinaThe paper presents an overview of the existing methods for solving the problem of automated identification of the author of the written text: substantiated the relevance of the research topic, analyzed the existing methods of solving the task, identified their advantages and disadvantages, selected the direction of further program implementation.Документ To the Problem of Creating Software to Determine the Level of Fluency of German(2020) Chervatiuk, Yuliia; Borysova, Natalia; Melnyk, KarinaThe paper presents an analysis of the problem of creating software for determining the fluency of the German language: the relevance of the research topic is substantiated, the existing approaches, tools and tools for determining the level of fluency of the German language are analyzed, their advantages and disadvantages are determined, the direction of further software implementation is selected.Документ Протокол RACPWP иерархической плановой системы для задач с высокой критичностью(Харківський національний університет Повітряних Сил ім. Івана Кожедуба, 2017) Шабанова-Кушнаренко, Любовь Владимировна; Борисова, Наталья Владимировна; Чередниченко, Ольга Юрьевна; Гонтарь, Юлия НиколаевнаИерархическое планирование используется для группировки приложений на основе их функциональности для улучшения агрегирования ресурсов и временной изоляции между приложениями. Коэффициент критичности задачи используется для сопоставления задач с ядрами в кластере подсистем. В статье представлены результаты полученного подхода иерархического планирования, когда планирование подсистем корректируется на основе уровня критичности, а не только строго установленного срока. Результатом является встроенная система, которая лучше приспособлена для адаптации к вычислительным вариациям, обеспечивающим гарантии времени для выполнения задач с высокой критичностью, что обеспечивает минимальный уровень обслуживания для снижения требований к критичности.Документ Efficiency estimation of methods for sentiment analysis of social network messages(Національний технічний університет "Харківський політехнічний інститут", 2019) Borysova, N. V.; Melnyk, K. V.The results of effectiveness evaluating of machine learning methods for sentiment analysis of social network messages are presented in this paper. The importance of the sentiment analysis problem as one of the important tasks of natural language processing in general and text ual information processing in particular is substantiated. A review of existing methods and software for sentiment analysis are ma de. The choice of classifiers for sentiment analysis of texts for this research is substantiated. The principles of functioning of a Naïve Bayesian Classifier and classifier based on a recurrent neural network are described. Classifiers were sequentially trained in two corpuses: first, in the RuTweetCorp corpus, the corpus of short messages from the social network Twitter, and then on the Slang corpus, the corpus of messages from social networks Facebook and Instagram and posts from the Pikabu website, second corpus have been marked up the tonality of slang words. Information about the tonality of slang words was taken from the youth slang dictionary obtained as a result of the survey of users. The separation of texts by tonality was carried out into three c lasses: positive, negative and neutral. The efficiency of these classifiers was evaluated. Efficiency evaluation was carried out according to standard metrics Recall, Precision, F-measure, Accuracy. For the naive Bayesian classifier, after training on the first corpus, the following metric values were obtained: Recall = 0,853; Precision = 0,869; F-measure = 0,861; Accuracy = 0,855; and after training on the second corpus such values were obtained: Recall = 0,948; Precision = 0,975; F-measure = 0,961; Accuracy = 0,960. For the classifier based on a recurrent neural network, after training on the first corpus, the following metric values were obtained: Recall = 0,870; Precision = 0,878; F-measure = 0,874; Accuracy = 0,861; and after training on the second corpus such values were obtained: Recall = 0,965; Precision = 0,982; F-measure = 0,973; Accuracy = 0,973. These results prove that additional training on the second corpus increased the efficiency of classifiers by 10–11%.