Адаптивні системи автоматичного управління : міжвідомчий науково-технічний збірник. – 2024. – № 2 (45)

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https://ela.kpi.ua/handle/123456789/70496

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Перегляд Адаптивні системи автоматичного управління : міжвідомчий науково-технічний збірник. – 2024. – № 2 (45) за Автор "Likhouzova, T."

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  • Ескіз
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    Models for analyzing and forecasting share prices on the stock exchange
    (КПІ ім. Ігоря Сікорського, 2024) Piznak, R.; Likhouzova, T.
    The work is devoted to the analysis and forecasting of share prices for four leading technology companies: Nvidia, Apple, Google, and Netflix. These companies are leaders in their fields and have a significant impact on the global economy. The goal is to study the dependencies affecting the share prices of companies, as well as to develop models for forecasting future trends. In the work, a thorough analysis of historical data on company share prices and their macroeconomic indicators was carried out. The study was based on the fundamental concepts of economic science. The study results are expected to provide a deeper understanding of the prospects of these companies.
  • Ескіз
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    Models for analyzing the complexity of english words in the text on the scale from A1 to C2
    (КПІ ім. Ігоря Сікорського, 2024) Bielikov, M.; Likhouzova, T.; Oliinyk, Y.
    At the current stage of globalization, English plays a key role as the language of international communication. This leads to the fact that more and more people become its carriers at various levels. The work is devoted to the analysis of English words on the scale from A1 to C2, which corresponds to the lowest and highest levels of proficiency according to the CEFR standards. A model that predicts the difficulty of words in a text can be used to improve the educational process. For example, it is possible to find a list of likely unknown and difficult words for the end user in any text depending on his level of English language proficiency. This approach will facilitate the language learning process by providing a personalized list of words to focus on. Also, the model can be useful for analyzing the complexity of texts depending on the number of words of each level of complexity in them. This can help teachers prepare materials that match the level of knowledge of their students, as well as identify words that may be difficult for them to understand. An application in the Python programming language is proposed, which receives a sample of data from the created storage, displays them graphically, performs intellectual analysis, trains and compares models according to accuracy, precision, recall and f1-score metrics. For data analysis and prediction of the level of complexity of English words, the following models were used: PchipInterpolator, logarithmic model, Gradient Boosting, Random Forest and XGB.