Browsing by Author "Ostrovska, Kateryna Yu."

Now showing 1 - 4 of 4
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    Application of Neural Networks for Prediction Financial Time Series
    (Scientific Publishing Center “Sci-conf.com.ua”, Perfect Publishing, 2024) Prokofiev, Taras; Ostrovska, Kateryna Yu.
    ENG: The article discusses some aspects and features of the use of neural networks for forecasting financial time series for the purpose of making a profit. The use of neural networks to analyze financial information is a promising alternative (or complement) to traditional research methods. Due to their adaptability, the same neural networks can be used to analyze several instruments and markets, while the patterns found by a player for a specific instrument using technical analysis methods may work worse or not work at all for other instruments.
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    Development and Research of a Chatbot Using the Linguistic Core of Amazon Lex V2
    (CEUR-WS Team, Aachen, Germany, 2024) Hnatushenko, Viktoriia V.; Ostrovska, Kateryna Yu.; Nosov, Valerii
    ENG: The main of this research is to develop and explore the configuration of a text and voice recognition system, integrate it into a specialized application, and deploy the application in a cloud environment. Amazon Lex service is built on chatbots that support Natural Language Understanding (NLU) and voice recognition. The developed chatbot elevates the user experience while engaging with voice consultants by offering flexible customization options. A chatbot has been designed with interactive text input fields and voice recording functions. The server architecture of the application is configured for seamless data transmission through the AWS SDK to Amazon Lex. The input information undergoes processing to ensure the generation of responses that are dynamically displayed on the web page. The structure of all intents – simulating banking services such as checking card balance, transaction history, and more. Testing the intents was done by creating a dataset with possible user statements and automated runs. The developed chatbot was tested through 6 runs, each consisting of up to 5 statements for recognition. The accuracy of text input recognition ranged from 60% to 99%, with voice input recognition accuracy being 10% lower.
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    Research of Image Classification Methods Using Neural Networks on GPUs
    (Scientific Publishing Center “Sci-conf.com.ua”, BoScience Publisher, 2024) Cherskyi, Serhii; Ostrovska, Kateryna Yu.
    ENG: The paper examines the classification of images on GPUs by means of neural networks, namely, using the example of the categorization of household goods. This topic is relevant, since in everyday life we are surrounded by images and it is easy for a person to interpret them, and it is much more difficult for a computer, all the more to classify or segment images. As a result, a system was created that automatically classifies goods, modifying existing approaches, and obtained a custom one that works better for this task. Having improved the product, it can be used for any organization where it would be convenient to automatically classify products.
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    Research of Image Classification Methods Using Neural Networks on GPUs
    (Scientific Publishing Center “Sci-conf.com.ua”, Perfect Publishing, 2024) Cherskyi, Serhii; Ostrovska, Kateryna Yu.
    ENG: The paper examines the classification of images on GPUs by means of neural networks, namely, using the example of the categorization of household goods. This topic is relevant, since in everyday life we are surrounded by images and it is easy for a person to interpret them, and it is much more difficult for a computer, all the more to classify or segment images. As a result, a system was created that automatically classifies goods, modifying existing approaches, and obtained a custom one that works better for this task. Having improved the product, it can be used for any organization where it would be convenient to automatically classify products.