Перегляд за Автор "Zaychenko, Yu."

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    A hybrid model of artificial intelligence integrated into GIS for predicting accidents in water supply networks
    (КПІ ім. Ігоря Сікорського, 2024) Zaychenko, Yu.; Starovoit, T.
    The search for an effective and reliable model for predicting accidents on water supply networks by determining their exact locations has always been important for effectively managing water distribution systems. This study, based on the adaptive neuro-fuzzy logical inference system (ANFIS) model, was developed to predict accidents in the city of Kyiv (Ukraine) water supply network. The ANFIS model was combined with genetic algorithms and swarm optimization (ACO) methods and integrated into a GIS to visualize results and determine locations. Forecasts were evaluated according to the following criteria: mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2 ). Depending on the amount and type of input data, ANFIS optimization with genetic algorithms and swarm optimization (ACO) can, on average, increase the accuracy of ANFIS predictions by 10.1% to 11%. The obtained results indicate that the developed hybrid model may be successfully applied to predict accidents on water supply networks.
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    Fuzzy portfolio optimization problem under uncertainty сonditions with application of computational intelligence methods
    (КПІ ім. Ігоря Сікорського, 2020) Zaychenko, H.; Zaychenko, Yu.
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    Hybrid system of computational intelligence based on bagging and group method of data handling
    (КПІ ім. Ігоря Сікорського, 2024) Bodyanskiy, Ye.; Kuzmenko, O.; Zaichenko, He.; Zaychenko, Yu.
    The paper considers the problem of short- and middle-term forecasting in the financial sphere. To solve this problem, a hybrid system of computational intelligence based on the group method of data handling (GMDH) and bagging, as well as an algorithm for its training, is proposed. The odd stacks of the hybrid system are formed by ensembles of parallel connected subsystems. ARIMA and the GMDHneo-fuzzy hybrid network were chosen as such subsystems. The proposed system does not require a large training data set, automatically determines the number of stacks during training, and provides online operation. The experimental investigations were conducted using the proposed hybrid system, as well as separately using ARIMA and GMDH-neo-fuzzy. The accuracy of the predictions obtained is compared, based on which the feasibility of using the proposed hybrid system is substantiated.
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    Investigation of computational intelligence methods in forecasting at financial markets
    (КПІ ім. Ігоря Сікорського, 2023) Zaychenko, Yu.; Zaichenko, He.; Kuzmenko, O.
    Abstract. The work considers intelligent methods for solving the problem of shortand middle-term forecasting in the financial sphere. LSTM DL networks, GMDH, and hybrid GMDH-neo-fuzzy networks were studied. Neo-fuzzy neurons were chosen as nodes of the hybrid network, which allows to reduce computational costs. The optimal network parameters were found. The synthesis of the optimal structure of hybrid networks was performed. Experimental studies of LSTM, GMDH, and hybrid GMDH-neo-fuzzy networks with optimal parameters for short- and middleterm forecasting have been conducted. The accuracy of the obtained experimental predictions is compared. The forecasting intervals for which the application of the researched artificial intelligence methods is the most expedient have been determined.
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    Investigation of сomputational intelligence methods in forecasting problems at stock exchanges
    (КПІ ім. Ігоря Сікорського, 2021) Zaychenko, Yu.; Hamidov, G.; Gasanov, A.
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    Medical images of breast tumors diagnostics with application of hybrid CNN–FNN network
    (КПІ ім. Ігоря Сікорського, 2018) Zaychenko, Yu.; Hamidov, G.; Varga, I.
    The problem of classification of breast tumors on medical images is considered. For its solution the new class of convolutional neural networks-hybrid CNN–FNN network is developed in which convolutional neural network VGG-16 is used as the feature extractor while fuzzy neural network NEFClass is used as the classifier. Training algorithms of FNN were implemented. The experimental investigations of the suggested hybrid network on the standard data set were carried out and comparison with known results was performed. The problem of data dimensionality reduction is considered and application of PCM method is investigated.
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    Multilayer gmdh-neuro-fuzzy network based on extended neo-fuzzy neurons and its application in online facial expression recognition
    (КПІ ім. Ігоря Сікорського, 2020) Bodyanskiy, Ye.; Zaychenko, Yu.; Hamidov, G.; Kulishova, N.
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    Using convolutional neural networks for breast cancer diagnosing
    (КПІ ім. Ігоря Сікорського, 2019) Naderan, M.; Zaychenko, Yu.; Napoli, A.
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    Исследование двойственной задачи оптимизации инвестиционного портфеля в нечетких условиях
    (Політехніка, 2011) Зайченко, Ю. П.; Ови Нафас Агаи Аг Гамиш; Зайченко, Юрій Петрович; Ові Нафас Агаі Аг Гаміш; Zaychenko, Yu.; Ovi Nafas Agai Ag Gamish