Кафедра комп’ютерних наук

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https://elar.khmnu.edu.ua/handle/123456789/582

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Перегляд Кафедра комп’ютерних наук за Автор "Hrypynska, Nadiia"

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  • Ескіз
    Документ
    A framework for exploring and modeling neural architecture search methods
    (CEUR-WS, 2020-05-14) Radiuk, Pavlo; Hrypynska, Nadiia
    For the past years, many researchers and engineers have been developing and optimising deep neural networks (DNN). The process of neural architecture design and tuning its hyperparameters remains monotonous, timeconsuming, and do not always ensure optimal results. In his regard, the automatic design of machine learning (AutoML) has been widely utilised, and neural architecture search (NAS) has been actively developing in recent years. Despite meaningful advances in the field of NAS, a unified, systematic approach to explore and compare search methods has not been established yet. In this paper, we aim to close this knowledge gap by summarising search decisions and strategies and propose a schematic framework. It applies quantitative and qualitative metrics for prototyping, comparing, and benchmarking the NAS methods. Moreover, our framework enables categorising critical areas to search for better neural architectures.
  • Ескіз
    Документ
    An ensemble machine learning approach for Twitter sentiment analysis
    (CEUR-WS, 2022-07-17) Radiuk, Pavlo; Pavlova, Olga; Hrypynska, Nadiia
    The presented study addresses the issue of classifying emotional expressions based on small texts (tweets) extracted from the social network Twitter. In this paper, we propose a novel approach to preprocessing tweets to fit them more effectively into the classification model. Moreover, we suggest utilizing two types of features, namely unigrams and bigrams, to expand the feature vector. The classification task of emotional expressions was performed according to several machine learning algorithms: raw random forest, gradient boosting random forest, support vector machine, multilayer perceptron, recurrent neural network, and convolutional neural network. The feature vector elements are presented as sparse and dense subvectors. As a result of computational experiments, it was found that the “appearance” in the reflection of the sparse vector provided higher performance than the “regularity.” The experiments also showed that deep learning approaches performed better than traditional machine learning techniques. Consequently, the best recurrent neural network achieved an accuracy of 83.0% on the test dataset, while the best convolutional neural network reached 83.34%. At the same time, it was discovered that the convolutional model with the support vector machine classifier showed better performance than the single convolutional neural network. Overall, the proposed ensemble method based on receiving the most votes according to the five best models’ predictions has reached an absolute accuracy of 85.71%, proving its practical usefulness.