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  <title>DSpace Зібрання:</title>
  <link rel="alternate" href="https://repository.hneu.edu.ua/handle/123456789/172" />
  <subtitle />
  <id>https://repository.hneu.edu.ua/handle/123456789/172</id>
  <updated>2026-04-12T02:35:11Z</updated>
  <dc:date>2026-04-12T02:35:11Z</dc:date>
  <entry>
    <title>Robust decentralization of information management systems</title>
    <link rel="alternate" href="https://repository.hneu.edu.ua/handle/123456789/39120" />
    <author>
      <name>Udovenko S. G.</name>
    </author>
    <author>
      <name>Zatkhey V. A.</name>
    </author>
    <author>
      <name>Teslenko O. V.</name>
    </author>
    <id>https://repository.hneu.edu.ua/handle/123456789/39120</id>
    <updated>2026-03-23T13:11:29Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Назва: Robust decentralization of information management systems
Автори: Udovenko S. G.; Zatkhey V. A.; Teslenko O. V.
Короткий огляд (реферат): An approach to determining control influences for a decentralized information management system that ensure its robustness to structural disturbances is considered. An algorithm for selecting a suboptimal strategy for forming controls over system objects is proposed.</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Neural network technology for detecting errors in text documents</title>
    <link rel="alternate" href="https://repository.hneu.edu.ua/handle/123456789/39115" />
    <author>
      <name>Udovenko S. G.</name>
    </author>
    <author>
      <name>Zatkhey V. A.</name>
    </author>
    <author>
      <name>Teslenko O. V.</name>
    </author>
    <id>https://repository.hneu.edu.ua/handle/123456789/39115</id>
    <updated>2026-03-23T12:12:51Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Назва: Neural network technology for detecting errors in text documents
Автори: Udovenko S. G.; Zatkhey V. A.; Teslenko O. V.
Короткий огляд (реферат): The goal of this work is to develop a modified technology for error detection in text documents using a multilayer perceptron neural network. &#xD;
The proposed technology is implemented using an autoassociative neural network trained on a corpus of parallel texts containing distorted and corrected sentences. The feasibility of using the principal component method for preprocessing input text data is investigated.&#xD;
The test results confirm the effectiveness of the application of the studied technology for detecting errors in polythematic text documents.</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Analysis of the effectiveness of software testing technologies for information systems</title>
    <link rel="alternate" href="https://repository.hneu.edu.ua/handle/123456789/38857" />
    <author>
      <name>Brynza N.</name>
    </author>
    <author>
      <name>Lobanov K.</name>
    </author>
    <id>https://repository.hneu.edu.ua/handle/123456789/38857</id>
    <updated>2026-03-01T14:17:10Z</updated>
    <published>2026-01-01T00:00:00Z</published>
    <summary type="text">Назва: Analysis of the effectiveness of software testing technologies for information systems
Автори: Brynza N.; Lobanov K.
Короткий огляд (реферат): The purpose of this work is to analyze the effectiveness of software testing. Modern approaches to software testing are analyzed, and their advantages, limitations, and areas of practical application are identified. To conduct a computational experiment, an experimental dataset in CSV format   was   created,   containing   approximately   15,000   records   that   characterize   the   testing   parameters,  specifically  costs,  execution  time,  and  the  number  of  defects  found.  Models  were  constructed to evaluate the economic efficiency of various testing types, enabling the determination of  time  limits  for  return  on  investment.  Based  on  the  results  of  the  experiment,  practical  recommendations  were  formulated  for  choosing  the  optimal  type  of  testing:  manual  testing  is  appropriate for small projects due to low costs, automated testing is appropriate for medium-sized projects with repetitive scenarios, and intelligent testing is suitable for large and dynamic systems where adaptability and high accuracy in detecting defects are critical.</summary>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Особливості застосування нечітких когнітивних карт сценарного моделювання функціонування критичної інфраструктури в умовах надзвичайних ситуацій воєнного характеру</title>
    <link rel="alternate" href="https://repository.hneu.edu.ua/handle/123456789/38759" />
    <author>
      <name>Тютюник В. В.</name>
    </author>
    <author>
      <name>Тютюник О. О.</name>
    </author>
    <author>
      <name>Усачов Д. В.</name>
    </author>
    <id>https://repository.hneu.edu.ua/handle/123456789/38759</id>
    <updated>2026-02-15T13:27:06Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Назва: Особливості застосування нечітких когнітивних карт сценарного моделювання функціонування критичної інфраструктури в умовах надзвичайних ситуацій воєнного характеру
Автори: Тютюник В. В.; Тютюник О. О.; Усачов Д. В.
Короткий огляд (реферат): Мета роботи: дослідження особливостей застосування нечітких когнітивних карт для сценарного моделювання функціонування об’єктів критичної інфраструктури в умовах надзвичайних ситуацій воєнного характеру.</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
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