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Browsing by Author "Golovko, Vladimir"

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    • A New Technique for Restricted Boltzmann Machine Learning 

      Golovko, Vladimir; Kroshchanka, Aliaksandr; Turchenko, Volodymyr; Jankowski, Stanislaw; Treadwell, Douglas (Information Science Warsaw University of Technology, 2015)
      Over the last decade, deep belief neural networks have been a hot topic in machine learning. Such networks can perform a deep hierarchical representation of input data. The first layer can extract low-level features, the second layer can extract high-level features and so on. In general, deep belief ...

      2023-11-01

    • A Steganographic Method Using Learning Vector Quantization 

      Gorbashkо, Larisa; Golovko, Vladimir (BSTU, 2006)
      The new technique for embedding image data is presented. The message is subjects to vector quantizer by neural network. The modified data is inserted into the coiner in the wavelet transform domain. The vector quantization enables to increase the capacity of embedded data. The experimental results ...

      2023-11-29

    • Behavior Patterns of adaptive Multi-Joined Robot learned by Multi-Agent Influence Reinforcement Learning 

      Kabysh, Anton; Golovko, Vladimir; Mikhniayeu, Andrei; Rubanau, Uladzimir; Lipnikas, Arunas (BSUIR, 2011)
      This paper describes behavior patterns produced by Multi-Joined Robot learned via Influence Reinforcement learning. This learning technique used for distributed, adaptive and self-organizing control in multi-agent system. This technique is quite simple and uses agent’s influences to estimate learning ...

      2023-10-23

    • Computing of Lyapunov Exponents Techniques Using Neural Networks 

      Golovko, Vladimir; Savitsky, Yury (BSUIR, 2003)
      The authors examine neural network techniques for computing of Lyapunov spectrum using observations from unknown dynamical system. Such an approach is based on applying of multilayer perceptron (MLP) for forecasting the next state of dynamical system from the previous one. It allows for evaluating the ...

      2023-11-17

    • Convergence and integration of artificial neural networks with knowledge bases in next-generation intelligent computer systems 

      Kovalev, Mikhail; Kroshchanka, Aliaksandr; Golovko, Vladimir (БГУИР, 2022)
      In the article, an approach to the integration and convergence of artificial neural networks with knowledge bases in next-generation intelligent computer systems through the representation and interpretation of artificial neural networks in a knowledge base is considered. The syntax, denotational, and ...

      2023-11-02

    • Deep Convolutional Neural Network for Detection of Solar Panels 

      Golovko, Vladimir; Kroshchanka, Alexander; Mikhno, Egor; Komar, Myroslav; Sachenko, Anatoliy (Springer, 2020)
      The article describes a method for detecting solar panels in satellite imagery. Due to the growing popularity of this technology, problems associated with themaintenance of solar panels are also becoming relevant.Many service companies are interested in obtaining information about potential customers. ...

      2023-11-01

    • Evolution of detectors in neural network immune system for pattern recognition 

      Bezobrazov, Sergei; Golovko, Vladimir (БГУ, 2011)
      In this paper we present the basic principles of the evolution of detectors in intelligent system for pattern recognition, such as malicious code detection. This system based on integration of both Al methods: artificial neural networks and artificial immune systems. The goal of the evolution is ...

      2023-11-20

    • Neural Network Model for Transient Ischemic Attacks Diagnostics 

      Golovko, Vladimir; Apanel, Elena; Mastykin, Alexander; Vaitsekhovich, Henadzi; Evstigneev, Victor (BSUIR, 2011)
      In this paper the neural network model for transient ischemic attacks recognition have been addressed. The proposed approach is based on integration of the NPCA neural network and multilayer perceptron. The dataset from clinic have been used for experiments performing. Combining two different neural ...

      2023-11-20

    • Neural Network Techniques for Intrusion Detection 

      Golovko, Vladimir; Vaitsekhovich, Leanid (BSTU, 2006)
      This paper presents the neural network approaches for building of intrusion detection system (IDS). Existing intrusion detection approaches have same limitations, namely low detection time and recognition accuracy. In order to overcome these limitations we propose several neural network systems for ...

      2023-11-29

    • Neural Networks for Chaotic Time Series Forecasting 

      Golovko, Vladimir; Savitsky, Yury (BSUIR, 2001)
      This paper examines neural network in order to predict behavior o f chaotic systems. The prediction is performed both on the level o f emergent structures and on the level o f individual data points. The network is tested using the Henon and Lorenz chaotic time series. The results o f experiments and ...

      2023-11-16

    • Neural networks in transient ischemic attacks diagnostics 

      Golovko, Vladimir; Apanel, Elena; Mastykin, Alexander; Vaitsekhovich, Henadzi (БГУ, 2011)
      In this paper the neural network model for transient ischemic attacks recognition have been addressed. The proposed approach is based on integration of the NPCA neural network and multilayer perceptron. The dataset from clinic have been used for experiments performing. Combining two different neural ...

      2023-11-20

    • Some approaches to line detection on a task of line-following 

      Kaliukhovich, Dzmitry; Golovko, Vladimir; Paczynski, Andreas (БрГТУ, 2007)
      Kaliukhovich, D. Some approaches to line detection on a task of line-following / D. Kaliukhovich, V. Golovko, A. Paczynski // Современные проблемы математики и вычислительной техники : материалы V Республиканской научной конференции молодых ученых и студентов, Брест, 28–30 ноября 2007 года / Министерство ...

      2020-03-02

    • Some Aspects of Chaotic Time Series Analysis 

      Golovko, Vladimir; Savitsky, Yury; Maniakov, Nikolaj; Rubanov, Vladimir (BSUIR, 2001)
      We address two aspects in chaotic time series analysis, namely the definition of embedding parameters and the largest Lyapunov exponent. It is necessary for performing state space reconstruction and identification of chaotic behavior. For the first aspect, we examine the mutual information for ...

      2023-11-16

    • The nature of unsupervised learning in deep neural networks: A new understanding and novel approach 

      Golovko, Vladimir; Kroshchanka, Aliaksandr; Treadwell, Douglas (Springer, 2016)
      Over the last decade, the deep neural networks are a hot topic in machine learning. It is breakthrough technology in processing images, video, speech, text and audio. Deep neural network permits us to overcome some limitations of a shallow neural network due to its deep architecture. In this paper we ...

      2023-11-01

    • Training of the recurrent neural networks for prediction 

      Savitsky, Jury; Golovko, Vladimir (BSUIR, 1999)
      In this paper the technique of creation of effective methods of training recurrent neural network for prediction problems are discussed. The various functions of activation of neural units are considered. The adaptive algorithms of training of neural networks with varied functions of activation of ...

      2023-11-15