In this paper, we investigated the effectiveness of an intelligent system for classifying radiographic images into three classes using transfer learning and fine-tuning methods. The convolutional neural network VGG16 structure was used to evaluate the effectiveness of predicting the presence of characteristic pathologies by the system. A catalog consisting of 4,228 chest X-ray images was used for training, verification of training and testing this neural network. A catalog was divided into three classes such as pneumonia, Covid-19 and in the absence of lung diseases. To provide modeling processes there were used deep learning libraries such as Keras and TensorFlow. In the course of the work, there were presented the results of the neural network accuracy of the classification of chest X-ray images by full-trained neural network model. We also demonstrated an increase in prediction accuracy by using transfer learning method, as well as with fine-tuning of the neural network structure. Based on the results of the work, the neural network has learned to recognize images with signs of pneumonia, Covid-19 and normal radiograph. In conclusion, the best accuracy index of 98.4 % was achieved by using the fine-tuning method for our convolutional neural network.

Keywords: chest X-ray, Covid-19, pneumonia, intelligent system, machine learning, deep learning, convolutional neural networks, transfer learning, fine-tuning of convolutional neural network

Reconstruction of industrial enterprises is a complex, time-consuming and expensive process. When implementing it, a number of significant problems arise that must be taken into account at the design stage. This will allow us to find approaches to solving the problems of reconstruction of industrial production carried out under various conditions and significantly reduce the costs of construction work. Reconstruction also has three main directions, covering certain tasks and differing in the scale of reconstruction activities. One of these areas is revitalization. When there is a need to solve problems focused on the socio-cultural life of the population on the territory of the existing urban development, revitalization becomes the optimal and modern way of solving. The result of the reconstruction should be an increase in production capacity and profitability of enterprises, a reduction in the negative impact on the environment, as well as an increase in social comfort in residential areas of cities.

Keywords: reconstruction, technical support, re-profiling, revitalization, production area, production capacity, radical reconstruction

In this work we have investigated the structure of individual single-walled and multi-walled carbon nanotubes by high-resolution electron microscopy and electron diffraction. To grow carbon nanotubes we used a catalytic chemical vapor deposition method. It was shown, that this synthesis protocol gave in general single-walled and double-walled carbon nanotubes with a high level of crystallinity. The diameters of the nanotubes were in the range 1.5 - 7 nm. We also observed that there was a certain level of amorphous carbon deposited on the nanotube surface during the growth. In this work we also present the structure analysis of the double-walled carbon nanotube by means of electron diffraction. We show that the structural date derived from electron microscopy and electron diffraction agree within the experimental error.

Keywords: Carbon nanotubes, electron diffraction, electron microscopy

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