Usage of neural networks in image recognition




neural network, object recognition, classification, domains


This article focuses on the operation of the classification of blueprint parts. Classification characteristic is the main part of the designation of the part or product and their design documents, solving a number of topical tasks from creation of a single information language for automated systems to unification and standardization.

Author Biographies

Olena KRYVORUCHKO, Kyiv National University of Trade and Economics

DSc (Engineering), Professor, Head of Department of Software Engineering
and Cyber Security

Karyna KHOROLSKA, Softorino Inc.

Server-side Developer,
Softorino Inc.

Vitalii CHUBAIEVSKYI, Kyiv National University of Trade and Economics

PhD (Political Sciences), Associate Professor of Department of Software Engineering and Cyber Security


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Andre, Esteva, & Brett, Kuprel (2017).Dermatologist-level classification of skin cancer with deep neural networks. (Vol. 542), (pp. 115–118). 02 February. Retrieved from DOI: [in English].

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Qian, Y., Dong, J., Wang, W., & Tan, T. (2015). Deep learning for steganalysis via convolutional neural networks. Media Watermarking, Security and Forensics. (Vol. 9409), (pp. 94 090J). DOI: [in English].

Lin, M., Chen, Q., & Yan, S. (2014). Network in network, in International Conference on Learning Representations [in English].

Ciresan, D. C., Meier, U. J., Masci, Gambardella L. M., & Schmidhuber J. (2011). High-performance neural networks for visual object classification. Arxiv preprint arXiv:1102.0183 [in English].

Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks, in Advances in neural information processing systems, (pp. 1097-1105) [in English].

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How to Cite

KRYVORUCHKO, O., KHOROLSKA, K., & CHUBAIEVSKYI, V. (2019). Usage of neural networks in image recognition. oreign rade: onomics, inance, aw, 104(3), 83–101.