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Study of the use of neural network in combination with graphical programs in light industry

https://doi.org/10.48184/2304-568X-2024-2-169-177

Abstract

Technology, currently at the peak of its development, provides an opportunity to increase the level of education of modern man. As a consequence, the development of new clothing models using neural networks is also becoming a more efficient way to optimize design processes. By applying neural networks, results can be achieved quickly and easily. Artificial intelligence empowers and optimizes work in the fashion industry: it recognizes clothes from photos, accesses virtual fitting sessions, and, without much effort, easily selects the perfect size. The program will automatically design the pattern and technically reproduce patterns by size, height and fullness. Artificial intelligence will require less creative work from the author, and the designers themselves will be able to spend more time on their creativity. The research paper utilizes Microsoft Bing to facilitate the creation of new designs for clothing patterns. It is an artificial intelligence that can be used to create images, solve problems and get new ideas. With the help of this program it is planned to create an experimental series of Kyrgyz national costumes, the details of which will be designed in the graphic program of Grafic 12 clothing designer.

About the Authors

I. A. Rysbaeva
I. Razzakov Kyrgyz State Technical University (KSTU)
Kyrgyzstan

Candidate of Technical Sciences, Associate Professor of the Department of Technology of Light Industry Products, Director of the Institute of Technology

Kyrgyzstan, 720044, Bishkek, Ch. Aitmatov Avenue 66 



B. T. Turganbaeva
I. Razzakov Kyrgyz State Technical University (KSTU)
Kyrgyzstan

Kyrgyzstan, 720044, Bishkek, Ch. Aitmatov Avenue 66 



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Review

For citations:


Rysbaeva I.A., Turganbaeva B.T. Study of the use of neural network in combination with graphical programs in light industry. The Journal of Almaty Technological University. 2024;144(2):169-177. https://doi.org/10.48184/2304-568X-2024-2-169-177

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ISSN 2304-568X (Print)
ISSN 2710-0839 (Online)