Deep Learning-based Creation of Fractal Art Graphics and Fractal Analysis of Information Flow
Year: 2025
Type: conference-paper
Abstract: This article discusses the creation of fractal art graphics based on deep learning technologies and the fractal analysis of information flow. The application of neural networks in the image creation process using unconventional structures of fractal geometry, along with the aesthetic and informational properties of the created graphics, is analyzed. Additionally, the methods of representing and evaluating the complex structure of information flows through fractal modeling are presented. The proposed approaches not only ensure the integration of fractal art with modern digital technologies but also serve to enhance the effectiveness of information systems. The results are presented through graphical images, statistical analyses, and visualizations, highlighting the practical importance of fractal structures in information technology. Furthermore, the article examines the role of artificial intelligence in transforming art design and education, and how deep learning opens up opportunities for developing both artistic and technical skills. The analysis of information flow during the design process is explored, shedding light on the potential of deep learning to open up new possibilities in graphic creation and improve the educational experience of students in the fields of art and design.
Author: Egamberdiyev Nodir Abdunazarovich, Uralova Iroda Abduvali qizi, Abdullayev Muxammadali Abdulaxad o'g'li
Language: English
Topic: Art, Technology, and Culture
Subfield: Visual Arts and Performing Arts
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6893307089
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