Version 1
: Received: 14 September 2024 / Approved: 16 September 2024 / Online: 16 September 2024 (11:39:45 CEST)
How to cite:
Jean, M.-D.; Zhang, C.; Li, X. A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques. Preprints2024, 2024091215. https://doi.org/10.20944/preprints202409.1215.v1
Jean, M.-D.; Zhang, C.; Li, X. A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques. Preprints 2024, 2024091215. https://doi.org/10.20944/preprints202409.1215.v1
Jean, M.-D.; Zhang, C.; Li, X. A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques. Preprints2024, 2024091215. https://doi.org/10.20944/preprints202409.1215.v1
APA Style
Jean, M. D., Zhang, C., & Li, X. (2024). A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques. Preprints. https://doi.org/10.20944/preprints202409.1215.v1
Chicago/Turabian Style
Jean, M., Chaoyang Zhang and Xiang Li. 2024 "A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques" Preprints. https://doi.org/10.20944/preprints202409.1215.v1
Abstract
The purpose of this paper is to construct an evaluation system for AI painting software based on generative adversarial network technology, which optimizes the performance of the related software in terms of functionality, ease of use, system performance and safety. The results of the questionnaires are statistically analyzed. In addition, exploratory factor analysis was supported to extract the data of the study, which was ultimately used to calculate the weight and importance of each index through fuzzy hierarchical analysis method. The study constructed an evaluation system for AI painting software based on generative adversarial network technology, including 16 indicators of functionality, 16 indicators of ease of use, 7 indicators of system performance, and 8 indicators of safety respectively that their alpha coefficients were 0.882, 0.962, 0.932, 0.932, and 0.932, respectively. In addition, the accumulated explanatory variances of their coefficients were 84.405%, 84.897%, 84.013%, and 72.606%, respectively, 73.013%, and 72.606%, respectively. It is clear that the items included in each of the indicators are homogeneous, with a high degree of internal consistency. This paper suggests that the development of AI painting software focusing on functionality, ease of use, system performance and safety can enhance the market competitiveness of the software.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
