A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques
The purpose of this paper is to construct an evaluation system for AI painting software based on generative adversarial network (GAN) technology, which optimizes the performance of the related software in terms of functionality, ease of use, system performance, and safety. The results of the questio...
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/21/10060 |
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| author | Chaoyang Zhang Xiang Li Ming-Der Jean |
| author_facet | Chaoyang Zhang Xiang Li Ming-Der Jean |
| author_sort | Chaoyang Zhang |
| collection | DOAJ |
| description | The purpose of this paper is to construct an evaluation system for AI painting software based on generative adversarial network (GAN) 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, an exploratory factor analysis was conducted to extract the data of the study, which were ultimately used to calculate the weight and importance of each index using the fuzzy hierarchical analysis method. This study constructed an evaluation system for AI painting software based on GAN technology, including 16 indicators of functionality, 16 indicators of ease of use, 7 indicators of system performance, and 8 indicators of safety, respectively, whose 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%, 72.606%, 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. |
| format | Article |
| id | doaj-art-a1c23c88d2564f0e89fd918c80eb50f5 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a1c23c88d2564f0e89fd918c80eb50f52024-11-08T14:34:18ZengMDPI AGApplied Sciences2076-34172024-11-0114211006010.3390/app142110060A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network TechniquesChaoyang Zhang0Xiang Li1Ming-Der Jean2College of Arts and Design, Jimei University, Xiamen 361021, ChinaCollege of Arts and Design, Jimei University, Xiamen 361021, ChinaCollege of Arts and Design, Jimei University, Xiamen 361021, ChinaThe purpose of this paper is to construct an evaluation system for AI painting software based on generative adversarial network (GAN) 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, an exploratory factor analysis was conducted to extract the data of the study, which were ultimately used to calculate the weight and importance of each index using the fuzzy hierarchical analysis method. This study constructed an evaluation system for AI painting software based on GAN technology, including 16 indicators of functionality, 16 indicators of ease of use, 7 indicators of system performance, and 8 indicators of safety, respectively, whose 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%, 72.606%, 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.https://www.mdpi.com/2076-3417/14/21/10060AI painting softwaregenerative adversarial networkfuzzy hierarchical analysisevaluation models |
| spellingShingle | Chaoyang Zhang Xiang Li Ming-Der Jean A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques Applied Sciences AI painting software generative adversarial network fuzzy hierarchical analysis evaluation models |
| title | A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques |
| title_full | A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques |
| title_fullStr | A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques |
| title_full_unstemmed | A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques |
| title_short | A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques |
| title_sort | survey system for artificial intelligence based painting using generative adversarial network techniques |
| topic | AI painting software generative adversarial network fuzzy hierarchical analysis evaluation models |
| url | https://www.mdpi.com/2076-3417/14/21/10060 |
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