在间隔估值的球形模糊环境下,使用CoCoSo方法评估用于视觉通信设计的生成AI工具
1Krirk University, Bangkhen, Bangkok, 10220, Thailand. 13707346215@163.com.
Scientific reports
|October 6, 2025
概括
生成型人工智能工具彻底改变了视觉通信. 一种新的区间值球形模糊组合妥协解决方案 (IVSF-CoCoSo) 方法将DeepArt确定为创造力和质量的顶级工具.
科学领域:
- 人工智能的人工智能
- 视觉传播 视觉传播
- 决策科学 决策科学 决策科学
背景情况:
- 生成型人工智能通过自动化创作过程来改变视觉通信.
- 评估生成性AI工具需要强大的决策框架.
- 现有的方法可能无法充分捕捉评估中的专家不确定性.
研究的目的:
- 为分析生成AI工具效率提出一个多标准决策 (MCDM) 方法.
- 引入一个区间值的球形模糊 (IVSF) 框架与CoCoSo方法相结合.
- 为普遍存在的生成性AI平台提供实际评估.
主要方法:
- 开发了一种新的IVSF-CoCoSo方法来处理评估中的专家不确定性.
- 将框架应用于一个案例研究,评估七个标准中的十个生成性AI平台.
- 在评估中包括创造力,成本,定制和整合能力.
主要成果:
- 在IVSF-CoCoSo方法中,DeepArt被确定为最有效的生成AI工具.
- 在创意改进,输出质量和定制方面,DeepArt表现出色.
- 敏感性分析证实了拟议模型的可靠性和灵活性.
结论:
- IVSF-CoCoSo方法为评估生成性AI工具提供了可靠和灵活的方法.
- 深度艺术推给寻求先进视觉通信解决方案的设计师和研究人员.
- 这项研究为在创意产业中实施生成性AI提供了实际见解.
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