Abstracts of the 5th National Conference On Medical Sciences 4th–5th May 1999 Held at the School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia

第五届全国医学科学会议摘要集,1999年5月4日至5日,马来西亚吉兰丹州马来西亚理科大学医学院举行

阅读:2

Abstract

At the age of intelligent design, generative AI tools are leading to a revolution in visual communication as the creative process is shifting to being fully automated, ideation is augmented, and production pipelines are fast-tracked. This paper will present a robust multi-criteria decision-making (MCDM) approach to analyze the comparative efficiency of prevalent tools of generative AI based on the combined compromise solution (CoCoSo) approach in an interval-valued (IVSF) spherical fuzzy framework. The IVSF framework is used to faithfully reflect the differences in how much or how little an expert would like to assign a particular degree of membership, non-membership, or abstinence, for the interval-valued membership, non-membership, and abstinence of degrees. These IVSF values are summed up, defuzzified, and then combined in the CoCoSo algorithm to provide an overall rating of the alternatives. One of the key features of this writing is the creation of a practical case study based on seven assessment criteria, including creativity, cost, customization, integration capability, and others, as well as ten generative AI platforms. DeepArt is presented as the most relevant tool by the proposed IVSF-CoCoSo approach, as it achieves a higher rate of creativity improvement, yields the best results in terms of output quality, and offers customization. Through comparison with other MCDM approaches, as well as sensitivity analysis, the proposed model proves to be highly reliable and flexible. The proposed study will provide practical knowledge to designers, researchers, and other industry stakeholders who aim to implement generative AI in visual communication processes successfully.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。