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Implementing The Use of AI for Analysis and Prediction in the Fashion Industry

Renaningtyas, Luri and Dwitasari, Putri and Ramadhani, Nugrahardi (2022) Implementing The Use of AI for Analysis and Prediction in the Fashion Industry. In: Sustainability in Creative Industry, 11-11-2022 - 11-11-2022, Surabaya - Indonesia.

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          Abstract

          The COVID-19 pandemic has made all aspects of human life assisted by technology and big data. It starts from the education sector, economy, communication, health, and manufacturing to fashion. As we all know fast fashion has become one of the most significant contributors of waste. During the flow of developing a collection, for example; the production and distribution process can cause ethical issues and contradict sustainability matters. Several studies from 2010 to date have initiated AI (Artificial Intelligent) technology, a computer vision that alleviates the use of carbon footprints in the fashion industry. AI presents robust evidence to the audience, since it is visual and statically calculated, furthermore it is less costly and energy saving. AI abstracts the similarities or differences across all clothing and collections from the dataset. Its implementation can be used in many fashion careers with different purposes. By reviewing across the computer vision journals complemented with fashion management literatures, this article eventually provides insights of the implementation of AI for analysis and prediction from fashion photos or dataset.

          Item Type: Conference or Workshop Item (Paper)
          Uncontrolled Keywords: Ai; Implementation; Fashion industry
          Subjects: T Technology
          Q Science > QA Mathematics > QA75 Electronic computers. Computer science
          H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
          Divisions: Faculty of Art and Design > Visual Communication Design Department
          Depositing User: Admin
          Date Deposited: 05 Dec 2022 15:45
          Last Modified: 30 Nov 2023 10:27
          URI: https://repository.petra.ac.id/id/eprint/19786

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