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

Renaningtyas, Luri and Dwitasari, Putri and Ramadhani, Nugrahardi (2023) Implementing The Use of AI for Analysis and Prediction in Fashion Industry. The International Journal on Academic Research Community Publication , 7 (1). pp. 1-9. ISSN 2357-0154

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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: Article
      Uncontrolled Keywords: Ai, Implementation,fashion industry
      Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
      T Technology
      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: 26 Feb 2023 19:01
      Last Modified: 19 Sep 2023 23:11
      URI: https://repository.petra.ac.id/id/eprint/21595

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