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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM28523
Volume:
8 |
2 |
Feb 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
This study investigates the potential application of artificial intelligence (AI) in the field of sustainable fashion by streamlining the production of clothing and incorporating trend analysis. By researching current fashion trends and consumer preferences to identify the most well-liked apparel patterns and designs, the article seeks to optimise supply and demand while reducing surplus production. Fashion garment production can be optimised to fulfil customer demand while minimising waste and lowering costs by incorporating trend research into the manufacturing process. Companies may now invest in clothing concepts that will sell thanks to AI trend forecasting, which reduces some of the uncertainty and human error that now hinder trend forecasting. The fashion industry is infamous for its detrimental effects on society and the environment, including creation of waste and pollution. While the fashion industry has started to adopt more sustainable practises, AI has the ability to quicken the process by offering creative answers to some of the sector’s most pressing problems. The study looks at a number of AI-related case studies in the fashion sector, including the use of predictive analytics to cut waste and streamline supply chains, computer vision to enhance textile recycling, and natural language processing to encourage openness and moral work practises. The paper also examines how AI could revolutionise the fashion business and hasten the shift to a more ethical and sustainable future. The report also highlights the necessity for careful evaluation of these concerns as the fashion industry develops by posing significant ethical and social implications for AI.
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Publication Date
12/31/45327
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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