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The role of artificial intelligence in revolutionizing the clothing and textile industry
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 The integration of AI technologies is revolutionizing various aspects of the apparel and textile industry, from design and manufacturing to customer experience and sustainability. Through the use of artificial intelligence algorithms, workers in the apparel and textile industry can take advantage of a wealth of opportunities for innovation, efficiency and creativity.
The research aims to display the enormous potential of artificial intelligence in the clothing and textile industry through published articles related to the title of the research using the Google Scholar search engine. The research contributes to the development of the cultural thought of researchers, designers, merchants and the consumer with the importance of integrating artificial intelligence technologies in the fields of the clothing and textile industry to keep pace with technological change.
The research found that the number of results for articles published in Google Scholar in the period of time from (2016-2023 AD) amounted to 1724, and 523 articles were published at a rate of (30%), which is the highest percentage for articles published in Google Scholar, and it was the lowest period of time from 2016-2017 AD. 50 published articles (3%), and the research recommended conducting more studies in the field of artificial intelligence and its applications in the clothing and textile industry.

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Publication Date
Wed Apr 25 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Different Estimation Methods for System Reliability Multi-Components model: Exponentiated Weibull Distribution
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        In this paper, estimation of system reliability of the multi-components in stress-strength model R(s,k) is considered, when the stress and strength are independent random variables and follows the Exponentiated Weibull Distribution (EWD) with known first shape parameter θ and, the second shape parameter α is unknown using different estimation methods. Comparisons among the proposed estimators through  Monte Carlo simulation technique were made depend on mean squared error (MSE)  criteria

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