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@article{200851,
        author = {Smt. Rupali Dhanyakumar Kasar and Smt. Charushila Suresha Garat and Smt. Anuradha Narayan Yadav},
        title = {The Role of Artificial Intelligence in Fashion Forecasting and Textile Design},
        journal = {International Journal of Innovative Research in Technology},
        year = {2018},
        volume = {5},
        number = {6},
        pages = {718-724},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200851},
        abstract = {Artificial Intelligence (AI) is playing a transformative role in the fashion industry by enhancing trend forecasting and textile design processes. Traditional forecasting methods rely on manual analysis and expert intuition, which are often time-consuming and lack accuracy in responding to rapidly changing consumer preferences. This study aims to analyze the impact of AI in improving forecasting efficiency and enabling innovative textile design. The research adopts a combined methodology, including a systematic review of recent studies and a model-based approach using machine learning and deep learning techniques. Models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Generative Adversarial Networks (GAN) are utilized for feature extraction, trend prediction, and design generation. The proposed hybrid model (LSTM + CNN) achieved the highest accuracy of 93.67%, outperforming traditional and individual AI models. The findings indicate that AI significantly improves prediction accuracy, reduces forecasting time, and enhances creativity in textile design. Additionally, AI enables real-time analysis of large datasets from social media and e-commerce platforms. The study concludes that AI-driven systems provide a competitive advantage by improving efficiency, scalability, and innovation in the fashion industry, while also highlighting future opportunities for sustainable and personalized fashion solutions.},
        keywords = {Artificial Intelligence, Fashion Forecasting, Textile Design, Machine Learning, Deep Learning, Generative Models},
        month = {November},
        }