Browsing by Author "De Noyel, K.T.S.P."

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    AI-Powered Fashion Trend Prediction: A Conceptual Framework and Comparative Analysis Using Social Media and Historical Data
    (Sri Lanka Institute of Information Technology, 2025-12) De Noyel, K.T.S.P.
    Fashion trend prediction is a complex process influenced by various factors have a role to play in. With emphasis on data observation and collection within an industry, interest in predicting trends using artificial intelligence (AI) has intensified over time. This paper outlines a theoretical framework of an AI-driven fashion trend prediction system that combines past sales data and social media posts. The model aims to use natural language processing (NLP) to explore Instagram, Facebook, fashion forums, and other fashion-related social media platforms to process information to capture the realtime opinion of the masses and identify trends. [1] At the same time, historical sales performance data, including products sold during seasons, subsequent years, purchasing behavior over time, and other consumer and market trends, assist in predicting demand in the marketplace. [2] The suggested framework describes how AI algorithms, especially machine learning and deep learning models can be used to these two complementary data sources to create actionable insights. When these datasets are combined, fashion trends are forecasted with more accuracy. This will assist brands and retailers in making proactive decisions regarding product and marketing strategies alongside inventory control. In particular, the paper underscores the conceptual architecture of the AI system, explaining the fundamental aspects of forecasting trends, consolidating data, and computation through an algorithm. This forms the basis of other studies that seek to apply AI Systems in forecasting the fashion industry trends while at the same time waiting to see the deployment of such systems. In addition, the challenges alongside ethical implications as well as opportunities for further development within this context strive to reduce the existing gap between fashion business practices and data science are provided.