Abstract
Data fusion techniques play a crucial role in enhancing the performance of artificial intelligence (AI) models by integrating data from multiple sources. This article explores the various methods and applications of data fusion in AI, focusing on how combining heterogeneous data types can improve model accuracy, robustness, and generalization. We examine the benefits of data fusion in fields such as computer vision, natural language processing, and autonomous systems, where data from different modalities, such as images, text, and sensor data, are combined. Additionally, the article discusses the challenges and future directions of data fusion in AI, including issues of data quality, model complexity, and computational efficiency.
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