Knowledge extraction in auction verification employing techniques from machine learning and fuzzy neural networks.

This paper explores the nuances of extracting knowledge for auction verification, utilizing advanced methodologies from machine learning and fuzzy neural networks. In a meticulous comparison with state-of-the-art approaches, the proposed model demonstrates an improvement in accurately distinguishing diverse auction scenarios. The study not only underscores the effectiveness of these sophisticated technologies in enhancing auction verification processes but also underscores the crucial role played by knowledge extraction through fuzzy rules. This extraction process emerges as a valuable asset, particularly for the detection and mitigation of potential fraud in auction settings.