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Publication.

Uncertainty Quantification (UQ)enhances the reliability of AI models by estimating confidence in predictions, crucial for fraud detection and defect identification.
In fraud detection, deep learning models struggle with unseen data and can make overconfident errors. Similarly, in manufacturing, CNN-based defect detection models face challenges with limited training data and real-world variations.
UQ methods, like Monte Carlo Dropout and Ensemble Learning, address these issues by identifying uncertain predictions, allowing expert intervention when needed.
This improves trust, interpretability, and decision-making in AI-driven systems.