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A hybrid deep learning framework combining SMOTE, autoencoders, CNNs, and an attention mechanism achieves over 99.9% accuracy and superior precision-recall balance in detecting credit card fraud within highly imbalanced datasets. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

A hybrid deep learning framework using synthetic oversampling, autoencoder, convolutional neural networks, and an attention mechanism for credit card fraud detection

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