Credit card fraud detection remains a significant challenge due to the growing complexity of fraudulent behaviour and the severe class imbalance in transaction data. This study presents a hybrid deep learning approach that combines three advanced models—an Artificial Neural Network (ANN) enhanced with Batch Normalization and Dropout, along with VGG16 and VGG19 architectures—to enhance detection accuracy and reliability. The system begins with extensive data pre-processing, including Standard Scaling for normalization, Synthetic Minority Over-sampling Technique (SMOTE) to balance class distribution, and Principal Component Analysis (PCA) for dimensionality reduction.
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Paperback. Zustand: new. Paperback. Credit card fraud detection remains a significant challenge due to the growing complexity of fraudulent behaviour and the severe class imbalance in transaction data. This study presents a hybrid deep learning approach that combines three advanced models-an Artificial Neural Network (ANN) enhanced with Batch Normalization and Dropout, along with VGG16 and VGG19 architectures-to enhance detection accuracy and reliability. The system begins with extensive data pre-processing, including Standard Scaling for normalization, Synthetic Minority Over-sampling Technique (SMOTE) to balance class distribution, and Principal Component Analysis (PCA) for dimensionality reduction. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9786209892257
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Credit card fraud detection remains a significant challenge due to the growing complexity of fraudulent behaviour and the severe class imbalance in transaction data. This study presents a hybrid deep learning approach that combines three advanced models-an Artificial Neural Network (ANN) enhanced with Batch Normalization and Dropout, along with VGG16 and VGG19 architectures-to enhance detection accuracy and reliability. The system begins with extensive data pre-processing, including Standard Scaling for normalization, Synthetic Minority Over-sampling Technique (SMOTE) to balance class distribution, and Principal Component Analysis (PCA) for dimensionality reduction. Bestandsnummer des Verkäufers 9786209892257
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Taschenbuch. Zustand: Neu. Credit Card Fraud Detection System Using Deep Learning Techniques | Detection of Fraudulent Transactions Using AI-Based Models | Megha Baghsawari | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209892257 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 135358934
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Paperback. Zustand: new. Paperback. Credit card fraud detection remains a significant challenge due to the growing complexity of fraudulent behaviour and the severe class imbalance in transaction data. This study presents a hybrid deep learning approach that combines three advanced models-an Artificial Neural Network (ANN) enhanced with Batch Normalization and Dropout, along with VGG16 and VGG19 architectures-to enhance detection accuracy and reliability. The system begins with extensive data pre-processing, including Standard Scaling for normalization, Synthetic Minority Over-sampling Technique (SMOTE) to balance class distribution, and Principal Component Analysis (PCA) for dimensionality reduction. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9786209892257
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