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Study of Advanced ML & DL Models for Credit Card Fraud Detection: A Comprehensive Survey on Advanced Techniques of Machine Learning and Deep Learning Approaches - Softcover
- PublisherLAP LAMBERT Academic Publishing
- Publication date2023
- ISBN 10 6206180662
- ISBN 13 9786206180661
- BindingPaperback
- LanguageEnglish
- Number of pages180
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Study of Advanced ML & DL Models for Credit Card Fraud Detection
Published by
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
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Softcover
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Study of Advanced ML & DL Models for Credit Card Fraud Detection
Published by
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
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Softcover
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Study of Advanced ML & DL Models for Credit Card Fraud Detection
Published by
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
New
Softcover
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Study of Advanced ML & DL Models for Credit Card Fraud Detection
Published by
LAP LAMBERT Academic Publishing Jul 2023, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning and deep learning (DL) techniques have shown promising results in detecting fraudulent activities. In this thesis, we propose approaches for credit card fraud detection that combine supervised and unsupervised learning techniques. We apply feature engineering techniques to extract relevant features from the credit card transaction dataset, followed by anomaly detection models that combine supervised ML, semi-supervised ML, and DL techniques. We analyze the dataset using various parameters and methods. Our study on various ML and DL methods in detecting fraudulent transactions are Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Support Vector Classifier (SVC) with Autoencoder, Linear Regression with Autoencoder, K-Nearest Neighbors (KNN), XGBoost, CatBoost, Adaboost, Gradient Boosting, Random Forest, Decision Tree, K-Means Clustering, LightBGM, Logistic Regression, logistic regression with undersampled data, Naive Bayes achieves, SVC achieves, Isolation Forest, and Local Outlier Factor. We evaluate our approach on a real-world credit card transaction dataset named Creditcard.csv from the Kaggle dataset. 180 pp. Englisch. Seller Inventory # 9786206180661
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Study of Advanced ML & DL Models for Credit Card Fraud Detection
Published by
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
New
Softcover
Seller: moluna, Greven, Germany
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Hassan Khondekar LutfulDR. Khondekar Lutful Hassan working as an assistant professor at Aliah University. He has published 1 book and 20 journals in various international journals. His research interest in Machine Learning, Deep Lear. Seller Inventory # 940131709
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Study of Advanced ML & DL Models for Credit Card Fraud Detection : A Comprehensive Survey on Advanced Techniques of Machine Learning and Deep Learning Approaches
Published by
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine learning and deep learning (DL) techniques have shown promising results in detecting fraudulent activities. In this thesis, we propose approaches for credit card fraud detection that combine supervised and unsupervised learning techniques. We apply feature engineering techniques to extract relevant features from the credit card transaction dataset, followed by anomaly detection models that combine supervised ML, semi-supervised ML, and DL techniques. We analyze the dataset using various parameters and methods. Our study on various ML and DL methods in detecting fraudulent transactions are Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Support Vector Classifier (SVC) with Autoencoder, Linear Regression with Autoencoder, K-Nearest Neighbors (KNN), XGBoost, CatBoost, Adaboost, Gradient Boosting, Random Forest, Decision Tree, K-Means Clustering, LightBGM, Logistic Regression, logistic regression with undersampled data, Naive Bayes achieves, SVC achieves, Isolation Forest, and Local Outlier Factor. We evaluate our approach on a real-world credit card transaction dataset named Creditcard.csv from the Kaggle dataset. Seller Inventory # 9786206180661
Quantity: 1 available