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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 - Brossura
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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.
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Study of Advanced ML & DL Models for Credit Card Fraud Detection
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LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
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Condizione: 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. Codice articolo 940131709
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Study of Advanced ML & DL Models for Credit Card Fraud Detection
Editore:
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
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Study of Advanced ML & DL Models for Credit Card Fraud Detection
Editore:
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
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Da: Biblios, Frankfurt am main, HESSE, Germania
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Condizione: New. PRINT ON DEMAND. Codice articolo 18401034034
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Study of Advanced ML & DL Models for Credit Card Fraud Detection
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LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
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Condizione: New. Print on Demand. Codice articolo 395375847
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Study of Advanced ML & DL Models for Credit Card Fraud Detection
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LAP LAMBERT Academic Publishing Jul 2023, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9786206180661
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Study of Advanced ML & DL Models for Credit Card Fraud Detection
Editore:
LAP LAMBERT Academic Publishing Jul 2023, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 180 pp. Englisch. Codice articolo 9786206180661
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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
Editore:
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206180662
ISBN 13: 9786206180661
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Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9786206180661
Quantità: 1 disponibili