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Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-0-387-09823-4_45openalex-snapshot
Data Mining for Imbalanced Datasets: An Overview
inferredcites →https://doi.org/10.1007/978-3-642-04003-0_10openalex-snapshot
Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks
inferredcites →https://doi.org/10.1016/j.dss.2010.08.008openalex-snapshot
Data mining for credit card fraud: A comparative study
inferredcites →https://doi.org/10.1016/j.eswa.2018.01.037openalex-snapshot
Sequence classification for credit-card fraud detection
inferredcites →https://doi.org/10.1016/j.eswa.2021.115736openalex-snapshot
Explaining anomalies detected by autoencoders using Shapley Additive Explanations
inferredcites →https://doi.org/10.1016/j.inffus.2020.07.001openalex-snapshot
Random forest explainability using counterfactual sets
inferredcites →https://doi.org/10.1080/00031305.1992.10475879openalex-snapshot
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
inferredcites →https://doi.org/10.1109/access.2018.2806420openalex-snapshot
Credit Card Fraud Detection Using AdaBoost and Majority Voting
inferredcites →https://doi.org/10.1109/access.2020.2971354openalex-snapshot
An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine
inferredcites →https://doi.org/10.1109/cnsc.2014.6906652openalex-snapshot
A study on classifying imbalanced datasets
inferredcites →https://doi.org/10.1109/i-smac49090.2020.9243602openalex-snapshot
Relative Analysis of ML Algorithm QDA, LR and SVM for Credit Card Fraud Detection Dataset
inferredcites →https://doi.org/10.1109/icaci.2016.7449810openalex-snapshot
An imbalanced data classification algorithm of improved autoencoder neural network
inferredcites →https://doi.org/10.1109/iccni.2017.8123782openalex-snapshot
Credit card fraud detection using machine learning techniques: A comparative analysis
inferredcites →https://doi.org/10.1109/ijcnn.2008.4633969openalex-snapshot
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
inferredcites →https://doi.org/10.1109/ijcnn.2019.8852415openalex-snapshot
GMM-based Undersampling and Its Application for Credit Card Fraud Detection
inferredcites →https://doi.org/10.1109/istel.2018.8661129openalex-snapshot
Credit Card Fraud Detection using autoencoder based clustering
inferredcites →https://doi.org/10.1109/snlp.2009.5340935openalex-snapshot
Comparing the performance of different neural networks for binary classification problems
inferredcites →https://doi.org/10.1109/tkde.2012.99openalex-snapshot
Anomaly Detection via Online Oversampling Principal Component Analysis
inferredcites →https://doi.org/10.1145/3297280.3297586openalex-snapshot
Multiple perspectives HMM-based feature engineering for credit card fraud detection
inferredcites →https://doi.org/10.1145/3377049.3377113openalex-snapshot
Credit Card Fraud Detection using Data Pre-processing on Imbalanced Data - both Oversampling and Undersampling
inferredcites →https://doi.org/10.1155/2014/252797openalex-snapshot
FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining
inferredcites →https://doi.org/10.1186/s12864-019-6413-7openalex-snapshot
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
inferredcites →https://doi.org/10.1371/journal.pone.0118432openalex-snapshot
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
inferredcites →https://doi.org/10.14569/ijacsa.2018.090103openalex-snapshot
Credit Card Fraud Detection using Deep Learning based on Auto-Encoder and Restricted Boltzmann Machine
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.21236/ada164453openalex-snapshot
Learning Internal Representations by Error Propagation
inferredcites →https://doi.org/10.24818/issn14531305/25.1.2021.06openalex-snapshot
Credit Card Fraud Detection using Deep Learning Techniques
inferredcites →https://doi.org/10.24963/ijcai.2020/752openalex-snapshot
An Anomaly Detection and Explainability Framework using Convolutional Autoencoders for Data Storage Systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Novelty and Similarity: Detection Using Data‐Driven Soft Independent Modeling of Class Analogy
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Predicting credit card fraud using multipurpose classification based on evolutionary rules
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Credit Card Fraud Detection Using Autoencoder Algorithm with SMOTE Technique
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An Efficient Credit Card Fraud Detection Using SMOTE Under Machine Learning Environment
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Credit Card Fraud Detection and Classification Using Deep Learning with Support Vector Machine Techniques
inferred← citeshttps://doi.org/10.1007/s00500-023-07954-yopenalex-snapshot
RETRACTED ARTICLE: Cybersecurity enhancement to detect credit card frauds in health care using new machine learning strategies
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Enhanced autoencoder-based fraud detection: a novel approach with noise factor encoding and SMOTE
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Combining Autoencoders and Deep Learning for Effective Fraud Detection in Credit Card Transactions
inferred← citeshttps://doi.org/10.1051/itmconf/20257802023openalex-snapshot
Credit Card Fraud Detection: Machine Learning and Deep Learning Advances, Challenges, and Future Directions
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A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection
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Ensemble Synthesized Minority Oversampling-Based Generative Adversarial Networks and Random Forest Algorithm for Credit Card Fraud Detection
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A Hybrid Deep Learning Ensemble Model for Credit Card Fraud Detection
inferred← citeshttps://doi.org/10.1109/access.2025.3620247openalex-snapshot
Operational Benchmarking of ML Models for Fraud Detection: A Comparative Study on AWS EC2 and ECS
inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10020419openalex-snapshot
GAN-based Data Augmentation for Credit Card Fraud Detection
inferred← citeshttps://doi.org/10.1109/bts-i2c63534.2024.10942037openalex-snapshot
Credit Card Fraud Detection Using Machine Learning Techniques: Dealing with Imbalanced Data using Over-Sampling and Under-Sampling Methods
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Blockchain-Assisted AI Framework for Enhanced ATM Card Fraud Prevention and Secure Transaction Monitoring
inferred← citeshttps://doi.org/10.1109/cosmic63293.2024.10871502openalex-snapshot
A Fusion Approach for Credit Card Fraud Detection: A Stacked Ensemble Framework
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Artificial Immune System and Machine Learning: A Synergistic Approach for Banking Fraud Detection
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inferred← citeshttps://doi.org/10.1109/icai-tems64510.2025.11467661openalex-snapshot
A Comparative Analysis of Machine Learning Algorithms for Credit Card Fraud Detection
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inferred← citeshttps://doi.org/10.1109/icce-taiwan58799.2023.10226905openalex-snapshot
Credit Card Fraud Detection Based on DeepInsight and Deep Learning
inferred← citeshttps://doi.org/10.1109/iccr67387.2025.11291785openalex-snapshot
Detecting Financial Fraud with Random Forest and SMOTE in Transactional Data
inferred← citeshttps://doi.org/10.1109/iccr67387.2025.11291822openalex-snapshot
Real-Time Anomaly Detection Algorithm in Financial Transactions Using Autoencoder Neural Networks
inferred← citeshttps://doi.org/10.1109/iceei68459.2025.11330606openalex-snapshot
AI-Powered Credit Card Fraud Detection Using Optimized Convolutional Neural Networks and Synthetic Data Augmentation
inferred← citeshttps://doi.org/10.1109/icisaa62385.2024.10828858openalex-snapshot
Enhancing Fraud Detection in Online E-Commerce Transactions through Deep Learning Auto encoder Model
inferred← citeshttps://doi.org/10.1109/icmlas67792.2026.11483932openalex-snapshot
Comparative Analysis of Machine Learning Algorithms for Financial Fraud Detection
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SMOTE-CHL-LightGBM: An Enhanced LightGBM for Credit Card Fraud Detection
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Advanced Machine Learning Techniques for Credit Card Fraud Detection: A Comprehensive Study
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Integrating Class Imbalance Solutions into Fraud Detection Systems: A Systematic Literature Review
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Unveiling Credit Card Fraud: Harnessing Artificial Neural Networks for Accurate Detection
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Design and Implementation of a Web-Based Credit Card Fraud Detection System
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Multivariate Bounded Support Kotz Mixture Model: Addressing Financial Fraud and Network Security Challenges
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Classification Using Random Forest on Imbalanced Credit Card Transaction Data
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A Hybrid Anomaly Detection Framework Combining Supervised and Unsupervised Learning for Credit Card Fraud Detection
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Probabilistic XGBoost Threshold Classification with Autoencoder for Credit Card Fraud Detection
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Optimizing Machine Learning Classifiers for Credit Card Fraud Detection on Highly Imbalanced Datasets Using PCA and SMOTE Techniques
inferred← citeshttps://doi.org/10.22630/mibe.2026.27.3.13openalex-snapshot
Mitigating Class Imbalance in U.S. Financial Security: A Latent Feature Augmentation Approach for High-Purity Fraud Detection
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Machine learning-based lung cancer diagnosis
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A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost
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Credit Card Fraud Detection Using Improved Deep Learning Models
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Credit Card Fraud Detection Method Based on RF-WGAN-TCN
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Dampak Pengambilan Sampel Data untuk Optimalisasi Data tidak seimbang pada Klasifikasi Penipuan Transaksi E-Commerce
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Selection of Potential Regions for the Creation of Intelligent Transportation Systems Based on the Machine Learning Algorithm Random Forest
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An Introduction to Machine Learning Methods for Fraud Detection
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Detecting Imbalanced Credit Card Fraud via Hybrid Graph Attention and Variational Autoencoder Ensembles
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Decentralized Federated Learning-Enabled Relation Aggregation for Anomaly Detection
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A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection
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AN AUTOENCODER-BASED UNSUPERVISED FRAUD DETECTION FRAMEWORK FOR NIGERIAN BANKING TRANSACTIONS
inferred← citeshttps://doi.org/10.7287/peerj-cs.1278v0.1/reviews/1openalex-snapshot
Peer Review #1 of "A systematic review of literature on credit card cyber fraud detection using machine and deep learning (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1278v0.1/reviews/2openalex-snapshot
Peer Review #2 of "A systematic review of literature on credit card cyber fraud detection using machine and deep learning (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1278v0.1/reviews/3openalex-snapshot
Peer Review #3 of "A systematic review of literature on credit card cyber fraud detection using machine and deep learning (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1278v0.2/reviews/1openalex-snapshot
Peer Review #1 of "A systematic review of literature on credit card cyber fraud detection using machine and deep learning (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1278v0.2/reviews/2openalex-snapshot
Peer Review #2 of "A systematic review of literature on credit card cyber fraud detection using machine and deep learning (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1278v0.2/reviews/3openalex-snapshot
Peer Review #3 of "A systematic review of literature on credit card cyber fraud detection using machine and deep learning (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1278v0.2/reviews/4openalex-snapshot
Peer Review #4 of "A systematic review of literature on credit card cyber fraud detection using machine and deep learning (v0.2)"
inferred← citeshttps://doi.org/10.7717/peerj-cs.1278openalex-snapshot
A systematic review of literature on credit card cyber fraud detection using machine and deep learning
inferred← citeshttps://doi.org/10.7717/peerj-cs.3907openalex-snapshot
MSGFD: a multi-scale temporal–metric graph ODE network for financial fraud detection
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