Works matching Credit card fraud
Results: 527
A voting ensemble machine learning based credit card fraud detection using highly imbalance data.
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- Multimedia Tools & Applications, 2024, v. 83, n. 18, p. 54729, doi. 10.1007/s11042-023-17766-9
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Novel Machine Learning Based Credit Card Fraud Detection Systems.
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- Mathematics (2227-7390), 2024, v. 12, n. 12, p. 1869, doi. 10.3390/math12121869
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Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection.
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- Mathematics (2227-7390), 2022, v. 10, n. 13, p. 2272, doi. 10.3390/math10132272
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AutoEncoder and LightGBM for Credit Card Fraud Detection Problems.
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- Symmetry (20738994), 2023, v. 15, n. 4, p. 870, doi. 10.3390/sym15040870
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Machine Learning Approach on Apache Spark for Credit Card Fraud Detection.
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- Ingénierie des Systèmes d'Information, 2020, v. 25, n. 1, p. 101, doi. 10.18280/isi.250113
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Adaptive Model for Credit Card Fraud Detection.
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- International Journal of Interactive Mobile Technologies, 2020, n. 3, p. 54, doi. 10.3991/ijim.v14i03.11763
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Hybrid Sampling and Similarity Attention Layer in Bidirectional Long Short Term Memory in Credit Card Fraud Detection.
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- International Journal of Intelligent Engineering & Systems, 2022, v. 15, n. 6, p. 35, doi. 10.22266/ijies2022.1231.04
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A NEW BINARY SUPPORT VECTOR SYSTEM FOR INCREASING DETECTION RATE OF CREDIT CARD FRAUD.
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- International Journal of Pattern Recognition & Artificial Intelligence, 2006, v. 20, n. 2, p. 227, doi. 10.1142/S0218001406004624
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Credit Card Fraud Detection using Machine Learning Algorithms - Study of Customer Behaviour.
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- Grenze International Journal of Engineering & Technology (GIJET), 2020, v. 6, n. 2, p. 143
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CUS-RF-Based Credit Card Fraud Detection with Imbalanced Data.
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- Journal of Risk Analysis & Crisis Response (JRACR), 2022, v. 12, n. 3, p. 110, doi. 10.54560/jracr.v12i3.332
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Credit Card Fraud Detection Model Based on Correlation Feature Selection.
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- Journal of Cybersecurity & Information Management, 2024, v. 14, n. 2, p. 334, doi. 10.54216/JCIM.140224
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Enhanced Credit Card Fraud Detection Using Deep Learning Techniques.
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- Journal of Cybersecurity & Information Management, 2024, v. 14, n. 2, p. 323, doi. 10.54216/JCIM.140223
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Synthesizing class labels for highly imbalanced credit card fraud detection data.
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- Journal of Big Data, 2024, v. 11, n. 1, p. 1, doi. 10.1186/s40537-024-00897-7
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Comparative analysis of binary and one-class classification techniques for credit card fraud data.
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- Journal of Big Data, 2023, v. 10, n. 1, p. 1, doi. 10.1186/s40537-023-00794-5
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The effect of feature extraction and data sampling on credit card fraud detection.
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- Journal of Big Data, 2023, v. 10, n. 1, p. 1, doi. 10.1186/s40537-023-00684-w
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Enhancing Credit Card Fraud Detection Using a Stacking Model Approach and Hyperparameter Optimization.
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- International Journal of Advanced Computer Science & Applications, 2024, v. 15, n. 10, p. 1080, doi. 10.14569/ijacsa.2024.01510110
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Research on Credit Card Fraud Prediction Model Based on GAN-DNN Imbalance Classification Algorithm.
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- International Journal of Advanced Computer Science & Applications, 2024, v. 15, n. 10, p. 517, doi. 10.14569/ijacsa.2024.0151054
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Optimizing Credit Card Fraud Detection: Classifier Performance and Feature Selection Empowered by Grasshopper Algorithm.
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- International Journal of Performability Engineering, 2024, v. 20, n. 3, p. 177, doi. 10.23940/ijpe.24.03.p6.177185
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Artificial Intelligence Based Credit Card Fraud Detection for Online Transactions Optimized with Sparrow Search Algorithm.
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- International Journal of Performability Engineering, 2023, v. 19, n. 9, p. 624, doi. 10.23940/ijpe.23.09.p7.624632
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MACHINE LEARNING AND DEEP LEARNING TECHNIQUES FOR DETECTING FRAUD INVOLVING CREDIT CARDS.
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- i-Manager's Journal on Information Technology, 2022, v. 11, n. 3, p. 1, doi. 10.26634/jit.11.2.18919
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Do Demographic, Psychological, and Financial Characteristics Increase the Likelihood to be Victims of Credit Card Fraud?
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- Journal of Personal Finance, 2023, v. 22, n. 2, p. 32
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An Empirical Study of AML Approach for Credit Card Fraud Detection-Financial Transactions.
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- International Journal of Computers, Communications & Control, 2019, v. 14, n. 6, p. 670, doi. 10.15837/ijccc.2019.6.3498
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EFFECT OF FEATURE RANKING ON THE DETECTION OF CREDIT CARD FRAUD: COMPARATIVE EVALUATION OF FOUR TECHNIQUES.
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- i-Manager's Journal of Pattern Recognition, 2018, v. 5, n. 3, p. 10, doi. 10.26634/jpr.5.3.15676
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A Survey and a Credit Card Fraud Detection and Prevention Model using the Decision Tree Algorithm.
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- Engineering, Technology & Applied Science Research, 2023, v. 13, n. 4, p. 11505, doi. 10.48084/etasr.6128
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Credit Card Fraud Detection using Random Forest Algorithm.
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- Journal of Cardiovascular Disease Research (Journal of Cardiovascular Disease Research), 2021, v. 12, n. 2, p. 531
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A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost.
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- Computers, Materials & Continua, 2023, v. 74, n. 3, p. 5951, doi. 10.32604/cmc.2023.035558
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Credit Card Fraud Detection Based on Machine Learning.
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- Computers, Materials & Continua, 2019, v. 61, n. 1, p. 185, doi. 10.32604/cmc.2019.06144
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Credit Card Fraud Detection using Deep Learning Techniques.
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- Informatica Economica, 2021, v. 25, n. 1, p. 70, doi. 10.24818/issn14531305/25.1.2021.06
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Leveraging graph-based learning for credit card fraud detection: a comparative study of classical, deep learning and graph-based approaches.
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- Neural Computing & Applications, 2024, v. 36, n. 34, p. 21873, doi. 10.1007/s00521-024-10397-7
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CFTNet: a robust credit card fraud detection model enhanced by counterfactual data augmentation.
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- Neural Computing & Applications, 2024, v. 36, n. 15, p. 8607, doi. 10.1007/s00521-024-09546-9
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Federated learning model for credit card fraud detection with data balancing techniques.
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- Neural Computing & Applications, 2024, v. 36, n. 11, p. 6231, doi. 10.1007/s00521-023-09410-2
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Credit Card Fraud Detection Using Machine Learning Techniques.
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- Future Computing & Informatics Journal, 2022, v. 7, n. 1, p. 13, doi. 10.54623/fue.fcij.7.1.2
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Forgery and Credit cards fraud - A Specific Criminal Offense against the Economy in the Criminal Legislation of Serbia.
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- Journal of Eastern European Criminal Law, 2017, n. 1, p. 181
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A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach.
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- Electronics (2079-9292), 2022, v. 11, n. 5, p. 756, doi. 10.3390/electronics11050756
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Enhanced Credit Card Fraud Detection Model Using Machine Learning.
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- Electronics (2079-9292), 2022, v. 11, n. 4, p. 662, doi. 10.3390/electronics11040662
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A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection.
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- Applied Sciences (2076-3417), 2025, v. 15, n. 3, p. 1356, doi. 10.3390/app15031356
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An Augmentation of Credit Card Fraud Detection using Random Undersampling.
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- Turkish Online Journal of Qualitative Inquiry, 2021, v. 12, n. 6, p. 664
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RHSOFS: Feature Selection Using the Rock Hyrax Swarm Optimization Algorithm for Credit Card Fraud Detection System.
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- Sensors (14248220), 2022, v. 22, n. 23, p. 9321, doi. 10.3390/s22239321
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A Performance Analysis of Machine Learning Techniques for Credit Card Fraud Detection.
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- Journal of Artificial Intelligence (2579-0021), 2024, v. 6, p. 1, doi. 10.32604/jai.2024.047226
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ANALYZING THE ECONOMIC IMPACT OF CREDIT CARD FRAUD: ACTIVATION, LIMIT UPGRADES, CASHBACK SCAMS, DISCOUNT FRAUD, AND OVERDRAFT RISKS.
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- Degrés, 2024, v. 9, n. 11, p. 1
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Weighted binary ELM optimized by the reptile search algorithm, application to credit card fraud detection.
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- Multimedia Tools & Applications, 2024, v. 83, n. 39, p. 86383, doi. 10.1007/s11042-024-19508-x
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Smart credit card fraud detection system based on dilated convolutional neural network with sampling technique.
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- Multimedia Tools & Applications, 2023, v. 82, n. 20, p. 31691, doi. 10.1007/s11042-023-15730-1
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An integration of deep learning model with Navo Minority Over-Sampling Technique to detect the frauds in credit cards.
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- Multimedia Tools & Applications, 2023, v. 82, n. 14, p. 21757, doi. 10.1007/s11042-023-14365-6
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An efficient fraud detection framework with credit card imbalanced data in financial services.
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- Multimedia Tools & Applications, 2023, v. 82, n. 3, p. 4139, doi. 10.1007/s11042-022-13434-6
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CCFD: Efficient Credit Card Fraud Detection Using Meta-Heuristic Techniques and Machine Learning Algorithms.
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- Mathematics (2227-7390), 2024, v. 12, n. 14, p. 2250, doi. 10.3390/math12142250
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RaKShA : A Trusted Explainable LSTM Model to Classify Fraud Patterns on Credit Card Transactions.
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- Mathematics (2227-7390), 2023, v. 11, n. 8, p. 1901, doi. 10.3390/math11081901
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High-Cardinality Categorical Attributes and Credit Card Fraud Detection.
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- Mathematics (2227-7390), 2022, v. 10, n. 20, p. 3808, doi. 10.3390/math10203808
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Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems.
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- Future Internet, 2024, v. 16, n. 6, p. 196, doi. 10.3390/fi16060196
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Black-Box Attack-Based Security Evaluation Framework for Credit Card Fraud Detection Models.
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- INFORMS Journal on Computing, 2023, v. 35, n. 5, p. 986, doi. 10.1287/ijoc.2023.1297
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Wave Hedges distance-based feature fusion and hybrid optimization-enabled deep learning for cyber credit card fraud detection.
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- Knowledge & Information Systems, 2024, v. 66, n. 11, p. 7005, doi. 10.1007/s10115-024-02177-5
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