Works matching DE "CREDIT card fraud"
Results: 377
Report from the 33rd Scientific Conference of the Classification and Data Analysis Section of the Polish Statistical Association.
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- Statistical Review / Przegląd Statystyczny, 2024, v. 71, n. 2, p. 39, doi. 10.59139/ps.2024.02.3
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Why SNAP Works: A Political History—and Defense—of the Food Stamp Program.
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- 2025
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- Book Review
An intelligent credit card fraudulent activity detection using hybrid deep learning algorithm.
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- Multimedia Tools & Applications, 2024, v. 83, n. 40, p. 87621, doi. 10.1007/s11042-024-18793-w
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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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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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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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Credit card fraud detection using ensemble data mining methods.
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- Multimedia Tools & Applications, 2023, v. 82, n. 19, p. 29057, doi. 10.1007/s11042-023-14698-2
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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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- Article
Book Review: Marx's Wager: Das Kapital and Classical Sociology.
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- Journal of Classical Sociology, 2023, v. 23, n. 3, p. 429, doi. 10.1177/1468795X221142464
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- Article
تحديد مستويات معيارية لبعض المهارات األساسية لرياضة الجمناستك لطالبات المرحمة الثانية في كميات التربية الرياضية .
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- Journal of Studies & Reseaches of Sport Education, 2020, v. 62, n. 1818, p. 1
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القيم العلمية ال ض منة يف حمتوى كتاب األحياء3 للصف الثالث ثانوي بالملكة العربية السعودية.
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- Journal of Education - Sohag University, 2021, v. 91, n. Part 10, p. 4575, doi. 10.12816/EDUSOHAG.2021.198472
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Privacy and Security – Together or Separate?.
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- ISSA Journal, 2021, v. 19, n. 8, p. 6
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- Article
未授權支付交易風險與 優勢風險承擔原則 ──以信用卡為例.
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- Taiwan Law Review, 2023, n. 339, p. 60, doi. 10.53106/1025593133904
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Identity Theft: Laws, Crimes, and Victims.
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- Journal of Consumer Affairs, 2004, v. 38, n. 2, p. 201, doi. 10.1111/j.1745-6606.2004.tb00863.x
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A Hybrid Deep Learning Approach with Generative Adversarial Network for Credit Card Fraud Detection.
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- Technologies (2227-7080), 2024, v. 12, n. 10, p. 186, doi. 10.3390/technologies12100186
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Research on Small-Sample Credit Card Fraud Identification Based on Temporal Attention-Boundary-Enhanced Prototype Network.
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- Mathematics (2227-7390), 2024, v. 12, n. 24, p. 3894, doi. 10.3390/math12243894
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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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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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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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Preface to "Swarm and Evolutionary Computation—Bridging Theory and Practice".
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- Mathematics (2227-7390), 2023, v. 11, n. 5, p. 1209, doi. 10.3390/math11051209
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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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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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Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture.
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- Mathematics (2227-7390), 2022, v. 10, n. 9, p. 1480, doi. 10.3390/math10091480
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Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest.
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- Mathematics (2227-7390), 2021, v. 9, n. 21, p. 2683, doi. 10.3390/math9212683
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- Article
ЗГОРТКОВА НЕЙРОМЕРЕЖА ДЛЯ ВИЯВЛЕННЯ ШАХРАЙСЬКИХ ОПЕРАЦІЙ З КРЕДИТНИМИ КАРТКАМИ
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- Automation of Technological & Business Processes / Avtomatizaciâ Tehnologiceskih i Biznes-Processov, 2019, v. 11, n. 3, p. 65
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The extent of cross-border crime in Europe: the view from Britain.
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- European Journal on Criminal Policy & Research, 1993, v. 1, n. 3, p. 57, doi. 10.1007/BF02249507
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Security and the Informational Sublime.
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- 2019
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- Book Review
Enhanced federated anomaly detection through autoencoders using summary statistics-based thresholding.
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- Scientific Reports, 2024, v. 14, n. 1, p. 1, doi. 10.1038/s41598-024-76961-2
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- Article
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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Special Issue: Machine Learning and Data Analysis.
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- Symmetry (20738994), 2023, v. 15, n. 7, p. 1397, doi. 10.3390/sym15071397
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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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KDOS: Kernel Density based Over Sampling: - A Solution to Skewed Class Distribution.
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- Journal of Information Assurance & Security, 2020, v. 15, n. 2, p. 44
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Sustainable Financial Fraud Detection Using Garra Rufa Fish Optimization Algorithm with Ensemble Deep Learning.
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- Sustainability (2071-1050), 2023, v. 15, n. 18, p. 13301, doi. 10.3390/su151813301
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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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The Link Between Childhood Overindulgence and Adult Financial Behaviors.
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- Journal of Financial Counseling & Planning, 2016, v. 27, n. 1, p. 80, doi. 10.1891/1052-3073.27.1.80
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THE LIMITS OF LAW AND AI.
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- University of Cincinnati Law Review, 2022, v. 90, n. 3, p. 923
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Predicting credit card fraud with Sarbanes‐Oxley assessments and Fama‐French risk factors.
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- Intelligent Systems in Accounting, Finance & Management, 2020, v. 27, n. 2, p. 95, doi. 10.1002/isaf.1472
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Predicting credit card delinquencies: An application of deep neural networks.
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- Intelligent Systems in Accounting, Finance & Management, 2018, v. 25, n. 4, p. 174, doi. 10.1002/isaf.1437
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LOST, STOLEN OR SKIMMED: Overcoming credit card fraud in South Africa.
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- SA Crime Quarterly, 2012, n. 40, p. 31
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Credit Card Risk Detection Based on Chaos Theory and Cloud Model.
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- International Review on Computers & Software, 2011, v. 6, n. 6, p. 1099
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From biased robots to race as technology.
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- Ephemera: Theory & Politics in Organization, 2022, v. 22, n. 2, p. 139
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MERCHANT LIABILITY TO FINANCIAL INSTITUTIONS FOR A DATA BREACH.
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- Northern Kentucky Law Review, 2017, v. 44, n. 1, p. 35
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INNOVATIVE METHODS TO ENHANCE TRANSACTION SECURITY OF BANKING APPLICATIONS.
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- Journal of Business Economics & Management, 2006, v. 7, n. 4, p. 243, doi. 10.3846/16111699.2006.9636145
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Culture’s impact on trust, distrust, and intentions in data theft environments: A cross-cultural exploratory study.
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- Journal of Global Information Technology Management, 2017, v. 20, n. 4, p. 214, doi. 10.1080/1097198X.2017.1388672
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A Breach in Security.
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- Public Relations Tactics, 2016, v. 23, n. 9, p. 6
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- Article
Sequential Adversarial Anomaly Detection for One-Class Event Data.
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- INFORMS Journal on Data Science, 2023, v. 2, n. 1, p. 45, doi. 10.1287/ijds.2023.0026
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Deep convolutional neural network based synthetic minority oversampling technique: a forfending model for fraudulent credit card transactions in financial institution.
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- Journal of Nigerian Society of Physical Sciences, 2024, v. 6, n. 2, p. 1, doi. 10.46481/jnsps.2024.2037
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Application of Machine Learning and Resampling Techniques to Credit Card Fraud Detection.
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- Journal of Nigerian Society of Physical Sciences, 2022, v. 4, n. 3, p. 1, doi. 10.46481/jnsps.2022.769
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Securing transactions: a hybrid dependable ensemble machine learning model using IHT-LR and grid search.
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- Cybersecurity (2523-3246), 2024, v. 7, n. 1, p. 1, doi. 10.1186/s42400-024-00221-z
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- Article