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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Random forest for credit card fraud detection
Shiyang Xuan, Guanjun Liu, Zhenchuan Li, et al.
(2018), pp. 1-6
Closed Access | Times Cited: 293

Showing 26-50 of 293 citing articles:

DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble
Fidelis Obukohwo Aghware, Rume Elizabeth Yoro, Patrick Ogholuwarami Ejeh, et al.
International Journal of Advanced Computer Science and Applications (2023) Vol. 14, Iss. 6
Open Access | Times Cited: 23
Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection
Fidelis Obukohwo Aghware, Arnold Adimabua Ojugo, Wilfred Adigwe, et al.
Journal of Computing Theories and Applications (2024) Vol. 1, Iss. 4, pp. 407-420
Open Access | Times Cited: 13
Integrating a Machine Learning-Driven Fraud Detection System Based on a Risk Management Framework
Lingfeng Guo, Runze Song, Jiang Wu, et al.
(2024)
Open Access | Times Cited: 9
Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review
Lucas Micol Policarpo, Diórgenes Eugênio da Silveira, Rodrigo da Rosa Righi, et al.
Computer Science Review (2021) Vol. 41, pp. 100414-100414
Closed Access | Times Cited: 53
A Proposed Model for Card Fraud Detection Based on CatBoost and Deep Neural Network
Nghia Hoai Nguyen, T.P.C. Duong, Tram Chau, et al.
IEEE Access (2022) Vol. 10, pp. 96852-96861
Open Access | Times Cited: 34
Credit Card Fraud Detection Based on Improved Variational Autoencoder Generative Adversarial Network
Yuanming Ding, Kang Wei, Jianxin Feng, et al.
IEEE Access (2023) Vol. 11, pp. 83680-83691
Open Access | Times Cited: 19
Forging a User-Trust Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study
Arnold Adimabua Ojugo, Maureen Ifeanyi Akazue, Patrick Ogholuwarami Ejeh, et al.
Journal of Computing Theories and Applications (2023) Vol. 1, Iss. 2, pp. 50-60
Open Access | Times Cited: 17
Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data
AlsharifHasan Mohamad Aburbeian, Huthaifa I. Ashqar
Lecture notes in networks and systems (2023), pp. 605-616
Open Access | Times Cited: 16
Credit Card Fraud Detection: Addressing Imbalanced Datasets with a Multi-phase Approach
Fatima Zohra El Hlouli, Jamal Riffi, Mohamed Adnane Mahraz, et al.
SN Computer Science (2024) Vol. 5, Iss. 1
Closed Access | Times Cited: 6
DaBO-BoostE: Enhanced Data Balancing via Oversampling Technique for a Boosting Ensemble in Card-Fraud Detection
Emakpor Augustine Otorokpo, Margaret Dumebi Okpor, Rume Elizabeth Yoro, et al.
Advances in Multidisciplinary & Scientific Research Journal Publication (2024) Vol. 12, pp. 45-66
Open Access | Times Cited: 6
Feature construction for fraudulent credit card cash-out detection
Yue Wu, Yunjie Xu, Jiaoyang Li
Decision Support Systems (2019) Vol. 127, pp. 113155-113155
Closed Access | Times Cited: 49
Accelerating Business Growth with Big Data and Artificial Intelligence
Awishkar Ghimire, Surendrabikram Thapa, Avinash Kumar Jha, et al.
2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (2020), pp. 441-448
Closed Access | Times Cited: 46
A Comparison of Data Sampling Techniques for Credit Card Fraud Detection
Abdulla Muaz, Manoj Jayabalan, V. Thiruchelvam
International Journal of Advanced Computer Science and Applications (2020) Vol. 11, Iss. 6
Open Access | Times Cited: 39
Credit Card Fraud Detection by Modelling Behaviour Pattern using Hybrid Ensemble Model
Venkatesh Karthik, Abinash Mishra, U. Srinivasulu Reddy
Arabian Journal for Science and Engineering (2021) Vol. 47, Iss. 2, pp. 1987-1997
Closed Access | Times Cited: 34
Analysis of the Regulatory Development Cryptocurrencies for Trading in Business with Deep Learning Techniques
Uma N. Dulhare, Shaik Rasool, Mohammad Naseer Khan, et al.
2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) (2022), pp. 651-657
Closed Access | Times Cited: 23
A Survey and a Credit Card Fraud Detection and Prevention Model using the Decision Tree Algorithm
Abdulaziz Saleh Alraddadi
Engineering Technology & Applied Science Research (2023) Vol. 13, Iss. 4, pp. 11505-11510
Open Access | Times Cited: 14
A Deep Learning Method of Credit Card Fraud Detection Based on Continuous-Coupled Neural Networks
Yanxi Wu, Liping Wang, Hongyu Li, et al.
Mathematics (2025) Vol. 13, Iss. 5, pp. 819-819
Open Access
Preferences and Usage of E-Banking Services Among Customers in Tripura, India
Supriya Dam
IGI Global eBooks (2025), pp. 155-186
Closed Access
Deep neural network for detection of fraudulent transaction
Fatima Zohra El Hlouli, Jamal Riffi, Mohamed Adnane Mahraz, et al.
Multimedia Tools and Applications (2025)
Closed Access
Graph-Based Syntactic Analysis for Cross-Domain Fraud Messages Detection
Ning-Fang Hsu, Yi-Shin Chen
Communications in computer and information science (2025), pp. 33-48
Closed Access
Adaptive Credit Card Fraud Detection Techniques Based on Feature Selection Method
Ajeet Singh, Anurag Jain
Advances in intelligent systems and computing (2019), pp. 167-178
Closed Access | Times Cited: 42
A Dual Approach for Credit Card Fraud Detection using Neural Network and Data Mining Techniques
Aanchal Sahu, Harshvardhan GM, Mahendra Kumar Gourisaria
2021 IEEE 18th India Council International Conference (INDICON) (2020)
Closed Access | Times Cited: 38
Credit Card Fraud Detection Techniques – A Survey
Nikita Shirodkar, Pratikesh Mandrekar, Rohit Shet Mandrekar, et al.
2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (2020), pp. 1-7
Closed Access | Times Cited: 36
On the classification of Microsoft-Windows ransomware using hardware profile
Sana Aurangzeb, Rao Naveed Bin Rais, Muhammad Aleem, et al.
PeerJ Computer Science (2021) Vol. 7, pp. e361-e361
Open Access | Times Cited: 30
A clustering-based flexible weighting method in AdaBoost and its application to transaction fraud detection
Chaofan Yang, Guanjun Liu, Chungang Yan, et al.
Science China Information Sciences (2021) Vol. 64, Iss. 12
Closed Access | Times Cited: 29

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