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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:

A survey of machine-learning and nature-inspired based credit card fraud detection techniques
Aderemi O. Adewumi, Andronicus A. Akinyelu
International Journal of Systems Assurance Engineering and Management (2016) Vol. 8, Iss. S2, pp. 937-953
Closed Access | Times Cited: 190

Showing 1-25 of 190 citing articles:

Deep Learning for Anomaly Detection: A Survey
Raghavendra Chalapathy, Sanjay Chawla
arXiv (Cornell University) (2019)
Open Access | Times Cited: 1178
Credit Card Fraud Detection Using AdaBoost and Majority Voting
Kuldeep Kaur Randhawa, Chu Kiong Loo, Manjeevan Seera, et al.
IEEE Access (2018) Vol. 6, pp. 14277-14284
Open Access | Times Cited: 367
An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine
Altyeb Taha, Sharaf J. Malebary
IEEE Access (2020) Vol. 8, pp. 25579-25587
Open Access | Times Cited: 297
Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances
Waleed Hilal, S. Andrew Gadsden, John Yawney
Expert Systems with Applications (2021) Vol. 193, pp. 116429-116429
Open Access | Times Cited: 260
Anomaly detection of defects on concrete structures with the convolutional autoencoder
Jun Kang Chow, Zhi-bin Su, Jianhua Wu, et al.
Advanced Engineering Informatics (2020) Vol. 45, pp. 101105-101105
Closed Access | Times Cited: 252
How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark
Nick Ryman‐Tubb, Paul Krause, Wolfgang Garn
Engineering Applications of Artificial Intelligence (2018) Vol. 76, pp. 130-157
Closed Access | Times Cited: 179
Insurance fraud detection: Evidence from artificial intelligence and machine learning
Faheem Aslam, Ahmed Imran Hunjra, Zied Ftiti, et al.
Research in International Business and Finance (2022) Vol. 62, pp. 101744-101744
Open Access | Times Cited: 74
Generative AI: A systematic review using topic modelling techniques
Priyanka Gupta, Bosheng Ding, Chong Guan, et al.
Data and Information Management (2024) Vol. 8, Iss. 2, pp. 100066-100066
Open Access | Times Cited: 58
A Survey on GAN Techniques for Data Augmentation to Address the Imbalanced Data Issues in Credit Card Fraud Detection
Emilija Strelcenia, Simant Prakoonwit
Machine Learning and Knowledge Extraction (2023) Vol. 5, Iss. 1, pp. 304-329
Open Access | Times Cited: 41
Naive Bayes classifier – An ensemble procedure for recall and precision enrichment
Or Peretz, Michal Koren, Oded Koren
Engineering Applications of Artificial Intelligence (2024) Vol. 136, pp. 108972-108972
Open Access | Times Cited: 19
E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review
Abed Mutemi, Fernando Bação
Big Data Mining and Analytics (2024) Vol. 7, Iss. 2, pp. 419-444
Open Access | Times Cited: 18
Combining Autoencoders and Deep Learning for Effective Fraud Detection in Credit Card Transactions
Mohammed Tayebi, Said El Kafhali
Operations Research Forum (2025) Vol. 6, Iss. 1
Closed Access | Times Cited: 2
Performance of machine learning techniques in the detection of financial frauds
Imane Sadgali, Nawal Sael, Faouzia Benabbou
Procedia Computer Science (2019) Vol. 148, pp. 45-54
Open Access | Times Cited: 139
One-class support vector classifiers: A survey
Shamshe Alam, Sanjay Kumar Sonbhadra, Sonali Agarwal, et al.
Knowledge-Based Systems (2020) Vol. 196, pp. 105754-105754
Closed Access | Times Cited: 114
A customized classification algorithm for credit card fraud detection
Alex G. C. de Sá, Adriano C. M. Pereira, Gisele L. Pappa
Engineering Applications of Artificial Intelligence (2018) Vol. 72, pp. 21-29
Closed Access | Times Cited: 109
Auto loan fraud detection using dominance-based rough set approach versus machine learning methods
Jerzy Błaszczyński, Adiel Teixeira de Almeida Filho, Anna Matuszyk, et al.
Expert Systems with Applications (2020) Vol. 163, pp. 113740-113740
Closed Access | Times Cited: 94
Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression
Ameer Hussein, Rihab Salah Khairy, Shaima Miqdad Mohamed Najeeb, et al.
International Journal of Interactive Mobile Technologies (iJIM) (2021) Vol. 15, Iss. 05, pp. 24-24
Open Access | Times Cited: 91
Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model
Salvatore Carta, Gianni Fenu, Diego Reforgiato Recupero, et al.
Journal of Information Security and Applications (2019) Vol. 46, pp. 13-22
Closed Access | Times Cited: 80
Machine learning assisted modeling of mixing timescale for LES/PDF of high-Karlovitz turbulent premixed combustion
Jinlong Liu, Haifeng Wang
Combustion and Flame (2021) Vol. 238, pp. 111895-111895
Open Access | Times Cited: 73
Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques
Abolfazl Mehbodniya, Izhar Alam, Sagar Dhanraj Pande, et al.
Security and Communication Networks (2021) Vol. 2021, pp. 1-8
Open Access | Times Cited: 65
Fraud detection and prevention in e-commerce: A systematic literature review
Vinicius Facco Rodrigues, Lucas Micol Policarpo, Diórgenes Eugênio da Silveira, et al.
Electronic Commerce Research and Applications (2022) Vol. 56, pp. 101207-101207
Closed Access | Times Cited: 43
Credit card fraud detection using the brown bear optimization algorithm
Shaymaa E. Sorour, Khalied M. Albarrak, Amr A. Abohany, et al.
Alexandria Engineering Journal (2024) Vol. 104, pp. 171-192
Closed Access | Times Cited: 10
A Multiple Classifiers System for Anomaly Detection in Credit Card Data With Unbalanced and Overlapped Classes
Suraya Nurain Kalid, Keng-Hoong Ng, Gee-Kok Tong, et al.
IEEE Access (2020) Vol. 8, pp. 28210-28221
Open Access | Times Cited: 65
A Novel Approach for Credit Card Fraud Detection using Decision Tree and Random Forest Algorithms
M. R. Dileep, A V Navaneeth, Manish Kumar Abhishek
2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (2021), pp. 1025-1028
Closed Access | Times Cited: 42
Entrepreneurship: What matters most
Ana Rosado-Cubero, Teresa Rubio, Adolfo Hernández
Journal of Business Research (2022) Vol. 144, pp. 250-263
Open Access | Times Cited: 35
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