Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Credit Card Fraud Detection: A Novel Approach Using Aggregation Strategy and Feedback Mechanism
Changjun Jiang, Jiahui Song, Guanjun Liu, et al.
IEEE Internet of Things Journal (2018) Vol. 5, Iss. 5, pp. 3637-3647
Closed Access | Times Cited: 148

Showing 26-50 of 148 citing articles:

Fraud Detection in Credit Cards using Logistic Regression
Hala Z Alenzi, O. Nojood
International Journal of Advanced Computer Science and Applications (2020) Vol. 11, Iss. 12
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
The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection
Yanfeng Zhang, Hongliang Lü, Hong-Fan Lin, et al.
Mobile Information Systems (2022) Vol. 2022, pp. 1-10
Open Access | Times Cited: 23
Ensemble Synthesized Minority Oversampling-Based Generative Adversarial Networks and Random Forest Algorithm for Credit Card Fraud Detection
Fuad A. Ghaleb, Faisal Saeed, Mohammed Al-Sarem, et al.
IEEE Access (2023) Vol. 11, pp. 89694-89710
Open Access | Times Cited: 15
UNMASKING FRAUDSTERS: Ensemble Features Selection to Enhance Random Forest Fraud Detection
Maureen Ifeanyi Akazue, Irene Alamarefa Debekeme, Abel Efe Edje, et al.
Journal of Computing Theories and Applications (2023) Vol. 1, Iss. 2, pp. 201-211
Open Access | Times Cited: 15
RaKShA: A Trusted Explainable LSTM Model to Classify Fraud Patterns on Credit Card Transactions
Jay S. Raval, Pronaya Bhattacharya, Nilesh Kumar Jadav, et al.
Mathematics (2023) Vol. 11, Iss. 8, pp. 1901-1901
Open Access | Times Cited: 14
Survey on extreme learning machines for outlier detection
Rasoul Kiani, Wei Jin, Victor S. Sheng
Machine Learning (2024) Vol. 113, Iss. 8, pp. 5495-5531
Closed Access | Times Cited: 5
Graph Convolutional Networks With Adaptive Neighborhood Awareness
Mingjian Guang, Chungang Yan, Yuhua Xu, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2024) Vol. 46, Iss. 11, pp. 7392-7404
Closed Access | Times Cited: 5
Performance Analysis of Machine Learning Techniques in Credit Card Fraud Detection
I Suganya, K Naveen, P Ragul, et al.
Journal of Soft Computing Paradigm (2025) Vol. 6, Iss. 4, pp. 390-400
Open Access
A novel drift detection method using parallel detection and anti-noise techniques
Qian Zhang, Guanjun Liu
Applied Intelligence (2025) Vol. 55, Iss. 6
Closed Access
Fraud detection for job placement using hierarchical clusters-based deep neural networks
Soo Hyun Kim, Hanjoon Kim, Hyoungrae Kim
Applied Intelligence (2019) Vol. 49, Iss. 8, pp. 2842-2861
Closed Access | Times Cited: 40
A SMOTe based Oversampling Data-Point Approach to Solving the Credit Card Data Imbalance Problem in Financial Fraud Detection
Nhlakanipho Michael Mqadi, Nalindren Naicker, Timothy T. Adeliyi
International Journal of Computing and Digital Systems (2021) Vol. 10, Iss. 1, pp. 277-286
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
Credit card fraud detection using a hierarchical behavior-knowledge space model
Asoke K. Nandi, Kuldeep Kaur Randhawa, Hong Siang Chua, et al.
PLoS ONE (2022) Vol. 17, Iss. 1, pp. e0260579-e0260579
Open Access | Times Cited: 22
Financial Fraud Identification Using Deep Learning Techniques
Fatima Adel Nama, Ahmed J. Obaid
Al-Salam Journal for Engineering and Technology (2024) Vol. 3, Iss. 1, pp. 141-147
Open Access | Times Cited: 4
Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems
Tahani Baabdullah, Amani Alzahrani, Danda B. Rawat, et al.
Future Internet (2024) Vol. 16, Iss. 6, pp. 196-196
Open Access | Times Cited: 4
Credit card fraud detection based on federated graph learning
Y. A. Tang, Yongquan Liang
Expert Systems with Applications (2024) Vol. 256, pp. 124979-124979
Closed Access | Times Cited: 4
Fraud Detection in E-commerce Platforms Using Data Mining Algorithms
Abhinav Parihar, Menachem Domb, Sujata Joshi
Lecture notes in networks and systems (2025), pp. 51-62
Closed Access
GMM-based Undersampling and Its Application for Credit Card Fraud Detection
Fengjun Zhang, Guanjun Liu, Zhenchuan Li, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2019), pp. 1-8
Closed Access | Times Cited: 33
A Feature Extraction Method for Credit Card Fraud Detection
Yu Xie, Guanjun Liu, Ruihao Cao, et al.
(2019), pp. 70-75
Closed Access | Times Cited: 32
Fraud detection in bank transaction with wrapper model and Harris water optimization-based deep recurrent neural network
Chandra Sekhar Kolli, Uma Devi Tatavarthi
Kybernetes (2020) Vol. 50, Iss. 6, pp. 1731-1750
Closed Access | Times Cited: 30
Credit Card Fraud Detection using Machine Learning
Anjali Singh Rathore, Ankit Kumar, Depanshi Tomar, et al.
(2021), pp. 167-171
Closed Access | Times Cited: 25
Sequential credit card fraud detection: A joint deep neural network and probabilistic graphical model approach
Javad Forough, Saeedeh Momtazi
Expert Systems (2021) Vol. 39, Iss. 1
Closed Access | Times Cited: 24
Credit Card Fraud Detection using Machine and Deep Learning Techniques
Shagun Sharma, Anjali Kataria, Jasminder Kaur Sandhu, et al.
2022 3rd International Conference for Emerging Technology (INCET) (2022), pp. 1-7
Closed Access | Times Cited: 18
Consumer Fraud in Online Shopping: Detecting Risk Indicators through Data Mining
Tobias Knuth, Dennis Ahrholdt
International Journal of Electronic Commerce (2022) Vol. 26, Iss. 3, pp. 388-411
Closed Access | Times Cited: 17

Scroll to top