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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:

Generative adversarial network based telecom fraud detection at the receiving bank
Yu‐Jun Zheng, Xiaohan Zhou, Weiguo Sheng, et al.
Neural Networks (2018) Vol. 102, pp. 78-86
Closed Access | Times Cited: 109

Showing 1-25 of 109 citing articles:

Deep Learning for Anomaly Detection: A Survey
Raghavendra Chalapathy, Sanjay Chawla
arXiv (Cornell University) (2019)
Open Access | Times Cited: 1178
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, et al.
Medical Image Analysis (2019) Vol. 54, pp. 30-44
Closed Access | Times Cited: 1110
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff, Jacob Kauffmann, Robert A. Vandermeulen, et al.
Proceedings of the IEEE (2021) Vol. 109, Iss. 5, pp. 756-795
Open Access | Times Cited: 690
A review of deep learning with special emphasis on architectures, applications and recent trends
Saptarshi Sengupta, Sanchita Basak, Pallabi Saikia, et al.
Knowledge-Based Systems (2020) Vol. 194, pp. 105596-105596
Open Access | Times Cited: 339
Generative Adversarial Networks
Zhipeng Cai, Zuobin Xiong, Honghui Xu, et al.
ACM Computing Surveys (2021) Vol. 54, Iss. 6, pp. 1-38
Closed Access | Times Cited: 269
GAN-based anomaly detection: A review
Xuan Xia, Xizhou Pan, Nan Li, et al.
Neurocomputing (2022) Vol. 493, pp. 497-535
Closed Access | Times Cited: 234
Cyber security in smart cities: A review of deep learning-based applications and case studies
Dongliang Chen, Paweł Wawrzyński, Zhihan Lv
Sustainable Cities and Society (2020) Vol. 66, pp. 102655-102655
Closed Access | Times Cited: 229
Flow-based network traffic generation using Generative Adversarial Networks
Markus Ring, Daniel Schlör, Dieter Landes, et al.
Computers & Security (2018) Vol. 82, pp. 156-172
Open Access | Times Cited: 170
FinTech as a Game Changer: Overview of Research Frontiers
Terrence Hendershott, Xiaoquan Zhang, J. Leon Zhao, et al.
Information Systems Research (2021) Vol. 32, Iss. 1, pp. 1-17
Open Access | Times Cited: 144
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
Modeling financial time-series with generative adversarial networks
Shuntaro Takahashi, Yu Chen, Kumiko Tanaka‐Ishii
Physica A Statistical Mechanics and its Applications (2019) Vol. 527, pp. 121261-121261
Open Access | Times Cited: 129
Convolutional and generative adversarial neural networks in manufacturing
Andrew Kusiak
International Journal of Production Research (2019) Vol. 58, Iss. 5, pp. 1594-1604
Closed Access | Times Cited: 108
Deep Learning for Anomaly Detection
Ruoying Wang, Kexin Nie, Tie Wang, et al.
(2020)
Closed Access | Times Cited: 73
A Comparative Study to Predict Bearing Degradation Using Discrete Wavelet Transform (DWT), Tabular Generative Adversarial Networks (TGAN) and Machine Learning Models
Keval Bhavsar, Vinay Vakharia, Rakesh Chaudhari, et al.
Machines (2022) Vol. 10, Iss. 3, pp. 176-176
Open Access | Times Cited: 40
Improving Classification Performance in Credit Card Fraud Detection by Using New Data Augmentation
Emilija Strelcenia, Simant Prakoonwit
AI (2023) Vol. 4, Iss. 1, pp. 172-198
Open Access | Times Cited: 31
Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives
Deepak Adhikari, Wei Jiang, Jinyu Zhan, et al.
Computer Science Review (2024) Vol. 54, pp. 100665-100665
Closed Access | Times Cited: 8
Machine Learning in NextG Networks via Generative Adversarial Networks
Ender Ayanoğlu, Kemal Davaslıoğlu, Yalin E. Sagduyu
IEEE Transactions on Cognitive Communications and Networking (2022) Vol. 8, Iss. 2, pp. 480-501
Open Access | Times Cited: 36
GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection
Xinxin Hu, Haotian Chen, Junjie Zhang, et al.
IEEE Transactions on Big Data (2024) Vol. 10, Iss. 4, pp. 528-542
Open Access | Times Cited: 6
Oversampling method using outlier detectable generative adversarial network
Joo-Hyuk Oh, Jae Yeol Hong, Jun‐Geol Baek
Expert Systems with Applications (2019) Vol. 133, pp. 1-8
Closed Access | Times Cited: 47
Generative adversarial dimensionality reduction for diagnosing faults and attacks in cyber-physical systems
Maryam Farajzadeh-Zanjani, Ehsan Hallaji, Roozbeh Razavi–Far, et al.
Neurocomputing (2021) Vol. 440, pp. 101-110
Closed Access | Times Cited: 37
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
Relaxed deep generative adversarial networks for real-time economic smart generation dispatch and control of integrated energy systems
Linfei Yin, Bin Zhang
Applied Energy (2022) Vol. 330, pp. 120300-120300
Closed Access | Times Cited: 27
BTG: A Bridge to Graph machine learning in telecommunications fraud detection
Xinxin Hu, Hongchang Chen, Shuxin Liu, et al.
Future Generation Computer Systems (2022) Vol. 137, pp. 274-287
Closed Access | Times Cited: 25
Cost-Sensitive GNN-Based Imbalanced Learning for Mobile Social Network Fraud Detection
Xinxin Hu, Haotian Chen, Hongchang Chen, et al.
IEEE Transactions on Computational Social Systems (2023) Vol. 11, Iss. 2, pp. 2675-2690
Open Access | Times Cited: 14
Research on the method of expanding the mineralization data based on generative adversarial networks
B. Li, Chunfang Kong, Kai Xu, et al.
Research Square (Research Square) (2025)
Closed Access
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