Research Article | Open Access | Download PDF
Volume 13 | Issue 4 | Year 2026 | Article Id. IJECE-V13I4P126 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I4P126Snow Leopard Green Anaconda Optimization-based Feature Selection with Credit Card Fraud Detection using Artificial Neural Network
Shuchita Sheokand, Sunita Beniwal
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 22 Jan 2026 | 22 Feb 2026 | 24 Mar 2026 | 30 Apr 2026 |
Citation :
Shuchita Sheokand, Sunita Beniwal, "Snow Leopard Green Anaconda Optimization-based Feature Selection with Credit Card Fraud Detection using Artificial Neural Network," International Journal of Electronics and Communication Engineering, vol. 13, no. 4, pp. 307-316, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I4P126
Abstract
Credit card holders are easy targets for fraud. With digitalization, online payments are increasingly prevalent through e-commerce and other websites. This rapid growth in digital transactions has significantly increased the risk of online fraud. Several challenges are faced by fraud detection systems, including delays in verification, concept drift, and class imbalance. However, many of the learning algorithms developed for fraud detection make assumptions that are often impractical in practical fraud detection systems. To tackle these issues, a model named Artificial Neural Network with Snow Leopard Green Anaconda Optimization (ANN with SLGAO) was proposed for detecting fraud in credit cards. Initially, input data from the credit card fraud dataset was pre-processed using quantile normalization, followed by feature selection using hybrid SLGAO, and Borderline- Synthetic Minority Over-sampling Technique (SMOTE) was used for data augmentation. Finally, fraudulent transactions were detected using an Artificial Neural Network (ANN). The proposed ANN with SLGAO achieved an accuracy of 91.848%, specificity of 91.685%, and sensitivity of 91.644%.
Keywords
Artificial neural network, Credit card fraud, Green Anaconda Optimization, Snow Leopard Optimization Algorithm, Synthetic Minority Over-sampling Technique.
References
- Rejwan Bin Sulaiman, Vitaly Schetinin, and Paul Sant, “Review of Machine Learning Approach on Credit Card Fraud Detection,” Human-Centric Intelligent Systems, vol. 2, pp. 55-68, 2022.
[CrossRef] [Google Scholar] [Publisher Link] - Asma Cherif et al., “Credit Card Fraud Detection in the Era of Disruptive Technologies: A Systematic Review,” Journal of King Saud University – Computer and Information Sciences, vol. 35, no. 1, pp. 145-174, 2023.
[CrossRef] [Google Scholar] [Publisher Link] - Saurabh C. Dubey, Ketan S. Mundhe, and Aditya A. Kadam, “Credit Card Fraud Detection Using Artificial Neural Network and Backpropagation,” 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, pp. 268-273, 2020.
[CrossRef] [Google Scholar] [Publisher Link] - Noor Saleh Alfaiz, and Suliman Mohamed Fati, “Enhanced Credit Card Fraud Detection Model using Machine Learning,” Electronics, vol. 11, no. 4, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link] - Pampana Gnana Venkata Sai, P. Srinivasulu, Tulasi Raju Nethala, “Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms,” International Journal of Novel Research and Development, vol. 9, no. 10, pp. 310-318, 2024.
[Publisher Link] - Abdul Rehman Khalid et al., “Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach,” Big Data and Cognitive Computing, vol. 8, no. 1, pp. 1-27, 2024.
[CrossRef] [Google Scholar] [Publisher Link] - Venkata Ratnam Ganji, Aparna Chaparala, and Radhika Sajja, “Shuffled Shepherd Political Optimization-Based Deep Learning Method for Credit Card Fraud Detection,” Concurrency and Computation: Practice and Experience, vol. 35, no. 10, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link] - Abdullah Alharbi et al., “A Novel Text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach,” Electronics, vol. 11, no. 5, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link] - J. Karthika, and A. Senthilselvi, “Smart Credit Card Fraud Detection System based on Dilated Convolutional Neural Network with Sampling Technique,” Multimedia Tools and Applications, vol. 82, no. 20, pp. 31691-31708, 2023.
[CrossRef] [Google Scholar] [Publisher Link] - V. S. S. Karthik, Abinash Mishra, and U. Srinivasulu Reddy, “Credit Card Fraud Detection by Modeling Behavioral Patterns using a Hybrid Ensemble Model,” Arabian Journal for Science and Engineering, vol. 47, pp. 1987-1997, 2022.
[CrossRef] [Google Scholar] [Publisher Link] - Emilija Strelcenia, and Simant Prakoonwit, “Improving Classification Performance in Credit Card Fraud Detection by using New Data Augmentation,” AI, vol. 4, no. 1, pp. 172-198, 2023.
[CrossRef] [Google Scholar] [Publisher Link] - Bandar Alshawi, “Utilizing GANs for Credit Card Fraud Detection: A Comparison of Supervised Learning Algorithms,” Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12264-12270, 2023.
[CrossRef] [Google Scholar] [Publisher Link] - Ibomoiye Domor Mienye, and Yanxia Sun, “A Deep Learning Ensemble with Data Resampling for Credit Card Fraud Detection,” IEEE Access, vol. 11, pp. 30628-30638, 2023.
[CrossRef] [Google Scholar] [Publisher Link] - Dijana Jovanovic et al., “Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection,” Mathematics, vol. 10, no. 13, pp. 1-30, 2022.
[CrossRef] [Google Scholar] [Publisher Link] - Bharat Kumar Padhi et al., “RHSOFS: Feature Selection Using the Rock Hyrax Swarm Optimization Algorithm for Credit Card Fraud Detection System,” Sensors, vol. 22, no. 23, pp. 1-18, 9321, 2022.
[CrossRef] [Google Scholar] [Publisher Link] - N. Prabhakaran, and R. Nedunchelian, “Oppositional Cat Swarm Optimization-Based Feature Selection Approach for Credit Card Fraud Detection,” Computational Intelligence and Neuroscience, vol. 2023, no. 1, 2023.
[CrossRef] [Google Scholar] [Publisher Link] - Credit Card Fraud Detection, Kaggle Dataset, 2025. [Online]. Available: https://www.kaggle.com/mlg-ulb/creditcardfraud
- Yaxing Zhao, Limsoon Wong, and Wilson Wen Bin Goh, “How to do Quantile Normalization Correctly for Gene Expression Data Analyses,” Scientific Reports, vol. 10, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link] - Petr Coufal et al., “Snow Leopard Optimization Algorithm: A New Nature-Based Optimization Algorithm for Solving Optimization Problems,” Mathematics, vol. 9, no. 21, pp. 1-26, 2021.
[CrossRef] [Google Scholar] [Publisher Link] - Mohammad Dehghani, Pavel Trojovský, and Om Parkash Malik, “Green Anaconda Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems,” Biomimetics, vol. 8, no. 1, pp. 1-60, 2023.
[CrossRef] [Google Scholar] [Publisher Link] - Taejun Lee, Minju Kim, and Sung-Phil Kim, “Data Augmentation Effects using Borderline-SMOTE on Classification of a P300-based BCI,” 2020 8th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South), pp. 1-4, 2020.
[CrossRef] [Google Scholar] [Publisher Link] - R.B. Asha, and Suresh Kumar, “Credit Card Fraud Detection using Artificial Neural Network,” Global Transitions Proceedings, vol. 2, no. 1, pp. 35-41, 2021.
[CrossRef] [Google Scholar] [Publisher Link] - J. Pradeep Kandhasamy, and S. Balamurali, “Performance Analysis of Classifier Models to Predict Diabetes Mellitus,” Procedia Computer Science, vol. 47, pp. 45-51, 2015.
[CrossRef] [Google Scholar] [Publisher Link]