Search feature selection in credit card fraud detection Cuckoo Hybrid Big Bang Big Crunch

Machine Learning


  • Khando, K., Islam, MS & Gao, S. New technologies for digital payments and related challenges: a systematic literature review. The future internet 15 (1), 21. (2022).

  • Ansarinasab, S., Ghassemi, F., Nazarimehr, F., Ghosh, D. & Jafari, S. Phase synchronization in cryptocurrency networks and their functions. int. J. mod. Phys. c(ijmpc). 35 (02), 1–21 (2024).

    Google Scholar

  • Mittal, S. & Tyagi, S. Calculation technology for real-time credit card fraud detection. in Computer Networking and Cybersecurity Handbook: Principles and Paradigms653–681 (2020).

  • De Best, R. The annual number of credit card transactions in India is 2012-2021, per capita (2024). https://www.statista.com/statistics/1309045/total-number-of-credit-card-payments-in-india/

  • De Best, R. Visa, MasterCard, UnionPay Transaction Volume Worldwide 2014–2022. https://www.statista.com/statistics/261327/number-of-per-card-credit-card-card-card-card-transactions-worldwide-by-brand-sy-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-of-

  • Mniai, A., Tarik, M. & Jebari, K. A new framework for credit card fraud detection. IEEE Access. 11112776–112786. (2023).

  • Dyvik, EH Card Scam and US Card Scam and World 2014-2021 (2023). https://www.statista.com/statistics/1264329/value-fraudulent-card-transactions-worldwide/

  • King, Y. Etal. August. Privacy that stores distributed deep learning and application to credit card fraud detection. in 2018 17th IEEE International Conference on Security and Privacy in Computing and Communications/12th IEEE International Conference Big Data Science and Engineering (Trustcom/BigDatase)1070–1078. (IEEE, 2018).

  • Alarfaj, FK et al. Detection of credit card fraud using cutting-edge machine learning and deep learning algorithms. IEEE Access. 1039700–39715 (2022).

    ArticleGoogle Scholar

  • Natarajan, R. Etal. Hybrid Big Bang with Ant Colony Optimization for Email Spam Detection – Big Crunch. International Journal of Modern Physics c, 33(04), p.2250051. (2022).

  • Xiong, Y., Zou, Z. & Cheng, J. J. Cuckoo Search Algorithm based on the cloud model and its applications. SCI. manager, 13 (1), 10098. (2023).

  • Erol, Ok & Eksin, I. New optimization method: Big Bang Big Crunch. Adv. Eng. Softw. 37 (2), 106–111 (2006).

    ArticleGoogle Scholar

  • Yang, XS & Deb, S. Engineering optimization with Cuckoo Search. int. J. Mathematics. Number of modeling. optimization. 1 (4), 330–343 (2010).

    ArticleGoogle Scholar

  • Hausain, eh et al. Hybrid Harris Hawks Optimization searches drug design and discovery in chemistry informatics. SCI. manager, 10 (1), 14439. (2020).

  • Hill, MQ et al. Deep convolutional neural networks facing caricatures. nut. Mach. Intel. 1 (11), 522–529 (2019).

    ArticleGoogle Scholar

  • Asha. By reducing feature dimensions with variations in deep neural network-based classification optimization gravity search algorithms. int. J. mod. Phys. c, 32 (10), 2150137. (2021).

  • Jindal, S., Sachdeva, M. & Kushwaha captivates a new quantum-capable binary firefly algorithm with gravity search algorithm to optimize features for human activity recognition. int. J. mod. Phys. c, 33 (11), 2250146. (2022).

  • Bhatt, A. Etal. A quantum-inspired metaheuristic algorithm with deep learning for facial expression recognition under different yaw angles. int. J. mod. Phys. c, 33 (04), 2250045. (2022).

  • Saheed, YK, Hambari, Massachusetts, Arrowolo, Missouri & Olaspo, YA November. Applying Naive Bayes, Random Forest and SVM GA feature selection for credit card fraud detection. in 2020 International Conference on Science and Applications for Decision-Making Aid (DASA)1091–1097. (IEEE, 2020).

  • Saheed, YK, BABA, UA & RAJI, Raj, Massachusetts, Big Data Analysis of Credit Card Fraud Detection Using Supervised Machine Learning Models. in Big data analysis of the insurance market31–56. (Emerald Publishing Limited, 2022).

  • Sailusha, R., Gnaneswar, V., Ramesh, R. & Rao, Gr May. Detection of credit card fraud using machine learning. in 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS)1264–1270. (IEEE, 2020).

  • Rajora, S. et al. A comparative study of machine learning techniques for credit card fraud detection based on time distribution. in 2018 IEEE Symposium Series on Computational Intelligence (SSCI)1958-1963. (IEEE, 2018).

  • Randhawa, K., Loo, CK, Seera, M., Lim, CP & Nandi, AK using credit card fraud detection and majority votes. IEEE Access, 614277–14284 (2018).

  • Sulaiman, R. B., Schetinin, V. & Sant, P. A review of the machine learning approach to credit card fraud detection. Human-centered Inter. syst. 2 (1), 55–68 (2022).

    ArticleGoogle Scholar

  • Kumar, S., Gunjan, V.K., Ansari, MD & Pathak, R. Credit card fraud detection using the Support Vector Machine. in Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart City and Applications: ICMISC 202127–37. (Springer, 2022).

  • Khan, MZ et al. Performance analysis of machine learning algorithms for credit card fraud detection. int. J. Online Biomed. Eng. 19 (3), 82–98 (2023).

    ArticleGoogle Scholar

  • Alfaiz, NS&FATI and SM have strengthened their credit card fraud detection model using machine learning. electronics 11 (4), 662 (2022).

  • Jurgovsky, J. Etal. Sequence classification of credit card fraud detection. Expert Syst. Appl. 100234–245 (2018).

    ArticleGoogle Scholar

  • Fiore, U., De Santis, A., Perla, F., Zanetti, P. & Palmieri, F. Inf. SCI. 479448–455 (2019).

    ArticleGoogle Scholar

  • Zioviris, G., Kolomvatsos, K. & Stamoulis, G. Credit card fraud detection using deep learning multi-stage model. J. Supercomputing. 78 (12), 14571–14596 (2022).

    ArticleGoogle Scholar

  • Forough, J. & Momtazi, S. Sequential credit card fraud detection: A joint deep neural network and a probabilistic graphical model approach. Expert Syst. 39 (1), E12795 (2022).

    ArticleGoogle Scholar

  • Kasasbeh, B., Aldabaybah, B. & Ahmad, H. A multilayer perceptron artificial neural network-based model for credit card fraud detection. Indonesian J. Electr. Eng. computer. SCI. 26 (1), 362–373 (2022).

    ArticleGoogle Scholar

  • Karthika, J. & Senthilselvi, A. A smart credit card fraud detection system based on an extended convolutional neural network with sampling technology. Multimedia Tool Appl. 82 (20), 31691–31708 (2023).

    ArticleGoogle Scholar

  • Ileberi, E., Sun, Y. & Wang, Z. Performance assessment of machine learning methods for credit card fraud detection using Small and Adaboost. IEEE Access. 9165286–165294 (2021).

    ArticleGoogle Scholar

  • Khalid, Ar et al. Enhanced credit card fraud: An ensemble machine learning approach. Big data recognition. computer. 8 (1), 6 (2024).

  • Singh, P., Singla, K., Piyush, P. & Chugh, B. 145632 Anomaly detection classifier for detecting credit card fraudulent transactions. in 4th International Conference on Electricity, Computing, Communication and Sustainable Technology Advancements in 2024 (ICAECT)1–6. (IEEE, 2024).

  • Mim, Ma, Majadi, N. & Mazumder, P. A soft voting ensemble learning approach for credit card fraud detection. Helion 10 (3), E25466. (2024).

  • Mienye, Id & Sun, Y. Deep learning ensemble with data resampling for credit card fraud detection. IEEE Access. 1130628–30638 (2023).

    ArticleGoogle Scholar

  • Ileberi, E., Sun, Y. & Wang, Z. Machine learning-based credit card fraud detection using GA algorithm for feature selection. J. Big Data, 9 (1), 24. (2022).

  • Geetha, N. & Dheepa, G. Transaction fraud detection using artificial bee colony (ABC)-based feature selection and enhanced neural network (ENN) classifiers. int. J. Mecha. Eng., 7 (3) (2022).

  • Geetha, N. & Dheepa, G. Hybrid deep learning and modified butterfly optimization-based feature selection for transactional credit card fraud detection. J. Positivity. School psychology. 6 (7), 5328–5345 (2022).

    Google Scholar

  • Arun, goalkeeper & Rajesh, P. Design of metaheuristic feature selection with deep learning-based credit card fraud detection model. in 2022 2nd International Conference on Artificial Intelligence and Smart Energy (ICAIS)191–197. (IEEE, 2022).

  • Karthika, J. & Senthilselvi, A. August. Detection of credit card fraud detection using HPO using Inception-based deep learning model. in 2023 5th International Conference on Invention Research in Computing Applications (ICIRCA)70–77. (IEEE, 2023).

  • Rawashdeh, E., Al-Ramahi, N., Ahmad, H. & Zaghloul, R. int. J.DataNetw. SCI. 8 (1), 463–472 (2024).

    ArticleGoogle Scholar

  • Yang, XS & Deb, S. Cuckoo search via Lévy flights. in 2009 World Conference on Nature and Biologically Inspired Computing (NABIC)210–214. (IEEE, 2009).

  • Andrea and Machine Learning Group – ULB, Credit Card Fraud Detection Dataset, Kaggle, San Francisco, California, USA. (2018). https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

  • Machine learning applications and resampling techniques for credit card fraud detection from Udeze, Cl, Eteng, IE & Ibor, AE. J. Nigeria Soc. Phys. SCI.769–769 (2022).



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