Open Access
Issue
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
Article Number 00076
Number of page(s) 18
DOI https://doi.org/10.1051/bioconf/20249700076
Published online 05 April 2024
  • Al-Faqir, S., & Ouda, O. (2022). Credit Card Frauds Scoring Model Based on Deep Learning Ensemble. Journal of Theoretical and Applied Information Technology, 31st July 2022, vol.100, No 14, 5223-5224. Little Lion Scientific. ISSN: 1992-8645. [Google Scholar]
  • Al-Shabi, Mohammed. (2019). Credit Card Fraud Detection Using Autoencoder Model in Unbalanced Datasets. Journal of Advances in Mathematics and Computer Science. 1–16. DOI: 10.9734/jamcs/2019/v33i530192. [CrossRef] [Google Scholar]
  • Hassan, N., Ola, A., Ayah, A.A. and Mutaz, Y., “Credit Card Fraud Detection Based on Machine and Deep Learning,” 2020 11th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan, 2020, pp. 204–208, DOI: 10.1109/ICICS49469.2020.239524. [Google Scholar]
  • Puh, M. and Ljiljana, B. “Detecting Credit Card Fraud Using Selected Machine Learning Algorithms.” 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (2019): 1250–1255. [CrossRef] [Google Scholar]
  • S.P. Maniraj & Saini, Aditya & Ahmed, Shadab & Sarkar, Swarna. (2019). Credit Card Fraud Detection using Machine Learning and Data Science. International Journal of Engineering Research and. 08. 10.17577/IJERTV8IS090031. [Google Scholar]
  • Tiwari, Pooja & Mehta, Simran & Sakhuja, Nishtha & Kumar, Jitendra & Singh, Ashutosh. (2021). Credit Card Fraud Detection using Machine Learning: A Study. [Google Scholar]
  • Mijwil, M. M., & Salem, I. E. (2020). Credit Card Fraud Detection in Payment Using Machine Learning Classifiers. Asian Journal of Computer and Information Systems, 8 (4), 50. Asian Online Journals. Retrieved from http://www.ajouronline.com. [CrossRef] [Google Scholar]
  • Ileberi, E., Sun, Y. & Wang, Z. A machine learning based credit card fraud detection using the GA algorithm for feature selection. J Big Data 9, 24 (2022). https://doi.org/10.1186/s40537-022-00573-8. [CrossRef] [Google Scholar]
  • Afriyie, Jonathan & Tawiah, Kassim & Pels, Wilhelmina & Addai-Henne, Sandra & Dwamena, Harriet & Emmanuel, Odame & Ayeh, Samuel & Eshun, John. (2023). A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions. Decision Analytics Journal. 6. 100163. DOI: 10.1016/j.dajour.2023.100163. [CrossRef] [Google Scholar]
  • Asma Cherif, Arwa Badhib, Heyfa Ammar, Suhair Alshehri, Manal Kalkatawi, Abdessamad Imine, Credit card fraud detection in the era of disruptive technologies: A systematic review, Journal of King Saud University -Computer and Information Sciences, Volume 35, Issue 1, 2023, Pages 145–174, ISSN 1319-1578, https://doi.org/10.1016/j.jksuci.2022.11.008. [CrossRef] [Google Scholar]
  • R. Asha, S. K.-G. T. Proceedings, and undefined 2021, “Credit card fraud detection using artificial neural network,” Elsevier, Accessed: Mar. 11, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666285X21000066. [Google Scholar]
  • Rtayli, Naoufal and Nourddine Enneya. “Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization.” J. Inf. Secur. Appl. 55 (2020): 102596. [Google Scholar]
  • Chang, Victor & Doan, Le Minh Thao & Di Stefano, Alessandro & Sun, Zhili & Fortino, Giancarlo. (2022). Digital payment fraud detection methods in digital ages and Industry 4.0. Computers & Electrical Engineering. 100. 107734. DOI: 10.1016/j.compeleceng.2022.107734. [Google Scholar]
  • Sadineni, Praveen Kumar. (2020). Detection of Fraudulent Transactions in Credit Card using Machine Learning Algorithms. 659–660. DOI: 10.1109/I-SMAC49090.2020.9243545. [Google Scholar]
  • Bryan K., Nathaniel P., Stephen R., Dewi S., Julian W., Yudy P., On the benefits of machine learning classification in cashback fraud detection, Procedia Computer Science, Volume 216, 2023, Pages 364–369, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2022.12.147. [CrossRef] [Google Scholar]
  • Błaszczyński, J., de Almeida Filho, A. T., Matuszyk, A., Szeląg, M., & Słowiński, R. (2021). “Auto loan fraud detection using dominance-based rough set approach versus machine learning methods.” Expert Systems with Applications, 163, 113740. [CrossRef] [Google Scholar]
  • S. Bagga, A. Goyal, N. Gupta, and A. Goyal, “Credit card fraud detection using pipeling and ensemble learning,” Procedia Comput. Sci., Vol. 173, pp. 104–112, 2020. [CrossRef] [Google Scholar]
  • M. M. Mijwil and I. E. Salem, “Credit Card Fraud Detection in Payment Using Machine Learning Classifiers,” Asian J. Comput. Inf. Syst., 8, no. 4, 2020. [Google Scholar]
  • S. Lakshmi and S. D. Kavilla, “Machine learning for credit card fraud detection system,” Int. J. Appl. Eng. Res., vol. 13, no. 24, pp. 16819–16824, 2018. [Google Scholar]
  • P. Gupta, A. Varshney, M. R. Khan, R. Ahmed, M. Shuaib, and S. Alam, “Unbalanced Credit Card Fraud Detection Data: A Machine Learning-Oriented Comparative Study of Balancing Techniques,” Procedia Comput. Sci., Vol. 218, pp. 2575–2584, 2023. [CrossRef] [Google Scholar]
  • A. Cherif, A. Badhib, H. Ammar, S. Alshehri, M. Kalkatawi, and A. Imine, “Credit card fraud detection in the era of disruptive technologies: A systematic review,” J. King Saud Univ. Inf. Sci., 2022. [Google Scholar]
  • D. Kajal and K. Kaur, “Credit card fraud detection using imbalance resampling method with feature selection,” Int. J., 10, no. 3, 2021. [Google Scholar]
  • E. Ileberi, Y. Sun, and Z. Wang, “A machine learning based credit card fraud detection using the GA algorithm for feature selection,” J. Big Data, vol. 9, no. 1, pp. 1–17, 2022. [CrossRef] [Google Scholar]
  • J. Błaszczyński, A. T. de Almeida Filho, A. Matuszyk, M. Szeląg, and R. Słowiński, “Auto loan fraud detection using dominance-based rough set approach versus machine learning methods,” Expert Syst. Appl., Vol. 163, p. 113740, 2021. [CrossRef] [Google Scholar]
  • E. N. Osegi and E. F. Jumbo, “Comparative analysis of credit card fraud detection in Simulated Annealing trained Artificial Neural Network and Hierarchical Temporal Memory,” Mach. Learn. with Appl., Vol. 6, p. 100080, 2021. [CrossRef] [Google Scholar]
  • A. Farhang Ghahfarokhi, T. Mansouri, M. R. Sadeghi Moghaddam, N. Bahrambeik, R. Yavari, and M. Fani Sani, “Credit card fraud detection using asexual reproduction optimization,” Kybernetes, vol. 51, no. 9, pp. 2852–2876, 2022. [CrossRef] [Google Scholar]
  • M. Rabbani et al., “A review on machine learning approaches for network malicious behavior detection in emerging technologies,” Entropy, vol. 23, no. 5, pp. 529, 2021. [CrossRef] [PubMed] [Google Scholar]
  • C. Sudha and D. Akila, “WITHDRAWN: Majority vote ensemble classifier for accurate detection of credit card frauds.” Elsevier, 2021. [Google Scholar]
  • Akshansh Sharma, Firoj Khan, Deepak Sharma, Dr. Sunil Gupta, “Python: The Programming Language of Future” INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY, May 2020 | IJIRT | Volume 6 Issue 12 | ISSN: 2349-6002. [Google Scholar]
  • Alolov, T. S. (2023). PYTHON INSTRUMENTLARI BILAN KATTA MA’LUMOTLARNI QAYTA ISHLASH. Educational Research in Universal Sciences, 2(10), 320–322. [Google Scholar]
  • Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020). https://doi.org/10.1038/s41586-020-2649-2 [NASA ADS] [CrossRef] [Google Scholar]
  • Dürr, O., Sick, B., & Murina, E. (2020). Probabilistic Deep Learning: With Python, Keras, and TensorFlow Probability. Retrieved from Google Books: https://books.google.com/ [Google Scholar]
  • Saabith, A. L. S., Vinothraj, T., & Fareez, M. M. M. (2020). Popular Python libraries and their application domains. International Journal of Advance Engineering and Research Development, 7 (11), 18. [Google Scholar]
  • Sial, A. H., Rashdi, S. Y. S., & Khan, A. H. (2021). Comparative analysis of data visualization libraries Matplotlib and Seaborn in Python. International Journal of Advanced Trends in Computer Science and Engineering, 10(1), Retrieved from http://www.warse.org/IJATCSE/static/pdf/file/ijatcse391012021.pdf. [Google Scholar]
  • Qin, Jian, et al. “Research and application of machinelearning for additive manufacturing.” AdditiveManufacturing (2022): 102691. [Google Scholar]
  • Scikit-learn. (2023). Scikit-learn: Machine Learning in Python. Retrieved from https://scikit-learn.org/stable/index.html. [Google Scholar]
  • Albadr, Musatafa Abbas Abbood, et al. “Speech emotion recognition using optimized genetic algorithm-extreme learning machine.” Multimedia Tools and Applications 81.17 (2022): 23963–23989. [CrossRef] [Google Scholar]
  • Albadr, M. A. A., Ayob, M., Tiun, S., AL-Dhief, F. T., Arram, A., & Khalaf, S. (2023). Breast cancer diagnosis using the fast learning network algorithm. Frontiers in Oncology, 13, 1150840. [CrossRef] [PubMed] [Google Scholar]
  • Albadr, Musatafa Abbas Abbood, et al. “Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection.” Frontiers in Public Health 10 (2022): 925901. [CrossRef] [PubMed] [Google Scholar]
  • Jaadi, Z. (2019, October 15). Everything you need to know about interpreting correlations. Towards Data Science. [Google Scholar]
  • Tamboli, N. (2023, July 14). Effective Strategies for Handling Missing Values in Data Analysis (Updated 2023). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.