Research_Design_CA1 - Computer Science bibliographies - in Harvard style

In-text: (Adadi and Berrada, 2018)

Your Bibliography: Adadi, A. and Berrada, M., 2018. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6(52), pp.138-160.

In-text: (Anowar and Sadaoui, 2020)

Your Bibliography: Anowar, F. and Sadaoui, S., 2020. Incremental Neural-Network Learning for Big Fraud Data. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 1(1), pp.1-4.

In-text: (Gee and Button, 2019)

Your Bibliography: Gee, J. and Button, M., 2019. The Financial Cost of Fraud 2019. [online] Crowe.com. Available at: <https://www.crowe.com/uk/croweuk/-/media/Crowe/Firms/Europe/uk/CroweUK/PDF-publications/The-Financial-Cost-of-Fraud-2019> [Accessed 16 October 2022].

In-text: (Nguyen, Tahir, Abdelrazek and Babar, 2022)

Your Bibliography: Nguyen, T., Tahir, H., Abdelrazek, M. and Babar, A., 2022. Deep Learning Methods for Credit Card Fraud Detection. [online] arXiv.org. Available at: <https://doi.org/10.48550/arXiv.2012.03754> [Accessed 16 October 2022].

In-text: (Psychoula et al., 2021)

Your Bibliography: Psychoula, I., Gutmann, A., Mainali, P., Lee, S., Dunphy, P. and Petitcolas, F., 2021. Explainable Machine Learning for Fraud Detection. Computer, 54(10), pp.49-59.

In-text: (RB and KR, 2021)

Your Bibliography: RB, A. and KR, S., 2021. Credit card fraud detection using artificial neural network. Global Transitions Proceedings, 2(1), pp.35-41.

In-text: (Sharma and Bathla, 2020)

Your Bibliography: Sharma, A. and Bathla, N., 2020. Review on credit card fraud detection and classification by Machine Learning and Data Mining approaches. Review on credit card fraud detection and classification by Machine Learning and Data Mining approaches, [online] 6, pp.687-692. Available at: <https://www.semanticscholar.org/paper/Review-on-credit-card-fraud-detection-and-by-and-Sharma-Bathla/b6c839cadb4c6281a934a8788fec93d5482e6af4> [Accessed 16 October 2022].

A new explainable artificial intelligence approach is ... presented. In this way, feature relationships that have a dominant effect on fraud detection are revealed.

In-text: (Sinanc, Demirezen and Sağıroğlu, 2021)

Your Bibliography: Sinanc, D., Demirezen, U. and Sağıroğlu, Ş., 2021. Explainable Credit Card Fraud Detection with Image Conversion. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(1), pp.63-76.

In-text: (Whatman, 2022)

Your Bibliography: Whatman, P., 2022. Credit card statistics 2022: 65+ facts for Europe, UK, and US. [online] Blog.spendesk.com. Available at: <https://blog.spendesk.com/en/credit-card-statistics-2020> [Accessed 16 October 2022].