KAYNAKÇA
Anderka, M., Klerx, T., Priesterjahn, S., ve Büning, H. K. (2014). Automatic ATM Fraud Detection as a Sequence-based Anomaly Detection Problem: Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, 759-764. https://doi.org/10.5220/0004922307590764
Bhowmik, M., Sai Siri Chandana, T., ve Rudra, B. (2021). Comparative Study of Machine Learning Algorithms for Fraud Detection in Blockchain. 2021 5th International Conference on Computing Methodologies and Communication, 539-541. https://doi.org/10.1109/ICCMC51019.2021.9418470
Botelho, J., ve Antunes, C. (2011). Combining Social Network Analysis with Semi-supervised Clustering: A Case Study on Fraud Detection. In Proceeding of Mining Data Semantics (MDS’2011) in Conjunction with SIGKDD, pp. 1–7.
Sayfa 77
Chen, T. (2013). A Collaborative and Artificial İntelligence Approach for Semiconductor Cost Forecasting. Computers & Industrial Engineering, 66(2), 476-484. https://doi.org/10.1016/j.cie.2013.07.014
Chen, T., ve Tsourakakis, C. (2022). AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2762-2770. https://doi.org/10.1145/3534678.3539100
de Roux, D., Perez, B., Moreno, A., Villamil, M. del P., ve Figueroa, C. (2018). Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 215-222. https://doi.org/10.1145/3219819.3219878
Didimo, W., Grilli, L., Liotta, G., Menconi, L., Montecchiani, F., ve Pagliuca, D. (2020). Combining Network Visualization and Data Mining for Tax Risk Assessment. IEEE Access, 8, 16073-16086. https://doi.org/10.1109/ACCESS.2020.2967974
Dong, X. L., ve Rekatsinas, T. (2018). Data Integration and Machine Learning: A Natural Synergy. Proceedings of the 2018 International Conference on Management of Data, 1645-1650. https://doi.org/10.1145/3183713.3197387
Dursun, Ö. O., ve Toraman, S. (2021). Uzun Kısa Vadeli Bellek Yöntemi ile Havayolu Yolcu Tahmini. Journal of Aviation, 5(2), 241-248. https://doi.org/10.30518/jav.1009331
Gao, Y., Shi, B., Dong, B., Wang, Y., Mi, L., ve Zheng, Q. (2023). Tax Evasion Detection With FBNE-PU Algorithm Based on PnCGCN and PU Learning. IEEE Transactions on Knowledge and Data Engineering, 35(1), 931-944. https://doi.org/10.1109/TKDE.2021.3090075
Guo, S. (2022). Intelligent Assessment Method of Enterprise Tax Risk Based on Deep Learning. Wireless Communications and Mobile Computing, 2022, 1-10. https://doi.org/10.1155/2022/5003935
Hui, X. (2020). Comparison and Application of Logistic Regression and Support Vector Machine in Tax Forecasting. 2020 International Signal Processing, Communications and Engineering Management Conference (ISPCEM), 48-52. https://doi.org/10.1109/ISPCEM52197.2020.00015
Sayfa 78
Junqué de Fortuny, E., Stankova, M., Moeyersoms, J., Minnaert, B., Provost, F., ve Martens, D. (2014). Corporate Residence Fraud Detection. Proceedings of the 20th ACM SIGKDD İnternational Conference on Knowledge Discovery and Data Mining, 1650-1659. https://doi.org/10.1145/2623330.2623333
Krizhevsky, A., Sutskever, I., ve Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25. https://papers.nips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html, (Erişim Tarihi: 20.08.2023)
Kureljusic, M., ve Reisch, L. (2022). Revenue Forecasting for European Capital Market-Oriented Firms: A Comparative Prediction Study Between Financial Analysts and Machine Learning Models. https://papers.ssrn.com/abstract=4057309, (Erişim Tarihi: 22.08.2023)
Lahann, J., Scheid, M., ve Fettke, P. (2019). Utilizing Machine Learning Techniques to Reveal VAT Compliance Violations in Accounting Data. 2019 IEEE 21st Conference on Business Informatics (CBI), 01, 1-10. https://doi.org/10.1109/CBI.2019.00008
Lei, H., ve Cailan, H. (2021). Comparison of Multiple Machine Learning Models Based on Enterprise Revenue Forecasting. 2021 Asia-Pacific Conference on Communications Technology and Computer Science, 354-359. https://doi.org/10.1109/ACCTCS52002.2021.00077
Miller, A., AlKindi, L., ve Alblooshi, A. (2020). Using A Database Approach, with Big Data and Unsupervised Machine Learning to Model Tax Behavior in the Expatriate Community. Solid State Technology, 63, 379-389.
Mishev, K., Gjorgjevikj, A., Vodenska, I., Chitkushev, L., Souma, W., ve Trajanov, D. (2019). Forecasting Corporate Revenue by Using Deep-Learning Methodologies. 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO), 115-120. https://doi.org/10.1109/ICCAIRO47923.2019.00026