R.J.M.A. Triepels

Triepels, R. J. M. A., Segers, M. H. M., Rosen, P., Nuijts, R. M. M. A., van den Biggelaar, F. J. H. M., Henry, Y. P., Stenevi, U., Tassignon, M. J., Young, D., Behndig, A., Lundstroem, M., & Dickman, M. M. (2023). Development of machine learning models to predict posterior capsule rupture based on the EUREQUO registry. Acta Ophthalmologica, 101(6), 644-650. https://doi.org/10.1111/aos.15648
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Triepels, R. (2023). SASHA: Hyperparameter Optimization by Simulated Annealing and Successive Halving. In I. Maglogiannis, L. Iliadis, J. MacIntyre, & M. Dominguez (Eds.), Artificial Intelligence Applications and Innovations: 19th IFIP WG 12.5 International Conference, AIAI 2023 León, Spain, June 14-17, 2023 Proceedings, Part II (pp. 491-502). Springer, Cham. https://doi.org/10.1007/978-3-031-34107-6_39
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Triepels, R. J. M. A., Segers, M. H. M., Nuijts, R. M. M. A., Rosen, P., Henry, Y. P., Stenevi, U., Tassignon, M. J., Young, D., Behndig, A., Lundstrom, M., & Dickman, M. M. (2022). Prediction of posterior capsule rupture with machine learning based on the EUREQUO. Acta Ophthalmologica, 100(s268), 37-38.
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Triepels, R. (2021). Anomaly Detection by Robust Feature Reconstruction. In L. Iliadis, J. MacIntyre, C. Jayne, & E. Pimenidis (Eds.), Proceedings of the 22nd Engineering Applications of Neural Networks Conference : EANN 2021 (pp. 42-53). Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_4
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Triepels, R., Daniels, H., & Berndsen, R. (2021). Monitoring Liquidity Management of Banks With Recurrent Neural Networks. Computational Economics, 57(1), 89-112. https://doi.org/10.1007/s10614-020-10067-5
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Triepels, R. (2019). Anomaly detection in the shipping and banking industry. [Doctoral Thesis, Center, Tilburg University]. CentER.
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Heuver, R., & Triepels, R. (2019). Liquidity Stress Detection in the European Banking Sector. In AP. Rocha, L. Steels, & J. VanDenHerik (Eds.), Proceedings of the 11th International Conference on Agents and Artificial Intelligence (Vol. 2, pp. 266-274). Scitepress - Science And Technology Publications. https://doi.org/10.5220/0007395602660274
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Triepels, R., Daniels, H., & Feelders, A. (2018). Data-driven fraud detection in international shipping. Expert Systems with Applications, 99, 193-202. https://doi.org/10.1016/j.eswa.2018.01.007
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Triepels, R., Daniels, H., & Heijmans, R. (2017). Anomaly Detection in Real-Time Gross Settlement Systems. In Proceedings of the 19th International Conference on Enterprise Information Systems (Vol. 3, pp. 433-441). Scitepress - Science And Technology Publications. https://doi.org/10.5220/0006333004330441
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Triepels, R., Daniels, H., & Heijmans, R. (2017). Detection and Explanation of Anomalous Payment Behavior in Real-Time Gross Settlement Systems. In S. Hammoudi, M. Smialek, O. Camp, & J. Filipe (Eds.), Enterprise Information Systems: 19th International Conference, ICEIS 2017 (Vol. 321, pp. 145-161). Springer Verlag. https://doi.org/10.1007/978-3-319-93375-7_8
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R.J.M.A. Triepels

Assistant Professor

Data Analytics and Digitalisation

School of Business and Economics

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R.J.M.A. Triepels