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Rafaralahy, \"Pairwise Learning to Rank for Chess Puzzle Difficulty\nPrediction,\" 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA, 2024, pp. 8385-8389, https:\/\/dx.doi.org\/10.1109\/BigData62323.2024.10825356.","DOI":"10.1109\/BigData62323.2024.10825356"},{"key":"ref4","doi-asserted-by":"publisher","unstructured":"D. Ruta, M. Liu and L. Cen, \"Moves Based Prediction of Chess\nPuzzle Difficulty with Convolutional Neural Networks,\" 2024 IEEE\nInternational Conference on Big Data (BigData), Washington, DC, USA,\n2024, pp. 8390-8395, https:\/\/dx.doi.org\/10.1109\/BigData62323.2024.10825595.","DOI":"10.1109\/BigData62323.2024.10825595"},{"key":"ref5","doi-asserted-by":"publisher","unstructured":"S. Milosz and P. Kapusta, \"Predicting Chess Puzzle Difficulty with\nTransformers,\" in 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA, 2024, pp. 8377-8384, https:\/\/dx.doi.org\/10.1109\/BigData62323.2024.10825919.","DOI":"10.1109\/BigData62323.2024.10825919"},{"key":"ref6","unstructured":"M. Omori and P. Tadepalli, \u201cEstimating Player Ratings and Puzzle\nDifficulty with CNN-LSTM Models,\u201d arXiv preprint https:\/\/arxiv.org\/abs\/2409.11506,\n2024."},{"key":"ref7","doi-asserted-by":"publisher","unstructured":"J. Zy\u015bko, M. \u015awiechowski, S. Stawicki, K. Jagie\u0142a, A. 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Andr\u00e9, \"Estimating Chess Puzzle Difficulty\nWithout Past Game Records Using a Human Problem-Solving Inspired\nNeural Network Architecture,\" 2024 IEEE International Conference on\nBig Data (BigData), Washington, DC, USA, 2024, pp. 8396-8402, https:\/\/dx.doi.org\/10.1109\/BigData62323.2024.10826087.","DOI":"10.1109\/BigData62323.2024.10826087"},{"key":"ref10","unstructured":"A. V. Dorogush, V. Ershov, and A. Gulin, \u201cCatBoost: gradient boosting\nwith categorical features support,\u201d in Proc. Workshop on ML Systems\n(MLSys), 2018."},{"key":"ref11","unstructured":"G. Ke, Q. Meng, T. Finley, et al., \u201cLightGBM: A highly efficient gradient\nboosting decision tree,\u201d in Advances in Neural Information Processing\nSystems (NeurIPS), 2017."},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"T. Chen and C. Guestrin, \u201cXGBoost: A scalable tree boosting system,\u201d\nin Proc. 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Ruta, \"Gradient Boosting Models for Cybersecurity Threat Detection with Aggregated Time Series Features,\" 2023 18th\nConference on Computer Science and Intelligence Systems (FedCSIS),\nWarsaw, Poland, 2023, pp. 1311-1315, https:\/\/dx.doi.org\/10.15439\/2023F4457.","DOI":"10.15439\/2023F4457"}],"event":{"name":"20th Conference on Computer Science and Intelligence Systems (FedCSIS)","theme":"Computer Science and Intelligence Systems","location":"Krak\u00f3w, Poland","acronym":"FedCSIS","number":"20","start":{"date-parts":[[2025,9,14]]},"end":{"date-parts":[[2025,9,17]]}},"container-title":["Annals of Computer Science and Information Systems","Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)"],"original-title":[],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:52:03Z","timestamp":1761119523000},"score":1,"resource":{"primary":{"URL":"https:\/\/annals-csis.org\/Volume_43\/drp\/4532.html"}},"subtitle":[],"proceedings-subject":"Computer Science and Information Systems","short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":13,"URL":"https:\/\/doi.org\/10.15439\/2025f4532","relation":{},"ISSN":["2300-5963"],"issn-type":[{"value":"2300-5963","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10,15]]}}}