Issue 68

M. Matin et alii, Frattura ed Integrità Strutturale, 68 (2024) 357-370; DOI: 10.3221/IGF-ESIS.68.24

[22] Matin, M. and Azadi, M. (2023). The effect of training data ratio and normalizing on fatigue lifetime prediction of aluminum alloys with machine learning. International Journal of Engineering. Available at: https://www.ije.ir/article_186504.html [23] Cohen, I. Huang, Y. Chen, J. Benesty, J. Benesty, J. Chen, J. Huang, Y. and Cohen, I. (2009). Pearson correlation coefficient. Noise reduction in speech processing, pp. 1-4. DOI: 10.1007/978-3-642-00296-0_5. [24] Lundberg, S. M. and Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30. ISBN: 9781510860964. [25] Wen, X., Xie, Y. Wu, L. and Jiang, L. (2021). Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP. Accident Analysis & Prevention, 159, 106261. DOI: 10.1016/j.aap.2021.106261. [26] Meng, Y. Yang, N. Qian, Z. and Zhang, G. (2020). What makes an online review more helpful: an interpretation framework using XGBoost and SHAP values. Journal of Theoretical and Applied Electronic Commerce Research, 16, pp. 466-490. DOI: 10.3390/jtaer16030029. [27] Biau, G. and Scornet, E. (2016). A random forest guided tour. Test, 25, 197-227. DOI: 10.1007/s11749-016-0481-7 [28] Cutler, A. Cutler, D. R. and Stevens, J. R. (2012). Random forests. Ensemble machine learning: Methods and applications, pp. 157-175. DOI: 10.1007/978-1-4419-9326-7_5. [29] Huang, Q. Mao, J. and Liu, Y. An improved grid search algorithm of SVR parameters optimization. (2012). IEEE 14th International Conference on Communication Technology, pp. 1022-1026. DOI: 10.1109/ICCT.2012.6511415. [30] Wu, C.H. Ho, J.M. and Lee, D.T. (2004). Travel-time prediction with support vector regression. IEEE transactions on intelligent transportation systems, 5, pp. 276-281. DOI: 10.1109/TITS.2004.837813. [31] Ahmadi-Nedushan, B. (2012). Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models. Construction and Building Materials, 36, pp. 665-673. DOI: 10.1016/j.conbuildmat.2012.06.002 [32] Putatunda, S. and Rama, K. (2018). A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost. Proceedings of the 2018 international conference on signal processing and machine learning, pp. 6-10. DOI: 10.1145/3297067.3297080. [33] Azadi, M. Shahsavand, A. and Parast, M. S. A. (2022). Analyzing experimental data from reciprocating wear testing on piston aluminum alloys, with and without clay nano-particle reinforcement. Data in Brief, 45, 108766. DOI: 10.1016/j.dib.2022.108766 [34] Nasiri, H. Azadi, M. and Dadashi, A. (2023). Interpretable Extreme Gradient Boosting Machine Learning Model for Fatigue Lifetimes in 3D-Printed Polylactic Acid Biomaterials. Preprint in SSRN. Available at SSRN 4364418. DOI: 10.2139/ssrn.4364418 [35] Zhu, S. Zhang, Y. Chen, X. He, Y. and Xu, W. (2023). A multi-algorithm integration machine learning approach for high cycle fatigue prediction of a titanium alloy in aero-engine. Engineering Fracture Mechanics, 289, 109485. DOI: 10.1016/j.engfracmech.2023.109485 [36] Long, X. Lu, C. Su, Y. and Dai, Y. (2023). Machine learning framework for predicting the low cycle fatigue life of lead free solders. Engineering Failure Analysis, 148, 107228. DOI: 10.1016/j.engfailanal.2023.107228 [37] Hao, W. Tan, L. Yang, X. Shi, D. Wang, M., Miao, G. and Fan, Y. (2023). A physics-informed machine learning approach for notch fatigue evaluation of alloys used in aerospace. International Journal of Fatigue, 170, 107536. DOI: 10.1016/j.ijfatigue.2023.107536 [38] Raudys, A. and Goldstein, E. (2022). Forecasting Detrended Volatility Risk and Financial Price Series Using LSTM Neural Networks and XGBoost Regressor. Journal of Risk and Financial Management, 15 (12), 602. DOI: 10.3390/jrfm15120602

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