[1] I. Chakraborty and P. Maity, “COVID-19 outbreak: Migration, effects on society, global environment and prevention,” Sci. Total Environ., vol. 728, 2020, pp. 138882.
[2] علی احمدیان رمکی، عباس رسولزادگان وعباس جوانجعفری، "تشخیص نفوذ مبتنی بر مدلهای مخفی مارکوف: روشها، کاربردها و چالشها"، نشریه مدلسازی در مهندسی، دوره 16، شماره 53، تیر 1397، صفحه 183- 206.
[3] الهام پارساییمهر، مهدی فرتاش و جواد اکبری ترکستانی، "بهبود استخراج ویژگی با استفاده از یک مدل یادگیری عمیق گروهی برای تشخیص موجودیت"، نشریه مدلسازی در مهندسی، دوره 20، شماره 69، تیر 1401، صفحه 103- 112.
[4] محمود معلم و علیاکبر پویان، "کشف ناهنجاری با استفاده از کدکننده خودکار مبتنی بر LSTM"، نشریه مدلسازی در مهندسی، دوره 17، شماره 56، اردیبهشت 1398، صفحه 191- 211.
[5] M. Ciotti et al., “COVID-19 Outbreak: An Overview,” Chemotherapy, vol. 64, no. 5–6, 2020, pp. 215–223.
[6] W. T. Li et al., “Using machine learning of clinical data to diagnose COVID-19: A systematic review and meta-analysis,” BMC Med. Inform. Decis. Mak., vol. 20, no. 1, Sep. 2020.
[7] Y. Zoabi, S. Deri-Rozov, and N. Shomron, “Machine learning-based prediction of COVID-19 diagnosis based on symptoms,” npj Digit. Med., vol. 4, no. 1, 2021, pp. 1–5.
[8] M. Soui, N. Mansouri, R. Alhamad, M. Kessentini, and K. Ghedira, “NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient’s symptoms,” Nonlinear Dyn., vol. 106, no. 2, 2021, pp. 1453–1475.
[9] S. Banik, S. Banik, A. Ghosh, and A. Mukherjee, “Probabilistic estimation of COVID-19 using patient’s symptoms,” in Data Driven Approach Towards Disruptive Technologies, Springer, 2021, pp. 369–378.
[10] S. N. Nan et al., “A prediction model based on machine learning for diagnosing the early COVID-19 patients,” pp. 1–12, 2020.
[11] A. Chansik et al., “Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study,” Scientific report in nature research, 2020.
[12] C. Fang et al., “Deep learning for predicting COVID-19 malignant progression,” in Medical Image Analysis, vol. 79, 2021.
[13] A. Mariot, S. Sgoifo, and M. Sauli, “I gozzi endotoracici: contributo casistico-clinico (20 casi),” Friuli Med., vol. 19, no. 6, 1964.
[14] Y. Xu, X. Zhao, Y. Chen, and Z. Yang, “Research on a Mixed Gas Classification Algorithm Based on Extreme Random Tree,” Appl. Sci., vol. 9, no. 9, 2019, pp. 1728.
[15] W. Wang, G. Chakraborty, and B. Chakraborty, “Predicting the risk of chronic kidney disease (Ckd) using machine learning algorithm,” Appl. Sci., vol. 11, no. 1, 2021, pp. 1–17.
[16] K. Song, F. Yan, T. Ding, L. Gao, and S. Lu, “A steel property optimization model based on the XGBoost algorithm and improved PSO,” Comput. Mater. Sci., vol. 174, 2020, pp. 109472.
[17] H. Wang, C. Liu, and L. Deng, “Enhanced prediction of hot spots at protein-protein interfaces using extreme gradient boosting,” Sci. Rep., vol. 8, no. 1, 2018, pp. 1–13.
[18] W. Zhang, C. Wu, H. Zhong, Y. Li, and L. Wang, “Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization,” Geosci. Front., vol. 12, no. 1, 2021, pp. 469–477.
[19] X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, “Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2097–2106.
[20] J. Ma, Y. Ding, J. C. P. Cheng, Y. Tan, V. J. L. Gan, and J. Zhang, “Analyzing the leading causes of traffic fatalities using XGBoost and grid-based analysis: a city management perspective,” IEEE Access, vol. 7, 2019, pp. 148059–148072.
[21] L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “CatBoost: unbiased boosting with categorical features,” Adv. Neural Inf. Process. Syst., vol. 31, 2018.
[22] A. V. Dorogush, V. Ershov, and A. Gulin, “CatBoost: gradient boosting with categorical features support,” arXiv Prepr. arXiv1810.11363, 2018.
[23] G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree,” Adv. Neural Inf. Process. Syst., vol. 2017-Decem, no. Nips, 2017, pp. 3147–3155.
[24] M. Ezzoddin, H. Nasiri, and M. Dorrigiv, “Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM,” in 2022 International Conference on Machine Vision and Image Processing (MVIP), 2022, pp. 1–7.
[25] C. Chen, Q. Zhang, Q. Ma, and B. Yu, “LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion,” Chemom. Intell. Lab. Syst., vol. 191, 2019, pp. 54–64.
[26] S. Chehreh Chelgani, H. Nasiri, and A. Tohry, “Modeling of particle sizes for industrial HPGR products by a unique explainable AI tool- A ‘Conscious Lab’ development,” Adv. Powder Technol., vol. 32, no. 11, 2021, pp. 4141–4148.
[27] S. C. Chelgani, H. Nasiri, and M. Alidokht, “Interpretable modeling of metallurgical responses for an industrial coal column flotation circuit by XGBoost and SHAP-A ‘conscious-lab’ development,” Int. J. Min. Sci. Technol., vol. 31, no. 6,2021, pp. 1135–1144.
[28] A. Movsessian, D. G. Cava, and D. Tcherniak, “Interpretable machine learning in damage detection using Shapley Additive Explanations,” 2021.
[29] H. Mao et al., “Driving safety assessment for ride-hailing drivers,” Accid. Anal. \& Prev., vol. 149, 2021, pp. 105574.
[30] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” Adv. Neural Inf. Process. Syst., vol. 30, 2017, pp. 4765–4774.
[31] N. Bussmann, P. Giudici, D. Marinelli, and J. Papenbrock, “Explainable machine learning in credit risk management,” Comput. Econ., vol. 57, no. 1, 2021, pp. 203–216.
[32] S. Mangalathu, S. H. Hwang, and J. S. Jeon, “Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach,” Eng. Struct., vol. 219, no. February,2020, pp. 110927.
[33] K. Zhou, S. Li, X. Zhou, Y. Hu, C. Zhang, and J. Liu, “Data-driven prediction and analysis method for nanoparticle transport behavior in porous media,” Measurement, vol. 172,2021, pp. 108869.
[34] S. Mangalathu, H. Shin, E. Choi, and J.-S. Jeon, “Explainable machine learning models for punching shear strength estimation of flat slabs without transverse reinforcement,” J. Build. Eng., vol. 39, 2021, pp. 102300.
[35] H. Nasiri and S. A. Alavi, “A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images,” Comput. Intell. Neurosci., vol. 2022, pp. 4694567.