1. Coury HJ. The effects of production changes on the musculoskeletal disorders in Brazil and South America. Int J Ind Ergon. 2000; 25(1):103-4.
2. World Health Organization. Musculoskeletal conditions. Web Site;2019. [Link]
3. United States bone and joint initiative: Prevalence, societal economic cost. 3rd ed. Illinois: The burden of musculoskeletal disease in the United States.2016. [Link]
4. European Trade :union: Institute (ETUI). Musculoskeletal disorders.2013.[Link]
5. Hedge A, Morimoto S, McCrobie D. Effects of keyboard tray geometry on upper body posture and comfort. Ergonomics. 1999;42(10):1333-49. [DOI: 10.1080/001401399184983] [PMID]
6. Mououdi M A, Sammak Amani A, Ghezelbash K, Ghahari M, Kebriyaee Nasab T. Musculoskeletal Disorders (MSDS) in the Administrative Staff of the National Iranian Gas Transmission Company-District 9 (NIGTC-D9). [In Persian]. Iran South Med J. 2022;25(3):250-60. [DOI: 10.52547/ismj.25.3.250 ]
7. Shinde PP, Shah S. A review of machine learning and deep learning applications. In2018 Fourth international conference on computing communication control and automation (ICCUBEA) 2018: 1-6. [DOI: 10.1109/ICCUBEA.2018.8697857]
8. Gomes, Mervyn Prediction of work-related musculoskeletal discomfort in the meat processing industry using statistical models. Int J Ind Ergon. 2020;75:102876. [DOI:10.1016/j.ergon.2019.102876]
9. Chandna, Pankaj. Pal, Mahesh. Infinite ensemble of support vector machines for prediction of musculoskeletal disorders risk. Int J Appl Sci Eng.2011; 3.6: 71-7. [DOI: 10.4314/ijest.v3i6.6]
10. Mahesh B, Machine learning algorithms-a review. Int j sci res. 2020;9(1):381-6. [DOI : 10.21275/ART20203995]
11. Ayodele T. Types of machine learning algorithms, New advances in machine learning. 3th ed by Zhang, Y. InTech;2010:19-48. [DOI: 10.5772/9385]
12. Ikotun, A M., Ezugwu A E, Abualigah L, Abuhaija B, and Heming J. K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Inf Sci.2022;622(C):178-210. [DOI:10.1016/j.ins.2022.11.139]
13. Wongvorachan, T, He S, Bulut O. A comparison of undersampling, oversampling, and SMOTE methods for dealing with imbalanced classification in educational data mining. Inf.2023; 14(1):54. [DOI:10.3390/info14010054]
14. Afifehzadeh-Kashani H, Choobineh A, Bakand S, et al. Validity and Reliability Farsi Version Cornell Musculoskeletal Discomfort Questionnaire (CMDQ).[In Persian]. Iran Occup Health .2011; 7(4): 10.
15. Zheng, Z, Yang, Y, Niu X, Dai H N, Zhou Y. Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Trans Industr Inform. 2018;14(4):1606–15. [DOI:10.1109/TII.2017.2785963]
16. Nam H, Kim HE. Batch-instance normalization for adaptively style-invariant neural networks. Advances in Neural Information Processing Systems. 2018;31. [DOI:10.48550/arxiv.1805.07925]
17. Pandey A K, Jain A. Comparative analysis of KNN algorithm using various normalization techniques. Int J Comput Netw Inf Secur. 2017; 9:36–42. [DOI: 10.5815/IJCNIS.2017.11.04]
18. Golalipour K, Akbari E, Hamidi S, Lee M, and Enayatifar R. From clustering to clustering ensemble selection: A review. Engineering Applications of Artificial Intelligence.2021;104:104388. [DOI:10.1016/j.engappai.2021.104388]
19. Aguiar G, Krawczyk B, Cano A. A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework. Mach Learn . 2023:1-79. [DOI: 10.1007/s10994-023-06353-6]
20. Elreedy D, Atiya, A.F. & Kamalov F. A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning. Mach Learn .2023:1-21. [DOI:10.1007/s10994-022-06296-4]
21. Gasparetto A, Marcuzzo M, Zangari A, and Albarelli A. A survey on text classification algorithms: From text to predictions. Information .2022;13)2):83. [DOI:10.3390/info13020083]
22. Desai Meha, and Shah M. An anatomization on breast cancer detection and diagnosis employing multilayer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth .2021;4:1-11. [DOI:10.1016/j.ceh.2020.11.002]
23. Egbueri J C, and Agbasi K C. Performances of MLR, RBF-NN, and MLP-NN in the evaluation and prediction of water resources quality for irrigation purposes under two modeling scenarios. Geocarto Int .2022;37(26):14399-431. [DOI:10.1080/10106049.2022.2087758]
24. Tao P, Cheng J, and Chen L. Brain-inspired chaotic backpropagation for MLP. Neural Net.2022;155(C):1-3. [DOI:10.1016/j.neunet.2022.08.004]
25. Pang, B., Nijkamp, E. and Wu, Y.N., 2020. Deep learning with tensorflow: A review. J Educ Behav Stat.2020;45(2):227-48. [DOI:10.3102/10769986198727]
26. Singh P, Manure A, Singh P and Manure A. Introduction to tensorflow 2.0. Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python. 2020 1-24. [DOI: 10.1007/978-1-4842-5558-2-1]
27. Grandini M, Bagli E, and Visani G. Metrics for multi-class classification: an overview. ArXiv.2020; abs/2008.05756. [DOI: 10.48550/arXiv.2008.05756]
28. Akbari J, Kazemi M, Mazareie A, Moradirad R, Razavi A. The Ergonomic assessment of exposure to risk factors that cause musculoskeletal disorders in Office workers by using ROSA.[In Persian]. J Ilam Uni Med Sci. 2017; 25(2) :8-17. [DOI: 10.29252/sjimu.25.2.8]
29. Mirmohammadi S T, Gook O, Mousavinasab SN, Mahmoodi Sharafe H. Investigating the Prevalence of Musculoskeletal Disorders in Melli Bank Staff and Determining Its Relationship with Office Tension in North Khorasan Province in 2019.[In Persian]. Iran J Ergon. 2020;7(4):31-9. [DOI: 10.30699/jergon.7.4.31]