The effects of age and body weight on powerlifters: An analysis model of powerlifting performance based on machine learning

Andronikos, G., Elumaro, A. I., Westbury, T., & Martindale, R. J. (2016). Relative age effect: implications for effective practice. Journal of sports sciences, 34(12), 1124-1131.10.1080/02640414.2015.109364726417709Search in Google Scholar

Anton, M. M., Spirduso, W. W., & Tanaka, H. I. R. O. F. U. M. I. (2004). Age-related declines in anaerobic muscular performance: weightlifting and powerlifting. Medicine and science in sports and exercise, 36(1), 143-147.10.1249/01.MSS.0000106283.34742.BE14707780Search in Google Scholar

Buckingham, G., Byrne, C. M., Paciocco, J., van Eimeren, L., & Goodale, M. A. (2014). Weightlifting exercise and the size–weight illusion. Attention, Perception, & Psychophysics, 76(2), 452-459.10.3758/s13414-013-0597-824306984Search in Google Scholar

Carey, D. L., Ong, K., Whiteley, R., Crossley, K. M., Crow, J., & Morris, M. E. (2018). Predictive modelling of training loads and injury in Australian football. International Journal of Computer Science in Sport, 17(1), 49-66.10.2478/ijcss-2018-0002Search in Google Scholar

Coker, N. A., Varanoske, A. N., Baker, K. M., Hahs-Vaughn, D. L., & Wells, A. J. (2018). Predictors of competitive success of national-level powerlifters: a multilevel analysis. International Journal of Performance Analysis in Sport, 18(5), 796-805.10.1080/24748668.2018.1519751Search in Google Scholar

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.10.1007/BF00994018Search in Google Scholar

Durguerian, A., Bougard, C., Drogou, C., Sauvet, F., Chennaoui, M., & Filaire, E. (2016). Weight loss, performance and psychological related states in high-level weightlifters. International journal of sports medicine, 37(03), 230-238.10.1055/s-0035-1555852Search in Google Scholar

Eime, R. M., Harvey, J. T., Charity, M. J., Casey, M. M., Westerbeek, H., & Payne, W. R. (2016). Age profiles of sport participants. BMC sports science, medicine and rehabilitation, 8(1), 6.10.1186/s13102-016-0031-3478889226973792Search in Google Scholar

Esfe, M. H., Tatar, A., Ahangar, M. R. H., & Rostamian, H. (2018). A comparison of performance of several artificial intelligence methods for predicting the dynamic viscosity of TiO2/SAE 50 nano-lubricant. Physica E: Low-dimensional Systems and Nanostructures, 96, 85-93.10.1016/j.physe.2017.08.019Search in Google Scholar

Geng, Y., Chen, J., Fu, R., Bao, G., & Pahlavan, K. (2016). Enlighten wearable physiological monitoring systems: On-body rf characteristics based human motion classification using a support vector machine. IEEE transactions on mobile computing, 15(3), 656-671.10.1109/TMC.2015.2416186Search in Google Scholar

Huang, G. B., Zhou, H., Ding, X., & Zhang, R. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 513-529.10.1109/TSMCB.2011.216860421984515Search in Google Scholar

Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501.10.1016/j.neucom.2005.12.126Search in Google Scholar

James, L. P., Comfort, P., Suchomel, T. J., Kelly, V. G., Beckman, E. M., & Haff, G. G. (2018). The impact of power clean ability and training age on adaptations to weightlifting-style training. Journal of Strength & Conditioning Research. Publish online.10.1519/JSC.0000000000002534Search in Google Scholar

Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471.10.1007/s10898-007-9149-xSearch in Google Scholar

Karaboga, D., Gorkemli, B., Ozturk, C., & Karaboga, N. (2014). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1), 21-57.10.1007/s10462-012-9328-0Search in Google Scholar

Li, Y., Zhu, Z., Hou, A., Zhao, Q., Liu, L., & Zhang, L. (2018). Pulmonary nodule recognition based on multiple kernel learning support vector machine- pso. Computational and mathematical methods in medicine, 2018.10.1155/2018/1461470594919029853983Search in Google Scholar

Lienhart, R., Einfalt, M., & Zecha, D. (2018). Mining automatically estimated poses from video recordings of top athletes. International Journal of Computer Science in Sport, 17(1), 94-112.10.2478/ijcss-2018-0005Search in Google Scholar

Mao, Y., Le, B. T., Xiao, D., He, D., Liu, C., Jiang, L., ... & Liu, X. (2019). Coal classification method based on visible-infrared spectroscopy and an improved multilayer extreme learning machine. Optics & Laser Technology, 114, 10-15.10.1016/j.optlastec.2019.01.005Search in Google Scholar

Pai, P. F., ChangLiao, L. H., & Lin, K. P. (2017). Analyzing basketball games by a support vector machines with decision tree model. Neural Computing and Applications, 28(12), 4159-4167.10.1007/s00521-016-2321-9Search in Google Scholar

Smith, K. L., Weir, P. L., Till, K., Romann, M., & Cobley, S. (2018). Relative age effects across and within female sport contexts: a systematic review and meta-analysis. Sports Medicine, 48(6), 1451-1478.10.1007/s40279-018-0890-829536262Search in Google Scholar

Solberg, P. A., Hopkins, W. G., Paulsen, G., & Haugen, T. A. (2019). Peak Age and Performance Progression in World-Class Weightlifting and Powerlifting Athletes. International journal of sports physiology and performance, Publish online.10.1123/ijspp.2019-009330958059Search in Google Scholar

Sun, L., Chen, T., & Zhang, Q. (2018). An Artificial Bee Colony Algorithm with Random Location Updating. Scientific Programming, 2018.10.1155/2018/2767546Search in Google Scholar

Wang, H., Li, J., Cui, W., Lu, X., Zhang, Z., Sheng, C., & Liu, Q. (2019). Mobile Robot Indoor Positioning System Based on K-ELM. Journal of Sensors, 2019.10.1155/2019/7547648Search in Google Scholar

Wang, J., Song, Y. F., & Ma, T. L. (2017). Mexican Hat Wavelet Kernel ELM for Multiclass Classification. Computational intelligence and neuroscience, 2017.10.1155/2017/7479140533963728321249Search in Google Scholar

Wu, Y. H., & Shen, H. (2018). Grey-related least squares support vector machine optimization model and its application in predicting natural gas consumption demand. Journal of Computational and Applied Mathematics, 338, 212-220.10.1016/j.cam.2018.01.033Search in Google Scholar

Xue, Y., Jiang, J., Zhao, B., & Ma, T. (2018). A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Computing, 22 (9), 2935-2952.10.1007/s00500-017-2547-1Search in Google Scholar

Zhao, D., Huang, C., Wei, Y., Yu, F., Wang, M., & Chen, H. (2017). An effective computational model for bankruptcy prediction using kernel extreme learning machine approach. Computational Economics, 49(2), 325-341.10.1007/s10614-016-9562-7Search in Google Scholar