Otwarty dostęp

Research on Classification of College Students’ Physical Fitness Test Scores Based on Neural Network

  
27 lut 2025

Zacytuj
Pobierz okładkę

Skote, M., Sandberg, M., & Westerberg, U. (2005). Numerical and experimental studies of wind environment in an urban morphology. Atmospheric Environment, 39(33), 6147-6158. SkoteM.SandbergM. & WesterbergU. (2005). Numerical and experimental studies of wind environment in an urban morphology. Atmospheric Environment, 39(33), 6147-6158. Search in Google Scholar

Zhao, B. (2013). Research on performance analysis and prediction of world’s top male athletes in decathlon. Sports Culture Guide, 2013(3), 76-79. ZhaoB. (2013). Research on performance analysis and prediction of world’s top male athletes in decathlon. Sports Culture Guide, 2013(3), 76-79. Search in Google Scholar

Jiang, T., & Wang, L. (2015). Application of data mining technology in sports science research. Agriculture Network Information, 11, 149-140, 156. JiangT. & WangL. (2015). Application of data mining technology in sports science research. Agriculture Network Information, 11, 149-140, 156. Search in Google Scholar

Huang, Q., & Shi, Y. (2009). Research on the application of data mining in sports training guidance. Journal of Guangzhou Institute of Physical Education, 29(6), 106-110. HuangQ. & ShiY. (2009). Research on the application of data mining in sports training guidance. Journal of Guangzhou Institute of Physical Education, 29(6), 106-110. Search in Google Scholar

Pallmann, P., & Hothorn, L. A. (2016). Analysis of means: A generalized approach using R. Journal of Applied Statistics, 43(5-8), 1541-1560. PallmannP. & HothornL. A. (2016). Analysis of means: A generalized approach using R. Journal of Applied Statistics, 43(5-8), 1541-1560. Search in Google Scholar

Boshnakov, G. N. (2012). Using R for data management, statistical analysis, and graphics. Journal of Applied Statistics, 39(6), 1382-1383. BoshnakovG. N. (2012). Using R for data management, statistical analysis, and graphics. Journal of Applied Statistics, 39(6), 1382-1383. Search in Google Scholar

Hess, A. S., & Hess, J. R. (2018). Principal component analysis. Transfusion, 58(7). HessA. S. & HessJ. R. (2018). Principal component analysis. Transfusion, 58(7). Search in Google Scholar

Metsalu, T., & Vilo, J. (2015). ClustVis: A web tool for visualizing the clustering of multivariate data using principal component analysis and heatmap. Nucleic Acids Research. MetsaluT. & ViloJ. (2015). ClustVis: A web tool for visualizing the clustering of multivariate data using principal component analysis and heatmap. Nucleic Acids Research. Search in Google Scholar

Behler, J. (2015). Constructing high-dimensional neural network potentials: A tutorial review. International Journal of Quantum Chemistry, 115(16). BehlerJ. (2015). Constructing high-dimensional neural network potentials: A tutorial review. International Journal of Quantum Chemistry, 115(16). Search in Google Scholar

Zhao, L.-z., Wen, X.-b., & Li, D. (2015). Amorphous localization algorithm based on BP artificial neural network. International Journal of Distributed Sensor Networks, 2015, 6. ZhaoL.-z.WenX.-b. & LiD. (2015). Amorphous localization algorithm based on BP artificial neural network. International Journal of Distributed Sensor Networks, 2015, 6. Search in Google Scholar

Tal, Y., & Jacob, W. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science: A Association for Psychological Science Journal, 12(6). TalY. & JacobW. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science: A Association for Psychological Science Journal, 12(6). Search in Google Scholar

Sommer, C., & Gerlich, D. W. (2013). Machine learning in cell biology - Teaching computers to recognize phenotypes. Journal of Cell Science, 126(24). SommerC. & GerlichD. W. (2013). Machine learning in cell biology - Teaching computers to recognize phenotypes. Journal of Cell Science, 126(24). Search in Google Scholar

Raissi, M., & Karniadakis, G. E. (2017). Hidden physics models: Machine learning of nonlinear partial differential equations. Journal of Computational Physics. RaissiM. & KarniadakisG. E. (2017). Hidden physics models: Machine learning of nonlinear partial differential equations. Journal of Computational Physics. Search in Google Scholar

Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11). AbiodunO. I.JantanA.OmolaraA. E.DadaK. V.MohamedN. A. & ArshadH. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11). Search in Google Scholar

Wu, Y. C., & Feng, J. W. (2018). Development and application of artificial neural network. Wireless Personal Communications, 102, 1645-1656. WuY. C. & FengJ. W. (2018). Development and application of artificial neural network. Wireless Personal Communications, 102, 1645-1656. Search in Google Scholar

Dongare, A. D., Kharde, R. R., & Kachare, A. D. (2012). Introduction to artificial neural network. International Journal of Engineering and Innovative Technology (IJEIT), 2(1), 189-194. DongareA. D.KhardeR. R. & KachareA. D. (2012). Introduction to artificial neural network. International Journal of Engineering and Innovative Technology (IJEIT), 2(1), 189-194. Search in Google Scholar

Hecht-Nielsen, R. (1992). Theory of the backpropagation neural network. In Neural networks for perception (pp. 65-93). Academic Press. Hecht-NielsenR. (1992). Theory of the backpropagation neural network. In Neural networks for perception (pp. 65-93). Academic Press. Search in Google Scholar

Haykin, S., & Network, N. (2004). A comprehensive foundation. Neural Networks, 2(2004), 41. HaykinS. & NetworkN. (2004). A comprehensive foundation. Neural Networks, 2(2004), 41. Search in Google Scholar

Beale, M. H., Hagan, M. T., & Demuth, H. B. (2010). Neural network toolbox. User’s guide. MathWorks, 2, 77-81. BealeM. H.HaganM. T. & DemuthH. B. (2010). Neural network toolbox. User’s guide. MathWorks, 2, 77-81. Search in Google Scholar

Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433-459. AbdiH. & WilliamsL. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433-459. Search in Google Scholar

Greenacre, M., Groenen, P. J., Hastie, T., d’Enza, A. I., Markos, A., & Tuzhilina, E. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1), 100. GreenacreM.GroenenP. J.HastieT.d’EnzaA. I.MarkosA. & TuzhilinaE. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1), 100. Search in Google Scholar

Kherif, F., & Latypova, A. (2020). Principal component analysis. In Machine learning (pp. 209-225). Academic Press. KherifF. & LatypovaA. (2020). Principal component analysis. In Machine learning (pp. 209-225). Academic Press. Search in Google Scholar

Shlens, J. (2014). A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100. ShlensJ. (2014). A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100. Search in Google Scholar

Tharwat, A. (2016). Principal component analysis tutorial. International Journal of Applied Pattern Recognition, 3(3), 197-240. TharwatA. (2016). Principal component analysis tutorial. International Journal of Applied Pattern Recognition, 3(3), 197-240. Search in Google Scholar

Język:
Angielski
Częstotliwość wydawania:
1 razy w roku
Dziedziny czasopisma:
Nauki biologiczne, Nauki biologiczne, inne, Matematyka, Matematyka stosowana, Matematyka ogólna, Fizyka, Fizyka, inne