Material analysis and big data monitoring of sports training equipment based on machine learning algorithm

Different machine learning algorithms predict the application effect of perovskite materials in sports training equipment. The sensitivity to material data is different on different ranges of data sets. Therefore, the algorithm needs to be selected according to specific material data samples. This study compares the prediction performance of neural network prediction algorithm (NN), genetic algorithm, and support vector machine-based machine learning algorithm (SVM) and uses statistical analysis to perform data analysis and draw corresponding curves. Moreover, this study uses a single perovskite material to verify the algorithm performance. In addition, based on the real data, the three machine learning algorithms of this study are applied to the related performance prediction, and the comparative analysis method is used to analyze the prediction performance of the machine learning algorithm. Through data analysis and chart analysis, we can see that machine learning algorithms have a certain effect in the application prediction of perovskite materials in sports training equipment. Among the three machine learning algorithms selected in this study, the performance of the machine learning algorithm based on support vector machine in all aspects is more excellent.

[1]  Engineering,et al.  Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques , 2016 .

[2]  Luke E K Achenie,et al.  Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening. , 2015, The journal of physical chemistry letters.

[3]  Zoubin Ghahramani,et al.  Probabilistic machine learning and artificial intelligence , 2015, Nature.

[4]  Ruchika Malhotra,et al.  A systematic review of machine learning techniques for software fault prediction , 2015, Appl. Soft Comput..

[5]  Daniel W. Davies,et al.  Machine learning for molecular and materials science , 2018, Nature.

[6]  James E. Gubernatis,et al.  Multi-fidelity machine learning models for accurate bandgap predictions of solids , 2017 .

[7]  Chetna Gupta,et al.  A Machine Learning based Efficient Software Reusability Prediction Model for Java Based Object Oriented Software , 2014 .

[8]  Evgeniy Gabrilovich,et al.  A Review of Relational Machine Learning for Knowledge Graphs , 2015, Proceedings of the IEEE.

[9]  Paul Raccuglia,et al.  Machine-learning-assisted materials discovery using failed experiments , 2016, Nature.

[10]  Atsuto Seko,et al.  Representation of compounds for machine-learning prediction of physical properties , 2016, 1611.08645.

[11]  Regina Barzilay,et al.  Prediction of Organic Reaction Outcomes Using Machine Learning , 2017, ACS central science.

[12]  Alexander Brenning,et al.  Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling , 2015, Comput. Geosci..

[13]  Chiho Kim,et al.  Machine learning in materials informatics: recent applications and prospects , 2017, npj Computational Materials.

[14]  George E. Dahl,et al.  Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. , 2017, Journal of chemical theory and computation.

[15]  Marvin N. Wright,et al.  SoilGrids250m: Global gridded soil information based on machine learning , 2017, PloS one.

[16]  K. Müller,et al.  Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space , 2015, The journal of physical chemistry letters.

[17]  Alok Choudhary,et al.  Combinatorial screening for new materials in unconstrained composition space with machine learning , 2014 .

[18]  Arun Mannodi-Kanakkithodi,et al.  Machine Learning Strategy for Accelerated Design of Polymer Dielectrics , 2016, Scientific Reports.

[19]  Yue Liu,et al.  Materials discovery and design using machine learning , 2017 .

[20]  G. Pilania,et al.  Machine learning bandgaps of double perovskites , 2016, Scientific Reports.

[21]  Andrea Grisafi,et al.  Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems. , 2017, Physical review letters.

[22]  Esko Sistonen,et al.  CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods , 2015 .

[23]  Atsuto Seko,et al.  Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids , 2013, 1310.1546.