Article Versions Notes

Joo C, Park H, Kwon H, Lim J, Shin E, Cho H, Kim J. Machine Learning Approach to Predict Physical Properties of Polypropylene Composites: Application of MLR, DNN, and Random Forest to Industrial Data. Polymers. 2022; 14(17):3500. https://doi.org/10.3390/polym14173500

Joo, Chonghyo, Hyundo Park, Hyukwon Kwon, Jongkoo Lim, Eunchul Shin, Hyungtae Cho, and Junghwan Kim. 2022. "Machine Learning Approach to Predict Physical Properties of Polypropylene Composites: Application of MLR, DNN, and Random Forest to Industrial Data" Polymers 14, no. 17: 3500. https://doi.org/10.3390/polym14173500

Joo, C., Park, H., Kwon, H., Lim, J., Shin, E., Cho, H., & Kim, J. (2022). Machine Learning Approach to Predict Physical Properties of Polypropylene Composites: Application of MLR, DNN, and Random Forest to Industrial Data. Polymers, 14(17), 3500. https://doi.org/10.3390/polym14173500