Dabiri, H.; Farhangi, V.; Moradi, M.J.; Zadehmohamad, M.; Karakouzian, M.
Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars. Appl. Sci. 2022, 12, 4851.
https://doi.org/10.3390/app12104851
AMA Style
Dabiri H, Farhangi V, Moradi MJ, Zadehmohamad M, Karakouzian M.
Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars. Applied Sciences. 2022; 12(10):4851.
https://doi.org/10.3390/app12104851
Chicago/Turabian Style
Dabiri, Hamed, Visar Farhangi, Mohammad Javad Moradi, Mehdi Zadehmohamad, and Moses Karakouzian.
2022. "Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars" Applied Sciences 12, no. 10: 4851.
https://doi.org/10.3390/app12104851
APA Style
Dabiri, H., Farhangi, V., Moradi, M. J., Zadehmohamad, M., & Karakouzian, M.
(2022). Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars. Applied Sciences, 12(10), 4851.
https://doi.org/10.3390/app12104851
