Kim, M.-Y.; Atakishiyev, S.; Babiker, H.K.B.; Farruque, N.; Goebel, R.; Zaïane, O.R.; Motallebi, M.-H.; Rabelo, J.; Syed, T.; Yao, H.;
et al. A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence. Mach. Learn. Knowl. Extr. 2021, 3, 900-921.
https://doi.org/10.3390/make3040045
AMA Style
Kim M-Y, Atakishiyev S, Babiker HKB, Farruque N, Goebel R, Zaïane OR, Motallebi M-H, Rabelo J, Syed T, Yao H,
et al. A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence. Machine Learning and Knowledge Extraction. 2021; 3(4):900-921.
https://doi.org/10.3390/make3040045
Chicago/Turabian Style
Kim, Mi-Young, Shahin Atakishiyev, Housam Khalifa Bashier Babiker, Nawshad Farruque, Randy Goebel, Osmar R. Zaïane, Mohammad-Hossein Motallebi, Juliano Rabelo, Talat Syed, Hengshuai Yao,
and et al. 2021. "A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence" Machine Learning and Knowledge Extraction 3, no. 4: 900-921.
https://doi.org/10.3390/make3040045
APA Style
Kim, M. -Y., Atakishiyev, S., Babiker, H. K. B., Farruque, N., Goebel, R., Zaïane, O. R., Motallebi, M. -H., Rabelo, J., Syed, T., Yao, H., & Chun, P.
(2021). A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence. Machine Learning and Knowledge Extraction, 3(4), 900-921.
https://doi.org/10.3390/make3040045
