Cite
Optimized feed-forward neural networks to address CO2-equivalent emissions data gaps – Application to emissions prediction for unit processes of fuel life cycle inventories for Canadian provinces.
MLA
Khadem, Sayyed Ahmad, et al. “Optimized Feed-Forward Neural Networks to Address CO2-Equivalent Emissions Data Gaps – Application to Emissions Prediction for Unit Processes of Fuel Life Cycle Inventories for Canadian Provinces.” Journal of Cleaner Production, vol. 332, Jan. 2022, p. N.PAG. EBSCOhost, https://doi.org/10.1016/j.jclepro.2021.130053.
APA
Khadem, S. A., Bensebaa, F., & Pelletier, N. (2022). Optimized feed-forward neural networks to address CO2-equivalent emissions data gaps – Application to emissions prediction for unit processes of fuel life cycle inventories for Canadian provinces. Journal of Cleaner Production, 332, N.PAG. https://doi.org/10.1016/j.jclepro.2021.130053
Chicago
Khadem, Sayyed Ahmad, Farid Bensebaa, and Nathan Pelletier. 2022. “Optimized Feed-Forward Neural Networks to Address CO2-Equivalent Emissions Data Gaps – Application to Emissions Prediction for Unit Processes of Fuel Life Cycle Inventories for Canadian Provinces.” Journal of Cleaner Production 332 (January): N.PAG. doi:10.1016/j.jclepro.2021.130053.