Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm

Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm

Abstract This paper provides a novel approach to estimating CO₂ emissions with high precision using machine learning based on DPRNNs with NiOA. The data preparation used in the present methodology involves sophisticated stages such as Principal Component Analysis (PCA) as well as Blind Source Separa...

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書目詳細資料
Main Authors: Anis Ben Ghorbal, Azedine Grine, Ibrahim Elbatal, Ehab M. Almetwally, Marwa M. Eid, El-Sayed M. El-Kenawy
格式: Article
語言:English
出版: Nature Portfolio 2025-02-01
叢編:Scientific Reports
主題:
CO2 emissions
Dual-path recurrent neural networks
Ninja optimizer
Machine learning
Environmental forecasting
Metaheuristics
在線閱讀:https://doi.org/10.1038/s41598-025-86251-0

因特網

https://doi.org/10.1038/s41598-025-86251-0

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