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使用深度学习和忍者元启发优化算法预测二氧化碳排放

Anis Ben Ghorbal1, Azedine Grine2, Ibrahim Elbatal2

  • 1Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11632, Riyadh, Saudi Arabia. assghorbal@imamu.edu.sa.

Scientific reports
|February 1, 2025
PubMed
概括

这项研究引入了一种新的机器学习方法,使用深度预测循环神经网络 (DPRNNs) 与氧化铁阳极 (NiOA) 进行精确的二氧化碳 (CO2) 排放估计. 该方法显著提高了准确性,并为决策者解决全球变暖提供了一个强大的框架.

关键词:
在二氧化碳排放方面,二氧化碳排放量双路径循环神经网络是双路径循环神经网络.环境预测 环境预测机器学习是机器学习.超听证学是一种超听证学.忍者优化器的优化器

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