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Работы, на которые ссылается эта работа
Работ: 84
Работа: Machine learning frameworks to accurately compute CO2 capture in porous liquids
Cocatalysts in Semiconductor‐based Photocatalytic CO<sub>2</sub> Reduction: Achievements, Challenges, and Opportunities
Jingrun Ran, Mietek Jaroniec, Shi‐Zhang Qiao
Обзорная статья2018Цитирований: 3ABIEmission and control of flue gas pollutants in CO2 chemical absorption system – A review
Mengxiang Fang, Ningtong Yi, Wentao Di +2
Обзорная статья2019Цитирований: 2ABIChemically modified carbonaceous adsorbents for enhanced CO2 capture: A review
Обзорная статья2021Цитирований: 2ABIPrediction of Consumer Behaviour using Random Forest Algorithm
Harsh Valecha, Aparna Varma, Ishita Khare +2
Статья2018Цитирований: 2ABIA novel variable selection algorithm for multi-layer perceptron with elastic net
Fangfang Zhang, Kai Sun, Xiuliang Wu
Статья2019Цитирований: 2ABIA two-layer feature selection method using Genetic Algorithm and Elastic Net
Статья2020Цитирований: 2ABIElastic-net based robust extreme learning machine for one-class classification
Weicheng Zhan, Kuaini Wang, Jinde Cao
Статья2023Цитирований: 2ABIMachine Learning Descriptors for CO2 Capture Materials
Ibrahim Orhan, Yuankai Zhao, Ravichandar Babarao +2
Обзорная статья2025Цитирований: 2ABI