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Работы, на которые ссылается эта работа

Работ: 84

Работа: Machine learning frameworks to accurately compute CO2 capture in porous liquids

  1. A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning

    Malti Bansal, Apoorva Goyal, Apoorva Choudhary

    Статья2022Цитирований: 10
    ABI

  2. Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data

    Junliang Fan, Xin Ma, Lifeng Wu +3

    Статья2019Цитирований: 9
    ABI

  3. Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables

    Gi-Wook Cha, Hyeun-Jun Moon, Young‐Chan Kim

    Статья2021Цитирований: 9
    ABI

  4. Без названия

    ДругоеЦитирований: 9
    ABI

  5. Decision tree-based diagnosis of coronary artery disease: CART model

    Mohammad M. Ghiasi, Sohrab Zendehboudi, Ali Asghar Mohsenipour

    Статья2020Цитирований: 8
    ABI

  6. Studying the effect of surfactant assisted low-salinity water flooding on clay-rich sandstones

    Saeed Khezerloo‐ye Aghdam, Alireza Kazemi, Mohammad Ahmadi

    Статья2023Цитирований: 8
    ABI

  7. Fast Monte Carlo reliability evaluation using support vector machine

    Claudio M. Rocco, José Alı́ Moreno

    Статья2002Цитирований: 7
    ABI

  8. Long‐Term Capturability of Atmospheric Water on a Global Scale

    Fangfang Li, H. F. Lü, Guang‐Qian Wang +1

    Статья2024Цитирований: 6
    ABI

  9. Highly efficient and regenerable amine-impregnated adsorbents: Mechanistic insights into glycerol modification for enhanced direct air capture

    Feng Yan, Heijin Chen, Tong Chi +5

    Статья2025Цитирований: 6
    ABI

  10. Convolutional neural networks

    Walter Hugo Lopez Pinaya, Sandra Vieira, Rafael Garcia‐Dias +1

    Глава2019Цитирований: 6
    ABI

  11. Gradient Boosting Machine

    V Kishore Ayyadevara

    Глава2018Цитирований: 6
    ABI

  12. Modeling of signal-response cascades using decision tree analysis

    Sampsa Hautaniemi, Sourabh Kharait, Akihiro Iwabu +2

    Статья2005Цитирований: 6
    ABI

  13. Intelligent interpolation by Monte Carlo machine learning

    Yongna Jia, Siwei Yu, Jianwei Ma

    Статья2017Цитирований: 6
    ABI

  14. CO<sub>2</sub> photo-reduction: insights into CO<sub>2</sub> activation and reaction on surfaces of photocatalysts

    Xiaoxia Chang, Tuo Wang, Jinlong Gong

    Статья2016Цитирований: 5
    ABI

  15. Gradient boosting machine for modeling the energy consumption of commercial buildings

    Samir Touzani, Jessica Granderson, Samuel Fernandes

    Статья2017Цитирований: 5
    ABI

  16. A comparative analysis of gradient boosting algorithms

    Candice Bentéjac, Anna Csörgő, Gonzalo Martínez-Muñoz

    Статья2020Цитирований: 5
    ABI

  17. Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme

    Emad A. El-Sebakhy

    Статья2008Цитирований: 5
    ABI

  18. Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN)

    Elham Heidari, Mohammad Amin Sobati, Salman Movahedirad

    Статья2016Цитирований: 5
    ABI

  19. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences

    M.W Gardner, Stephen Dorling

    Статья1998Цитирований: 4
    ABI

  20. MRD parameters using immunophenotypic detection methods are highly reliable in predicting survival in acute myeloid leukaemia

    N Feller, M.A. van der Pol, Amber van Stijn +5

    Статья2004Цитирований: 4
    ABI

  21. A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide

    Jie Chen, Kees de Hoogh, John Gulliver +19

    Статья2019Цитирований: 4
    ABI

  22. Linear regression

    Thomas M.H. Hope

    Глава2019Цитирований: 4
    ABI

  23. Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review

    Sebastian Sado, Ilona Jastrzębska, W Zelik +1

    Обзорная статья2023Цитирований: 4
    ABI

  24. Adsorbent Materials for Carbon Dioxide Capture from Large Anthropogenic Point Sources

    Sunho Choi, Jeffrey H. Drese, Christopher W. Jones

    Обзорная статья2009Цитирований: 4
    ABI

  25. Prediction of CO2 solubility in ionic liquids using machine learning methods

    Zhen Song, Huaiwei Shi, Xiang Zhang +1

    Статья2020Цитирований: 3
    ABI