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

  1. A Critique of Some Ridge Regression Methods

    Gary Smith, Frank W. Campbell

    Мақола19803 иқтибос
    ABI
  2. The Generalized LASSO

    Volker Röth

    Мақола20043 иқтибос
    ABI
  3. Ridge Regression: A Historical Context

    Roger W. Hoerl

    Мақола20203 иқтибос
    ABI
  4. Emission and control of flue gas pollutants in CO2 chemical absorption system – A review

    Mengxiang Fang, Ningtong Yi, Wentao Di +2

    Шарҳ мақола20192 иқтибос
    ABI
  5. Chemically modified carbonaceous adsorbents for enhanced CO2 capture: A review

    Urooj Kamran, Soo‐Jin Park

    Шарҳ мақола20212 иқтибос
    ABI
  6. Prediction of Consumer Behaviour using Random Forest Algorithm

    Harsh Valecha, Aparna Varma, Ishita Khare +2

    Мақола20182 иқтибос
    ABI
  7. A novel variable selection algorithm for multi-layer perceptron with elastic net

    Fangfang Zhang, Kai Sun, Xiuliang Wu

    Мақола20192 иқтибос
    ABI
  8. A two-layer feature selection method using Genetic Algorithm and Elastic Net

    Fatemeh Amini, Guiping Hu

    Мақола20202 иқтибос
    ABI
  9. Elastic-net based robust extreme learning machine for one-class classification

    Weicheng Zhan, Kuaini Wang, Jinde Cao

    Мақола20232 иқтибос
    ABI
  10. Machine Learning Descriptors for CO2 Capture Materials

    Ibrahim Orhan, Yuankai Zhao, Ravichandar Babarao +2

    Шарҳ мақола20252 иқтибос
    ABI
  11. Сарлавҳасиз

    Бошқа1 иқтибос
    ABI
  12. Сарлавҳасиз

    Бошқа1 иқтибос
    ABI
  13. Сарлавҳасиз

    Бошқа1 иқтибос
    ABI