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Работы, на которые ссылается эта работа

Работ: 63

Работа: New insights into lithium recovery from unconventional water resources

  1. Reporting physisorption data for gas/solid systems with special reference to the determination of surface area and porosity (Recommendations 1984)

    K. S. W. Sing

    Статья1985Цитирований: 16
    ABI

  2. Pseudo-second order model for sorption processes

    Yuh‐Shan Ho, Gordon McKay

    Статья1999Цитирований: 13
    ABI

  3. Identification of cyclin protein using gradient boost decision tree algorithm

    Hasan Zulfiqar, Shi-Shi Yuan, Qin-Lai Huang +4

    Статья2021Цитирований: 10
    ABI

  4. 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: 10
    ABI

  5. The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling

    Yile Ao, Hongqi Li, Liping Zhu +2

    Статья2018Цитирований: 7
    ABI

  6. A comparative analysis of gradient boosting algorithms

    Candice Bentéjac, Anna Csörgő, Gonzalo Martínez-Muñoz

    Статья2020Цитирований: 7
    ABI

  7. An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine

    Altyeb Taha, Sharaf J. Malebary

    Статья2020Цитирований: 6
    ABI

  8. Synthesis of porous fiber-supported lithium ion-sieve adsorbent for lithium recovery from geothermal water

    Kaiyu Zhao, Bojia Tong, Xiaoping Yu +3

    Статья2021Цитирований: 5
    ABI

  9. Building more accurate decision trees with the additive tree

    José Marcio Luna, Efstathios D. Gennatas, Lyle Ungar +7

    Статья2019Цитирований: 5
    ABI

  10. Elastic Net Nonparallel Hyperplane Support Vector Machine and Its Geometrical Rationality

    Kai Qi, Hu Yang

    Статья2021Цитирований: 5
    ABI

  11. 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: 5
    ABI

  12. Ridge Regression: A Historical Context

    Roger W. Hoerl

    Статья2020Цитирований: 5
    ABI

  13. You only design once (YODO): Gaussian Process-Batch Bayesian optimization framework for mixture design of ultra high performance concrete

    Eman Saleh, Ahmad Tarawneh, M.Z. Naser +2

    Статья2022Цитирований: 5
    ABI

  14. An efficient extraction method of journal-article table data for data-driven applications

    Deng Jianxin, Gang Liu, Ling Wang +2

    Статья2024Цитирований: 4
    ABI

  15. Lithium recovery using electrochemical technologies: Advances and challenges

    Wu Lei, Changyong Zhang, Seoni Kim +3

    Обзорная статья2022Цитирований: 4
    ABI

  16. Machine-learning-assisted materials discovery using failed experiments

    Paul Raccuglia, Katherine C. Elbert, Philip Adler +7

    Статья2016Цитирований: 4
    ABI

  17. A comparative analysis on linear regression and support vector regression

    S. Kavitha, S. Varuna, R. Ramya

    Статья2016Цитирований: 4
    ABI

  18. High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

    Frank Emmert‐Streib, Matthias Dehmer

    Статья2019Цитирований: 4
    ABI

  19. Без названия

    ДругоеЦитирований: 4
    ABI

  20. <scp>M‐PINN</scp>: A mesh‐based physics‐informed neural network for linear elastic problems in solid mechanics

    Lu Wang, Guangyan Liu, Wang Guanglun +1

    Статья2024Цитирований: 3
    ABI

  21. Без названия

    ДругоеЦитирований: 3
    ABI

  22. Без названия

    ДругоеЦитирований: 3
    ABI

  23. Без названия

    ДругоеЦитирований: 3
    ABI

  24. Без названия

    ДругоеЦитирований: 3
    ABI

  25. Без названия

    ДругоеЦитирований: 2
    ABI