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Работы, цитирующие эту работу

Работ: 6

Работа: The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling

  1. A micromechanical nested machine learning model for characterizing materials behaviors of bulk metallic glasses

    Moustafa Sahnoune Chaouche, Hani K. Al-Mohair, Shavan Askar +3

    СтатьяMetallic Glasses and Amorphous AlloysJournal of Non-Crystalline Solids2023Цитирований: 1
    ABI

  2. Predictive modeling of oil rate for wells under gas lift using machine learning

    Fang Ma, Farag M. A. Altalbawy, Pinank Patel +9

    СтатьяReservoir Engineering and Simulation MethodsScientific Reports2025Цитирований: 0
    ABI

  3. Development of robust machine learning models to estimate hydrochar higher heating value and yield based upon biomass proximate analysis

    Guoliang Hou, Ahmed Alkhayyat, Ahmad Almalkawi +8

    СтатьяThermochemical Biomass Conversion ProcessesBioresources and Bioprocessing2025Цитирований: 0
    ABI

  4. Predicting filter cake thickness in drilling fluids using machine learning techniques

    Minghui OU, Mohammed Al-Bahra, Raman Kumar +10

    СтатьяDrilling and Well EngineeringPhysics and Chemistry of the Earth Parts A/B/C2025Цитирований: 0
    ABI

  5. Machine learning approaches for estimating formation enthalpies in energetic materials

    Jianfeng Li, Farag M. A. Altalbawy, Hardik Doshi +8

    СтатьяMachine Learning in Materials ScienceJournal of Energetic Materials2025Цитирований: 0
    ABI

  6. Developing rigorous machine learning models to accurately estimate pesticide photodegradation by ZnO-based photocatalysts in water

    Farag M. A. Altalbawy, Shoira Formanova, Ahmad Almalkawi +7

    СтатьяWater Quality Monitoring and AnalysisJournal of the Indian Chemical Society2025Цитирований: 0
    ABI