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Работы, цитирующие эту работу

Работ: 9

  1. 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

  2. Accurate estimation of permeability reduction resulted from low salinity water flooding in clay-rich sandstones

    Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora +10

    СтатьяHydraulic Fracturing and Reservoir AnalysisJournal of Petroleum Exploration and Production Technology2025Цитирований: 0
    ABI

  3. 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

  4. 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

  5. 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

  6. New insights into residue formation due to oxidation of crude oil via data-driven methods

    Mohammad Abushuhel, Ibrahim Khersan, Tariq Abdulkader Alrihaim +7

    СтатьяPetroleum Processing and AnalysisGeosystem Engineering2025Цитирований: 0
    ABI

  7. Predicting the Storage Modulus of Chromium‐Crosslinked Polymer Gels Through Advanced Machine Learning Techniques

    Farag M. A. Altalbawy, Raed H. C. Alfilh, Tarak Vora +8

    СтатьяMachine Learning in Materials SciencePolymers for Advanced Technologies2026Цитирований: 0
    ABI

  8. Estimating solubility of nitrogen in various ionic liquids: application of machine-learning

    Kassem Al Attabi, Farzona Alimova, Anupam Yadav +7

    СтатьяIonic liquids properties and applicationsChemical Papers2026Цитирований: 0
    ABI

  9. Machine learning frameworks to accurately compute CO2 capture in porous liquids

    Farag M. A. Altalbawy, Soumya V. Menon, Anupam Yadav +7

    СтатьяCarbon Dioxide Capture TechnologiesChemical Papers2026Цитирований: 0
    ABI