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28 ta ish

Ish: Machine-learning based prediction of hydrogen/methane mixture solubility in brine

  1. Machine learning-based estimation of nano-lubricants viscosity in different operating conditions

    Amin Bemani, Mohammad Madani, Alireza Kazemi

    Maqola202325 iqtibos
    ABI

  2. Storage of hydrogen, natural gas, and carbon dioxide – Geological and legal conditions

    Radosław Tarkowski, Barbara Uliasz‐Misiak, Piotr Tarkowski

    Maqola20214 iqtibos
    ABI

  3. Particle swarm optimization

    Riccardo Poli, James Kennedy, Tim Blackwell

    Maqola20072 iqtibos
    ABI

  4. A comprehensive review of value-added CO2 sequestration in subsurface saline aquifers

    Sunil Kumar, Jalal Foroozesh, Katriona Edlmann +2

    Sharh maqola20202 iqtibos
    ABI

  5. Prediction of hydrogen solubility in aqueous solutions: Comparison of equations of state and advanced machine learning-metaheuristic approaches

    Sajjad Ansari, Majid Safaei-Farouji, Saeid Atashrouz +3

    Maqola20222 iqtibos
    ABI

  6. Data-driven machine learning models for the prediction of hydrogen solubility in aqueous systems of varying salinity: Implications for underground hydrogen storage

    Hung Vo Thanh, Hemeng Zhang, Zhenxue Dai +3

    Maqola20232 iqtibos
    ABI

  7. Solubility of H2-CH4 mixtures in brine at underground hydrogen storage thermodynamic conditions

    Michel Tawil, Eloisa Salina Borello, Sergio Bocchini +6

    Maqola20242 iqtibos
    ABI

  8. Least Squares Support Vector Machine Classifiers

    Johan A. K. Suykens, Joos Vandewalle

    Maqola19992 iqtibos
    ABI

  9. An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange

    Melek Acar Boyacıoğlu, Derya Avcı

    Maqola20102 iqtibos
    ABI

  10. Benchmarking Least Squares Support Vector Machine Classifiers

    Tony Van Gestel, Johan A. K. Suykens, Bart Baesens +5

    Maqola20032 iqtibos
    ABI

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