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Peer-reviewedCC BYFrontiers in medicine

Leakage-aware machine learning for data-driven performance prediction of metal–organic framework systems

  • Nidal H. Abu-Hamdeh1
  • Mohammed N. Ajour2
  • Ahmad H. Milyani2
  • Khalid H. Almitani3
  • Saeed Shirazian4
4 affiliations
  1. 1Center of Research Excellence in Renewable Energy and Power Systems/Energy Efficiency Group, Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University , ,
  2. 2Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University , ,
  3. 3Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University , ,
  4. 4Institute of Research and Development, Duy Tan University , ,
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Cite

BibTeXRIS
BibTeX
@article{abuhamdeh2026leakageaware,
  title = {{Leakage-aware machine learning for data-driven performance prediction of metal–organic framework systems}},
  author = {Nidal H. Abu-Hamdeh and Mohammed N. Ajour and Ahmad H. Milyani and Khalid H. Almitani and Saeed Shirazian},
  journal = {Frontiers in medicine},
  year = {2026},
  month = oct,
  url = {https://europepmc.org/article/PMC/PMC13654774},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Leakage-aware machine learning for data-driven performance prediction of metal–organic framework systems
AU  - Nidal H. Abu-Hamdeh
AU  - Mohammed N. Ajour
AU  - Ahmad H. Milyani
AU  - Khalid H. Almitani
AU  - Saeed Shirazian
PY  - 2026
DA  - 2026/10/16
JO  - Frontiers in medicine
UR  - https://europepmc.org/article/PMC/PMC13654774
SN  - 2296-858X
ER  -