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Machine learning-based hybrid preprocessing techniques for UAV spare parts demand forecasting

Jae Dong Kim et al.

European Journal of Industrial Engineering2026https://doi.org/10.1504/ejie.2026.151158article
AJG 2
Weight
0.37

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https://doi.org/https://doi.org/10.1504/ejie.2026.151158

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@article{jae2026,
  title        = {{Machine learning-based hybrid preprocessing techniques for UAV spare parts demand forecasting}},
  author       = {Jae Dong Kim et al.},
  journal      = {European Journal of Industrial Engineering},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ejie.2026.151158},
}

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Machine learning-based hybrid preprocessing techniques for UAV spare parts demand forecasting

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.