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Advanced AQI interpretation using deep convolutional neural networks - a case study for Delhi

Kavita Pabreja et al.

Interdisciplinary Environmental Review2025https://doi.org/10.1504/ier.2025.147801article
ABDC C
Weight
0.50

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https://doi.org/https://doi.org/10.1504/ier.2025.147801

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@article{kavita2025,
  title        = {{Advanced AQI interpretation using deep convolutional neural networks - a case study for Delhi}},
  author       = {Kavita Pabreja et al.},
  journal      = {Interdisciplinary Environmental Review},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ier.2025.147801},
}

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Advanced AQI interpretation using deep convolutional neural networks - a case study for Delhi

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Evidence weight

0.50

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

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
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.