Search alternatives:
significant decrease » significant increase (Expand Search), significantly increased (Expand Search)
greater decrease » greatest decrease (Expand Search), greater increase (Expand Search), greater disease (Expand Search)
green decrease » mean decrease (Expand Search), teer decrease (Expand Search), greatest decrease (Expand Search)
significant decrease » significant increase (Expand Search), significantly increased (Expand Search)
greater decrease » greatest decrease (Expand Search), greater increase (Expand Search), greater disease (Expand Search)
green decrease » mean decrease (Expand Search), teer decrease (Expand Search), greatest decrease (Expand Search)
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MXene/Bi<sub>2</sub>O<sub>3</sub> Nanocomposites as Supercapacitors for Portable Electronic Devices
Published 2025Subjects: -
1224
Seasonal spatial-temporal trend of NDVI of burned forested areas in Africa (2001–2020).
Published 2025“…<p>Areas with increasing (green) or decreasing (red) vegetation, based on the Mann-Kendall test and statistically significance values (<i>p</i> < 0.05) for DJF (December-February), MAM (March-May), JJA (June-August) and SON (September-November). …”
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Seasonal spatial-temporal trend of NDVI in burned areas across Africa (2001–2020).
Published 2025“…<p>Areas with increasing (green) or decreasing (red) vegetation, based on the Mann-Kendall test and statistically significance values (<i>p</i> < 0.05) for DJF (December-February), MAM (March-May), JJA (June-August), and SON (September-November). …”
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S1 File -
Published 2025“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
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Seasonal spatial-temporal trend of NDVI of burned forested areas in Africa (2001–2020) by countries and territories.
Published 2025“…<p>Areas with increasing (green) or decreasing (red) vegetation, based on the Mann-Kendall test and statistically significance values (<i>p</i> < 0.05) for December-February (DJF), March-May (MAM), June-August (JJA) and September-November (SON). …”
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Confusion matrix for ClinicalBERT model.
Published 2025“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
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Confusion matrix for LastBERT model.
Published 2025“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
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Student model architecture.
Published 2025“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
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Configuration of the LastBERT model.
Published 2025“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
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Confusion matrix for DistilBERT model.
Published 2025“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
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ROC curve for LastBERT model.
Published 2025“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
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Sample Posts from the ADHD dataset.
Published 2025“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
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Top-level overview for ADHD classification study.
Published 2025“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
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