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Improving Machine Learning Models of Land Cover Change Using Spatial Sample Weights Based on Patch‐Level Landscape Metrics

open access: yesTransactions in GIS, Volume 30, Issue 3, May 2026.
ABSTRACT Machine learning (ML) is prevalent in land change modeling for automating transition rule specification. However, aspatial strategies are typically used to mitigate the imbalances characterizing multitemporal land cover (LC) datasets. While spatialized cost‐sensitive learning strategies including spatial sample weights (SSWs) have demonstrated
Alysha van Duynhoven   +2 more
wiley   +1 more source

Advanced Embryo Ploidy Classification Using Vision Transformers: Integration of Sequential Time-Lapse Imaging and Undersampling Techniques: A Retrospective Study. [PDF]

open access: yesJ Hum Reprod Sci
Avidiansyah MF   +9 more
europepmc   +1 more source

Enhancing cardiotocography classification via ensemble learning and threshold optimization. [PDF]

open access: yesSci Rep
Kong L   +8 more
europepmc   +1 more source

Feasibility Study for Dual Phase Free Breathing 3D bSSFP MRA With Preserved Eddy-Current Tolerance With Adaptive k-Space Lengths (PETAL) Acquisition. [PDF]

open access: yesMagn Reson Med
Ribeiro Salles Moura T   +11 more
europepmc   +1 more source

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