Results 141 to 150 of about 13,903 (231)

Rebalancing Software Defect Datasets via Mutation: Performance Insights From Prediction Models Based on Software Measures

open access: yesSoftware Testing, Verification and Reliability, Volume 36, Issue 5, August 2026.
A mutation‐based approach (MBA) to rebalance defect datasets improves recall, particularly in cross‐project prediction, but increases false alarms and does not consistently enhance MCC or AUC. These findings highlight both the potential and limitations of mutation‐based rebalancing in software defect prediction.
Dinçer Güner   +2 more
wiley   +1 more source

Bathymetry Prediction With SWOT Gravity Anomaly Using Machine Learning Methods: Paper 1–Model Development

open access: yesJournal of Geophysical Research: Solid Earth, Volume 131, Issue 8, August 2026.
Abstract Only one quarter of the global ocean floor has been directly surveyed; the remaining three quarters are inferred from satellite altimeter‐derived gravity data using techniques developed in the 1990s. These classical methods correlate gravity anomalies with known depths and extrapolate bathymetry in unsounded regions.
David Sandwell   +13 more
wiley   +1 more source

Simulation and Prediction of Fungal Community Evolution Based on RBF Neural Network.

open access: yesComput Math Methods Med, 2021
Cai XW, Bao YQ, Hu MF, Liu JB, Zhu JM.
europepmc   +1 more source

iS‐GNN: Interpolation of Crustal Stress Maps Using a Graph Neural Network Model

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Estimating the orientation of the maximum horizontal stress (SHmax) from sparse and unevenly distributed geophysical observations remains a persistent challenge in tectonic and geomechanical stress studies. Classical interpolation methods often neglect the multiscale tectonic–geological heterogeneity of the crust, leading to biased estimates ...
Kwame A. Gyamfi, Michele M. C. Carafa
wiley   +1 more source

Optimal deep learning activity recognition pipeline using animal bio‐logging data

open access: yesMethods in Ecology and Evolution, Volume 17, Issue 8, Page 2309-2329, August 2026.
Abstract Animal activity recognition using supervised machine learning on bio‐logging data has revolutionized behavioural studies on wildlife and livestock over the past decade. These studies' analytic pipelines differ substantially though, being based on various sensors, features, algorithms and hyperparameter values, which has caused suboptimal or ...
Jasper A. J. Eikelboom
wiley   +1 more source

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