Results 171 to 180 of about 17,625 (278)

Advancing Gastrointestinal Cancer Risk Prediction With Patient-Centered Machine Learning: Machine Learning Modeling Study. [PDF]

JMIR Med Inform
Baublyte D, Lee J, Gunathilake M, Kim J.
europepmc   +1 more source

Task‐Evoked Functional Activation and Coupling With CSF Flow Detected in the Human Brain With Ultrashort Echo Time fMRI at 7 T

NMR in Biomedicine, Volume 39, Issue 8, August 2026.
Ultrashort and zero echo time (UTE and ZTE) MRI techniques generate functional contrast through inflow effects, making them suitable for studying cerebral blood flow and cerebrospinal fluid (CSF) dynamics. In a 7‐T study of 13 participants performing a visual task, we demonstrate that UTE produced robust, reproducible activations with group‐level ...
Sara Ponticorvo, Ekaterina Paasonen, Pavel Filip, Douglas L. Rothman, Juha S. Valjakka, Olli Gröhn, Shalom Michaeli, Silvia Mangia   +7 more
wiley   +1 more source

Comparison of Machine Learning Models and the FMF Competing-Risks Algorithm for First-Trimester Preeclampsia Screening in a Romanian Cohort. [PDF]

Diagnostics (Basel)
Cristofor AE, Carauleanu A, Vasilache IA, Condriuc I, Bica O, Nemescu D.   +5 more
europepmc   +1 more source

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

Software 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, Görkem Giray, Onur Demirörs   +2 more
wiley   +1 more source

Domain-Conditioned and Temporal-Guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction. [PDF]

NMR Biomed
Zhang L, Yuwen Zhou I, Montesi SB, Feng L, Liu F.   +4 more
europepmc   +1 more source

Enhancing Classification Performance on Imbalanced Datasets Through Complexity‐Guided Oversampling With SMOTE

Expert Systems, Volume 43, Issue 8, August 2026.
ABSTRACT Improving classification performance on imbalanced datasets remains a challenging problem in machine learning. Synthetic oversampling techniques such as SMOTE are widely used to address class imbalance; however, their random interpolation strategy often ignores structural data properties, which may affect classifier generalisation.
Jose L. Morillo‐Salas, Veronica Bolón‐Canedo, Laura Morán‐Fernández, Amparo Alonso‐Betanzos   +3 more
wiley   +1 more source

Unified comparison of machine learning paradigms for blood transfusion prediction in pediatric congenital heart surgery. [PDF]

iScience
Yin MW, Li J, Huang J, Zhu Z, Chen BH, Shi Z, Jiang Q, Chen XJ, Yu G.   +8 more
europepmc   +1 more source

Clustering-Based Undersampling for Class-imbalanced Data

, 2017
[[abstract]]Class imbalance is often a problem in various real-world data sets, where one class (i.e. the minority class) contains a small number of data points and the other (i.e. the majority class) contains a large number of data points. It is notably
林維昭;Wei-Chao Lin;Tsai, C.-F.;Hu, Y.-H.;Jhang, J.-S.
core  

On the Importance of Imbalance‐Aware Evaluation for Edge‐Of‐Field Runoff Prediction: A Commentary on Ford et al. (2022)

Geophysical Research Letters, Volume 53, Issue 13, 16 July 2026.
Abstract Field‐scale runoff prediction is critical for managing nutrient losses. Ford et al. (2022, https://doi.org/10.1029/2022gl100667) present an innovative hybrid modeling and regionalization framework that integrates cluster analysis, National Water Model (NWM) outputs, and machine learning to extend edge‐of‐field (EOF) runoff prediction across ...
M. S. Jahangir, S. Steinschneider
wiley   +1 more source

Dynamic Transformer Based on Wavelet and Diffusion Prior Guidance for Cardiac Cine MRI Reconstruction. [PDF]

Sensors (Basel)
Zhao B, Lyu J.
europepmc   +1 more source