Results 51 to 60 of about 545,363 (263)

Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses

Bioengineering, 2023
Ultrasound (US) is often used to diagnose liver masses. Ensemble learning has recently been commonly used for image classification, but its detailed methods are not fully optimized.
Norio Nakata, Tsuyoshi Siina
doaj   +1 more source

Functional and Structural Evidence of Neurofluid Circuit Aberrations in Huntington Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Disrupted neurofluid regulation may contribute to neurodegeneration in Huntington disease (HD). Because neurofluid pathways influence waste clearance, inflammation, and the distribution of central nervous system (CNS)–delivered therapeutics, understanding their dysfunction is increasingly important as targeted treatments emerge.
Kilian Hett, Abigail Dubois, Melanie Leguizamon, Alexander Song, Paula Trujillo, Colin D. McKnight, Ciaran M. Considine, Manus J. Donahue, Daniel O. Claassen   +8 more
wiley   +1 more source

Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

Journal of Cheminformatics, 2020
Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure–activity
Chia-Hsiu Chen, Kenichi Tanaka, Masaaki Kotera, Kimito Funatsu   +3 more
doaj   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Learning-based Ensemble Average Propagator Estimation

, 2017
By capturing the anisotropic water diffusion in tissue, diffusion magnetic resonance imaging (dMRI) provides a unique tool for noninvasively probing the tissue microstructure and orientation in the human brain.
C Ye, DC Essen Van, E Garyfallidis, E Özarslan, H Johansen-Berg, HE Assemlal, M Descoteaux, SL Merlet, V Golkov, Y LeCun, YC Wu   +10 more
core   +1 more source

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Advanced Engineering Materials, EarlyView.
A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba, Sang‐Hyeok Lee, Christina Gasper, Martina Freund, Sandra Korte‐Kerzel, Ulrich Kerzel   +5 more
wiley   +1 more source

Foundations and Innovations in Data Fusion and Ensemble Learning for Effective Consensus

Mathematics
Ensemble learning and data fusion techniques play a crucial role in modern machine learning, enhancing predictive performance, robustness, and generalization.
Ke-Lin Du, Rengong Zhang, Bingchun Jiang, Jie Zeng, Jiabin Lu   +4 more
doaj   +1 more source

dropCyclic: Snapshot Ensemble Convolutional Neural Network Based on a New Learning Rate Schedule for Land Use Classification

IEEE Access, 2022
The ensemble learning method is a necessary process that provides robustness and is more accurate than the single model. The snapshot ensemble convolutional neural network (CNN) has been successful and widely used in many domains, such as image ...
Sangdaow Noppitak, Olarik Surinta
doaj   +1 more source

Markov Network Structure Learning via Ensemble-of-Forests Models [PDF]

, 2013
Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models.
Arvaniti, Eirini, Claassen, Manfred
core  

Dynamic Metric Learning from Pairwise Comparisons

, 2016
Recent work in distance metric learning has focused on learning transformations of data that best align with specified pairwise similarity and dissimilarity constraints, often supplied by a human observer.
Greenewald, Kristjan, Hero III, Alfred, Kelley, Stephen   +2 more
core   +1 more source