Results 151 to 160 of about 90,025 (264)

Comparative performance of bagging and boosting ensemble models for predicting lumpy skin disease with multiclass-imbalanced data. [PDF]

Sci Rep
Gouda HF, Abdallah FDM.
europepmc   +1 more source

Interaction‐Driven Assembly Pathways in Heterogeneous Active Microrobotic Systems

Advanced Functional Materials, EarlyView.
Peanut‐ and cube‐shaped light‐powered microrobots exhibit distinct magnetic and photocatalytic responses that determine their heterogeneous self‐assembly. In the dark, magnetic dipole interactions drive colloidal copolymerization with cubes acting as nucleation centers, whereas under light irradiation, phoretic interactions compete with magnetic ...
Domenico Calabrò, Roshan Velluvakandy, Martin Pumera, Francesco Priolo, Salvo Mirabella, Mario Urso   +5 more
wiley   +1 more source

MEFL: Meta-Equilibrize Federated Learning for Imbalanced Data in IoT. [PDF]

Entropy (Basel)
Tang J, Gao Y, Li X, Jia J.
europepmc   +1 more source

Dynamic learning for imbalanced data in learning chest X-ray and CT images. [PDF]

Heliyon, 2023
Iqbal S, Qureshi AN, Li J, Choudhry IA, Mahmood T.   +4 more
europepmc   +1 more source

Electrostatic Superlattices Beyond 1:1 Stoichiometry

Advanced Functional Materials, EarlyView.
Polymer‐mediated charge regulation drives two hierarchical routes to higher‐stoichiometry nanoparticle superlattices. Tuning the polymer's molecular weight promotes progressive interstitial filling, transforming ZnS into the fully occupied CaF2${\rm CaF}_2$ lattice. Exchanging polymer assignment between large and small nanoparticles instead breaks CsCl
Binay P. Nayak, Prapti Kakkar, Wesley P. Korba, Honghu Zhang, Wenjie Wang, Surya K. Mallapragada, Alex Travesset, David Vaknin   +7 more
wiley   +1 more source

A review of machine learning methods for imbalanced data challenges in chemistry. [PDF]

Chem Sci
Jiang J, Zhang C, Ke L, Hayes N, Zhu Y, Qiu H, Zhang B, Zhou T, Wei GW.   +8 more
europepmc   +1 more source

A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare. [PDF]

BioData Min, 2023
Kosolwattana T, Liu C, Hu R, Han S, Chen H, Lin Y.   +5 more
europepmc   +1 more source

Snap‐Through–Driven Liquid Crystal Elastomers for High‐Power Remote Microneedle Actuation Toward Smart Livestock Systems

Advanced Functional Materials, EarlyView.
A magnetically assisted snap‐through liquid crystal elastomer transforms photochemical deformation into a high‐power mechanical impulse, enabling remote microneedle actuation and highlighting its potential for future smart livestock intervention systems.
Chae Won Lee, Nankyung Kim, SeungHyun Park, Dae Seok Kim   +3 more
wiley   +1 more source

Predicting road traffic accident severity from imbalanced data using VAE attention and GCN. [PDF]

Sci Rep
Shangguan A, Feng N, Hei X, Fei R, Jin Y, Mu L, Li Y.   +6 more
europepmc   +1 more source

An empirical evaluation of sampling methods for the classification of imbalanced data. [PDF]

PLoS One, 2022
Kim M, Hwang KB.
europepmc   +1 more source