Results 111 to 120 of about 823,383 (284)
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
On a strategy to develop robust and simple tariffs from motor vehicle insurance data [PDF]
The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features.
Christmann, Andreas
core
Wet aggregate stability modeling based on support vector machine in multiuse soils
, 2022 Ruizhi Zhai +3 more
openalex +1 more source
Advanced Functional Materials, EarlyView.This study investigates electromechanical PUFs that improve on traditional electric PUFs. The electron transport materials are coated randomly through selective ligand exchange. It produces multiple keys and a key with motion dependent on percolation and strain, and approaches almost ideal inter‐ and intra‐hamming distances.
Seungshin Lim +7 more
wiley +1 more source
Advanced Functional Materials, EarlyView.It is demonstrated that severe lattice distortion and coherent nanoprecipitates overcome the strength–ductility dilemma and mitigate discontinuous plastic flow (DPF) at 4.2 K. Such microstructural design enhances both thermal and athermal stress components, leading to exceptional mechanical performance.
Min Young Sung +11 more
wiley +1 more source
Support Vector Machine Implementations for Classification & Clustering
BMC Bioinformatics, 2006 Background We describe Support Vector Machine (SVM) applications to classification and clustering of channel current data. SVMs are variational-calculus based methods that are constrained to have structural risk minimization (SRM), i.e., they provide ...
Winters-Hilt Stephen +3 more
doaj +1 more source
Prediction of human disease‐associated phosphorylation sites with combined feature selection approach and support vector machine [PDF]
, 2015 Xiaoyi Xu, Ao Li, Minghui Wang
openalex +1 more source
Advanced Functional Materials, EarlyView.Molecular engineering of a nonconjugated radical polymer enables a significant enhancement of the glass transition temperature. The amorphous nature and tunability of the polymer, arising from its nonconjugated backbone, facilitates the fabrication of organic memristive devices with an exceptionally high yield (>95%), as well as substantial ...
Daeun Kim +14 more
wiley +1 more source
An electric power generation forecasting method using support vector machine [PDF]
, 2018 Li Guo +3 more
openalex +1 more source
Two‐Dimensional Materials as a Multiproperty Sensing Platform
Advanced Functional Materials, EarlyView.Various sensing modalities enabled and/or enhanced by two‐dimensional (2D) materials are reviewed. The domains considered for sensing include: 1) optoelectronics, 2) quantum defects, 3) scanning probe microscopy, 4) nanomechanics, and 5) bio‐ and chemosensing.
Dipankar Jana +11 more
wiley +1 more source