Results 91 to 100 of about 1,003,291 (283)
Advanced Engineering Materials, EarlyView.A novel workflow for investigating hydride vapor phase epitaxy for GaN bulk crystal growth is proposed. It combines Design of experiments (DoE) with physical simulations of mass transport and crystal growth kinetics, serving as an intermediate step between DoE and experiments.
J. Tomkovič +7 more
wiley +1 more source
Regression depth and support vector machine [PDF]
The regression depth method (RDM) proposed by Rousseeuw and Hubert [RH99] plays an important role in the area of robust regression for a continuous response variable.
Christmann, Andreas
core
Prediksi Mahasiswa Drop Out Menggunakan Metode Support Vector Machine [PDF]
, 2015 Tingginya tingkat keberhasilan mahasiswa dan rendahnya tingkat kegagalan mahasiswa dapat mencerminkan kualitas dari suatu perguruan tinggi. Salah satu indikator kegagalan mahasiswa adalah kasus drop out.
kusrini, K. (Kusrini) +2 more
core
In Situ Micromechanical Study of Bimodal γ′–γ″ Precipitate Assemblies in Ni–Cr–Al–Nb Superalloy
Advanced Engineering Materials, EarlyView.A Ni–Cr–Al–Nb superalloy with a bimodal γ′–γ″ precipitate distribution is developed. Composite precipitate assemblies form through heterogeneous nucleation, effectively impeding dislocation motion. Micropillar compression reveals high strength at room and elevated temperatures, governed by precipitate shearing, with coupled faulting mechanisms ...
Ujjval Bansal +4 more
wiley +1 more source
Biased support vector machine and weighted-smote in handling class imbalance problem
IJAIN (International Journal of Advances in Intelligent Informatics), 2018 Class imbalance occurs when instances in a class are much higher than in other classes. This machine learning major problem can affect the predicted accuracy.
Hartono Hartono +3 more
doaj +1 more source
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
Predicting Atomic Charges in MOFs by Topological Charge Equilibration
Advanced Functional Materials, EarlyView.An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi +2 more
wiley +1 more source
On robustness properties of convex risk minimization methods for pattern recognition [PDF]
The paper brings together methods from two disciplines: machine learning theory and robust statistics. Robustness properties of machine learning methods based on convex risk minimization are investigated for the problem of pattern recognition ...
Christmann, Andreas, Steinwart, Ingo
core
Advanced Functional Materials, EarlyView.We propose a suture‐complementary approach that integrates optical skin clearing with a strain‐programmable luminescent adhesive patch. Hyaluronic acid promotes transdermal delivery of tartrazine to improve optical clearing and stabilizes its interaction with a photosensitizer. Optical clearing increases the penetration depth of visible light into skin,
Seong‐Jong Kim +6 more
wiley +1 more source
, 2002 This chapter gives a short introduction to support vector machines, the basic learning method used, extended, and analyzed for text classification throughout this work. Support vector machines [Cortes and Vapnik, 1995][Vapnik, 1998] were developed by Vapnik et al.
openaire +2 more sources