Results 71 to 80 of about 110,596 (265)
Hyperparameters for the ULMFiT classifiers.
, 2022 Hyperparameters for the ULMFiT classifiers.
Marco Bruno Miranda Clementino (13170864) +1 more
core +1 more source
Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
Neural network hyperparameters for GA.
, 2022 Neural network hyperparameters for GA.
Denise Bernadette Ramos (12536476) +11 more
core +1 more source
Advanced Engineering Materials, EarlyView.This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source
Communications in Science and TechnologyFacial Electromyography (EMG) signals offer a promising modality for intuitive human-machine interfaces (HMIs). The development of robust control systems, however, remains challenging in view of the inherent complexity, noise susceptibility, and ...
Irfan Wahyu Ramadhan +1 more
doaj +1 more source
IEEE Access, 2020 The work reported in this article explores a novel Particle Swarm Optimization (PSO) tuned Support Vector Regression (SVR) based technique to develop the small-signal behavioral model for GaN High Electron Mobility Transistor (HEMT).
Ahmad Khusro +4 more
doaj +1 more source
Optimised features and model hyperparameters.
, 2021 Optimised features and model hyperparameters.
Paul Tafforeau (118114) +8 more
core +1 more source
A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
Advanced Engineering Materials, EarlyView.A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle +5 more
wiley +1 more source
New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design
Advanced Engineering Materials, EarlyView.This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare +5 more
wiley +1 more source
Frontiers in Artificial IntelligenceAutomatic speech recognition (ASR) systems for the Telugu language can significantly enhance speech-based interaction and digital accessibility. Although Telugu is among the top languages in the Dravidian family in terms of the number of speakers, there ...
Anvita Manne +3 more
doaj +1 more source