Results 131 to 140 of about 7,571,754 (304)
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
Detecting frog calling activity based on acoustic event\ud detection and multi-label learning [PDF]
, 2016 Frog population has been declining the past decade for habitat loss, invasive species, climate change, and so forth. Therefore, it is becoming ever more important to monitor the frog population.
Towsey, Michael +3 more
core +1 more source
Metric Learning-Based Multi-Instance Multi-Label Classification With Label Correlation
IEEE Access, 2019 In multi-instance multi-label learning (MIML) problems, predicting the labels of unseen bags becomes difficult when the labels of their instances are not provided directly.
Haifeng Hu +3 more
doaj +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
Identify RNA-associated subcellular localizations based on multi-label learning using Chou's 5-steps rule. [PDF]
BMC Genomics, 2021 Wang H, Ding Y, Tang J, Zou Q, Guo F.
europepmc +1 more source
Online discriminative dictionary learning via label information for multi task object tracking
, 2014 In this paper, a supervised approach to online learn a structured sparse and discriminative representation for object tracking is presented. Label information from training data is incorporated into the dictionary learning process to construct a compact ...
Fan BJ(范保杰) +3 more
core
Partial multi-label learning method based on deep forest
上海师范大学学报. 自然科学版This paper proposes a biased multi label learning model based on deep forest architecture:the biased multi label deep forest (PMLDF) model, which combines the advantages of metric sensitive multi label deep forest and biased label forest, effectively ...
YUE Fan, QIU Feng
doaj +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
Advanced Engineering Materials, EarlyView.The OntOMat ontology establishes a structured framework for polymer matrix fiber reinforced composite materials, integrating manufacturing processes, characterization methods, and multiscale design through the VDI/VDE 3682 formalized process description standard.
Nicolas Christ +19 more
wiley +1 more source
Advanced Functional Materials, EarlyView.An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone +11 more
wiley +1 more source