EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms.
Sign Up to like & getrecommendations! Published in 2021 at "Analytical chemistry"
DOI: 10.1021/acs.analchem.1c01309
Abstract: Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data relies on the recognition of extracted ion chromatogram (EIC) peak shapes using peak picking algorithms. Unfortunately, all peak picking algorithms present a significant drawback of generating… read more here.
Keywords: extracted ion; deep learning; model trained; model ... See more keywords
How Much a Model be Trained by Passive Learning Before Active Learning?
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3162253
Abstract: Most pool-based active learning studies have focused on query strategy for active learning. In this paper, via empirical analysis on the effect of passive learning before starting active learning, we reveal that the amount of… read more here.
Keywords: active learning; much model; learning; passive learning ... See more keywords
A gait phase prediction model trained on benchmark datasets for evaluating a controller for prosthetic legs
Sign Up to like & getrecommendations! Published in 2023 at "Frontiers in Neurorobotics"
DOI: 10.3389/fnbot.2022.1064313
Abstract: Powered lower-limb assistive devices, such as prostheses and exoskeletons, are a promising option for helping mobility-impaired individuals regain functional gait. Gait phase prediction plays an important role in controlling these devices and evaluating whether the… read more here.
Keywords: gait phase; prediction; phase prediction; model trained ... See more keywords