AIMC Topic: Machine Learning

Clear Filters Showing 8971 to 8980 of 34417 articles

Characterizing Autism Spectrum Disorder Through Fusion of Local Cortical Activation and Global Functional Connectivity Using Game-Based Stimuli and a Mobile EEG System.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The deficit in social interaction skills among individuals with autism spectrum disorder (ASD) is strongly influenced by personal experiences and social environments. Neuroimaging studies have previously highlighted the link between social impairment...

Online Privacy-Preserving EEG Classification by Source-Free Transfer Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applications. Recent studies have utilized transfer learning to assist the learning task in the new subject, i.e., target domain, by leveraging beneficial inf...

Explainable machine-learning-based prediction of QCT/FEA-calculated femoral strength under stance loading configuration using radiomics features.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Finite element analysis can provide precise femoral strength assessment. However, its modeling procedures were complex and time-consuming. This study aimed to develop a model to evaluate femoral strength calculated by quantitative computed tomography...

Navigating the maze of mass spectra: a machine-learning guide to identifying diagnostic ions in O-glycan analysis.

Analytical and bioanalytical chemistry
Structural details of oligosaccharides, or glycans, often carry biological relevance, which is why they are typically elucidated using tandem mass spectrometry. Common approaches to distinguish isomers rely on diagnostic glycan fragments for annotati...

Using machine-learning approaches to investigate the volatile-compound fingerprint of fishy off-flavour from beef with enhanced healthful fatty acids.

Meat science
Machine learning classification approaches were used to discriminate a fishy off-flavour identified in beef with health-enhanced fatty acid profiles. The random forest approach outperformed (P < 0.001; receiver operating characteristic curve: 99.8 %,...

The development of machine learning approaches in two-dimensional NMR data interpretation for metabolomics applications.

Analytical biochemistry
Metabolomics has been widely applied in human diseases and environmental science to study the systematic changes of metabolites over diverse types of stimuli. NMR-based metabolomics has been widely used, but the peak overlap problems in the one-dimen...

Center of Pressure- and Machine Learning-based Gait Score and Clinical Risk Factors for Predicting Functional Outcome in Acute Ischemic Stroke.

Archives of physical medicine and rehabilitation
OBJECTIVES: To investigate whether machine learning (ML)-based center of pressure (COP) analysis for gait assessment, when used in conjunction with clinical information, offers additive benefits in predicting functional outcomes in patients with acut...

Metabolic signatures derived from whole-brain MR-spectroscopy identify early tumor progression in high-grade gliomas using machine learning.

Journal of neuro-oncology
PURPOSE: Recurrence for high-grade gliomas is inevitable despite maximal safe resection and adjuvant chemoradiation, and current imaging techniques fall short in predicting future progression. However, we introduce a novel whole-brain magnetic resona...

Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models.

The Science of the total environment
The traditional prediction of the Cd content in grains (Cd) of crops primarily relies on the multiple linear regression models based on soil Cd content (Cd) and pH, neglecting inter-factorial interactions and nonlinear causal links between external e...

Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California.

The Science of the total environment
The potential for machine learning to answer questions of environmental science, monitoring, and regulatory enforcement is evident, but there is cause for concern regarding potential embedded bias: algorithms can codify discrimination and exacerbate ...