AIMC Topic: Machine Learning

Clear Filters Showing 10701 to 10710 of 34417 articles

Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model.

IEEE transactions on neural networks and learning systems
Recently, brain networks have been widely adopted to study brain dynamics, brain development, and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phen...

Machine Learning Tools to Assist the Synthesis of Antibacterial Carbon Dots.

International journal of nanomedicine
INTRODUCTION: The emergence and rapid spread of multidrug-resistant bacteria (MRB) caused by the excessive use of antibiotics and the development of biofilms have been a growing threat to global public health. Nanoparticles as substitutes for antibio...

Machine learning with taxonomic family delimitation aids in the classification of ephemeral beaked whale events in passive acoustic monitoring.

PloS one
Passive acoustic monitoring is an essential tool for studying beaked whale populations. This approach can monitor elusive and pelagic species, but the volume of data it generates has overwhelmed researchers' ability to quantify species occurrence for...

Prediction of pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II based on clinical risk.

PloS one
OBJECTIVE: To assess the effectiveness of different machine learning models in estimating the pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II diagnosis, based on the clinical risk index determined by the a...

Phenotype clustering of hospitalized high-risk patients with COVID-19 - a machine learning approach within the multicentre, multinational PCHF-COVICAV registry.

Cardiology journal
IMTRODUCTION: The high-risk population of patients with cardiovascular (CV) disease or risk factors (RF) suffering from COVID-19 is heterogeneous. Several predictors for impaired prognosis have been identified. However, with machine learning (ML) app...

Machine learning assisted classification between diabetic polyneuropathy and healthy subjects using plantar pressure and temperature data: a feasibility study.

Computer methods in biomechanics and biomedical engineering
Automated and early detection of diabetics with polyneuropathy in an ambulatory health monitoring setup may reduce the major risk factors for diabetic patients. Increased and localized plantar pressure associated with impaired pain and temperature is...

Au nanozyme-based colorimetric sensor array integrates machine learning to identify and discriminate monosaccharides.

Journal of colloid and interface science
As different monosaccharides exhibit different redox characteristics, this paper presented a novel colorimetric sensor array based on the glucose oxidase-like (GOx-like) activity of Au nanoparticles (NPs) for monosaccharides identification. AuNPs can...

Meta-learning based blind image super-resolution approach to different degradations.

Neural networks : the official journal of the International Neural Network Society
Although recent studies on blind single image super-resolution (SISR) have achieved significant success, most of them typically require supervised training on synthetic low resolution (LR)-high resolution (HR) paired images. This leads to re-training...

Facial micro-expression recognition using stochastic graph convolutional network and dual transferred learning.

Neural networks : the official journal of the International Neural Network Society
Micro-expression recognition (MER) has drawn increasing attention due to its wide application in lie detection, criminal detection and psychological consultation. However, the best recognition accuracy on recent public dataset is still low compared t...

New Model and Public Online Prediction Platform for Risk Stratification of Vocal Cord Leukoplakia.

The Laryngoscope
OBJECTIVE: To extract texture features from vocal cord leukoplakia (VCL) images and establish a VCL risk stratification prediction model using machine learning (ML) techniques.