AIMC Topic: Unsupervised Machine Learning

Clear Filters Showing 141 to 150 of 828 articles

Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches.

Scientific reports
Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This st...

Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation.

Neural networks : the official journal of the International Neural Network Society
Unsupervised Domain Adaptation (UDA) is a machine learning technique that facilitates knowledge transfer from a labeled source domain to an unlabeled target domain, addressing distributional discrepancies between these domains. Existing UDA methods o...

Unsupervised learning for real-time and continuous gait phase detection.

PloS one
Individuals with lower limb impairment after a stroke or spinal cord injury require rehabilitation, but traditional methods can be challenging for both patients and therapists. Robotic systems have been developed to help; however, they currently cann...

A general adaptive unsupervised feature selection with auto-weighting.

Neural networks : the official journal of the International Neural Network Society
Feature selection (FS) is essential in machine learning and data mining as it makes handling high-dimensional data more efficient and reliable. More attention has been paid to unsupervised feature selection (UFS) due to the extra resources required t...

Enhancing long-term water quality modeling by addressing base demand, demand patterns, and temperature uncertainty using unsupervised machine learning techniques.

Water research
Water quality modelling in Water Distribution systems (WDS) is frequently affected by uncertainties in input variables such as base demand and decay constants. When utilizing simulation tools like EPANET, which necessitate exact numerical inputs, the...

A comparative analysis of unsupervised machine-learning methods in PSG-related phenotyping.

Journal of sleep research
Obstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover various obstructive sleep apnea phenotypic clusters. However, the selection of the clustering metho...

Structure preservation constraints for unsupervised domain adaptation intracranial vessel segmentation.

Medical & biological engineering & computing
Unsupervised domain adaptation (UDA) has received interest as a means to alleviate the burden of data annotation. Nevertheless, existing UDA segmentation methods exhibit performance degradation in fine intracranial vessel segmentation tasks due to th...

GMNI: Achieve good data augmentation in unsupervised graph contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Graph contrastive learning (GCL) shows excellent potential in unsupervised graph representation learning. Data augmentation (DA), responsible for generating diverse views, plays a vital role in GCL, and its optimal choice heavily depends on the downs...

Utilizing echocardiography and unsupervised machine learning for heart failure risk identification.

International journal of cardiology
BACKGROUND: Global longitudinal strain (GLS) is recognized as a powerful predictor of heart failure (HF). However, the entire strain curve may entail important prognostic information regarding HF risk that might be undiscovered by only focusing on th...

A Scoping Review of Machine Learning Applied to Peripheral Nerve Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Peripheral nerve interfaces (PNIs) can enable communication with the peripheral nervous system and have a broad range of applications including in bioelectronic medicine and neuroprostheses. They can modulate neural activity through stimulation or mo...