AIMC Topic: Supervised Machine Learning

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Enhanced Room Temperature Sensing Properties of Tin Oxide Gas Sensors Exploiting Carbon Nanotubes: High-Accuracy Ammonia Gas Classification via Supervised Learning Regression Algorithms.

ACS sensors
The sensing properties of tin oxide (SnO) gas sensors, enhanced by the exploitation of carbon nanotubes (CNTs), were explored at room temperature. The CNT/tin oxide hybrid sensors demonstrated superior performance at room temperature compared to sing...

Early detection of vascular catheter-associated infections employing supervised machine learning - a case study in Lleida region.

BMC medical informatics and decision making
Healthcare-associated infections (HAIs), particularly Vascular Catheter-Associated Infections (VCAIs), are a significant concern, accounting for over 7% of all infections and are often linked to medical devices. Early detection of VCAIs before invasi...

Prediction of human pathogenic start loss variants based on self-supervised contrastive learning.

BMC biology
BACKGROUND: Start loss variants are a class of genetic variants that affect the bases of the start codon, disrupting the normal translation initiation process and leading to protein deletions or the production of different proteins. Accurate assessme...

Semi-supervised medical image segmentation based on multi-stage iterative training and high-confidence pseudo-labeling.

Biomedical physics & engineering express
Due to the scarcity and high cost of pixel-level annotations for training data, semi-supervised learning has gradually become a key solution. Most existing methods rely on consistency regularization and pseudo-label generation, often adopting multi-b...

Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets.

BMC bioinformatics
As single-cell sequencing technology became widely used, scientists found that single-modality data alone could not fully meet the research needs of complex biological systems. To address this issue, researchers began simultaneously collect multi-mod...

Prediction of blown pack in vacuum-packaged beef based on microbiome profiles and supervised machine learning.

International journal of food microbiology
The preservation of vacuum-packaged beef products is essential for maintaining shelf life. However, the occurrence of blown pack phenomenon, characterized by the expansion of packaging due to gas production by spoilage microorganisms, is still a chal...

Self-supervised pre-training with joint-embedding predictive architecture boosts ECG classification performance.

Computers in biology and medicine
Accurate diagnosis of heart arrhythmias requires the interpretation of electrocardiograms (ECG), which capture the electrical activity of the heart. Automating this process through machine learning is challenging due to the need for large annotated d...

A hybrid supervised and unsupervised machine learning approach for identifying nucleoside drugs using nanopore readouts.

Nanoscale
Nucleoside drugs, mimics of natural nucleosides, have become cornerstone treatments in clinical approaches to combat cancer and viral infections. The analysis of nucleoside drugs is commonly performed using liquid chromatography-tandem mass spectrome...

Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning.

BMC medical informatics and decision making
BACKGROUND: In the Intensive Care Unit (ICU), data stored in patient data management systems (PDMS) is commonly used in clinical practice and research. Parameters from point-of-care arterial blood gas (BG) analysis are used in the diagnosis and defin...

Normal Pressure Hydrocephalus Classification using Weakly-Supervised Local Feature Extraction.

Computers in biology and medicine
Normal Pressure Hydrocephalus (NPH) presents diagnostic challenges because its symptoms often overlap with other neurological conditions. A key radiological NPH indicator is ventricular cerebrospinal fluid (CSF) volume, assessed by neuroradiologists ...