AIMC Topic: Machine Learning

Clear Filters Showing 4021 to 4030 of 34417 articles

Gold nanorods as multidimensional optical nanomaterials: machine learning-enhanced quantitative fingerprinting of proteins for diagnostic applications.

Nanoscale
The rapid and precise quantification and identification of proteins as key diagnostic biomarkers hold significant promise in allergy testing, disease diagnosis, clinical treatment, and proteomics. This is crucial because alterations in disease-associ...

A prediction model based on machine learning: prognosis of HBV-induced HCC male patients with smoking and drinking habits after local ablation treatment.

Frontiers in immunology
BACKGROUND: Liver cancer, particularly hepatocellular carcinoma (HCC), is a major health concern globally and in China, possibly shows recurrence after ablation treatment in high-risk patients. This study investigates the prognosis of early-stage mal...

Unveiling the power of Treg.Sig: a novel machine-learning derived signature for predicting ICI response in melanoma.

Frontiers in immunology
BACKGROUND: Although immune checkpoint inhibitor (ICI) represents a significant breakthrough in cancer immunotherapy, only a few patients benefit from it. Given the critical role of Treg cells in ICI treatment resistance, we explored a Treg-associate...

Explainable machine learning for predictive modeling of blowing snow detection and meteorological feature assessment using XGBoost-SHAP.

PloS one
Accurate forecasting of blowing snow events is vital for improving numerical models of snow processes, yet traditional predictive methods often lack interpretability. This study leverages eXtreme Gradient Boosting (XGBoost) to detect blowing snow eve...

A Novel, Interpretable Machine Learning Model to Predict Neurological Outcomes Following Venoarterial Extracorporeal Membrane Oxygenation.

Neurocritical care
BACKGROUND: We used machine learning models incorporating rich electronic medical record (EMR) data to predict neurological outcomes after venoarterial extracorporeal membrane oxygenation (VA-ECMO).

MRI-based radiomics for prediction of biochemical recurrence in prostate cancer: a systematic review and meta-analysis.

Abdominal radiology (New York)
BACKGROUND AND PURPOSE: Biochemical recurrence (BCR) following prostate cancer (PCa) treatment is a significant indicator of metastasis and mortality. Early prediction of BCR can guide treatment decisions, and optimize patient management strategies. ...

Enhancing target speaker extraction with Hierarchical Speaker Representation Learning.

Neural networks : the official journal of the International Neural Network Society
Target speaker extraction aims to obtain the speech of the specific speaker from a mixture of multiple voices. The conventional approach exploits the target speaker embeddings from a pre-recorded speech segment as auxiliary information, providing pri...

Using samples with label noise for robust continual learning.

Neural networks : the official journal of the International Neural Network Society
Recent studies have shown that effectively leveraging samples with label noise can enhance model robustness by uncovering more reliable feature patterns. While existing methods, such as label correction methods and loss correction techniques, have de...

Optimal cybersecurity framework for smart water system: Detection, localization and severity assessment.

Water research
The digital transformation of water distribution systems has streamlined monitoring and control through the integration of smart devices such as pressure sensors, smart meters, and level switches, all communicating with supervisory control and data a...

Adaptive bigraph-based multi-view unsupervised dimensionality reduction.

Neural networks : the official journal of the International Neural Network Society
As a crucial machine learning technology, graph-based multi-view unsupervised dimensionality reduction aims to learn compact low-dimensional representations for unlabeled multi-view data using graph structures. However, it faces several challenges, i...