AIMC Topic: Machine Learning

Clear Filters Showing 27591 to 27600 of 34417 articles

Comparison between chemometrics and machine learning for the prediction of macronutrients in cheese using Imaging spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Traditional methods for assessing cheese's nutritional content are often labor-intensive, destructive, and environmentally taxing. This study explores the non-destructive spectral imaging technique, also called Hyperspectral Imaging (HSI) combined wi...

Hyperspectral imaging and machine learning for quality assessment of apples with different bagging types.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study examined how different bagging types (unbagged, mesh-bagged, and paper-bagged) affect the internal and external quality of fruits. Spectral images of 307 apples were collected using a visible-near infrared hyperspectral imaging system, and...

Diagnosis of uterine diseases by label-free serum SERS fingerprints with machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Early detection of uterine diseases is critically important for women's reproductive health. Here, we propose a novel and robust serum-based SERS analysis platform that integrates machine learning algorithms. This is the first application of it in th...

Combined magnetic resonance imaging and serum analysis reveals distinct multiple sclerosis types.

Brain : a journal of neurology
Multiple sclerosis (MS) is a highly heterogeneous disease in its clinical manifestation and progression. Predicting individual disease courses is key for aligning treatments with underlying pathobiology. We developed an unsupervised machine learning ...

Machine learning-enabled non-targeted metabolomics reveals nutritional and metabolic responses of Brachypodium distachyon to drought and elevated CO2.

Journal of experimental botany
Rising atmospheric CO2 and intensified drought are reshaping nutrient dynamics in C3 plants, with implications for ecosystem function and food security. To investigate how these stressors jointly affect nutrient homeostasis, we examined Brachypodium ...

Can CTA-Based Machine Learning Identify Patients for Whom Successful Endovascular Stroke Therapy Is Insufficient?

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Despite advances in endovascular stroke therapy (EST) devices and techniques, many patients are left with substantial disability, even if the final infarct volumes (FIVs) remain small. Here, we evaluate the performance of a ma...

Machine Learning-Based Prediction of Delayed Neurologic Sequelae in Carbon Monoxide Poisoning Using Automatically Extracted MR Imaging Features.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Delayed neurologic sequelae are among the most serious complications of carbon monoxide poisoning. However, no reliable tools are available for evaluating their potential risk. We aimed to assess whether machine learning model...

Pulling back the curtain: the road from statistical estimand to machine-learning-based estimator for epidemiologists (no wizard required).

American journal of epidemiology
Epidemiologists increasingly use causal inference methods that rely on machine learning, as these approaches can relax unnecessary model specification assumptions. While deriving and studying asymptotic properties of such estimators is a task usually...