AIMC Topic: Machine Learning

Clear Filters Showing 21861 to 21870 of 34417 articles

Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.

PloS one
Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescri...

Supervised machine learning for source allocation of per- and polyfluoroalkyl substances (PFAS) in environmental samples.

Chemosphere
Environmental contamination by per- and polyfluoroalkyl substances (PFAS) is widespread, because of both their decades of use, and their persistence in the environment. These factors can make identification of the source of contamination in samples a...

Data science and machine learning in anesthesiology.

Korean journal of anesthesiology
Machine learning (ML) is revolutionizing anesthesiology research. Unlike classical research methods that are largely inference-based, ML is geared more towards making accurate predictions. ML is a field of artificial intelligence concerned with devel...

Learning in the machine: To share or not to share?

Neural networks : the official journal of the International Neural Network Society
Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sh...

Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning.

Sensors (Basel, Switzerland)
Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-co...

Food Liking-Based Diet Quality Indexes (DQI) Generated by Conceptual and Machine Learning Explained Variability in Cardiometabolic Risk Factors in Young Adults.

Nutrients
The overall pattern of a diet (diet quality) is recognized as more important to health and chronic disease risk than single foods or food groups. Indexes of diet quality can be derived theoretically from evidence-based recommendations, empirically fr...

Alzheimer's disease, mild cognitive impairment, and normal aging distinguished by multi-modal parcellation and machine learning.

Scientific reports
A 360-area surface-based cortical parcellation is extended to study mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy control (HC) using the joint human connectome project multi-modal parcellation (JHCPMMP) proposed by us. We ...

IMPContact: An Interhelical Residue Contact Prediction Method.

BioMed research international
As an important category of proteins, alpha-helix transmembrane proteins (TMPs) play an important role in various biological activities. Because the solved αTMP structures are inadequate, predicting the residue contacts among the transmembrane segmen...

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015.

Scientific reports
Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric histo...