AIMC Topic: Machine Learning

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Confirm or refute?: A comparative study on citation sentiment classification in clinical research publications.

Journal of biomedical informatics
Quantifying scientific impact of researchers and journals relies largely on citation counts, despite the acknowledged limitations of this approach. The need for more suitable alternatives has prompted research into developing advanced metrics, such a...

Compact Hardware Synthesis of Stochastic Spiking Neural Networks.

International journal of neural systems
Spiking neural networks (SNN) are able to emulate real neural behavior with high confidence due to their bio-inspired nature. Many designs have been proposed for the implementation of SNN in hardware, although the realization of high-density and biol...

Monitor-Based Spiking Recurrent Network for the Representation of Complex Dynamic Patterns.

International journal of neural systems
Neural networks are powerful computation tools for mimicking the human brain to solve realistic problems. Since spiking neural networks are a type of brain-inspired network, called the novel spiking system, Monitor-based Spiking Recurrent network (Mb...

Machine learning identifies "rsfMRI epilepsy networks" in temporal lobe epilepsy.

European radiology
OBJECTIVES: Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning met...

Proximal detection of guide wire perforation using feature extraction from bispectral audio signal analysis combined with machine learning.

Computers in biology and medicine
Artery perforation during a vascular catheterization procedure is a potentially life threatening event. It is of particular importance for the surgeons to be aware of hidden or non-obvious events. To minimize the impact it is crucial for the surgeon ...

Machine Learning in Nuclear Medicine: Part 1-Introduction.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
This article, the first in a 2-part series, provides an introduction to machine learning (ML) in a nuclear medicine context. This part addresses the history of ML and describes common algorithms, with illustrations of when they can be helpful in nucl...

DG-GL: Differential geometry-based geometric learning of molecular datasets.

International journal for numerical methods in biomedical engineering
MOTIVATION: Despite its great success in various physical modeling, differential geometry (DG) has rarely been devised as a versatile tool for analyzing large, diverse, and complex molecular and biomolecular datasets because of the limited understand...

Machine learning for MEG during speech tasks.

Scientific reports
We consider whether a deep neural network trained with raw MEG data can be used to predict the age of children performing a verb-generation task, a monosyllable speech-elicitation task, and a multi-syllabic speech-elicitation task. Furthermore, we ar...

Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images.

Scientific reports
Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classificat...

Predicting centre of mass horizontal speed in low to severe swimming intensities with linear and non-linear models.

Journal of sports sciences
We aimed to compare multilayer perceptron (MLP) neural networks, radial basis function neural networks (RBF) and linear models (LM) accuracy to predict the centre of mass (CM) horizontal speed at low-moderate, heavy and severe swimming intensities us...