AIMC Topic: Neural Networks, Computer

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Testing hypotheses about the underlying deficit of apraxia of speech through computational neural modelling with the DIVA model.

International journal of speech-language pathology
A recent behavioural experiment featuring a noise masking paradigm suggests that Apraxia of Speech (AOS) reflects a disruption of feedforward control, whereas feedback control is spared and plays a more prominent role in achieving and maintaining se...

[Basis and perspectives of artificial intelligence in radiation therapy].

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Artificial intelligence is a highly polysemic term. In computer science, with the objective of being able to solve totally new problems in new contexts, artificial intelligence includes connectionism (neural networks) for learning and logics for reas...

A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG.

Computational and mathematical methods in medicine
Motion artifacts and myoelectrical noise are common issues complicating the collection and processing of dynamic electrocardiogram (ECG) signals. Recent signal quality studies have utilized a binary classification metric in which ECG samples are dete...

Cascade and Fusion of Multitask Convolutional Neural Networks for Detection of Thyroid Nodules in Contrast-Enhanced CT.

Computational intelligence and neuroscience
With the development of computed tomography (CT), the contrast-enhanced CT scan is widely used in the diagnosis of thyroid nodules. However, due to the artifacts and high complexity of thyroid CT images, traditional machine learning has difficulty in...

A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species.

Computational biology and chemistry
Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional methods involved cost-intensive and time-consuming molecular biology techniques but they proved to be ...

A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.

Scandinavian cardiovascular journal : SCJ
In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predictive value of echocardiographic strain features alone and in combination with other features to differ...

A broad class of discrete-time hypercomplex-valued Hopfield neural networks.

Neural networks : the official journal of the International Neural Network Society
In this paper, we address the stability of a broad class of discrete-time hypercomplex-valued Hopfield-type neural networks. To ensure the neural networks belonging to this class always settle down at a stationary state, we introduce novel hypercompl...

A smoothing neural network for minimization l-l in sparse signal reconstruction with measurement noises.

Neural networks : the official journal of the International Neural Network Society
This paper investigates a smoothing neural network (SNN) to solve a robust sparse signal reconstruction in compressed sensing (CS), where the objective function is nonsmooth l-norm and the feasible set satisfies an inequality of l-norm 2≥p...

Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study.

European radiology
OBJECTIVE: To classify motion-induced blurred images of calcified coronary plaques so as to correct coronary calcium scores on nontriggered chest CT, using a deep convolutional neural network (CNN) trained by images of motion artifacts.

The application of artificial neural networks in metabolomics: a historical perspective.

Metabolomics : Official journal of the Metabolomic Society
BACKGROUND: Metabolomics data, with its complex covariance structure, is typically modelled by projection-based machine learning (ML) methods such as partial least squares (PLS) regression, which project data into a latent structure. Biological data ...