AIMC Topic: Neural Networks, Computer

Clear Filters Showing 7711 to 7720 of 31376 articles

Artificial neural network-assisted thermogravimetric analysis of thermal degradation in combustion reactions: A study across diverse organic samples.

Environmental research
During gasification the kinetic and thermodynamic parameter depend on both the feedstock and the process conditions. As a result, one needs to enhance the understanding of how to model numerically these parameters using thermogravimetric analyzer. Co...

A comprehensive and reliable feature attribution method: Double-sided remove and reconstruct (DoRaR).

Neural networks : the official journal of the International Neural Network Society
The limited transparency of the inner decision-making mechanism in deep neural networks (DNN) and other machine learning (ML) models has hindered their application in several domains. In order to tackle this issue, feature attribution methods have be...

Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images.

Cornea
PURPOSE: The aim of this study was to evaluate the efficacy of artificial intelligence-derived morphometric parameters in characterizing Fuchs corneal endothelial dystrophy (FECD) from specular microscopy images.

Detection of caries around restorations on bitewings using deep learning.

Journal of dentistry
OBJECTIVE: Secondary caries lesions adjacent to restorations, a leading cause of restoration failure, require accurate diagnostic methods to ensure an optimal treatment outcome. Traditional diagnostic strategies rely on visual inspection complemented...

Transforming clinical cardiology through neural networks and deep learning: A guide for clinicians.

Current problems in cardiology
The rapid evolution of neural networks and deep learning has revolutionized various fields, with clinical cardiology being no exception. As traditional methods in cardiology encounter limitations, the integration of advanced computational techniques ...

Improving abdominal image segmentation with overcomplete shape priors.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The extraction of abdominal structures using deep learning has recently experienced a widespread interest in medical image analysis. Automatic abdominal organ and vessel segmentation is highly desirable to guide clinicians in computer-assisted diagno...

Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture.

Cell reports. Medicine
Federated learning (FL) is a distributed machine learning framework that is gaining traction in view of increasing health data privacy protection needs. By conducting a systematic review of FL applications in healthcare, we identify relevant articles...

Automated detection of fatal cerebral haemorrhage in postmortem CT data.

International journal of legal medicine
During the last years, the detection of different causes of death based on postmortem imaging findings became more and more relevant. Especially postmortem computed tomography (PMCT) as a non-invasive, relatively cheap, and fast technique is progress...

ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation.

Medical & biological engineering & computing
Early intervention in tumors can greatly improve human survival rates. With the development of deep learning technology, automatic image segmentation has taken a prominent role in the field of medical image analysis. Manually segmenting kidneys on CT...

One-step Bayesian example-dependent cost classification: The OsC-MLP method.

Neural networks : the official journal of the International Neural Network Society
Example-dependent cost classification problems are those where the decision costs depend not only on the true and the attributed classes but also on the sample features. Discriminative algorithms that carry out such classification tasks must take thi...