AIMC Topic: Deep Learning

Clear Filters Showing 22741 to 22750 of 28423 articles

Deep Neural Networks as Scientific Models.

Trends in cognitive sciences
Artificial deep neural networks (DNNs) initially inspired by the brain enable computers to solve cognitive tasks at which humans excel. In the absence of explanations for such cognitive phenomena, in turn cognitive scientists have started using DNNs ...

A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals.

International journal of environmental research and public health
Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environme...

Rehab-Net: Deep Learning Framework for Arm Movement Classification Using Wearable Sensors for Stroke Rehabilitation.

IEEE transactions on bio-medical engineering
In this paper, we present a deep learning framework "Rehab-Net" for effectively classifying three upper limb movements of the human arm, involving extension, flexion, and rotation of the forearm, which, over the time, could provide a measure of rehab...

Medical image classification using synergic deep learning.

Medical image analysis
The classification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Although deep learning has shown proven advantages over traditional methods that rely on the handcrafted features, it remains c...

DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins.

BMC bioinformatics
BACKGROUND: Protein ubiquitination occurs when the ubiquitin protein binds to a target protein residue of lysine (K), and it is an important regulator of many cellular functions, such as signal transduction, cell division, and immune reactions, in eu...

Training improvements for ultrasound beamforming with deep neural networks.

Physics in medicine and biology
This paper investigates practical considerations of training ultrasound deep neural network (DNN) beamformers. First, we studied training DNNs using the combination of multiple point target responses instead of single point target responses. Next, we...

Phonocardiogram classification using deep neural networks and weighted probability comparisons.

Journal of medical engineering & technology
Cardiac auscultation is one of the most conventional approaches for the initial assessment of heart disease, however the technique is highly user-dependent and with low repeatability. Several computational approaches based on the analysis of the phon...

Deep neural networks in psychiatry.

Molecular psychiatry
Machine and deep learning methods, today's core of artificial intelligence, have been applied with increasing success and impact in many commercial and research settings. They are powerful tools for large scale data analysis, prediction and classific...

Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy.

Laboratory investigation; a journal of technical methods and pathology
Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessm...

Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity.

Journal of chemical information and modeling
Blockade of the human ether-à-go-go-related gene (hERG) channel by small molecules induces the prolongation of the QT interval which leads to fatal cardiotoxicity and accounts for the withdrawal or severe restrictions on the use of many approved drug...