Results 41 to 50 of about 525,499 (288)
Geometric Deep Learning for Protein–Protein Interaction Predictions
IEEE Access, 2022 This work introduces novel approaches, based on geometrical deep learning, for predicting protein–protein interactions. A dataset containing both interacting and non-interacting proteins is selected from the Negatome Database.
Gabriel St-Pierre Lemieux +3 more
doaj +1 more source
Analysis and training of a traffic sign recognition neural network model
Вестник Дагестанского государственного технического университета: Технические науки, 2023 Objective. The purpose of the research is to develop and train a neural network model based on convolutional neural networks for effective recognition of road signs in images.Method.
A. U. Mentsiev +2 more
doaj +1 more source
Sparse 3D convolutional neural networks
, 2015 We have implemented a convolutional neural network designed for processing sparse three-dimensional input data. The world we live in is three dimensional so there are a large number of potential applications including 3D object recognition and analysis ...
Graham, Ben
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Cognitive impairment, fatigue, and depression are common in multiple sclerosis (MS), potentially due to disruption of regional functional connectivity caused by white matter (WM) lesions. We explored whether WM lesions functionally connected to specific brain regions contribute to these MS‐related manifestations.
Alessandro Franceschini +7 more
wiley +1 more source
Computation, 2023 Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition.
Mohammad Mustafa Taye
doaj +1 more source
, 2019 Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution.
Ashukha, Arsenii +4 more
core +1 more source
Kernel Graph Convolutional Neural Networks
, 2018 Graph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM classifier is trained based on the features defined implicitly by this kernel.
Meladianos, Polykarpos +4 more
core +2 more sources
Diffusion Tractography Biomarker for Epilepsy Severity in Children With Drug‐Resistant Epilepsy
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To develop a novel deep‐learning model of clinical DWI tractography that can accurately predict the general assessment of epilepsy severity (GASE) in pediatric drug‐resistant epilepsy (DRE) and test if it can screen diverse neurocognitive impairments identified through neuropsychological assessments.
Jeong‐Won Jeong +7 more
wiley +1 more source
Efficient Deep Feature Learning and Extraction via StochasticNets
, 2015 Deep neural networks are a powerful tool for feature learning and extraction given their ability to model high-level abstractions in highly complex data.
Fieguth, Paul +3 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah +7 more
wiley +1 more source