Results 41 to 50 of about 552,282 (301)

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

, 2018
We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4x4 Ising model. Using its success at this task, we motivate the study of the larger 8x8 Ising model, showing that the deep neural ...
Mills, Kyle, Tamblyn, Isaac
core   +1 more source

ICU‐EEG Pattern Detection by a Convolutional Neural Network

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Patients in the intensive care unit (ICU) often require continuous EEG (cEEG) monitoring due to the high risk of seizures and rhythmic and periodic patterns (RPPs). However, interpreting cEEG in real time is resource‐intensive and heavily relies on specialized expertise, which is not always available.
Giulio Degano, Hervé Quintard, Andreas Kleinschmidt, Nikita Francini, Oana E. Sarbu, Pia De Stefano   +5 more
wiley   +1 more source

Application of Deep Learning in the Prediction of Benign and Malignant Thyroid Nodules on Ultrasound Images

IEEE Access, 2020
In this paper, ultrasound imaging of benign and malignant thyroid nodules to predict the depth of the learning algorithm, built on circulation volume product thyroid ultrasound image neural network forecasting model.
Yinghui Lu, Yi Yang, Wan Chen
doaj   +1 more source

Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

IEEE Access, 2021
A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design ...
Kaisheng Liao, Yaodong Zhao, Jie Gu, Yaping Zhang, Yi Zhong   +4 more
doaj   +1 more source

Learning to Detect Violent Videos using Convolutional Long Short-Term Memory

, 2017
Developing a technique for the automatic analysis of surveillance videos in order to identify the presence of violence is of broad interest. In this work, we propose a deep neural network for the purpose of recognizing violent videos.
Lanz, Oswald, Sudhakaran, Swathikiran
core   +1 more source

Lesion Location and Functional Connections Reveal Cognitive Impairment Networks in Multiple Sclerosis

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, Paolo Preziosa, Paola Valsasina, Damiano Mistri, Monica Margoni, Federica Esposito, Massimo Filippi, Maria A. Rocca   +7 more
wiley   +1 more source

Research on road extraction of remote sensing image based on convolutional neural network

EURASIP Journal on Image and Video Processing, 2019
Road is an important kind of basic geographic information. Road information extraction plays an important role in traffic management, urban planning, automatic vehicle navigation, and emergency management.
Yuantao Jiang
doaj   +1 more source

Interpretable Convolutional Neural Networks [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
This paper proposes a method to modify traditional convolutional neural networks (CNNs) into interpretable CNNs, in order to clarify knowledge representations in high conv-layers of CNNs. In an interpretable CNN, each filter in a high conv-layer represents a certain object part.
Ying Nian Wu, Quanshi Zhang, Song-Chun Zhu   +2 more
openaire   +3 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, Min‐Hee Lee, Hiroshi Uda, Yoon Ho Hwang, Michael Behen, Aimee Luat, Csaba Juhász, Eishi Asano   +7 more
wiley   +1 more source

Research onconvolutional neural network for reservoir parameter prediction

Tongxin xuebao, 2016
As the branch of artificial intelligence,artificial neural network solved many difficult practical problems in pattern recognition and classification prediction field successfully.However,they cannot learn the feature from networks.In recent years,deep ...
You-xiang DUAN, Gen-tian LI, Qi-feng SUN
doaj   +2 more sources