Results 51 to 60 of about 298,507 (214)
Monkeypox detection using deep neural networks
BMC Infectious Diseases, 2023 Abstract Background In May 2022, the World Health Organization (WHO) European Region announced an atypical Monkeypox epidemic in response to reports of numerous cases in some member countries unrelated to those where the illness is endemic. This issue has raised concerns about the widespread nature of this disease around
Azar, Amir Sorayaie +5 more
openaire +5 more sources
Fault Diagnosis Method for Aerial Sensor Based on Deep Learning [PDF]
Jisuanji gongcheng, 2017 To solve the problem of over-fitting and limited generalization ability during sensor fault diagnosis by traditional neural network,a fault diagnosis method for aerial sensor based on deep belief network observer is proposed.Shallow layer neural network ...
ZHENG Xiaofei,GUO Chuang,YAO Bin,FENG Huaxin
doaj +1 more source
, 2017 Deep Neural Networks (DNNs) are universal function approximators providing state-of- the-art solutions on wide range of applications. Common perceptual tasks such as speech recognition, image classification, and object tracking are now commonly tackled via DNNs.
Balestriero, Randall, Baraniuk, Richard
openaire +2 more sources
Real-Time Phase-Only Nulling Based on Deep Neural Network With Robustness
IEEE Access, 2019 Phase-only nulling under sidelobe and mainlobe constraints is a problem of interest in array synthesis which is a nonlinear problem without analytical solution.
Zhonghui Zhao +3 more
doaj +1 more source
Deep Neural Networks and PIDE Discretizations
SIAM Journal on Mathematics of Data Science, 2022 In this paper, we propose neural networks that tackle the problems of stability and field-of-view of a Convolutional Neural Network (CNN). As an alternative to increasing the network's depth or width to improve performance, we propose integral-based spatially nonlocal operators which are related to global weighted Laplacian, fractional Laplacian and ...
Bastian Bohn +2 more
openaire +3 more sources
A Solution Method for Differential Equations Based on Taylor PINN
IEEE Access, 2023 Based on deep neural network, elliptic partial differential equations in complex regions are solved. Accurate and effective strategies and numerical methods for elliptic partial differential equations are proposed by implementing deep feedforward ...
Yajuan Zhang +3 more
doaj +1 more source
Deep neural networks for ultrasound beamforming [PDF]
2017 IEEE International Ultrasonics Symposium (IUS), 2017 We investigate the use of deep neural networks (DNNs) for suppressing off-axis scattering in ultrasound channel data. Our implementation operates in the frequency domain via the short-time Fourier transform. The inputs to the DNN consisted of the separated real and imaginary components (i.e.
Adam C. Luchies, Brett C. Byram
openaire +2 more sources
Convergence of deep convolutional neural networks
Neural Networks, 2022 arXiv admin note: text overlap with arXiv:2107 ...
Yuesheng Xu, Haizhang Zhang
openaire +4 more sources
Memory management in deep learning:a survey
大数据, 2020 In recent years,deep learning has achieved great success in many fields.As the deep neural network develops towards a deeper and wider direction,the training and inference of a deep neural network face huge memory pressure.The limited memory space of ...
Weiliang MA +4 more
doaj
Deep neural network model for group activity recognition using contextual relationship
Engineering Science and Technology, an International Journal, 2019 In this paper, we present contextual relationship-based learning model using deep neural network for recognizing the activities performed by a group of people in a video sequence.
S.A. Vahora, N.C. Chauhan
doaj +1 more source