Advances in Surface-Wave Analysis Using Single Sensor Seismic Data and Deep Neural Network Algorithm for Near Surface Characterization

2019 ◽  
Author(s):  
Taqi Yousuf Alyousuf ◽  
Daniele Colombo
Keyword(s):  
Neural Network ◽  
Surface Wave ◽  
Seismic Data ◽  
Surface Characterization ◽  
Deep Neural Network ◽  
Wave Analysis ◽  
Near Surface ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Single Sensor

scholarly journals MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

2021 ◽  
Author(s):  
Liangrui Pan ◽  
boya ji ◽  
Xiaoqi wang ◽  
shaoliang peng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Test Accuracy ◽  
Learning Networks ◽  
X Ray ◽  
Network Algorithm ◽  
Life Saving ◽  
Chest X Ray ◽  
Neural Network Algorithm

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

scholarly journals Analysis Model of the Impact of Refined Intervention in Operating Room on Patients’ Recovery Quality and Complications after Thoracic Surgery Based on Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jun He ◽  
Jing Wen
Keyword(s):  
Neural Network ◽  
Thoracic Surgery ◽  
Operating Room ◽  
Test Data ◽  
Deep Neural Network ◽  
Quality Of Recovery ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Room Nursing

To improve the nursing effect in patients after thoracic surgery, this paper proposes a refined intervention method in the operating room based on traditional operating room nursing and applies this method to the nursing of patients after thoracic surgery. Moreover, this paper improves the traditional neural network algorithm and uses the deep neural network algorithm to process test data. In addition, it includes patients accepted by the hospital as samples for test analysis and formulates detailed intervention methods for the operating room. Finally, this paper collects the corresponding test data by setting up test and control groups and visually displays the data using mathematical statistics. The statistical parameters of the experiment in this paper include the quality of recovery, complications, satisfaction score, and recovery effect. The comparative test shows that the refined intervention in the operating room based on the neural network proposed in this paper can achieve a certain effect in the postoperative nursing of thoracic surgery, effectively promote the quality of recovery, and reduce the possibility of complications.

Crop Yield Forecast at Field Scale Using Deep Neural Network Algorithm

2021 ◽  
Author(s):  
Mehdi Hosseini ◽  
Inbal Becker-Reshef ◽  
Ritvik Sahajpal ◽  
Lucas Fontana ◽  
Pedro Lafluf ◽  
...  
Keyword(s):  
Neural Network ◽  
Crop Yield ◽  
Deep Neural Network ◽  
Field Scale ◽  
Network Algorithm ◽  
Yield Forecast ◽  
Neural Network Algorithm

scholarly journals A New Iterative Deep Neural Network Algorithm to Significantly Improve Image Segmentation

2016 ◽  
Vol 110 (3) ◽  
pp. 331a
Author(s):  
Xundong Wu ◽  
Yong Wu ◽  
Riccardo Olcese ◽  
Ligia Toro ◽  
Enrico Stefani
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Deep Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm
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