A deep learning algorithm for multi-source data fusion to predict water quality of urban sewer networks

2021 ◽  
pp. 128533
Author(s):  
Yiqi Jiang ◽  
Chaolin Li ◽  
Lu Sun ◽  
Dong Guo ◽  
Yituo Zhang ◽  
...  
Keyword(s):  
Water Quality ◽  
Deep Learning ◽  
Data Fusion ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Source Data ◽  
Sewer Networks

scholarly journals Applying deep learning for adverse pregnancy outcome detection with pre-pregnancy health data

2018 ◽  
Vol 189 ◽  
pp. 10014 ◽  
Author(s):  
Yu Mu ◽  
Kai Feng ◽  
Ying Yang ◽  
Jingyuan Wang
Keyword(s):  
Deep Learning ◽  
Pregnancy Outcome ◽  
Pregnancy Outcomes ◽  
Learning Algorithm ◽  
Adverse Pregnancy Outcome ◽  
Health Data ◽  
Adverse Pregnancy Outcomes ◽  
Deep Learning Algorithm ◽  
Adverse Pregnancy

Adverse pregnancy outcomes can bring enormous losses to both families and the society. Thus, pregnancy outcome prediction stays a crucial research topic as it may help reducing birth defect and improving the quality of population. However, recent advances in adverse pregnancy outcome detection are driven by data collected after mothers having been pregnant. In this situation, if a bad pregnancy outcome is diagnosed, the parents will suffer both physically and emotionally. In this paper, we develop a deep learning algorithm which is able to detect and classify adverse pregnancy outcomes before parents getting pregnant. We train a multi-layer neural network by using a dataset of 75542 couples’ multidimension pre-pregnancy health data. Our model outperforms some of algorithms in accuracy, recall and F1 score.

scholarly journals Deep Learning Approach for COVID-19 Diagnosis Using X-Ray Images

2020 ◽  
Author(s):  
Muntasir Al-Asfoor
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Normal Subjects ◽  
Learning Approach ◽  
Computer Aided Detection ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
Chest X Ray ◽  
The Times

Abstract During the times of pandemics, faster diagnosis plays a key role in the response efforts to contain the disease as well as reducing its spread. Computer-aided detection would save time and increase the quality of diagnosis in comparison with manual human diagnosis. Artificial Intelligence (AI) through deep learning is considered as a reliable method to design such systems. In this research paper, an AI based diagnosis approach has been suggested to tackle the COVID-19 pandemic. The proposed system employs a deep learning algorithm on chest x-ray images to detect the infected subjects. An enhanced Convolutional Neural Network (CNN) architecture has been designed with 22 layers which is then trained over a chest x-ray dataset. More after, a classification component has been introduced to classify the x-ray images into two categories (Covid-19 and not Covid-19) of infection. The system has been evaluated through a series of observations and experimentation. The experimental results have shown a promising performance in terms of accuracy. The system has diagnosed Covid-19 with accuracy of 95.7% and normal subjects with accuracy of 93.1 while it showed 96.7 accuracy on Pneumonia.

Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (FOG) of sewer networks in urban commercial areas

2021 ◽  
pp. 117797
Author(s):  
Yiqi Jiang ◽  
Chaolin Li ◽  
Yituo Zhang ◽  
Ruobin Zhao ◽  
Kefen Yan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Data Driven ◽  
Oil And Grease ◽  
Deep Learning Algorithm ◽  
Sewer Networks

scholarly journals A deep learning algorithm for multiple observation data fusion in sonar system

2018 ◽  
Vol 48 (12) ◽  
pp. 1614-1621
Author(s):  
Qihu LI ◽  
Chonghua WEI ◽  
Shanhua XUE
Keyword(s):  
Deep Learning ◽  
Data Fusion ◽  
Learning Algorithm ◽  
Observation Data ◽  
Deep Learning Algorithm ◽  
Sonar System
Sign in / Sign up

Export Citation Format

Share Document