Feature extraction using a deep learning algorithm for uncertainty quantification of channelized reservoirs

2018 ◽  
Vol 171 ◽  
pp. 1007-1022 ◽  
Author(s):  
Kyungbook Lee ◽  
Jungtek Lim ◽  
Seongin Ahn ◽  
Jaejun Kim
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Uncertainty Quantification ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Channelized Reservoirs

scholarly journals Deep learning for constructing microblog behavior representation to identify social media user's personality

2016 ◽  
Author(s):  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Feature Learning ◽  
Extraction Methods ◽  
Deep Learning Algorithm ◽  
Personality Prediction ◽  
Behavior Description

Due to the rapid development of information technology, Internet has become part of everyday life gradually. People would like to communicate with friends to share their opinions on social networks. The diverse social network behavior is an ideal users' personality traits reflection. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic. Although they work fairly well, but it is hard to extend and maintain. In this paper, for personality prediction, we utilize deep learning algorithm to build feature learning model, which could unsupervised extract Linguistic Representation Feature Vector (LRFV) from text published on Sina Micro-blog actively. Compared with other feature extraction methods, LRFV, as an abstract representation of Micro-blog content, could describe use's semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of prediction model respectively. The results show that LRFV performs more excellently in micro-blog behavior description and improve the performance of personality prediction model.

scholarly journals Scene Matching Method for Children’s Psychological Distress Based on Deep Learning Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Junli Su
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Psychological Distress ◽  
Learning Algorithm ◽  
Matching Method ◽  
Scene Matching ◽  
Deep Learning Algorithm ◽  
Person Perspective ◽  
Huge Data ◽  
Study Results

In the process of children’s psychological development, various levels of psychological distress often occur, such as attention problems, emotional problems, adaptation problems, language problems, and motor coordination problems; these problems have seriously affected children’s healthy growth. Scene matching in the treatment of psychological distress can prompt children to change from a third-person perspective to a first-person perspective and shorten the distance between scene contents and child’s perceptual experience. As a part of machine learning, deep learning can perform mapping transformations in huge data, process huge data with the help of complex models, and extract multilayer features of scene information. Based on the summary and analysis of previous research works, this paper expounded the research status and significance of the scene matching method for children’s psychological distress, elaborated the development background, current status, and future challenges of deep learning algorithm, introduced the methods and principles of depth spatiotemporal feature extraction algorithm and dynamic scene understanding algorithm, constructed a scene matching model for children’s psychological distress based on deep learning algorithm, analyzed the scene feature extraction and matching function construction of children’s psychological distress, proposed a scene matching method for children’s psychological distress based on deep learning algorithm, performed scene feature matching and information processing of children’s psychological distress, and finally conduced a simulation experiment and analyzed its results. The results show that the deep learning algorithm can have a deep and abstract mining on the characteristics of children’s psychological distress scenes and obtain a large amount of more representative characteristic information through training on large-scale data, thereby improving the accuracy of classification and matching of children’s psychological distress scenes. The study results of this paper provide a reference for further researches on the scene matching method for children’s psychological distress based on deep learning algorithm.

scholarly journals Deep learning for constructing microblog behavior representation to identify social media user's personality

2016 ◽  
Author(s):  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Feature Learning ◽  
Extraction Methods ◽  
Deep Learning Algorithm ◽  
Personality Prediction ◽  
Behavior Description

Due to the rapid development of information technology, Internet has become part of everyday life gradually. People would like to communicate with friends to share their opinions on social networks. The diverse social network behavior is an ideal users' personality traits reflection. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic. Although they work fairly well, but it is hard to extend and maintain. In this paper, for personality prediction, we utilize deep learning algorithm to build feature learning model, which could unsupervised extract Linguistic Representation Feature Vector (LRFV) from text published on Sina Micro-blog actively. Compared with other feature extraction methods, LRFV, as an abstract representation of Micro-blog content, could describe use's semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of prediction model respectively. The results show that LRFV performs more excellently in micro-blog behavior description and improve the performance of personality prediction model.

A deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging

2021 ◽  
pp. 1-14
Author(s):  
Dechun Zhao ◽  
Renpin Jiang ◽  
Mingyang Feng ◽  
Jiaxin Yang ◽  
Yi Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Eye Movement ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Rapid Eye Movement ◽  
Sleep Staging ◽  
Deep Learning Algorithm ◽  
Feature Extraction And Selection ◽  
Long Short Term Memory

BACKGROUND: Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection. OBJECTIVE: This paper proposed a deep learning algorithm without feature extraction based on one-dimensional convolutional neural network and long short-term memory. METHODS: The algorithm can automatically divide sleep into 5 phases including awake period, non-rapid eye movement sleep period (N1 ∼ N3) and rapid eye movement using the electroencephalogram signals. The raw signal was processed by the wavelet transform. Then, the processed signal was directly input into the deep learning algorithm to obtain the staging result. RESULTS: The accuracy of staging is 93.47% using the Fpz-Cz electroencephalogram signal. When using the Fpz-Cz and electroencephalogram signal, the algorithm can obtain the highest accuracy of 94.15%. CONCLUSION: These results show that this algorithm is suitable for different physiological signals and can realize end-to-end automatic sleep staging without any manual feature extraction.

Sign in / Sign up

Export Citation Format

Share Document