A deep learning based dynamic COD prediction model for urban sewage

2019 ◽  
Vol 5 (12) ◽  
pp. 2210-2218
Author(s):  
Zifei Wang ◽  
Yi Man ◽  
Yusha Hu ◽  
Jigeng Li ◽  
Mengna Hong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Urban Sewage ◽  
Model Based ◽  
Deep Learning Algorithm ◽  
Aeration Control

An influent COD prediction model based on the CNN-LSTM deep learning algorithm is proposed as the basis of aeration control in WWTPs.

scholarly journals Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yifan Jian ◽  
Xianguo Qing ◽  
Yang Zhao ◽  
Liang He ◽  
Xiao Qi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Mechanism Model ◽  
Model Based ◽  
Deep Learning Algorithm ◽  
Stacked Autoencoders ◽  
Different Types ◽  
Operation Status ◽  
Types Of Faults

The coal mill is one of the important auxiliary engines in the coal-fired power station. Its operation status is directly related to the safe and steady operation of the units. In this paper, a model-based deep learning algorithm for fault diagnosis is proposed to effectively detect the operation state of coal mills. Based on the system mechanism model of coal mills, massive fault data are obtained by analyzing and simulating the different types of faults. Then, stacked autoencoders (SAEs) are established by combining the said data with the deep learning algorithm. The SAE model is trained by the fault data, which provide it with the learning and identification capability of the characteristics of faults. According to the simulation results, the accuracy of fault diagnosis of coal mills based on SAE is high at 98.97%. Finally, the proposed SAEs can well detect the fault in coal mills and generate the warnings in advance.

scholarly journals Construction of cardiac arrhythmia prediction model using deep learning and gradient boosting

2021 ◽  
Vol 13 (3) ◽  
pp. 114-119
Author(s):  
Dhanar Bintang Pratama ◽  
Favian Dewanta ◽  
Syamsul Rizal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Medical Staff ◽  
Heart Attack ◽  
Learning Algorithm ◽  
Gradient Boosting ◽  
Ecg Signal ◽  
Deep Learning Algorithm ◽  
Boosting Algorithm

Arrhythmia is a condition in which the rhythm of heartbeat becomes irregular. This condition in extreme cases can lead to fatal heart attack accidents. In order to reduce heart attack risk, appropriate early treatments should be conducted right after getting results of Arrhythmia condition, which is generated by electrocardiography ECG tools. However, reading ECG results should be done by qualified medical staff in order to diagnose the existence of arrhythmia accurately. This paper proposes a deep learning algorithm method to classify and detect the existence of arrhythmia from ECG reading. Our proposed method relies on Convolutional Neural Network (CNN) to extract feature from a single lead ECG signal and also Gradient Boosting algorithm to predict the final outcome of single lead ECG reading. This method achieved the accuracy of 96.18% and minimized the number of parameters used in CNN Layer.

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 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 Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence

2020 ◽  
Vol 12 (15) ◽  
pp. 6143
Author(s):  
Min-Seung Kim ◽  
Jeong-Hee Lee ◽  
Yong-Ju Jang ◽  
Chan-Ho Lee ◽  
Ji-Hye Choi ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Deep Learning ◽  
Prediction Model ◽  
National Health ◽  
Respiratory Diseases ◽  
Learning Algorithm ◽  
Atmospheric Pollutants ◽  
Atmospheric Environment ◽  
Deep Learning Algorithm

Due to recent advancements in industrialization, climate change and overpopulation, air pollution has become an issue of global concern and air quality is being highlighted as a social issue. Public interest and concern over respiratory health are increasing in terms of a high reliability of a healthy life or the social sustainability of human beings. Air pollution can have various adverse or deleterious effects on human health. Respiratory diseases such as asthma, the subject of this study, are especially regarded as ‘directly affected’ by air pollution. Since such pollution is derived from the combined effects of atmospheric pollutants and meteorological environmental factors, and it is not easy to estimate its influence on feasible respiratory diseases in various atmospheric environments. Previous studies have used clinical and cohort data based on relatively a small number of samples to determine how atmospheric pollutants affect diseases such as asthma. This has significant limitations in that each sample of the collections is likely to produce inconsistent results and it is difficult to attempt the experiments and studies other than by those in the medical profession. This study mainly focuses on predicting the actual asthmatic occurrence while utilizing and analyzing the data on both the atmospheric and meteorological environment officially released by the government. We used one of the advanced analytic models, often referred to as the vector autoregressive model (VAR), which traditionally has an advantage in multivariate time-series analysis to verify that each variable has a significant causal effect on the asthmatic occurrence. Next, the VAR model was applied to a deep learning algorithm to find a prediction model optimized for the prediction of asthmatic occurrence. The average error rate of the hybrid deep neural network (DNN) model was numerically verified to be about 8.17%, indicating better performance than other time-series algorithms. The proposed model can help streamline the national health and medical insurance system and health budget management in South Korea much more effectively. It can also provide efficiency in the deployment and management of the supply and demand of medical personnel in hospitals. In addition, it can contribute to the promotion of national health, enabling advance alerts of the risk of outbreaks by the atmospheric environment for chronic asthma patients. Furthermore, the theoretical methodologies, experimental results and implications of this study will be able to contribute to our current issues of global change and development in that the meteorological and environmental data-driven, deep-learning prediction model proposed hereby would put forward a macroscopic directionality which leads to sustainable public health and sustainability science.

A Two-Level Computation Model Based on Deep Learning Algorithm for Identification of piRNA and Their Functions via Chou’s 5-Steps Rule

2019 ◽  
Vol 26 (2) ◽  
pp. 795-809 ◽  
Author(s):  
Salman Khan ◽  
Mukhtaj Khan ◽  
Nadeem Iqbal ◽  
Tahir Hussain ◽  
Sher Afzal Khan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Computation Model ◽  
Model Based ◽  
Deep Learning Algorithm
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