scholarly journals Research on Community Public Service Information Collaborative Governance Based on Deep Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yajing Liu
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Public Service ◽  
Public Information ◽  
Collaborative Governance ◽  
Learning Model ◽  
Social Risks ◽  
Service Information ◽  
Analysis Theory ◽  
Deep Learning Model

The communities have significantly increased in number and the environment has become complex. There are problems such as poor information collection in community public service information governance, lack of relevant analysis standards and models, and unreliable prediction results. In order to forecast and manage the risk information of the community, this research analyzes the public information of the community through the collaborative deep learning model. First of all, the information characteristic factors are selected that affect social risks based on the correlation analysis theory. Secondly, the convolutional neural network is used in deep learning for simulation of the community risk prediction model. Finally, through the comparative analysis of the model prediction results, it can be concluded that the accuracy rate of the proposed prediction model reaches 92.5%. An effective collaborative deep learning model is used to govern community public service information.

scholarly journals Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Liqun Gao ◽  
Yujia Liu ◽  
Hongwu Zhuang ◽  
Haiyang Wang ◽  
Bin Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Public Opinion ◽  
Prediction Model ◽  
Network Structure ◽  
Early Warning ◽  
Feature Fusion ◽  
Learning Model ◽  
Feature Model ◽  
Underlying Network ◽  
Deep Learning Model

With the rapid popularity of agent technology, a public opinion early warning agent has attracted wide attention. Furthermore, a deep learning model can make the agent more automatic and efficient. Therefore, for the agency of a public opinion early warning task, the deep learning model is very suitable for completing tasks such as popularity prediction or emergency outbreak. In this context, improving the ability to automatically analyze and predict the virality of information cascades is one of the tasks that deep learning model approaches address. However, most of the existing studies sought to address this task by analyzing cascade underlying network structure. Recent studies proposed cascade virality prediction for agnostic-networks (without network structure), but did not consider the fusion of more effective features. In this paper, we propose an innovative cascade virus prediction model named CasWarn. It can be quickly deployed in intelligent agents to effectively predict the virality of public opinion information for different industries. Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.

scholarly journals Apply VGGNet-Based Deep Learning Model of Vibration Data for Prediction Model of Gravity Acceleration Equipment

2020 ◽  
Author(s):  
SeonWoo Lee ◽  
HyeonTak Yu ◽  
HoJun Yang ◽  
InSeo Song ◽  
JaeHeung Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Learning Model ◽  
Learning Models ◽  
Vibration Signals ◽  
Vibration Data ◽  
Amplitude Data ◽  
Feature Based ◽  
Gravity Acceleration ◽  
Deep Learning Model

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hy-pergravity accelerator. The method proposed in this paper was to convert vibration signals to spectograms and perform classification training using a deep learning model. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accel-erometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The data were converted to a two-dimensional spectrogram, and classification training was performed using a deep learning model for four conditions of the equipment: Unbalance, Misalignment, Shaft Rubbing, and Normal. The ex-perimental results showed that the proposed method had a 99.5% F1-Score, which was up to 23% higher than the 76.25% for existing feature-based learning models.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model
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