scholarly journals Monitoring and Recognizing Enterprise Public Opinion from High-Risk Users Based on User Portrait and Random Forest Algorithm

Axioms ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 106
Author(s):  
Tinggui Chen ◽  
Xiaohua Yin ◽  
Lijuan Peng ◽  
Jingtao Rong ◽  
Jianjun Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Public Opinion ◽  
High Risk ◽  
Random Forest ◽  
Recognition Accuracy ◽  
Rapid Development ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Random Forest Algorithm ◽  
Negative Comments

With the rapid development of “We media” technology, netizens can freely express their opinions regarding enterprise products on a network platform. Consequently, online public opinion about enterprises has become a prominent issue. Negative comments posted by some netizens may trigger negative public opinion, which can have a significant impact on an enterprise’s image. From the perspective of helping enterprises deal with negative public opinion, this paper combines user portrait technology and a random forest algorithm to help enterprises identify high-risk users who have posted negative comments and thus may trigger negative public opinion. In this way, enterprises can monitor the public opinion of high-risk users to prevent negative public opinion events. Firstly, we crawled the information of users participating in discussions of product experience, and we constructed a portrait of enterprise public opinion users. Then, the characteristics of the portraits were quantified into indicators such as the user’s activity, the user’s influence, and the user’s emotional tendency, and the indicators were sorted. According to the order of the indicators, the users were divided into high-risk, moderate-risk, and low-risk categories. Next, a supervised high-risk user identification model for this classification was established, based on a random forest algorithm. In turn, the trained random forest identifier can be used to predict whether the authors of newly published public opinion information are high-risk users. Finally, a back propagation neural network algorithm was used to identify users and compared with the results of model recognition in this paper. The results showed that the average recognition accuracy of the back propagation neural network is only 72.33%, while the average recognition accuracy of the model constructed in this paper is as high as 98.49%, which verifies the feasibility and accuracy of the proposed random forest recognition method.

The use of artificial neural networks for modelling rumen fill

2020 ◽  
Author(s):  
Rasheed Adekunle Adebayo ◽  
Mehluli Moyo ◽  
Evariste Bosco Gueguim-Kana ◽  
Ignatius Verla Nsahlai
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Random Forest Model ◽  
Ann Model ◽  
Forest Model ◽  
Artificial Neural ◽  
Cattle And Sheep

Artificial Neural Network (ANN) and Random Forest models for predicting rumen fill of cattle and sheep were developed. Data on rumen fill were collected from studies that reported body weights, measured rumen fill and stated diets fed to animals. Animal and feed factors that affected rumen fill were identified from each study and used to create a dataset. These factors were used as input variables for predicting the weight of rumen fill. For ANN modelling, a three-layer Levenberg-Marquardt Back Propagation Neural Network was adopted and achieved 96% accuracy in prediction of the weight of rumen fill. The precision of the ANN model’s prediction of rumen fill was higher for cattle (80%) than sheep (56%). On validation, the ANN model achieved 95% accuracy in prediction of the weight of rumen fill. A Random Forest model was trained using a binary tree-based machine-learning algorithm and achieved 87% accuracy in prediction of rumen fill. The Random Forest model achieved 16% (cattle) and 57% (sheep) accuracy in validation of the prediction of rumen fill. In conclusion, the ANN model gave better predictions of rumen fill compared to the Random Forest model and should be used in predicting rumen fill of cattle and sheep.

Offline Signature Recognition using Back Propagation Neural Network

2016 ◽  
Vol 4 (3) ◽  
pp. 678
Author(s):  
Asyrofa Rahmi ◽  
Vivi Nur Wijayaningrum ◽  
Wayan Firdaus Mahmudy ◽  
Andi Maulidinnawati A. K. Parewe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recognition Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Learning Rate ◽  
Signature Recognition ◽  
Artificial Neural Network Ann ◽  
Number Of Iterations

The signature recognition is a difficult process as it requires several phases. A failure in a phase will significantly reduce the recognition accuracy. Artificial Neural Network (ANN) believed to be used to assist in the recognition or classification of the signature. In this study, the ANN algorithm used is Back Propagation. A mechanism to adaptively adjust the learning rate is developed to improve the system accuracy. The purpose of this study is to conduct the recognition of a number of signatures so that can be known whether the recognition which is done by using the Back Propagation is appropriate or not. The testing results performed by using learning rate of 0.64, the number of iterations is 100, and produces an accuracy value of 63%.

scholarly journals Random Forest Feature Selection and Back Propagation Neural Network to Detect Fire Using Video

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Jin-Xing Liang ◽  
Jian-Fu Zhao ◽  
Ning Sun ◽  
Bao-Jun Shi
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Random Forest ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Fire Detection ◽  
Back Propagation Neural Network ◽  
Detection Algorithm ◽  
Low Sensitivity ◽  
Fire Recognition

As the most common serious disaster, fire may cause a lot of damages. Early detection and treatment of fires are of great significance to ensure public safety and to reduce losses caused by fires. However, traditional fire detectors are facing some focus issues such as low sensitivity and limited detection scenes. To overcome these problems, a video fire detection hybrid method based on random forest (RF) feature selection and back propagation (BP) neural network is proposed. The improved flame color model in RGB and HSI space and the visual background extractor (ViBe) in moving target detection algorithm are used to segment the suspected flame regions. Then, multidimensional features of flames are extracted from the suspected regions, and these extracted features are combined and selected according to the RF feature importance analysis. Finally, a BP neural network model is constructed for multifeature fusion and fire recognition. The test results on several experimental video sets show that the proposed method can effectively avoid feature interference and has an excellent recognition effect on fires in a variety of scenarios. The proposed method is applicable for fire recognition applied in video surveillance and detection robots.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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