Enterprise Financial Influencing Factors and Early Warning Based on Decision Tree Model

Liao, Shiyun; Liu, Zhangsheng

doi:10.1155/2022/6260809

Cited by 10 publications

(8 citation statements)

References 14 publications

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“…In the model, the parameters of the economic activities of agricultural and industrial enterprises are characterized by some quantitative and qualitative indicators, which can reflect the level of economic security [ 1 , 2 ]. The economic security level assessment model in that research does not use too much big data analysis technology and has a narrow scope of application, but its theory can provide help for this article.…”

Section: Introductionmentioning

confidence: 99%

Construction of Economic Security Early Warning System Based on Cloud Computing and Data Mining

Yuan

Xie

Tan

2022

Computational Intelligence and Neuroscience

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Economic security is a core theoretical issue in economics. In modern economic conditions, the ups and downs caused by economic instability in any economic system will affect the stability of the financial market, bring huge losses to the economy, and affect the development of the whole national economy. Therefore, research on the regularity of economic security and economic fluctuations is one of the important contents to ensure economic stability and scientific development. Accurate monitoring and forecasting of economic security are an indispensable link in economic system regulation, and it is also an important reference factor for any economic organization to make decisions. This article focuses on the construction of an economic security early warning system as the main research content. It integrates cloud computing and data mining technologies and is supported by CNN-SVM algorithm and designs an early warning model that can adaptively evaluate and warn the economic security state. Experiments show that when the CNN network in the model uses ReLU activation function and SVM uses RBF function, the prediction accuracy can reach 0.98, and the prediction effect is the best. The data set is verified, and the output Q province’s 2018 economic security early warning comprehensive index is 0.893. The 2019 economic security early warning index is 0.829, which is consistent with the actual situation.

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Section: Introductionmentioning

confidence: 99%

Construction of Economic Security Early Warning System Based on Cloud Computing and Data Mining

Yuan

Xie

Tan

2022

Computational Intelligence and Neuroscience

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“…e text classification method in this study adopts a naive Bayesian classification model and decision tree classification model [28][29][30][31][32][33][34][35][36][37].…”

Section: Discussionmentioning

confidence: 99%

Analysis of Public Big Data Management under Text Analysis

Zhu

Kan

2022

Mathematical Problems in Engineering

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Based on text analysis, public big data management is studied. The public data management of Mount Wutai tourism network travel notes is discussed. The positive, neutral, and negative effects of the naive Bayesian classification model and decision tree classification model on the tourism sentiment attitude of Mount Wutai are compared. The relationship between tourism resources, tourism facilities, tourism services, tourism environment, and tourism sentiment and attitude of Wutai Mountain is analyzed. The results show that the true positive rate, true negative rate, and F-measure of the Bayesian decision tree classifier to classify positive text are 86.64%, 81.27%, and 84.62%, respectively. The true positive rate for neutral text is 82.05%, the true negative rate is 78.89%, and the F-measure is 77.11%. The true positive rate for negative text is 83.67%, the true negative rate is 98.29%, and the F-measure is 82.83%. The Bayesian decision tree classifier can evaluate positive and negative texts better than neutral texts. The true positive rate of the C4.5 decision tree classifier for positive text is 91.44%, the true negative rate is 86.57%, and the F-measure is 89.45%. The true positive rate for neutral text is 90.17%, the true negative rate is 83.28%, and the F-measure is 84.06%. The true positive rate for negative text is 91.84%, the true negative rate is 99.05%, and the F-measure is 90.91%. The decision tree classifier has a better evaluation effect on positive and negative texts than on neutral texts. The ROC curve of the evaluation effect of the two classifiers shows that the evaluation effect of the two classifiers has a better evaluation effect on positive text than that of the neutral and negative texts, and the evaluation effect of the C4.5 decision tree classifier is better than that of the Bayesian classifier. The promotion degree of tourism resources and facilities in forwarding online travel notes is obviously higher, and there is a high correlation between tourism resources and facilities and forward online travel notes. In negative online travel notes, the promotion degree of tourism service and tourism environment is high, and the correlation between tourism service and tourism environment and negative online travel notes is high. In summary, improving the quality of tourism services and the tourism environment of Mount Wutai scenic spots can better enhance the recognition and satisfaction of tourists with Mount Wutai tourism.

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“…This study selects the animation scene as the evaluation object of users In addition, the selection of machine learning algorithm is a key problem in the implementation of 3D animation data processing and development platform. Among the machine learning methods, the method based on decision tree is widely used [ 14 – 19 ]. First, because the decision tree model is easy to understand, the problem-solving process can be intuitively understood through the working process of decision tree, and second because the decision tree can give accurate solutions to a wide range of problems.…”

Section: Framework Of Platformmentioning

confidence: 99%

Design and Implementation of 3D Animation Data Processing Development Platform Based on Artificial Intelligence

Gao

2022

Computational Intelligence and Neuroscience

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Based on the whole process of computer-aided technology, a 3D animation data processing development platform based on artificial intelligence is designed and implemented. A random forest model for animation data processing and development is designed to mine the experience that can guide animation generation from the accumulated animation data. Based on the design goal and implementation principle of animation data processing and development platform, the attributes and categories of random forest model are abstracted. After standardizing a large number of historical data, the training sample set is obtained, and the random forest model is obtained after training. The parameters of the random forest model are continuously optimized by experiments, so that the learning model can better guide the dynamic animation data processing and development platform to generate animation to the satisfaction of users. The designed three-dimensional animation data processing and development platform interacts with the animation generation module, users, and system administrators. It can continuously receive the sample data of the animation generation module, automatically expand the number of training samples, analyze the status of the sample database, and put forward suggestions to the system administrator to update the learning model, so as to realize the initiative of learning. The experimental results show that the designed 3D animation data processing and development platform is effective and feasible.

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Enterprise Financial Influencing Factors and Early Warning Based on Decision Tree Model

Cited by 10 publications

References 14 publications

Construction of Economic Security Early Warning System Based on Cloud Computing and Data Mining

Construction of Economic Security Early Warning System Based on Cloud Computing and Data Mining

Analysis of Public Big Data Management under Text Analysis

Design and Implementation of 3D Animation Data Processing Development Platform Based on Artificial Intelligence

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