[Retracted] Risk Assessment of Government Debt Based on Machine Learning Algorithm

Chen, Dan

doi:10.1155/2021/3686692

Cited by 12 publications

(10 citation statements)

References 26 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e derivative of the unipolar sigmoid function, formula (14), is applied. For multihidden layer BPNN, it is only necessary to reversely derive the weight adjustment formula according to the above rules [14].…”

Section: Bp Neural Network and Its Algorithmmentioning

confidence: 99%

Mechanical Motion Trajectory Control Tracking System Based on Machine Learning Algorithm

Zhao

Wang

2022

Mobile Information Systems

View full text Add to dashboard Cite

Machine learning is a key problem in the field of artificial intelligence. It is the study of statistical learning methods. It enables computers to simulate the learning behavior of humans, accumulate experience, and continuously improve and perfect their performances. Mechanical motion has always been an important subject in the field of automation, and trajectory tracking control is an important technology for mobile robots. Therefore, its research has important theoretical and practical significance. This study proposes a research based on the machine learning algorithm applied to the control and tracking system of mechanical motion trajectory. It expounds the neural network model, support vector machine algorithm, clustering algorithm, and K-means algorithm. Zeroing in on the issue of mechanical movement direction following control, this study concentrates on the plan of the mechanical movement direction following regulator in view of the BP brain network in the AI calculation and checks the accuracy and plausibility of the regulator plan. The exploratory outcomes demonstrate the way that the ideal following direction can be acquired by taking ε = 5. It makes the tracking trajectory more accurate, and the error convergence speed is faster.

show abstract

Section: Bp Neural Network and Its Algorithmmentioning

confidence: 99%

Mechanical Motion Trajectory Control Tracking System Based on Machine Learning Algorithm

Zhao

Wang

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…It can better solve fuzzy and difficult to quantify problems and is suitable for solving all kinds of uncertain problems. Literature [10] adopted that BP neural network is used to construct the college English teaching mode in the new era, and the teaching quality is effectively evaluated. This method has strong self-learning and self-adaptive ability, and the generalization ability is also good.…”

Section: Advances In Multimediamentioning

confidence: 99%

College English Audio-Visual-Oral Teaching Mode from the Perspective of Artificial Intelligence

Ling

2022

Advances in Multimedia

View full text Add to dashboard Cite

The “New Era Artificial Brain Power Improvement Plan” issued by the State Council and the “Computer Reasoning Development Activity Plan for Colleges and Universities” issued by the Ministry of Education clearly pointed out that it is necessary to attach importance to the combination of modern education and artificial intelligence. As a global language, English is becoming more and more important in global trade. Under the premise of educational plan changes in the new environment, English teaching is gradually reflected in the cultivation of college students’ English application ability. This research studies the current college English audio-visual-oral teaching mode from the perspective of artificial intelligence and analyzes the current situation of traditional English teaching by explaining the meaning of artificial intelligence, which mainly includes four stages: literature research, model construction, empirical research, and result analysis. In light of BP neural network, the showing assessment model of school English audio-visual-oral is laid out, and the change technique of showing method of school English audio-visual-oral under AI is advanced. Experiments show that this model achieves better teaching quality than the models constructed by traditional GA algorithm and BSA algorithm and is more suitable for promotion in college English teaching.

show abstract

“…Ma et al [4] have analyzed the development of China's peerto-peer online lending and the credit risks of borrowers, developed a credit risk assessment indicator system, and used the indicator system to develop a back-propagation neural network to complete the risk assessment model. Chen [5] has stated that, in order to prevent and control government debt risk, we need early warning and early prevention. First, we consider the selection of macrolevel indicators.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Debt Default Risk Assessment Based on the XGBoost Algorithm: An Empirical Study from China

Wang

Rong

Zhang

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The bond market is an important part of China’s capital market. However, defaults have become frequent in the bond market in recent years, and consequently, the default risk of Chinese credit bonds has become increasingly prominent. Therefore, the assessment of default risk is particularly important. In this paper, we utilize 31 indicators at the macroeconomic level and the corporate microlevel for the prediction of bond defaults, and we conduct principal component analysis to extract 10 principal components from them. We use the XGBoost algorithm to analyze the importance of variables and assess the credit debt default risk based on the XGBoost prediction model through the calculation of evaluation indicators such as the area under the ROC curve (AUC), accuracy, precision, recall, and F1-score, in order to evaluate the classification prediction effect of the model. Finally, the grid search algorithm and k -fold cross-validation are used to optimize the parameters of the XGBoost model and determine the final classification prediction model. Existing research has focused on the selection of bond default risk prediction indicators and the application of XGBoost algorithm in default risk prediction. After optimization of the parameters, the optimized XGBoost algorithm is found to be more accurate than the original algorithm. The grid search and k -fold cross-validation algorithms are used to optimize the XGBoost model for predicting the default risk of credit bonds, resulting in higher accuracy of the proposed model. Our research results demonstrate that the optimized XGBoost model has a significantly improved prediction accuracy, compared to the original model, which is beneficial to improving the prediction effect for practical applications.

show abstract

[Retracted] Risk Assessment of Government Debt Based on Machine Learning Algorithm

Cited by 12 publications

References 26 publications

Mechanical Motion Trajectory Control Tracking System Based on Machine Learning Algorithm

Mechanical Motion Trajectory Control Tracking System Based on Machine Learning Algorithm

College English Audio-Visual-Oral Teaching Mode from the Perspective of Artificial Intelligence

Credit Debt Default Risk Assessment Based on the XGBoost Algorithm: An Empirical Study from China

Contact Info

Product

Resources

About