Energy financial risk early warning model based on Bayesian network

BackgroundThis study aims to establish a Bayesian network risk prediction model for gastric cancer using data mining methods. It explores both direct and indirect factors influencing the incidence of gastric cancer and reveals the interrelationships among these factors.MethodsData were collected from early cancer screenings conducted at the People’s Hospital of Lincang between 2022 and 2023. Initial variable selection was performed using Least Absolute Shrinkage and Selection Operator (Lasso) and Sliding Windows Sequential Forward Selection (SWSFS), and the screened variables and demographic characteristics features were used as variables for constructing the Bayesian network (BN) model. Subsequently, the performance of the models was evaluated, and the optimal model was selected for network mapping and Bayesian inference using the best model.ResultsThe incidence rate of gastric cancer in this region’s high-risk population was determined to be 7.09%. The BN model constructed from the set of variables consisting of Lasso’s selection variables and demographic characteristics had better performance. A total of 12 variables were incorporated into the BN model to form a network structure consisting of 13 nodes and 18 edges. The model shows that age, gender, ethnicity, current address, upper gastrointestinal symptoms (nausea, acid reflux, vomiting), alcohol consumption, smoking, SGIM gastritis, and family history are important risk factors for gastric cancer development.ConclusionThe Bayesian network model provides an intuitive framework for understanding the direct and indirect factors contributing to the early onset of gastric cancer, elucidating the interrelationships among these factors. Furthermore, the model demonstrates satisfactory predictive performance, which may facilitate the early detection of gastric cancer and enhance the levels of early diagnosis and treatment among high-risk populations.

The accurate calculation of the height of water-flowing fractured zone (WFFZ) in coal mine is a critical factor in ensuring mine safety and protecting surficial eco-environment. In view of inapplicability of traditional empirical formula for predicting the height of WFFZ, the correlation of the height of WFFZ and influence factors was analyzed firstly based on 82 collected groups of coalfield measured data in China. Results show that the mining thickness and mining depth have a significant effect on the height of WFFZ. Subsequently, the measured data were divided into two parts: 80% for training models and the remaining 20% for validation. Two prediction models, i.e., the multiple regression (MR) model and BP neural network (BPNN) model, were established and trained. A new merging model, multiple regression-BP neural network (MR-BPNN) model, was proposed by combining the multiple regression and BP neural network model. The prediction accuracy and generalization ability of the three models were verified by the recorded testing samples. Results of comparison suggest that three models all had better applicability for predicting the height of WFFZ in the coalmine, compared with the existing empirical prediction methods. More importantly, the MR-BPNN merging model combined the nonlinear mapping ability of neural network and empiric of multiple regression model, which could provide high-accurate, strong-generalized, and practical application for predicting the height of WFFZ of coalfield. In addition, the reason for the inapplicability of traditional empirical formula and the practicability of the proposed neural network-based prediction models were discussed.

Increasingly complex economic backgrounds and power consumption have created more difficulties for medium and long-term load forecasting. Traditional load forecasting models such as linear regression and time series models have a good effect on linear data, but the accuracy of nonlinear electric load data in Xinjiang is poor. The BP neural network model has better processing ability for nonlinear data, but it also has the problem of poor fitting and generalization ability. Aiming at this problem, this paper establishes a medium- and long-term power load forecasting model of Bayesian regularized BP neural network. Compared with the general gradient descent method BP neural network model, it is proved that the proposed model has a good effect on the electricity consumption forecast in Xinjiang.

The intermittence and fluctuation of renewable energy bring significant uncertainty to the power system, which enormously increases the operational risks of the power system. The development of efficient interval prediction models can provide data support for decision making and help improve the economy and reliability of energy interconnection operation. The performance of Bayesian deep learning models and Bayesian shallow neural networks in short-term interval prediction of photovoltaic power is compared in this study. Specifically, an LSTM Approximate Bayesian Neural Network model (ABNN-I) is built on the basis of the deep learning and Monte Carlo Dropout method. Meanwhile, a Feedforward Bayesian Neural Network (ABNN-II) model is introduced by Feedforward Neural Network and the Markov Chain Monte Carlo method. To better compare and verify the interval prediction capability of the ABNN models, a novel clustering method with three-dimensional features which include the number of peaks and valleys, the average power value, and the non-stationary measurement coefficient is proposed for generating sunny and non-sunny clustering sets, respectively. Results show that the ABNN-I model has an excellent performance in the field of photovoltaic short-term interval forecasting. At a 95% confidence level, the interval coverage from ABNN-I to ABNN-II can be increased by up to 3.1% and the average width of the interval can be reduced by 56%. Therefore, with the help of the high computational capacity of deep learning and the inherent ability to quantify uncertainty of the interval forecast from Bayesian methods, this research provides high-quality interval prediction results for photovoltaic power prediction and solves the problem of difficult modeling for over-fitting that exists in the training process, especially on the non-sunny clustering sets.

Sound quality evaluation may be conducted with multiple different objective evaluation models, but reliability of evaluation results is different.Noise samples of two ears of the driver were gathered when the car operated at the speed of 60km/h and different music was played in the car.After equal loudness, frequency band and filtering treatment of sound samples, subjective evaluation of sound quality was carried out.Based on calculation of objective parameters, MLR (Multiple Linear Regression) objective evaluation model for sound quality, objective evaluation model for sound quality based on BP neural network and objective evaluation model for sound quality based on RBF (radial basis function) neural network were established.The test samples were substitute into the three models, and their sound quality prediction effects at each frequency band were compared.The results show that MLR model is simple and has fast calculation speed, so it is suitable for linear problem analysis; BP neural network model is suitable for solving large sample problem with complex internal mechanism; RBF neural network model has high precision and small relative error, so it is suitable for analysis of nonlinear problem of small sample.Under experimental conditions, prediction effect of RBF model is superior to MLR model and BP neural network model.

The Sixth International Symposium on Neural Networks (ISNN 2009)

The complete financial early warning model has the function of early warning memory, which enables management to trace past early warning information or crisis events that have occurred. If the same early warning signal occurs, it can repeatedly prompt early warning information or interrupt business functions, helping enterprises avoid A similar financial crisis happened again. The purpose of this paper is to study commercial bank credit information disclosure and financial crisis early warning based on artificial neural network. On the basis of reviewing a large number of domestic and foreign literatures about BP neural network economic early warning, it is pointed out that we need to introduce a financial crisis early warning model to carry out credit risk management suitable for my country’s national conditions. From the perspective of credit information disclosure of commercial banks, through the establishment of neural network model and BP neural network model improved by genetic algorithm, the main variable indicators are evaluated and analyzed, and the financial status of sampled enterprises is controlled and predicted. Through empirical research and comparison of models, it is found that the improved model has its incomparable advantages, and the prediction accuracy is as high as 98%. It can be seen that the improvement of the BP neural network model based on the GA algorithm is an effective method for judging and predicting the financial crisis of enterprises for credit risk management method.

Aiming at the problems of insufficiently comprehensive prediction content and scope as well as the methods of prediction to be improved in the current research on chemical process safety risk prediction, this paper is based on Bayesian network (BN) for real-time assessment and early warning of chemical process safety risk of ammonia synthesis. The constructed risk warning model CNN-ATT-LSTM-BN is divided into two parts: one is the CNN-ATT-LSTM prediction model that combines CNN, LSTM, and attention mechanism, and the other is the safety risk traceability model based on BN. It is shown that the optimal values of linear regression correlation coefficient R2 and root mean square error RMSE of the CNN-ATT-LSTM model are 0.98794 and 0.00088, respectively, which have very high accuracy and are better than the other compared models. Meanwhile, the chemical process risk early warning was realized by risk assessment of the prediction results and risk change curves were obtained. The risk traceability results show that the probability that a safety problem may eventually occur in the chemical production stage is 15.04%, in which the human risk factors (85.74%), followed by mechanical and process factors (22.35%), have the greatest impact on safety. In addition, this paper proposes a scheme to realize risk early warning decision-making by using knowledge mapping, which can be used as a reference for subsequent risk prevention and treatment and other work.

This paper proposes a Bayesian Network (BN) based prediction model for a layer-based selection and its application to an operation assistance for the environmental control system Bio-Remote (BR). In the proposed method, each node of the BN model is involved in the layer-based selection function, which corresponds to an individual operation command, appliance, etc., and previous logs of operation commands and time division are used as input factors to predict the user's intended operation. The prediction results are displayed on the layer-based selection for the BR, and the number of times of operations and time taken for the operations can be reduced with the proposed prediction model. In the experiments, life-logs were collected from a cervical spinal injury patient who used the BR in daily life, and the proposed model was trained based on these recorded life-logs. The prediction accuracy for control devices of the BR system using the proposed model was 84.3 ± 6.5 %. The results indicated that the proposed prediction model could be useful for the operation assistance of the BR system.

Systemic financial risk early warning of financial market in China using Attention-LSTM model

The polynomial model and wood model have been extensively applied to predict the milk yield of cows, which aims to measure and partially explain the uncertainty under single factor condition. However, different sample data would affect the goodness of fit of the models. To investigate the milk yield regularities of the Chinese Holstein cows in Yunnan Standardized Pasture (YSP), data of cows with 1–3 parities using different observation periods were derived from the 401,497 records of 1,826 cows collected from YSP, and fitted in the BP neural network (BPNN) model, the Quadrinomial model and the Wood model. Prediction results of three models were compared. The results show that, for group data (mean), the Quadrinomial model was likely to over-fit under the single factor condition with small sample data, while the Wood model had a better goodness of fit; the BPNN model was more suitable for multi-factor and large sample data analysis.

Tang, Y., 2020. Financial risk and early warning based on Qingdao marine economic forecast. In: Li, L. and Huang, X. (eds.), Sustainable Development in Coastal Regions: A Perspective of Environment, Economy, and Technology. Journal of Coastal Research, Special Issue No. 112, pp. 195-198. Coconut Creek (Florida), ISSN 0749-0208.China's marine economy is in the stage of rapid development, which requires us to strengthen the prediction of marine economic development. By determining the marine economic development, we can determine the future trend and strategic objectives of marine economic development in Qingdao. Qingdao's marine economy will face a very complex domestic and international environment, which will face major opportunities and challenges. Financial risk is essential in the production and operation, which will better respond to the financial risk early warning. Through model prediction, we can provide a reliable data budget. Therefore, based on the prediction of Qingdao's marine economy, this paper develops the future financial risk assessment model, which can better provide early warning for enterprises. This paper mainly introduces the grey prediction model. Then, this paper puts forward some suggestions, which can better strengthen the risk early warning. Through the establishment of a sound financial early warning mechanism, Qingdao's marine economy will get a healthy development.

This article aims to explore a more suitable prediction method for tourism complex environment, to improve the accuracy of tourism prediction results and to explore the development law of China’s domestic tourism so as to better serve the domestic tourism management and tourism decision‐making. This study uses grey system theory, BP neural network theory, and the combination model method to model and forecast tourism demand. Firstly, the GM (1, 1) model is established based on the introduction of grey theory. The regular data series are obtained through the transformation of irregular data series, and the prediction model is established. Secondly, in the structure algorithm of the BP neural network, the BP neural network model is established using the data series of travel time and the number of people. Then, combining BP neural network with the grey model, the grey neural network combination model is established to forecast the number of tourists. The prediction accuracy of the model is analyzed by the actual time series data of the number of tourists. Finally, the experimental analysis shows that the combination forecasting makes full use of the information provided by each forecasting model and obtains the combination forecasting model and the best forecasting result so as to improve the forecasting accuracy and reliability.

Due to the influence of internal and external factors, stock prices often fluctuate greatly, which is also a big difficulty in the field of data mining and machine learning. This paper uses linear regression model, BP neural network model, and hard voting model to forecast Google stock. The results and accuracy of these three prediction models were observed respectively. Then a more accurate and practical model was selected to make a reasonable prediction of the closing price of Google stock in the following 15 days. The data set used is divided into two subsets, with some data used to fit the model and the rest used to evaluate the model. The results prove that the BP neural network model has the best prediction effect. Therefore, this model was selected for the next step of prediction, and the conclusive prediction result proved that the closing price of Google stock would decline in the following 15 days.

More attention to supply chain finance as a new form of financing channel is paid with the increasing highlight of the financing difficulty of small and medium-sized enterprises (SMEs). It is crucial for scientific and accurate risk assessment to control the control of the credit risk of supply chain finance. BP neural network model is used to open up a new way for credit risk assessment of SMEs based on the establishment and improvement of an index system of credit risk evaluation for SMEs. BP neural network model is a smart algorithm with self-learning and adaptive characteristics. The BP artificial neural network model was analyzed and detected in a practical case. The feasibility of the model was proved. The result is satisfactory. The model can be used to assess SMEs’ credit risk in the supply chain.