scholarly journals An Early Risk Warning Model for Electronic Financial Crime Based on Big Data

2017 ◽  
Author(s):  
Jianying Xiong
Keyword(s):  
Big Data ◽  
Financial Crime ◽  
Early Risk ◽  
Risk Warning ◽  
Warning Model

scholarly journals Construction of an Early Risk Warning Model of Organizational Resilience: An Empirical Study Based on Samples of R&D Teams

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Si-hua Chen
Keyword(s):  
Factor Analysis ◽  
Evaluation Method ◽  
Factor Model ◽  
Five Factor Model ◽  
Organizational Resilience ◽  
Incremental Change ◽  
Early Risk ◽  
Risk Warning ◽  
Fierce Competition ◽  
Warning Model

Facing fierce competition, it is critical for organizations to keep advantages either actively or passively. Organizational resilience is the ability of an organization to anticipate, prepare for, respond to, and adapt to incremental change and sudden disruptions in order to survive and prosper. It is of particular importance for enterprises to apprehend the intensity of organizational resilience and thereby judge their abilities to withstand pressure. By conducting an exploratory factor analysis and a confirmatory factor analysis, this paper clarifies a five-factor model for organizational resilience of R&D teams. Moreover, based on it, this paper applies fuzzy integrated evaluation method to build an early risk warning model for organizational resilience of R&D teams. The application of the model to a company shows that the model can adequately evaluate the intensity of organizational resilience of R&D teams. The results are also supposed to contribute to applied early risk warning theory.

scholarly journals Predicting the occurrence of venous thromboembolism: construction and verification of Risk Warning Model

2020 ◽  
Author(s):  
Chen Shen ◽  
Binqian Ge ◽  
Xiaoqin Liu ◽  
Hao Chen ◽  
Yi Qin ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Venous Thromboembolism ◽  
Test Performance ◽  
Assessment Scale ◽  
Youden Index ◽  
Validation Data ◽  
Data Set ◽  
Early Warning Model ◽  
Risk Warning ◽  
Warning Model

Abstract Background: The onset of venous thromboembolism is insidious and the prognosis is poor. In this study, we aimed to construct a VTE risk early warning model and explore the clinical application value of the VTE risk early warning model. Methods: Preliminary construction of the VTE risk warning model was carried out according to the independent risk warning indicators of VTE screened by Logistic regression analysis in previous studies. The truncated value of screening VTE was obtained and the model was evaluated. ROC curve analysis was used to compare the test performance of Caprini risk assessment scale and VTE risk warning model on VTE. The validation data set was established, and the cut-off value of the VTE risk warning model was used to evaluate the test effectiveness of the model for VTE patients with validation data set. Results: The VTE risk warning model is p = ex / ( 1+ ex ) , x = -4.840 + 2.557 • X10(1) + 1.432 • X14(1) + 2.977 • X15(1) + 3.445 • X18(1) + 1.086 • X25(1) + 0.249 • X34 + 0.282 • X41. ROC curve results show that: AUC (95%CI), cutoff value (95%CI), accuracy, Youden index (95%), sensitivity, specificity and other evaluation indexes, Caprini risk assessment scale is 0.596 (0.552, 0.638), > 5 (> 4, > 5), 61.3%, 0.226 (0.167, 0.290), 26.07%, 96.50%, VTE risk warning model is 0.960 (0.940, 0.976), > 0.438 (> 0.263), respectively. >, 0.504), 92.2%, 0.844 (0.789, 0.879), 92.61%, 91.83%, with statistically significant differences (Z=14.521, P < 0.0001). The accuracy and Youden index of VTE screening using VTE risk warning model were 81.8% and 62.5%, respectively. Conclusions: VTE risk warning model has high accuracy in predicting the occurrence of VTE in hospitalized patients, and its test performance is higher than Caprini risk assessment scale. It also has high test performance for VTE in external population.

scholarly journals Research on Pump Inspection Cycle Early Warning Method Based on Big Data

2021 ◽  
Vol 245 ◽  
pp. 02026
Author(s):  
Du Lihong ◽  
Liu Yufang ◽  
Cao Fei ◽  
Li Fang ◽  
Min Guizhi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Influencing Factors ◽  
Early Warning ◽  
Learning Technology ◽  
Inspection Period ◽  
Engineering Technology ◽  
Early Warning Model ◽  
Single Well ◽  
Warning Model

At present, the existing indicator diagram can only be used for expost judgment and can not give early warning, and the influencing factors of pump inspection period are nonlinear, multi constrained and multi variable. In this paper, big data machine learning method is used to carry out relevant research. Firstly, around the influencing factors of pump inspection cycle, relevant data are collected and the evaluation index of pump inspection cycle is designed. Then, based on feature engineering technology, the production parameters of oil wells in different pump inspection periods are calculated to form the analysis sample set of pump inspection period. Finally, the early warning model of pump inspection period is established by using machine learning technology. The experimental results show that: the pump inspection cycle early warning model established by stochastic forest algorithm can identify the pump inspection status of single well, and the accuracy rate is about 85%.

scholarly journals Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zijun Dang ◽  
Shunshun Liu ◽  
Tong Li ◽  
Liang Gao
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Human Behavior ◽  
Temporal Relationship ◽  
Recognition Method ◽  
Risk Warning ◽  
Neural Network Algorithm ◽  
Feature Information ◽  
Relationship Network ◽  
Warning Model

In this paper, a deep confidence neural network algorithm is used to design and deeply analyze the risk warning model for stadium operation. Many factors, such as video shooting angle, background brightness, diversity of features, and the relationship between human behaviors, make feature attribute-based behavior detection a focus of researchers’ attention. To address these factors, researchers have proposed a method to extract human behavior skeleton and optical flow feature information from videos. The key of the deep confidence neural network-based recognition method is the extraction of the human skeleton, which extracts the skeleton sequence of human behavior from a surveillance video, where each frame of the skeleton contains 18 joints of the human skeleton and the confidence value estimated for each frame of the skeleton, and builds a deep confidence neural network model to classify the dangerous behavior based on the obtained skeleton feature information combined with the time vector in the skeleton sequence and determine the danger level of the behavior by setting the corresponding threshold value. The deep confidence neural network uses different feature information compared with the spatiotemporal graph convolutional network. The deep confidence neural network establishes the deep confidence neural network model based on the human optical flow information, combined with the temporal relational inference of video frames. The key of the temporal relationship network-based recognition method is to extract some frames from the video in an orderly or random way into the temporal relationship network. In this paper, we use several methods for comparison experiments, and the results show that the recognition method based on skeleton and optical flow features is significantly better than the algorithm of manual feature extraction.

scholarly journals Analysis of Financial Risk Early Warning Systems of High-Tech Enterprises under Big Data Framework

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Maotao Lai
Keyword(s):  
Big Data ◽  
Financial Crisis ◽  
Early Warning ◽  
Logistic Model ◽  
High Tech ◽  
Financial Indicators ◽  
Early Warning Model ◽  
Early Warning Indicators ◽  
Warning Model ◽  
Warning Indicators

With the further development of China's market economy, the competition faced by companies in the market has become more intense, and many companies have difficulty facing pressure and risks. Among the many types of enterprises, high-tech enterprises are the riskiest. The emergence of big data technologies and concepts in recent years has provided new opportunities for financial crisis early warning. Through in-depth study of the theoretical feasibility and practical value of big data indicators, the use of big data indicators to develop an early warning system for financial crises has important theoretical value for breaking through the stagnant predicament of financial crisis early warning. As a result of the preceding context, this research focuses on the influence of big data on the financial crisis early warning model, selects and quantifies the big data indicators and financial indicators, designs the financial crisis early warning model, and verifies its accuracy. The specific research design ideas include the following: (1) We make preliminary preparations for model construction. Preliminary determination and screening of training samples and early warning indicators are carried out, the samples needed to build the model and the early warning indicator system are determined, and the principles of the model methods used are briefly described. First, we perform a significant analysis of financial indicators and screen out early warning indicators that can clearly distinguish between financial crisis companies and nonfinancial crisis companies. (2) We analyze the sentiment tendency of the stock bar comment data to obtain big data indicators. Then, we establish a logistic model based on pure financial indicators and a logistic model that introduces big data indicators. Finally, the two models are tested and compared, the changes in the model's early warning effect before and after the introduction of big data indicators are analyzed, and the optimization effect of big data indicators on financial crisis early warning is tested.

scholarly journals Model of Electricity Trading Risk Warning under The New Power System Reform

2019 ◽  
Vol 136 ◽  
pp. 02003
Author(s):  
Ning Bu ◽  
Wei Zhong-kang ◽  
Zhu Tian-bo ◽  
Niu Jia-qiang ◽  
Liu Jun
Keyword(s):  
Power System ◽  
Market Price ◽  
System Reform ◽  
Price Fluctuation ◽  
Electricity Trading ◽  
Trading Market ◽  
Risk Warning ◽  
Warning Model

Under the new power system reform, the scale of electricity trading market in China grows rapidly, burdening power companies with more uncertainty risk relate to market trading. This paper constructs a risk warning model for electricity trading, and take market price fluctuation as the signal of market trading risk to carry out risk warning.

Sign in / Sign up

Export Citation Format

Share Document