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Zhao Xin

Bio: Zhao Xin is an academic researcher from North China Electric Power University. The author has contributed to research in topics: Auditor's report & Support vector machine. The author has an hindex of 1, co-authored 3 publications receiving 5 citations.

Papers
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Proceedings ArticleDOI
29 Sep 2009
TL;DR: This article applies statistical sampling techniques to audit, selected 30 companies as samples from Shanghai and Shenzhen stock markets, use multiple linear regression method to build prediction models about the audit opinion and carried out a effectiveness test.
Abstract: This article applies statistical sampling techniques to audit, selected 30 companies as samples from Shanghai and Shenzhen stock markets, use multiple linear regression method to build prediction models about the audit opinion, and carried out a effectiveness test. The results show that the analytical model have a higher accuracy rate and with excellent interoperability, provide a new way of thinking about the prediction for the audit opinion.

5 citations

Proceedings ArticleDOI
29 Sep 2009
TL;DR: Based on the combination of ant colony optimization (ACO) and Support Vector Machines (SVM) theory, the model of project financing risk assessment is established to recognizing the financing risk of project by making use of ACO obtaining appropriate parameters.
Abstract: Based on the combination of ant colony optimization(ACO)and Support Vector Machines (SVM) theory, the model of project financing risk assessment is established to recognizing the financing risk of project. By making use of ACO obtaining appropriate parameters we can improve the general recognizing ability of SVM. After that, these parameters are used to develop classification rules and train SVM. The effectiveness of our methodology was verified by experiments comparing BP neural networks with our approach.

1 citations


Cited by
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Proceedings ArticleDOI
26 May 2022
TL;DR: A real-time insurance cost price prediction system named ML Health Insurance Prediction System (MLHIPS) using ML algorithms which will aid the insurance companies in the market for easy and rapid determination of values of premiums and thereby curb down health expenditure.
Abstract: India's government spends 1.5 percent of its annual GDP on public healthcare, which is significantly less than that of other countries. Global public health spending, on the other hand, has almost doubled in line with inflation in the last two decades, reaching US ${\$}8.5$ trillion in 2019, or 9.8% of global GDP. Multinational multi-private sectors provide around 60% of comprehensive medical treatments and 70% of out-patient care, which charge patients astronomically high fees. Because of the rising expense of quality healthcare, increased life expectancy, and the epidemiological shift toward non-communicable diseases, health insurance is becoming an essential commodity for everyone. Insurance data has increased dramatically in the last decade, and carriers now have access to it. The health insurance system explores predictive modeling to boost its business operations and services. Computer algorithms and Machine Learning (ML) is used to study and analyze the past insurance data and predict new output values based on trends in customer behavior, insurance policies, and data-driven business decisions, and support in formulating new schemes. Additionally, ML has found enormous and potential applications in the insurance industry. Thus, this paper develops a real-time insurance cost price prediction system named ML Health Insurance Prediction System (MLHIPS) using ML algorithms which will aid the insurance companies in the market for easy and rapid determination of values of premiums and thereby curb down health expenditure. The proposed model incorporates and demonstrates different models of regression such as Ridge Regression, Lasso Regression, Simple Linear Regression, Multiple Linear Regression and Polynomial Regression to anticipate insurance costs and assess model outcomes. In the proposed model, the Polynomial Regression model has achieved better results with an RMSE value of 5100.53 and R -squared value of 0.80 compared to all the other models.

8 citations

Journal ArticleDOI
TL;DR: To find the best performing machine learning algorithms to use with Snort so as to improve its detection, the application of combined and optimized MLAs worked quite well.
Abstract: In this paper, an existing rule-based intrusion detection system (IDS) is made more intelligent through the application of machine learning. Snort was chosen as it is an open source software and th...

5 citations

Journal ArticleDOI
TL;DR: A new method for determining abscissas of the breakpoint for segmented regression, minimizing the standard deviation based on multidimensional paraboloid usage is proposed.
Abstract: The main purpose of mathematical model building while employing statistical data analysis is to obtain high accuracy of approximation within the range of observed data and sufficient predictive properties. One of the methods for creating mathematical models is to use the techniques of regression analysis. Regression analysis usually applies single polynomial functions of higher order as approximating curves. Such an approach provides high accuracy; however, in many cases, it does not match the geometrical structure of the observed data, which results in unsatisfactory predictive properties. Another approach is associated with the use of segmented functions as approximating curves. Such an approach has the problem of estimating the coordinates of the breakpoint between adjacent segments. This article proposes a new method for determining abscissas of the breakpoint for segmented regression, minimizing the standard deviation based on multidimensional paraboloid usage. The proposed method is explained by calculation examples obtained using statistical simulation and real data observation.

4 citations

Journal ArticleDOI
01 Apr 2020
TL;DR: An experiment of prediction using Machine Learning technique with the Long Short Term Memory (LSTM) regression method indicated that LSTM regression had a sales prediction value with evaluation model through RMSE of 286,465,424 for data training and of 187,013,430 for data testing.
Abstract: In striving to win for competition in the market, pharmaceutical companies must produce quality pharmaceutical products. To produce a quality product, good and efficient production planning is needed. One basis for production planning is the sale prediction. PT. Metiska Farma has applied the prediction method in the production process, but the predictions resulted are not so accurate that the fulfillment of market demand is not optimal. To minimize the inaccuracy in prediction process, this research conducted an experiment of prediction using Machine Learning technique with the Long Short Term Memory (LSTM) regression method. The proposed technique used in the experiment was “X” product sales dataset from PT. Metiska Farma with performance parameters of Root Mean Squared Error (RMSE) and MAPE (Mean Absolute Percentage Error). The results of this research were in the form of average error evaluation value from the modeling of data training and data testing. The results indicated that LSTM regression had a sales prediction value with evaluation model through RMSE of 286,465,424 for data training and of 187,013,430 for data testing. Meanwhile, MAPE value was 787% and 309% for data training and data testing respectively.

4 citations

Journal ArticleDOI
TL;DR: In this paper, a dam break database system DFDS is built, and within which basic dam break information including location, failure time, type, height, storage volume, fatalities number and failure cause, are complied.
Abstract: Dam break database system DFDS is built, and within which basic dam break information including location, failure time, type, height, storage volume, fatalities number and failure cause, are complied. Correspondence analysis is used to study the relationships of the information. The statistical analysis indicates that dam break rate is high in 1970s in China. This is mainly because the dam construction standard is low and the technology is backward. Insufficient discharge ability and poor quality are the main factors that induce dam failure. Correspondence analysis shows that earth dam break accident is associated with foundation failure, inadequate management, and overtopping.

1 citations