scholarly journals Exploring the Medication Pattern of Chinese Medicine for Peptic Ulcer Based on Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Guigui Li ◽  
Youlei Guo
Keyword(s):  
Neural Network ◽  
Peptic Ulcer ◽  
Chinese Medicine ◽  
Trend Analysis ◽  
Test Method ◽  
Chinese Medicines ◽  
Cumulative Distance ◽  
Periodicity Analysis ◽  
Modern Application ◽  
Ulcer Treatment

During the last decades, Chinese medicine has been widely used for curing various diseases in the healthcare domain. Based on the databases of medicine wisdom and modern application of prescriptions, we have explored the medication pattern of ancient and modern prescriptions for the treatment of peptic ulcer in various patients. In this paper, we have proposed a neural network model which is based on the time series decomposition and is able to mine and predict the medication pattern of peptic ulcer treatment in Chinese medicine. For this purpose, cumulative distance level method, Mann–Kendall trend analysis, Hurst exponent, and characteristic point methods are used for the trend analysis. Likewise in the proposed model, the wavelet analysis method is used for the periodicity analysis and Mann–Kendall mutation test method along with Pettitt methods is used for mutability analysis. In addition, autocorrelation and unit root methods are utilized to test the random terms. The Chinese herbal formulas (where the main diseases are peptic ulcer, peptic ulcer, cerebral leakage, and cerebral abscess) are collected from the databases of medicine wisdom and modern application of prescriptions. Furthermore, methods of frequency analysis, association rule analysis, and factor analysis are used to evaluate the grouping pattern of prescriptions for peptic ulcer treatment. The error in the proposed scheme between the predicted and the measured values of 87 prescriptions, which involve five Chinese medicines for peptic ulcer and 160 Chinese medicines, obtained from the neural network was 16.79%.

Financial market trend analysis based on autoregressive conditional heteroscedasticity model and BP neural network prediction

2021 ◽  
pp. 002072092098849
Author(s):  
Xinzhe Yin ◽  
Jinghua Li
Keyword(s):  
Neural Network ◽  
Trend Analysis ◽  
Financial Market ◽  
Prediction Method ◽  
Good Effect ◽  
Trend Prediction ◽  
Bp Network ◽  
Conditional Heteroscedasticity ◽  
Bp Model ◽  
Autoregressive Conditional Heteroscedasticity

Many experts and scholars at home and abroad have studied this topic in depth, laying a solid foundation for the research of financial market prediction. At present, the mainstream prediction method is to use neural network and autoregressive conditional heteroscedasticity to build models, which is a more scientific way, and also verified the feasibility of the way in many studies. In order to improve the accuracy of financial market trend prediction, this paper studies in detail the neural network system represented by BP and the autoregressive conditional heterogeneous variance model represented by GARCH. Analyze its structure and algorithm, combine the advantages of both, create a GARCH-BP model, and transform its combination structure and optimize the algorithm according to the uniqueness of the financial market, so as to meet the market as much as possible Characteristics. The novelty of this paper is the construction of the autoregressive conditional heteroscedasticity model, which lays the foundation for the prediction of financial market trends through the construction of the model. However, there are some shortcomings in this article. The overall overview of the financial market is not very clear, and the prediction of the BP network is not so comprehensive. Finally, through the actual data statistics of market transactions, the effectiveness of the GARCH-BP model was tested, analyzed and researched. The final results show that model has a good effect on the prediction and trend analysis of market, and its accuracy and availability greatly improved compared with the previous conventional approach, which is worth further study and extensive research It is believed that the financial market prediction model will become one of the mainstream tools in the industry after its later improvement.

Survival after peptic ulcer perforation: A time trend analysis

1996 ◽  
Vol 49 (12) ◽  
pp. 1363-1371 ◽  
Author(s):  
Cecilie Svanes ◽  
Rolv T. Lie ◽  
Stein A. Lie ◽  
Gunnar Kvåle ◽  
Knut Svanes ◽  
...  
Keyword(s):  
Peptic Ulcer ◽  
Trend Analysis ◽  
Time Trend ◽  
Peptic Ulcer Perforation ◽  
Ulcer Perforation

A New Approach to Machinery Monitoring and Diagnostics Using Self-Organizing Maps

1995 ◽  
Author(s):  
Siyu Zhang ◽  
R. Ganesan ◽  
T. S. Sankar
Keyword(s):  
Neural Network ◽  
Service Life ◽  
Trend Analysis ◽  
New Approach ◽  
Machine System ◽  
Network Algorithm ◽  
On Line ◽  
Neural Network Algorithm ◽  
Monitoring And Diagnostics ◽  
Self Organizing

Abstract The problem of estimating an unknown multivariate function from on-line vibration measurements, for determining the conditions of a machine system and for estimating its service life is considered. This problem is formulated into a multiple-index based trend analysis problem and the corresponding indices for trend analysis are extracted from the on-line vibration data. Selection of these indices is based on the simultaneous consideration of commonly-observed faults or malfunctions in the machine system being monitored. A neural network algorithm that has been developed by the present authors for multiple-index based regression is adapted to perform the trend analysis of a machine system. Applications of this neural network algorithm to the condition monitoring and life estimation of both a bearing system as well as a gearbox are fully demonstrated. The efficiency and computational supremacy of the new algorithm are established through comparing with the performance of Self-Organizing Mapping (SOM) and Constrained Topological Mapping (CTM) algorithms. Further, the usefulness of multiple-index based trend analysis in precisely predicting the condition and service life of a machine system is clearly demonstrated. Using on-line vibration signal to constitute the set of variables for trend analysis, and employing the newly-developed self-organizing neural algorithm for performing the trend analysis, a new approach is developed for machinery monitoring and diagnostics.

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