The research on rule of Acupoints and Massage Manipulations selection for Post-ischemic Stroke Constipation based on association rule and entropy clustering analysis

Constipation is a common complication of stroke,and it is increasing year by year,which is worthy of attention.In fact,as an effective treatment for Post-ischemic Stroke Constipation,massage has been recognized by doctors at home and abroad.However,In the known research reports,massage prescriptions are complicated,therefore,a simple and effective massage prescription is urgently needed to effectively guide the clinic and promote it.In this study,we used association rule and entropy clustering analysis methods to mine clinical literature on Post-ischemic Stroke Constipation in 7 databases,and combined with data analysis,traditional chinese massage theory and clinical practice,a core new prescription is summarized.The core new prescription of massage in treating Post-ischemic Stroke Constipation take tonifying spleen,nourishing Qi and generating Body Fluid,promoting Qi,invigorating the circulation of blood and eliminating phlegm as the principle of treatment,which is accord with the pathogenesis of this disease,can better guide the clinical practice and facilitate the popularization and application of massage therapy.

A study of TCM master Yan Zhenghua’s medication rule in prescriptions for digestive system diseases based on Apriori and complex system entropy cluster

Objective:To explore Yan Zhenghua’s drug selection rule for treating digestive system diseases using data mining.Methods:The 609 medical records of digestive system diseases treated by Yan Zhenghua were collected and the herbs in these recipes were examined using a data mining technique.The correlativity between herb pairs and association rules was studied using an Apriori algorithm and the correlativity among multi-herbs was studied using a complex system entropy cluster technique.Results:Yan Zhenghua’s treatment of digestive system diseases featured 15 herbs prescribed at least 159 times each,22 herb pairs prescribed at least 155 times each,and eight frequently used herb core combinations.A confidence greater than 0.91 and a support level greater than 20%were achieved using the modified mutual information method.Conclusion:The data mining results conformed to findings from clinical practice.The data mining method is a valuable technique with which to study the experience of famous,elderly traditional Chinese medicine physicians.

HTTP-sCAN:Detecting HTTP-Flooding Attack by Modeling Multi-Features of Web Browsing Behavior from Noisy Web-Logs

HTTP-flooding attack disables the victimized web server by sending a large number of HTTP Get requests.Recent research tends to detect HTTP-flooding with the anomaly-based approaches,which detect the HTTP-flooding by modeling the behavior of normal web surfers.However,most of the existing anomaly-based detection approaches usually cannot filter the web-crawling traces from unknown searching bots mixed in normal web browsing logs.These web-crawling traces can bias the base-line profile of anomaly-based schemes in their training phase,and further degrade their detection performance.This paper proposes a novel web-crawling tracestolerated method to build baseline profile,and designs a new anomaly-based HTTP-flooding detection scheme(abbr.HTTP-sCAN).The simulation results show that HTTP-sCAN is immune to the interferences of unknown webcrawling traces,and can detect all HTTPflooding attacks.

Empirical Analysis of Forest Pest Control Efficiency from 2003 to 2014 in China

Three indexes including forest pest occurrence area,control area and input fund of 31 provinces from 2003 to 2014 were selected from Forestry Statistical Yearbook,to establish dynamic interaction index evaluation system with clustering robust regression model and Stata 13. 0 software. Total forest pest control efficiency in China was determined according to the computing result of entropy method. Suggestions such as improving forest pest control efficiency,increasing service efficiency and input amount of forest pest control input funds were put forward. It will provide empirical basis for target management evaluation of forest pest control work and accountability system.

Study on the Drug Selection Law for Treatment of Chronic Gastritis with Spleen Deficiency and Stomach Dryness by Complex System Entropy Cluster

Objective:To study on Prof. GAO Zhong-ying’s drug selection law for treatment of chronic gastritis with data mining technique. Methods: The 407 medical records of chronic gastritis treated by Prof. GAO Zhong-ying were collected and the study on these drugs in the recipes was carried out with data mining method. Among them, the recipe composed of one drug was studied with frequency statistical method, correlativity between drug pairs with improved mutual information, correlativity among multi-drugs with complex system entropy cluster technique. Results: In treatment of chronic gastritis by Prof. GAO Zhong-ying there were 30 drugs with a higher use frequency of over 38 times, 94 commonly-used drug pairs with correlation coefficient of over 0.05, 11 commonly-used drug core combinations. Conclusion: The results attained with data mining technique for studying experience of famous and old TCM physicians conform to the clinical practice and the method is of an important significance for summarization of famous and old TCM physicians’ experiences.

Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network

Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation.However,this model requires the number of clusters to be set manually,resulting in a low automation degree due to the complexity of the iterative clustering process.To address this problem,a segmentation method based on a self-learning super-pixel network(SLSP-Net)and modified automatic fuzzy clustering(MAFC)is proposed.SLSP-Net performs feature extraction,non-iterative clustering,and gradient reconstruction.A lightweight feature embedder is adopted for feature extraction,thus expanding the receiving range and generating multi-scale features.Automatic matching is used for non-iterative clustering,and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters,providing a better irregular super-pixel neighborhood structure.An optimized density peak algorithm is adopted for MAFC.Based on the obtained super-pixel image,this maximizes the robust decision-making interval,which enhances the automation of regional clustering.Finally,prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result.Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance,realizing not only automatic image segmentation,but also good segmentation results.