Rule of prescribing traditional Chinese medicine in the treatment of pneumoconiosis basedon association rules and k-means clustering algorithm

Objective:To analyze the rule of prescribing traditional Chinese medicine for treating pneumoconiosis,so as to provide reference for differential diagnosis and treatment of pneumoconiosis as well as for the development of new drugs for treatingthe disease.Methods:We searched China National Knowledge Infrastructure,Wanfang Database and VIP Chinese PublicationDatabase to retrieve relevant literatures which were then screened according to the enrollment criteria to establish a prescriptiondatabase of traditional Chinese medicine for the treatment of pneumoconiosis.The inheritance calculation platform of traditionalChinese medicine was used to analyze the prescribing rule of traditional Chinese medicine in the treatment of pneumoconiosisbased on association rules,k-means clustering algorithm and regression model analysis.Results:A total of 131 related literature were preliminarily selected,from which 97 prescriptions of traditional Chinese medicine with a total of 195 herbs were included.The most frequently prescribed herbs included Radix astragali,Platycodon grandiflorum,Pinellia ternata,licorice,Codonopsispilosula,Salvia miltiorrhiza,bitter almond etc.A total of 14 association rules,13 high-frequency herb pairs were found and 5groups of formulas were revealed by cluster analysis.Conclusion:The prescriptions for the treatment of pneumoconiosis are mainly composed of herbs for tonifying deficiency,resolving phlegm,relieving cough and asthma,activating blood circulation and removingblood stasis,which are supplemented with herbs for clearing heat,relieving appearance,regulating qi,promoting waterand permeating dampness,etc.,The prescribing rules reflect the basic pathological characteristics of lung deficiency and collateral arthralgia in pneumoconiosis,which provides some ideas for the clinical differentiation and treatment of pneumoconiosis in traditionalChinese medicine.It also provides reference for the research and development of new treatment methods.

Horror Video Recognition Based on Fuzzy Comprehensive Evolution

Technique for horror video recognition is important for its application in web content filtering and surveillance, especially for preventing children from being threaten. In this paper, a novel horror video recognition algorithm based on fuzzy comprehensive evolution model is proposed. Three low-level video features are extracted as typical features, and they are video key-light, video colour energy and video rhythm. Analytic Hierarchy Process （AHP） is adopted to estimate the weights of extracted features in fuzzy evolution model. Horror evaluation （membership function） is on shot scale and it is constructed based on the knowledge that videos which share the same affective have similar low-level features. K-Means algorithm is implemented to help finding the most representative feature vectors. The experimental results demonstrate that the proposed approach has good performance in recognition precision, recall rate and F1 measure.