Focusing on the issues of poor real-time, low classification recognition rate and less features of the crowd abnormal detection, an abnormal behavior detection algorithm of small and medium crowd based on intelligent video surveillance was proposed. Firstly, the rapid population density detection algorithm was employed to extract the change information of crowd amount. Secondly, the improved Lucas-Kanade optical flow method was utilized to extract the average kinetic energy, the direction entropy and the distance potential energy of the crowd. Finally, the crowd behaviors were classified by using the Extreme Learning Machine (ELM) algorithm. UMN common data set was used for test, compared to abnormal crowd behavior detection algorithm in high and medium density and abnormal behavior detection algorithm based on Kinetic Orientation Distance (KOD) energy feature, the recognition rate of ELM algorithm in abnormal behavior detection of small and medium crowd increased by 7.13 percentage points and 5.89 percentage points respectively. On the part of the crowd density estimation, compared to the high and medium crowd density detection algorithm, the processing time for each frame of ELM algorithm reduced 106 ms almost 1/3, approximately. The experiments show that the proposed abnormal behavior detection of small and medium crowd based on intelligent video surveillance can effectively improve recognition rate and real-time performance of the abnormal behavior detection.

Focusing on the issue that imbalanced dataset influencing the effect of prediction for retweeting behavior in microblogs, a novel predicting algorithm based on oversampling techniques and Random Forest (RF) algorithm was proposed. Firstly, the retweeting-related features, including individual information, social relationships and topic information, were defined. The key feature selection method was implemented based on information gain algorithm. Secondly, by considering the characteristics of the microblogs feature data, an improved algorithm for oversampling based on Synthetic Minority Over-sampling Technique (SMOTE) was proposed. In the course of this algorithm, the probability distribution of the original dataset was estimated based on nonparametric distribution estimation. In order to ensure a balanced number of positive examples and negative examples, an oversampling method was executed based on the improved SMOTE method, according to approximate probability distribution of the original dataset. Finally, a classifier based on random forest algorithm was trained, according to retweeting-related key features. The algorithm parameters of random forest were selected by analyzing the error estimation of Out Of Bag (OOB) data. By comparison with Decision Tree (DT), Support Vector Machine (SVM), Naive Bayesian (NB) and RF algorithms, which were used in the analysis for microblog retweeting behavior, the overall performance of the proposed method is superior to the method based on SVM, which obtains optimal results in all the baseline methods. The recall rate and F-measure of the proposed method are improved by 8%, 5% respectively. The experimental results show that the proposed method can effectively improve the prediction accuracy of microblog retweeting behavior analysis in practical application.

The research on the evolutionary mechanisms and models of Protein-Protein Interaction (PPI) network is significant for understanding the evolution of the biological systems as well as the formation process of the organisms. So far, there have been kinds of models based on different evolutionary mechanisms. All of these models exhibit certain topological characteristics emerging from the protein-protein interaction networks, while some limitations exist simultaneously. This paper focused on several classic protein-protein interaction network models, analyzing the main ideas of these models and comparing the topological characteristics derived from them with those of real protein-protein interaction networks. A summary of the features for each model was given based on the experiments. At last, several viewpoints for the future research of protein-protein interaction network models were also proposed to provide a useful reference for further studies.