Aiming at the problems of the single result of recommending similar users and insufficient understanding of user interests and behavior information for single social network platforms, a User Recommendation method of Cross-Platform based on Knowledge graph and Restart random walk (URCP-KR) was proposed. First, in the similar subgraphs segmented and matched by the target platform graph and the auxiliary platform graph, an improved multi-layer Recurrent Neural Network (RNN) was used to predict the candidate user entities. And the similar users were selected by comprehensive use of the similarity of topological structure features and user portrait similarity. Then, the relationship information of similar users in the auxiliary platform graph was used to complete the target platform graph. Finally, the probabilities of the users in the target platform graph walking to each user in the community were calculated, so that the interest similarity between users was obtained to realize the user recommendation. Experimental results show that the proposed method has higher recommendation precision and diversity than Collaborative Filtering (CF) algorithm, User Recommendation algorithm based on Cross-Platform online social network (URCP) and User Recommendation algorithm based on Multi-developer Community (UR-MC) with the recommendation precision up to 95.31% and the recommendation coverage up to 88.42%.

In order to optimize the Mel-Frequency Cepstral Coefficient (MFCC) feature extraction algorithm, improve the recognition accuracy of respiratory sound signals, and achieve the purpose of identifying Chronic Obstructive Pulmonary Disease (COPD), a feature extraction algorithm with the fusion of MFCC based on Hilbert-Huang Transform (HHT) and short-term Energy, named HHT-MFCC+Energy, was proposed. Firstly, the preprocessed respiratory sound signal was used to calculate the Hilbert marginal spectrum and marginal spectrum energy through HHT. Secondly, the spectral energy was passed through the Mel filter to obtain the eigenvector, and then the logarithm and discrete cosine transform of the eigenvector were performed to obtain the HHT-MFCC coefficients. Finally, the short-term energy of signal was fused with the HHT-MFCC eigenvector to form a new feature, and the signal was identified by Support Vector Machine (SVM). Three feature extraction algorithms including MFCC, HHT-MFCC and HHT-MFCC+Energy were combined with SVM to recognize the respiratory sound signal. Experimental results show that the proposed feature fusion algorithm has better respiratory sound recognition effect for both COPD patients and healthy people compared with the other two algorithms:the average recognition rate of the proposed algorithm can reach 97.8% on average when extracting 24-dimensional features and selecting 100 training samples, which is 6.9 percentage points and 1.4 percentage points higher than those of MFCC and HHT-MFCC respectively.

OpenFlow enhances the Quality of Service (QoS) of traditional networks, but it has disadvantage that its network session identification efficiency is low and the network packet forwarding path is poor and so on. On the basis of the current study of the OpenFlow, GPU OpenFlow Massive Data Network Analysis (GOMDI) model was proposed by this paper, through integrating the biological sequence algorithm, GPU parallel computing algorithm and machine learning methods. The network session matching algorithm and path selection algorithm of GOMDI were designed. The experimental results show that the speedup of the GOMDI network session matching algorithm is over 300 higher than the CPU environment in real network, and the network packet loss rate of its path selection algorithm is lower than 5%, the network delay is less than 20ms. Thus, the GOMDI model can effectively improve network performance and meet the needs of the real-time processing for massive information in big data environment.