Subjects: Library Science，Information Science >> Library Science submitted time 2023-10-08 Cooperative journals: 《知识管理论坛》
Abstract： [Purpose/significance] It is of great theoretical and practical significance to explore the influence mechanism of social capital on the user stickiness of virtual academic communities, which is to stabilize the user group of this community in the industry competition. [Method/process] From the perspectives of the users’ personal perception of virtual academic communities, this paper made a deep exploration on its dimensions of social capital, as a breakthrough point to explore the theoretical structure model on social capital, knowledge exchanges efficiency, user stickiness, and analyzed the adjustment role of the individual knowledge power derived from the virtual academic communities. The relationship between these variables was verified by the regression analysis of 270 samples data from the typical virtual community platforms. [Result/conclusion] The research shows that social capital contributes to the user stickiness of virtual academic communities. The knowledge exchange efficiency plays a complete mediation role between social interaction, social trust and user stickiness, and plays a partial mediation role between common vision and user stickiness. The influence of knowledge exchange efficiency on user stickiness is negatively regulated by knowledge power, that is, the higher the degree of knowledge power, the higher the efficiency of knowledge exchange will have a negative impact on user stickiness.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-06-06 Cooperative journals: 《计算机应用研究》
Abstract： In the large data environment, there are many problems in the parallel deep convolution neural network (DCNN) algorithm, such as excessive data redundancy, slow convolution layer operation and poor convergence of loss function. This paper proposed a parallel deep convolution neural network optimization algorithm based on the Im2col method. First, the algorithm proposed a parallel feature extraction strategy based on Marr-Hildreth operator to extract target features from data as input of convolution neural network, which can effectively avoid the problem of excessive data redundancy. Secondly, the algorithm designed a parallel model training strategy based on the Im2col method. The redundant convolution kernel is removed by designing the Mahalanobis distance center value, and the convolution layer operation speed is improved by combining the MapReduce and Im2col methods. Finally, the algorithm proposed an improved small-batch gradient descent strategy, which eliminates the effect of abnormal data on the batch gradient and solves the problem of poor convergence of the loss function. The experimental results show that IA-PDCNNOA algorithm performs well in deep convolution neural network calculation under large data environment and is suitable for parallel DCNN model training of large datasets.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-04-01 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the problem that the accuracy and recall of the protein complexes identification algorithm based on ant colony and fuzzy C-means (FCM) clustering are not high and the running efficiency is low, this paper proposed a novel protein complex recognition algorithm named FAC-PC (algorithm for identifying weighted protein complexes based on fuzzy ant colony clustering) . Firstly, combing with the Pearson correlation coefficient and edge aggregation coefficient, it constructed the weighted protein network. Secondly, in order to overcome the defects of massive merger, filter, repeated pick-up and drop-down operations in ant colony clustering algorithm, it designed the EPS (essential protein selection) metric to select essential protein, and designed the PFC (protein fitness calculation) metric to traverse neighbors of essential proteins to obtain essential group proteins, then the essential group protein replaced the seed node in the process of ant colony clustering, which improved results that the accuracy and time performance. Furthermore, it proposed the SI (similarity improvement) metric to optimize the probability of picking and dropping operations of ant colony to obtain the number of clustering. Finally, according to the improved ant colony algorithm, it obtained the essential protein and the number of clustering to initialize the FCM algorithm, and designed the membership update strategy to optimize the membership update, at the same time, a new FCM objective function which took a balance between intra-clustering and proposed inter-clustering variation, finally identified the protein complex by improved FCM algorithm. It used FAC-PC algorithm to identify protein complexes on DIP data. The experimental results show that FAC-PC algorithm has better performance on accuracy and recall, which is more reasonable to identify protein complexes.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-12-13 Cooperative journals: 《计算机应用研究》
Abstract： Since static PPI networks are difficult to truly reflect the dynamic character of cells, the convergence speed is slow, cluster precision and recall is low in mining protein complex based on ant colony clustering, this paper proposes an ant colony clustering algorithm based on fuzzy granular and closeness degree to mine protein complexes in dynamic weighted PPI network, named FGCDACC-DPC. First, based on the topological and biological characteristics of the PPI network, a comprehensive weight metric (CWM) is designed to accurately describe the interaction between proteins. Second, this method constructs a series of dense and highly co-expressed complex core based on the basic characteristic of the complexes, then it employs the picking and dropping operations, which based on fuzzy granular and closeness degree, to cluster the nodes in PPI networks, in order to reduce effectively the computational complexity and randomness, speed up the clustering speed. Finally, this algorithm designs a local and global strategy founded on function transmission and timing functional relevance theory for weight’s update, which achieve the function transmission between different generations of ant colonies and networks at different times to effectively improve clustering accuracy. FGCDACC-DPC algorithm is used to mine protein complexes on DIP data. Experimental results demonstrate that this algorithm has better performance on precision and recall, which is more reasonable to identify protein complexes.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-10-11 Cooperative journals: 《计算机应用研究》
Abstract： According to the problem of frequent occurrence of regional energy events in our country, this paper makes a deep study on the implicit characteristics of regional energy security exogenous source. This research constructs the attribute set and data set of regional energy security exogenous source through extracting different cases, and in the light of the data set features, designs a multi-dimensional association rule mining model on regional energy security exogenous source. Based on the idea of multi-dimensional attribute fusion, the model firstly maps the multi-dimensional attributes to one dimension by dividing the attributes into items, and then mines the rules with the basic principle of Apriori algorithm. On the basis, this paper uses this model to analyze the implicit characteristics of the regional energy security exogenous source, which makes a research on the relationship between these attributes in order to output the strong association rules. The research results show that multi-dimensional association rule model can find the implicit characteristics of the regional energy security exogenous source, which are derivative, seasonality, harmfulness and durability.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-19 Cooperative journals: 《计算机应用研究》
Abstract： In the clustering study of landslide prediction, the difficulties of determining the number of clusters which traditional clustering algorithm needs to set in advance and accurately measuring the important factor of Landslide induced-rainfall leads to bad prediction effect. Therefore, this paper proposes a new clustering algorithm-Uncertain PAHT algorithm , the algorithm introduces a kind of uncertain data model called M-D distance, which effectively measure the uncertain rainfall; and based on the hierarchical clustering thinking, through finding the best threshold p* to determine the k value. Contrast experiment in Yenan Baota district as an example, the experimental results verified the effectiveness of uncertain M-D distance and PAHT algorithm and the feasibility of uncertain PAHT algorithm on the landslide hazard prediction.
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