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Copyright of Piezoelectrics & Acoustooptics is the property of Piezoelectric & Acoustooptic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the disadvantages of negative link feature fusion and effective information mining in signed social networks,resulting in low prediction performance,a new feature fusion negative link prediction method is proposed. Based on the classic structural balance theory and social status theory,this method constructed four features related to negative signs,including node feature,structural feature,similarity feature,and scoring feature,and used logistic regression algorithms to realize negative links prediction.Its effectiveness was verified on Epinions and Slashdot two typical signed network data sets. Experimental results show that compared with the benchmark method,the accuracy of this method is increased by about 4.5% and 10.4% on the two data sets,respectively,and the F1 score is increased by about 27.3% and 31.5%,which achieve the goal of improving the effect of negative link prediction. References: [ABSTRACT FROM AUTHOR]

GWO algorithm has drawbacks such as the irrationality of convergence. At present, there are few ways to improve the convergence of GWO algorithm. In addition, in the GWO iteration to the later stage, all gray individual wolves approach a wolf, B wolf and 8 wolf, causing the algorithm to fall into a local optimum. In order to address the above problems, this paper proposed an enhanced GWO algorithm DSF-GWO. This algorithm introduced a disturbance factor to balance the mining and exploration capabilities. Secondly, it introduced a somersault foraging strategy to prevent the group from falling into a local optimum, while also making the group's diversity slightly improve at the early stage. This paper verified the optimization per formance of DSF-CWO by 14 unimodal and multimodal objective functions. Under the same parameter settings, the experimental results show that DSF-CWO algorithm has obvious advantages in optimization performance over GWO algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problem that the traditional sensitive information early warning algorithm is difficult to cluster information effectively. a new sensitive information early warning algorithm is proposed. Firstly the sensitive information of high word frequency in the network was collected. Secondly, the accurate mining of high sensitive information was completed by set ting a reasonable threshold. Thirdly, the high sensitive information was effectively clustered by deep neural network and the adjacency matrix was constructed. Finally, the connection average of network parameter nodes was calculated. Combined with the above clustering on the monitoring and early warning of high sensitive information were realized. The experimental results showed that the data sent by the network nodes using the traditional algorithm was not regular. and the warning response time was more than 2 s. while the network dat using the deep neural network algorithm presents fully connected state. and the sensitive information could show a high degree of consistency and the warning response of the algorithm to the sensitive information could be completed in The research show that the early warning algorithm based on deep neural network can be applied in the field of network security. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To further improve the performance of personalized tag recommendation, this paper argued that traditional methods ignore the potential and informative tags hidden in the context of users and items. Aimed at this, this paper proposed a novel personalized tag recommendation method BPR-PITF-P based on potential tag mining and fine-grained preference . Firstly, BPR-PITF-P leveraged the context information of both users and got to mine potential and useful tags, and got three kinds of tags, positive tags, potential tags, and negative tags. Based on the above, it translated the traditional pairwise preference into fine-grained preference relationship among user-item post and tags. This kind of treatment helped alleviate the sparse problem of tagging data. Second, combined with pairwise interaction tensor factorization method to predict preference value, BPR-PITF-P modeled the preference relationship based on the optimization criteria of Bayesian personalized ranking, and developed a personalized tag recommendation model followed by optimization algorithm. The comparison results show that this proposed method could improve tag recommendation performance in the premise of guarantee convergence speed. [ABSTRACT FROM AUTHOR]

In order to solve the problems that existing in the maximal frequent itemset mining algorithms, such as the mining time is too long and the memory consumption is too large, this paper presented a maximal frequent itemset mining algorithm BMFI which employed B-list to mining frequent itemsets and employed the whole sequence search tree as the search space. Then, it used the parent equivalence pruning technique to reduce the search space. Finally, which combined with the MFItree-based projection strategy to achieve superset detection to improve the efficiency of the algorithm. The experimental results show that the performance of BMFI algorithm is superior to FPMAX algorithm and MFIN algorithm in terms of time efficiency and spatial efficiency. The proposed algorithm has good performance when mining the maximal frequent itemset in dense data set and sparse data set. [ABSTRACT FROM AUTHOR]