scholarly journals A Constant Time Complexity Spam Detection Algorithm for Boosting Throughput on Rule-Based Filtering Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 82653-82661 ◽  
Author(s):  
Tian Xia
2014 ◽  
Vol 10 (10) ◽  
pp. 2135-2140 ◽  
Author(s):  
Saini Jacob Soman ◽  
S. Murugappan

2014 ◽  
Vol 519-520 ◽  
pp. 833-837
Author(s):  
Hui Yan Qu ◽  
Wei Zhao

In order to solve the virtual objects intersect problem in virtual environment, real-time fast CD application must be used in virtual environment. Collision detection algorithm based on a SIMD parallel was proposed. For ordered lists we used methods for N processors to search ordered sequence simultaneously, to select a valid range through an iterative, to retain the public segment as an iterative sequence so that to complete the division of the interval and data selection. It can improve the reconstruction of the bounding box of the data selected when these two algorithms applied to the detection of the CD. Experimental results show that compared with the classical reconstruction algorithm, the proposed algorithm has better time complexity and improve the efficiency of the CD.


2010 ◽  
Vol 36 (2) ◽  
pp. 407-423 ◽  
Author(s):  
F. Chávez ◽  
F. Fernández ◽  
R. Alcalá ◽  
J. Alcalá-Fdez ◽  
G. Olague ◽  
...  

2014 ◽  
Vol 599-601 ◽  
pp. 1369-1373
Author(s):  
Huang Bin You ◽  
Xue Wu Zhang ◽  
Huai Yong Fu ◽  
Zhuo Zhang ◽  
Min Li ◽  
...  

The community structure is a vital property of complex networks. As special networks the weighted networks also have community structure. Nowadays the studies of overlapping community draw attentions of researchers. However, the scale of networks become huge, so it requires the algorithm has lower time complexity and higher classification accuracy. Many existing algorithms cannot meet these two requirements at the same time. So we propose a novel overlapping community detection algorithm. Firstly we apply maximum degree node and its some special adjacent nodes as the initial community, and then expand the initial community by adding eligible nodes to it, finally other communities can be found by repeating these two steps. Experiments results show that our algorithm can detect overlapping community structure from weighted networks successfully, and also reveal that our method has higher division accuracy and lower time complexity than many previously proposed methods.


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