Advertisement

Cluster Computing

, Volume 22, Supplement 6, pp 13619–13625 | Cite as

Research on image tagging algorithm on internet

  • Junyang Zhang
  • Yang Guo
  • Xiao HuEmail author
Article
  • 55 Downloads

Abstract

At the age of big data, the information changes quickly. How to extract the key information timely seems to be quite important. Therefore, improving the execution speed of BFS algorithm means a lot to the processing of big data. This paper firstly introduces the implementation flow, features and performance evaluation criteria of the breadth-first search algorithm, and secondly introduce the research status of BFS algorithm based on current CPU platform both at home and abroad. Thirdly, this paper optimizes the algorithm by using the local principle of program, load balancing method and so on. Finally, the comparison of the algorithm performance is shown in this paper: the program optimized in this paper gets good performance and could be popularized further in practice.

Keywords

BFS algorithm Graph search Deep learning algorithm 

References

  1. 1.
    Pan, S., Wu, J., Zhu, X., et al.: Graph ensemble boosting for imbalanced noisy graph stream classification. IEEE Trans. Cybern. 45(5), 954–968 (2015)CrossRefGoogle Scholar
  2. 2.
    Chagaspaula, D.A., Oliveira, T.B., Zhang, T., et al.: Prediction of anti-inflammatory plants and discovery of their biomarkers by machine learning algorithms and metabolomic studies. Planta Med. 81(6), 450–458 (2015)CrossRefGoogle Scholar
  3. 3.
    Wu, X., Yang, Z., Li, Z.H., et al.: Disease related knowledge summarization based on deep graph search. Biomed. Res. Int. 12(7), 269–272 (2015)Google Scholar
  4. 4.
    Corbellini, A., Mateos, C., Godoy, D., et al.: An architecture and platform for developing distributed recommendation algorithms on large-scale social networks. J. Inf. Sci. 41(5), 686–704 (2015)CrossRefGoogle Scholar
  5. 5.
    Beheshtifard, Z., Meybodi, M.R.: Maximal throughput scheduling based on the physical interference model using learning automata. Ad Hoc Netw. 45(9), 65–79 (2016)CrossRefGoogle Scholar
  6. 6.
    Joeris, B., Lindzey, N., Mcconnell, R.M., et al.: Simple DFS on the complement of a graph and on partially complemented digraphs. Inf. Process. Lett. 117(2), 35–39 (2017)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chang, K.W., He, H., Iii, H.D., et al.: Learning to search for dependencies. Comput. Sci. 7(3), 96–100 (2015)Google Scholar
  8. 8.
    Zhai, Z., Li, S., Liu, Y.: Parameter determination of milling process using a novel teaching-learning-based optimization algorithm. Math. Probl. Eng. 9(3), 1–14 (2015)Google Scholar
  9. 9.
    Bellodi, E., Riguzzi, F.: Structure learning of probabilistic logic programs by searching the clause space. Theory Pract. Logic Program. 15(2), 169–212 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer, National University of Defense TechnologyChangshaChina

Personalised recommendations