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RETRACTED ARTICLE: Situational English Language Information Intelligent Retrieval Algorithm Based on Wireless Sensor Network

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This article was retracted on 07 November 2022

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Abstract

In order to improve the accuracy of situational English language information intelligent retrieval and shorten the retrieval time, a situational English language information intelligent retrieval algorithm based on wireless sensor network is proposed. Firstly, the principle of situational English information intelligent retrieval algorithm is analyzed. Then, the model based on wireless sensor network is established, including the establishment of communication model and communication routing mechanism. Finally, the intelligent retrieval of situational English information is realized through information filtering, probability retrieval model, organization and storage of structured documents and secondary filtering. The experimental results show that the situational English language information intelligent retrieval algorithm of this study has high accuracy and can effectively shorten the retrieval time.

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All data, models, and code generated or used during the study appear in the submitted article.

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References

  1. Y. Qiang, X. Yang, J. Zhao, et al., Lung nodule image retrieval based on convolutional neural networks and hashing[J], Journal of Beijing Institute of Technology, Vol. 28, No. 01, pp. 21–30, 2019.

    Google Scholar 

  2. B. Wang, Z. Tian, W. Zhang, et al., Retrieval of green-up onset date from modis derived ndvi in grasslands of inner mongolia[J], IEEE Access, Vol. 7, No. 1, pp. 77885–77893, 2019.

    Article  Google Scholar 

  3. K. S. Arun, V. K. Govindan and S. D. M. Kumar, Enhanced bag of visual words representations for content based image retrieval: a comparative study [J], Artificial Intelligence Review, Vol. 53, No. 3, pp. 1615–1653, 2020.

    Article  Google Scholar 

  4. P. Zhou, K. Wang, J. Xu, et al., Differentially-private and trustworthy online social multimedia big data retrieval in edge computing [J], IEEE Transactions on Multimedia, Vol. 21, No. 3, pp. 539–554, 2019.

    Article  Google Scholar 

  5. Q. Men and H. Leung, Retrieval of spatial-temporal motion topics from 3D skeleton data [J], Visual Computer, Vol. 35, No. 8, pp. 973–984, 2019.

    Article  Google Scholar 

  6. R. Freij-Hollanti, O. W. Gnilke, C. Hollanti, A. L. Horlemann-Trautmann, D. Karpuk and I. Kubjas, $t$ -private information retrieval schemes using transitive codes, IEEE Transactions on Information Theory, Vol. 65, No. 4, pp. 2107–2118, 2019.

    Article  MathSciNet  MATH  Google Scholar 

  7. T. Vale and E. S. De Almeida, Experimenting with information retrieval methods in the recovery of feature-code SPL traces [J], Empirical Software Engineering, Vol. 24, No. 3, pp. 1328–1368, 2019.

    Article  Google Scholar 

  8. S. R. Mashwani and S. Khusro, 360° semantic file system: augmented directory navigation for nonhierarchical retrieval of files [J], IEEE Access, Vol. 7, No. 1, pp. 9406–9418, 2019.

    Article  Google Scholar 

  9. G. Baechler, M. Krekovic, J. Ranieri, et al., Super resolution phase retrieval for sparse signals [J], IEEE Transactions on Signal Processing, Vol. 67, No. 18, pp. 4839–4854, 2019.

    Article  MATH  Google Scholar 

  10. E. Yaakobi and J. Bruck, On the uncertainty of information retrieval in associative memories [J], IEEE Transactions on Information Theory, Vol. 65, No. 4, pp. 2155–2165, 2018.

    Article  MathSciNet  MATH  Google Scholar 

  11. Y. P. Wei and S. Ulukus, The capacity of private information retrieval with private side information under storage constraints [J], IEEE Transactions on Information Theory, Vol. 66, No. 4, pp. 2023–2031, 2020.

    Article  MathSciNet  MATH  Google Scholar 

  12. J. Lavauzelle, Private information retrieval from transversal designs [J], IEEE Transactions on Information Theory, Vol. 65, No. 2, pp. 1189–1205, 2019.

    Article  MathSciNet  MATH  Google Scholar 

  13. K. Banawan and S. Ulukus, Asymmetry hurts: private information retrieval under asymmetric traffic constraints [J], IEEE Transactions on Information Theory, Vol. 65, No. 11, pp. 7628–7645, 2019.

    Article  MathSciNet  MATH  Google Scholar 

  14. S. Kadhe, B. Garcia, A. Heidarzadeh, et al., Private information retrieval with side information[J], IEEE Transactions on Information Theory, Vol. 66, No. 4, pp. 2032–2043, 2017.

    Article  MathSciNet  MATH  Google Scholar 

  15. Y. P. Wei, K. Banawan and S. Ulukus, The capacity of private information retrieval with partially known private side information [J], IEEE Transactions on Information Theory, Vol. 65, No. 12, pp. 8222–8231, 2017.

    Article  MathSciNet  MATH  Google Scholar 

  16. T. Mutton and D. Ridley, Understanding similarities and differences between two prominent web-based chemical information and data retrieval tools: comments on searches for research topics, substances, and reactions, Journal of Chemical Education, Vol. 96, No. 10, pp. 2167–2179, 2019.

    Article  Google Scholar 

  17. N. Goyal, M. Dave and A. K. Verma, Data aggregation in underwater wireless sensor network: Recent approaches and issues [J], Journal of King Saud University - Computer and Information Sciences, Vol. 31, No. 3, pp. 275–286, 2019.

    Article  Google Scholar 

  18. Pascal Lorenz, et al., New path centrality based on operator calculus approach for wireless sensor network deployment, IEEE Transactions on Emerging Topics in Computing, Vol. 7, No. 1, pp. 162–173, 2019.

    Article  Google Scholar 

  19. I. Benkhelifa, S. Moussaoui and I. Demirkol, Intertwined localization and error-resilient geographic routing for mobile wireless sensor networks [J], Wireless Networks, Vol. 26, No. 3, pp. 1731–1753, 2018.

    Article  Google Scholar 

  20. M. Kanthimathi, R. Amutha and K. S. Kumar, Energy efficiency analysis of differential cooperative algorithm in wireless sensor network [J], Cluster Computing, Vol. 22, No. 12, pp. 1–9, 2019.

    Google Scholar 

  21. S. Sivasakthiselvan and V. Nagarajan, A new localization technique for node positioning in wireless sensor networks [J], Cluster Computing, Vol. 22, No. 1, pp. 4027–4034, 2019.

    Article  Google Scholar 

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Correspondence to Qing Ye.

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Ye, Q. RETRACTED ARTICLE: Situational English Language Information Intelligent Retrieval Algorithm Based on Wireless Sensor Network. Int J Wireless Inf Networks 28, 287–296 (2021). https://doi.org/10.1007/s10776-021-00516-9

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  • DOI: https://doi.org/10.1007/s10776-021-00516-9

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