doi: 10.3934/dcdss.2020202

Design of intelligent retrieval algorithm for literature information resources in digital library

Library of Zhejiang A&F University, Hangzhou 311300, China

* Corresponding author: Wujian Yang

Received  March 2019 Revised  May 2019 Published  December 2019

The intelligent retrieval method of information resources is the key technology of literature information search in digital library. The detection results directly determine the performance of subsequent literature information search. Therefore, an intelligent retrieval algorithm for literature information resources in digital library is designed. Firstly, the Gaussian smoothing filter and the wavelet are used to process the literature information resources in digital library to hide the useless information, and construct a new literature information resource signal in digital library. Secondly, the information is divided into the target information and the database information through the threshold. The iterative method is used to retrieve the optimal threshold of digital literature information, and the structural elements of literature information resources in digital library under two different scales are weighted and combined to obtain the final literature information resources and intelligently detected. Finally, the simulation results show that the designed algorithm can accurately extract the literature information resources in digital library, and has a wide range of practicability and application value.

Citation: Qing Chang, Wujian Yang. Design of intelligent retrieval algorithm for literature information resources in digital library. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020202
References:
[1]

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D. YuH. Liu and C. Bresser, Peak load management based on hybrid power generation and demand response, Energy, 163 (2018), 969-985.  doi: 10.1016/j.energy.2018.08.177.  Google Scholar

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show all references

References:
[1]

A. Q. BaigM. Naeem and W. Gao, Revan and hyper-revan indices of octahedral and icosahedral networks, Applied Mathematics and Nonlinear Sciences, 3 (2018), 33-39.  doi: 10.21042/AMNS.2018.1.00004.  Google Scholar

[2]

Q. Bi and J. Liu, Study on the method of aggregation and service recommendation of digital resource based on domain ontology, Journal of the China Society for Scientific and Technical Information, 36 (2017), 452-460.   Google Scholar

[3]

Q. CuiX. Yuan and H. Deng, The robust design of literature retrieval service for university library based on taguchi method, Journal of Modern Information, 23 (2016), 19-26.   Google Scholar

[4]

W. Gao and W. Wang, New isolated toughness condition for fractional (g, f, n) - critical graph, Colloquium Mathematicum, 147 (2017), 55-65.  doi: 10.4064/cm6713-8-2016.  Google Scholar

[5]

W. Gao and W. Wang, A tight neighborhood union condition on fractional (g, f, n', m)-critical deleted graphs, Colloquium Mathematicum, 149 (2017), 291-298.  doi: 10.4064/cm6959-8-2016.  Google Scholar

[6]

T. Ichimura and S. Kamada, Clustering and retrieval method of immunological memory cell in clonal selection algorithm // joint, international conference on soft computing and intelligent systems, IEEE, (2018), 1351–1356. Google Scholar

[7]

S. C. JiangS. B. GeX. Wu and et al., Treating n-butane by activated carbon and metal oxides, Toxicological and Environmental Chemistry, 99 (2017), 753-759.  doi: 10.1080/02772248.2017.1279432.  Google Scholar

[8]

J. Jin and W. Mi, An aimms-based decision-making model for optimizing the intelligent stowage of export containers in a single bay, Discrete and Continuous Dynamical Systems Series S, 12 (2019), 1101-1115.   Google Scholar

[9]

L. KangH. L. Du and X. Du, Study on dye wastewater treatment of tunable conductivity solid-waste-based composite cementitious material catalyst, Desalination and Water Treatment, 125 (2018), 296-301.  doi: 10.5004/dwt.2018.22910.  Google Scholar

[10]

F. Khellat and M. B. Khormizi, A global solution for a reaction-diffusion equation on bounded domains, Applied Mathematics and Nonlinear Sciences, 3 (2018), 15-22.  doi: 10.21042/AMNS.2018.1.00002.  Google Scholar

[11]

C. MiJ. WangW. MiY. HuangZ. ZhangY. YangJ. Jiang and P. Octavian, An aimms-based decision-making model for optimizing the intelligent stowage of export containers in a single bay, Discrete and Continuous Dynamical Systems Series S, 12 (2019), 1117-1133.   Google Scholar

[12]

T. K. MohammedA. GovardhanH. S. Saini and et al., Bio-medical literature retrieval from mixed literature bank with the aid of multi kernel fuzzy c-means technique (mk-fcm), Journal of Theoretical and Applied Information Technology, 95 (2017), 2698-2710.   Google Scholar

[13]

Y. F. Qi, Y. X. Zhao and Q. H. Zhu, Study on the semantic search framework in digital library based on bibframe, Library and Information, (2017), 74–81. Google Scholar

[14]

K. RajaA. J. Sauer and R. P. Garg, A hybrid citation retrieval algorithm for evidence-based clinical knowledge summarization: Combining concept extraction, Vector Similarity and Query Expansion for High Precision, 48 (2016), 55-62.   Google Scholar

[15]

S. K. Sunny and M. Angadi, Evaluating the effectiveness of thesauri in digital information retrieval systems, Electronic Library, 36 (2018), 55-70.  doi: 10.1108/EL-02-2017-0033.  Google Scholar

[16]

T. Y. XuH. R. Ren and G. B. Zhang, The construction and implementation of image retrieval framework for digital library-based on the non sampling contourlet transform, Modern Information, 37 (2017), 55-60.   Google Scholar

[17]

D. YuH. Liu and C. Bresser, Peak load management based on hybrid power generation and demand response, Energy, 163 (2018), 969-985.  doi: 10.1016/j.energy.2018.08.177.  Google Scholar

[18]

J. Zhang, A textbook embedded with knowledge of scholarly publishing: Review on the book literature information retrieval and paper writing, Library Journal, 35 (2016), 11-17.   Google Scholar

[19]

L. I. ZhengdaF. Wang and Q. Geng, The existing problems in the literature retrieval course in higher vocational college based on the information literacy education and countermeasures, Journal of Library and Information Science, 12 (2016), 105-113.   Google Scholar

Figure 1.  Comparison chart of recognition accuracy for hidden information
Table 1.  Accuracy of different methods in different useless information environments/%
Methods Useless information hiding ratio of single pulse noise/% Useless information hiding ratio of Gaussian white noise/% Useless information hiding ratio of limited white noise/%
Intelligent retrieval method based on omnidirectional wavelet 87.3416 88.6317 83.7895
The proposed method 96.8764 89.3421 93.2419
Methods Useless information hiding ratio of single pulse noise/% Useless information hiding ratio of Gaussian white noise/% Useless information hiding ratio of limited white noise/%
Intelligent retrieval method based on omnidirectional wavelet 87.3416 88.6317 83.7895
The proposed method 96.8764 89.3421 93.2419
Table 2.  Accuracy of different methods under different useless information densities/Bit
Methods Gaussian useless information density
20 Bit 25 Bit 30 Bit 35 Bit 40 Bit
Intelligent retrieval method based on omnidirectional wavelet 84.63 82.83 81.86 89.37 88.64
The proposed method 92.54 93.21 93.54 92.19 91.06
Methods Gaussian useless information density
20 Bit 25 Bit 30 Bit 35 Bit 40 Bit
Intelligent retrieval method based on omnidirectional wavelet 84.63 82.83 81.86 89.37 88.64
The proposed method 92.54 93.21 93.54 92.19 91.06
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