In order to improve the application of teaching resources and reduce delays in the integration process of multimedia network, a rational resource allocation method for multimedia network teaching reform based on Bayesian partition data mining is proposed. Bayesian partition is used to preprocess the multimedia network teaching resources (MNTR), adjusting the recognition probability of MNTR in each partition based on its attributes. By performing Bayesian quantitative classification using samples of MNTR, the prior probability is adjusted through maximization analysis. The partitioned resources undergo sample data mining to obtain the data category collection of all MNTR. A prediction model is then built to forecast the demand for teaching resources at specific times in the future. MNTR can be rationally allocated based on the prediction results. Experimental results demonstrate that this method reduces delays in MNTR application and improves the accuracy and utilization of teaching resources.
Citation: Juan Li, Geng Sun. A rational resource allocation method for multimedia network teaching reform based on Bayesian partition data mining[J]. Electronic Research Archive, 2023, 31(10): 5959-5975. doi: 10.3934/era.2023303
In order to improve the application of teaching resources and reduce delays in the integration process of multimedia network, a rational resource allocation method for multimedia network teaching reform based on Bayesian partition data mining is proposed. Bayesian partition is used to preprocess the multimedia network teaching resources (MNTR), adjusting the recognition probability of MNTR in each partition based on its attributes. By performing Bayesian quantitative classification using samples of MNTR, the prior probability is adjusted through maximization analysis. The partitioned resources undergo sample data mining to obtain the data category collection of all MNTR. A prediction model is then built to forecast the demand for teaching resources at specific times in the future. MNTR can be rationally allocated based on the prediction results. Experimental results demonstrate that this method reduces delays in MNTR application and improves the accuracy and utilization of teaching resources.
| [1] |
R. Liang, Analysis of the influence of multimedia network hybrid teaching method on college students learning ability in physical education, Int. J. Contin. Eng. Educ. Life-Long Learn., 29 (2019), 101–112. https://doi.org/10.1504/IJCEELL.2019.10018933 doi: 10.1504/IJCEELL.2019.10018933
|
| [2] |
B. V. Tucker, M. C. Kelley, C. Redmon, A place to share teaching resources: speech and language resource bank, J. Acoust. Soc. Am., 149 (2021), 147. https://doi.org/10.1121/10.0005365 doi: 10.1121/10.0005365
|
| [3] |
J. T. Gomez, A. Rodriguez-Hidalgo, Y. V. J. Naranjo, C. Pelaez-Moreno, Teaching differently: the digital signal processing of multimedia content through the use of liberal arts, IEEE Signal Process. Mag., 38 (2021), 94–104. https://doi.org/10.1109/MSP.2021.3053218 doi: 10.1109/MSP.2021.3053218
|
| [4] |
T. Wei, L. Chen, A risk preference model for teaching resource allocation based on functional link fuzzy neural network algorithm classifier, Int. J. Comput. Appl. Technol., 61 (2019), 88–93. https://doi.org/10.1504/IJCAT.2019.10023594 doi: 10.1504/IJCAT.2019.10023594
|
| [5] |
C. Q. Liu, Y. Wei, H. F. Ren, Q. Zhou, J. Chen, H. X. Guo, et al., Strategies and criteria targeting the reform and development for the teaching resource of nursing undergraduate: a multi-center cross-sectional study, Medicine, 100 (2021), 25853. https://doi.org/10.1097/MD.0000000000025853 doi: 10.1097/MD.0000000000025853
|
| [6] |
S. Bayat, A. Khalili, S. Zargari, M. R. Mili, H. Zhu, Multi-objective resource allocation for D2D and enabled MC-NOMA networks by tchebycheff method, IEEE Trans. Veh. Technol., 70 (2021), 4464–4470. https://doi.org/10.1109/TVT.2021.3068855 doi: 10.1109/TVT.2021.3068855
|
| [7] |
J. Sun, Research on resource allocation of vocal music teaching system based on mobile edge computing, Comput. Commun., 160 (2020), 342–350. https://doi.org/10.1016/j.comcom.2020.05.016 doi: 10.1016/j.comcom.2020.05.016
|
| [8] | S. Ao, X. Peng, Z. Liu, Multi role optimization strategic grey wolf optimizer-symbiotic organisms search algorithm, J. Chin. Comput. Syst., 42 (2021), 2276–2283. |
| [9] |
L. Lu, J. Zhou, Research on mining of applied mathematics educational resources based on edge computing and data stream classification, Mobile Inf. Syst., 2021 (2021), 1–8. https://doi.org/10.1155/2021/5542718 doi: 10.1155/2021/5542718
|
| [10] |
Y. Jin, R. Guo, X. Yu, L. Shen, Explosive synchronization of multi-layer complex networks based on inter-layer star network connection, Chin. Phys. B, 30 (2021), 120505. https://doi.org/10.1088/1674-1056/abff41 doi: 10.1088/1674-1056/abff41
|
| [11] |
X. Wu, H. Wang, D. Wei, M. Shi, ANFIS with natural language processing and gray relational analysis based cloud computing framework for real time energy efficient resource allocation, Comput. Commun., 150 (2020), 122–130. https://doi.org/10.1016/j.comcom.2019.11.015 doi: 10.1016/j.comcom.2019.11.015
|
| [12] |
P. Rubio, L. Chamoin, F. Louf, Real-time bayesian data assimilation with data selection, correction of model bias, and on-the-fly uncertainty propagation, C. R. Mec., 347 (2019), 762–779. https://doi.org/10.1016/j.crme.2019.11.004 doi: 10.1016/j.crme.2019.11.004
|
| [13] |
C. A. Furia, R. Feldt, R. Torkar, Bayesian data analysis in empirical software engineering research, IEEE Trans. Software Eng., 47 (2021), 1786–1810. https://doi.org/10.1109/TSE.2019.2935974 doi: 10.1109/TSE.2019.2935974
|
| [14] |
T. Liu, Y. Liu, J. Liu, L. Wang, L. Xu, G. Qiu, et al., A Bayesian learning based scheme for online dynamic security assessment and preventive control, IEEE Trans. Power Syst., 35 (2020), 4088–4099. https://doi.org/10.1109/TPWRS.2020.2983477 doi: 10.1109/TPWRS.2020.2983477
|
| [15] |
A. Esuli, A. Molinari, F. Sebastiani, A critical reassessment of the saerens-latinne-decaestecker algorithm for posterior probability adjustment, ACM Trans. Inf. Syst., 39 (2020), 1–34. https://doi.org/10.1145/3433164 doi: 10.1145/3433164
|
| [16] |
B. A. Armstrong, E. P. Sparrow, J. Spaniol, The effect of information formats and incidental affect on prior and posterior probability judgments, Med. Decis. Making, 40 (2020). https://doi.org/10.1177/0272989X20938056 doi: 10.1177/0272989X20938056
|
| [17] | L. Zhao, B. Tian, H. Chen, Node location algorithm for LTE-5G sensor network based on data bubble correction, Comput. Simul., 38 (2021), 253–258. |
| [18] |
Y. Fan, L. Yu, X. Shi, Uncertainty quantification and partition for multivariate risk inferences through a factorial multimodel Bayesian copula (FMBC) system, J. Hydrol., 598 (2021), 126406. https://doi.org/10.1016/j.jhydrol.2021.126406 doi: 10.1016/j.jhydrol.2021.126406
|
| [19] |
L. Yu, D. Lin, Bayesian-based search decision framework and search strategy analysis in probabilistic search, Sci. Program., 2020 (2020), 8865381. https://doi.org/10.1155/2020/8865381 doi: 10.1155/2020/8865381
|
| [20] |
X. Yang, Research on integration method of ai teaching resources based on learning behavior data analysis, Int. J. Contin. Eng. Educ. Life-Long Learn., 30 (2020), 492–508. https://doi.org/10.1504/IJCEELL.2020.10030602 doi: 10.1504/IJCEELL.2020.10030602
|
| [21] |
E. Sauta, A. Demartini, F. Vitali, A. Riva, R. Bellazzi, A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks, BMC Bioinf., 21 (2020), 219. https://doi.org/10.1186/s12859-020-3510-1 doi: 10.1186/s12859-020-3510-1
|
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Juan Li, Geng Sun. A rational resource allocation method for multimedia network teaching reform based on Bayesian partition data mining[J]. Electronic Research Archive, 2023, 31(10): 5959-5975. doi: 10.3934/era.2023303
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