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. 2021 Dec 17;11:24138. doi: 10.1038/s41598-021-03019-y

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Training time analysis of the HHO-DBN model under the epsilon dataset. The results portrayed that CPIO-FS with the HHO-DBN technique resulted in the least training runtime compared to the other methods. For instance, with no FS, the HHO-DBN technique has gained a lower runtime of 318.54 s, whereas the SVMC, LRC, and NBC techniques have accomplished a higher runtime of 400.38 s, 430.48 s, and 340.42 s, respectively. Moreover, sequential CHC, the HHO-DBN approach has attained a minimal runtime of 265.98 s, whereas the SVMC, LRC, and NBC approaches have accomplished superior runtimes of 345.27 s, 398.07 s, and 300.21 s, respectively. Furthermore, MR-EFS, the HHO-DBN method has reached an increased runtime of 243.09 s, whereas the SVMC, LRC, and NBC methodologies have accomplished a higher runtime of 334.18 s, 367.29 s, and 264.26 s, respectively. Finally, CPIO-FS, the HHO-DBN method has attained a lower runtime of 218.96 s, whereas the SVMC, LRC, and NBC algorithms have accomplished superior runtimes of 298.36 s, 312.78 s, and 220.90 s, respectively.