Open Access
Int. J. Simul. Multidisci. Des. Optim.
Volume 15, 2024
Article Number 5
Number of page(s) 10
Published online 12 April 2024
  1. B. Wang, F. Tao, X. Fang, C. Liu, Y. Liu, T. Freiheit, Smart manufacturing and intelligent manufacturing: a comparative review, Engineering 7, 738–757 (2021) [Google Scholar]
  2. Y. Fu, Y. Hou, Z. Wang, X. Wu, K. Gao, L. Wang, Distributed scheduling problems in intelligent manufacturing systems, Tsinghua Sci. Technol. 26, 625–645 (2021) [Google Scholar]
  3. H. Dai, H. Wang, G. Xu, J. Wan, M. Lmran, Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies, Enterprise Inform. Syst. 14, 1279–1303 (2020) [Google Scholar]
  4. C. Zhang, G. Zhou, H. Li, C. Yan, Manufacturing blockchain of things for the configuration of a data-and knowledge-driven digital twin manufacturing cell, IEEE Internet Things J. 7, 11884–11894 (2020) [Google Scholar]
  5. M. Barma, U. Modibbo, Multiobjective mathematical optimization model for municipal solid waste management with economic analysis of reuse/recycling recovered waste materials, J. Comput. Cogn. Eng. 1, 122–137 (2022) [Google Scholar]
  6. L. Li, J. Zhang, Research and analysis of an enterprise E-commerce marketing system under the big data environment, J. Organizational End User Comput. 33, 1–19 (2021) [Google Scholar]
  7. R. Li, J. Rao, L. Wan, The digital economy, enterprise digital transformation, and enterprise innovation, Manag. Decis. Econ. 43, 2875–2886 (2022) [Google Scholar]
  8. G. Du, Y. Lin, Brand connection and entry in the shopping mall ecological chain: evidence from consumer behavior big data analysis based on two-sided markets, J. Cleaner Product. 364, 1–12 (2022) [Google Scholar]
  9. O. Kulkarni, S. Jena, V. Sankar, MapReduce framework based big data clustering using fractional integrated sparse fuzzy C means algorithm, IET Image Process. 14, 2719–2727 (2020) [Google Scholar]
  10. Z. Chen, Y. Meng, R. Wang, T. Chen, Water quality big data analysis of the river basin with artificial intelligence ADV monitoring, Membrane Water Treat. 13, 219–225 (2022) [Google Scholar]
  11. H. Thai, J. Huh, Optimizing patient transportation by applying cloud computing and big data analysis, J. Supercomput. 78, 18061–18090 (2022) [Google Scholar]
  12. S. Sasikala, S. Gomathi, V. Geetha, L. Murali, A proposed framework for cloud-aware multimodal multimedia big data analysis toward optimal resource allocation, Comput. J. 64, 880–894 (2021) [Google Scholar]
  13. M. Agersted, K. Babak, Y. Liu, W. Melle, T. Klevjer, Application of an unsupervised clustering algorithm on in situ broadband acoustic data to identify different mesopelagic target types, ICES J. Mar. Sci. 78, 2907–2921 (2021) [Google Scholar]
  14. E. Zhang, H. Li, Y. Huang, S. Hong, L. Zhao, C. Ji, Practical multi-party private collaborative k-means clustering, Neurocomputing 467, 256–265 (2022) [Google Scholar]
  15. D. Luo, H. Liu, E. Qi, Recognition and labeling of faults in wind turbines with a density-based clustering algorithm, Data Technol. Appl. 55, 841–868 (2021) [Google Scholar]
  16. A. Cupak, G. Kaczor, Regionalization of low flow for chosen catchments of the upper Vistula river basin using non-hierarchical cluster analysis, Idojaras 126, 27–45 (2022) [Google Scholar]
  17. X. Tang, X. Ji, J. Liu, Predicting aircraft taxiing estimated time of arrival by cluster analysis, IET Intell. Trans. Syst. 16, 252–262 (2022) [Google Scholar]
  18. A. Brintrup, J. Pak, D. Ratiney, T. Pearce, P. Wichmann, P. Woodall, Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing, Int. J. Product. Res. 58, 3330–3341 (2020) [Google Scholar]
  19. H. Henderi, T. Wahyuningsih, E. Rahwanto, Comparison of Min-Max normalization and Z-score normalization in the K-nearest neighbor (kNN) algorithm to test the accuracy of types of breast cancer, Int. J. Inform. Inform. Syst. 4, 13–20 (2021) [Google Scholar]
  20. P. Anitha, M. Patil, RFM model for customer purchase behavior using K-means algorithm, J. King Saud Univ. Comput. Inf. Sci. 34, 1785–1792 (2022) [Google Scholar]

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