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Advancement of Statistical Analysis, Machine Learning and Decision Analysis Based on the Fourteenth ICMSEM Proceedings

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Proceedings of the Fourteenth International Conference on Management Science and Engineering Management (ICMSEM 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1190))

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Abstract

Over the past few years, there have been significant developments in Management Science and Engineering Management (MSEM), across all fields and industries, which together have contributed to global socio-economic development. In this paper, the basic concepts covered in the fourteenth ICMSEM proceedings Volume I are first described, after which a review of the key areas in management science (MS) research are given: statistical analysis, machine learning and decision analysis. And the related research in Proceedings Volume I are discussed. The research trends from both MSEM journals and the ICMSEM are then summarized using the CiteSpace tool. As always, ICMSEM is committed to providing an international forum for academic exchange and communication and plans to continue these MSEM innovations in the future.

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References

  1. Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2020)

    Google Scholar 

  2. Chen, C.: Citespace ii: detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 57(3), 359–377 (2006)

    Article  Google Scholar 

  3. Clemen, R.T.: Making Hard Decisions: An Introduction to Decision Analysis. Brooks/Cole Publishing Company, Pacific Grove (1996)

    Google Scholar 

  4. Giraldo, R., Dabo-Niang, S., Martinez, S.: Statistical modeling of spatial big data: an approach from a functional data analysis perspective. Stat. Probab. Lett. 136, 126–129 (2018)

    Article  MathSciNet  Google Scholar 

  5. Guo, Y., Wang, N., Xu, Z.Y., Wu, K.: The internet of things-based decision support system for information processing in intelligent manufacturing using data mining technology. Mech. Syst. Sig. Process. 142, 106630 (2020)

    Google Scholar 

  6. Johanna, S.M., David, O.Y., Javier, R.I., et al.: Predictive analysis of urban waste generation for the city of Bogota, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon 5(11) (2019)

    Google Scholar 

  7. Michie, D., Spiegelhalter, D.J., Taylor, C., et al.: Machine learning. Neural Stat. Classif. 13(1994), 1–298 (1994)

    MATH  Google Scholar 

  8. Narula, S.C., Vassilev, V.S., Genova, K.B., et al.: A reference neighbourhood interactive method for solving a class of multiple criteria decision analysis problem. IFAC Proc. 37(19), 131–137 (2004)

    Article  Google Scholar 

  9. Rai, S.N., Qian, C., Pan, J., et al.: Microbiome data analysis with an application to a pre-clinical study using qiime2: statistical considerations. Genes Dis. (2019)

    Google Scholar 

  10. Rezapour, M., Molan, A.M., Ksaibati, K.: Analyzing injury severity of motorcycle at-fault crashes using machine learning techniques, decision tree and logistic regression models. Int. J. Transp. Sci. Technol. (2019)

    Google Scholar 

  11. Smith, C.B.: Adaptive management on the central platte river–science, engineering, and decision analysis to assist in the recovery of four species. J. Environ. Manag. 92, 1414–1419 (2010)

    Google Scholar 

  12. Taji, I., Ghorbani, S., De Brito, J., et al.: Application of statistical analysis to evaluate the corrosion resistance of steel rebars embedded in concrete with marble and granite waste dust. J. Cleaner Prod. 210, 837–846 (2019)

    Article  Google Scholar 

  13. Verma, R.: Management science, theory of constraints/optimized production technology and local optimization. Omega 25(2), 189–200 (1997)

    Article  Google Scholar 

  14. Xu, J., Zhou, L.: An adaptive trivariate dimension-reduction method for statistical moments assessment and reliability analysis. Appl. Math. Model. 82, 748–765 (2020)

    Google Scholar 

  15. Xu, Z.: Uncertain Multi-attribute Decision Making: Methods and Applications. Springer, Heidelberg (2015)

    Google Scholar 

Download references

Acknowledgements

The author gratefully acknowledges Tingting Liu and Ruolan Li’s efforts on the paper collection and classification, Zongmin Li and Yidan Huang’s efforts on data collation and analysis, and Rongwei Sun and Zhiwen Liu’s efforts on the chart drawing.

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Correspondence to Jiuping Xu .

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Xu, J. (2020). Advancement of Statistical Analysis, Machine Learning and Decision Analysis Based on the Fourteenth ICMSEM Proceedings. In: Xu, J., Duca, G., Ahmed, S., García Márquez, F., Hajiyev, A. (eds) Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. ICMSEM 2020. Advances in Intelligent Systems and Computing, vol 1190. Springer, Cham. https://doi.org/10.1007/978-3-030-49829-0_1

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