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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34843-3_4
Reference56 articles.
1. Adikaram, K. K. L. B., Hussein, M. A., Effenberger, M., & Becker, T. (2015). Data transformation technique to improve the outlier detection power of Grubbs’ test for data expected to follow linear relation. Journal of Applied Mathematics, 2015, e708948. https://doi.org/10.1155/2015/708948
2. An, J., Kim, K., Mortara, L., & Lee, S. (2018). Deriving technology intelligence from patents: Preposition-based semantic analysis. Journal of Informetrics, 12(1), 217–236. https://doi.org/10.1016/j.joi.2018.01.001
3. Aslam, M. (2020). Introducing Grubbs’s test for detecting outliers under neutrosophic statistics – An application to medical data. Journal of King Saud University – Science, 32(6), 2696–2700. https://doi.org/10.1016/j.jksus.2020.06.003
4. Aversa, A., Saboori, A., Marchese, G., Iuliano, L., Lombardi, M., & Fino, P. (2021). Recent progress in beam-based metal additive manufacturing from a materials perspective: A review of patents. Journal of Materials Engineering and Performance, 30(12), 8689–8699. https://doi.org/10.1007/s11665-021-06273-3
5. Benarous, L., Kadri, B., & Bouridane, A. (2017). A survey on cyber security evolution and threats: biometric authentication solutions. In R. Jiang, S. Al-maadeed, A. Bouridane, D. Crookes, & A. Beghdadi (Eds.), Biometric security and privacy: Opportunities & challenges in The Big Data Era (pp. 371–411). Springer. https://doi.org/10.1007/978-3-319-47301-7_15
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