Root Cause Analysis of Reduced Accessibility in 4G Networks-Reference-Cited by-同舟云学术

Root Cause Analysis of Reduced Accessibility in 4G Networks

Published:2020 Issue: Volume: Page:117-133
ISSN:0302-9743
Container-title:Machine Learning for Networking
language:
Short-container-title:

Author:

Ferreira Diogo^ORCID,Senna Carlos^ORCID,Salvador Paulo^ORCID,Cortesão Luís,Pires Cristina,Pedro Rui,Sargento Susana^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-45778-5_9

Reference11 articles.

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2. Bennacer, L., Amirat, Y., Chibani, A., Mellouk, A., Ciavaglia, L.: Self-diagnosis technique for virtual private networks combining bayesian networks and case-based reasoning. IEEE Trans. Autom. Sci. Eng. 12(1), 354–366 (2015). https://doi.org/10.1109/tase.2014.2321011

3. Chigurupati, A., Lassar, N.: Root cause analysis using artificial intelligence. In: 2017 Annual Reliability and Maintainability Symposium (RAMS), pp. 1–5, January 2017. https://doi.org/10.1109/RAM.2017.7889651

4. Kennedy, D.: D2.6 final report on programme progress and KPIs (2017). https://5g-ppp.eu/wp-content/uploads/2017/10/Euro-5G-D2.6_Final-report-on-programme-progress-and-KPIs.pdf

5. Fisher, A., Rudin, C., Dominici, F.: All models are wrong but many are useful: variable importance for black-box, proprietary, or misspecified prediction models, using model class reliance (2018)

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