A Machine-Learning-Based Action Recommender for Network Operation Centers
Article Properties
-
DOI (url)
-
Publication Date2021/09/01
-
Indian UGC (journal)
-
Refrences50
-
Citations3
-
Shady A. Mohammed ORCID (unauthenticated)
-
Ayse Rumeysa Mohammed ORCID (unauthenticated)
-
David Cote ORCID (unauthenticated)
-
Shervin Shirmohammadi ORCID (unauthenticated)
Cite
Mohammed, Shady A., et al. “A Machine-Learning-Based Action Recommender for Network Operation Centers”. IEEE Transactions on Network and Service Management, vol. 18, no. 3, 2021, pp. 2702-13, https://doi.org/10.1109/tnsm.2021.3095463.
Mohammed, S. A., Mohammed, A. R., Cote, D., & Shirmohammadi, S. (2021). A Machine-Learning-Based Action Recommender for Network Operation Centers. IEEE Transactions on Network and Service Management, 18(3), 2702-2713. https://doi.org/10.1109/tnsm.2021.3095463
Mohammed, Shady A., Ayse Rumeysa Mohammed, David Cote, and Shervin Shirmohammadi. “A Machine-Learning-Based Action Recommender for Network Operation Centers”. IEEE Transactions on Network and Service Management 18, no. 3 (2021): 2702-13. https://doi.org/10.1109/tnsm.2021.3095463.
1.
Mohammed SA, Mohammed AR, Cote D, Shirmohammadi S. A Machine-Learning-Based Action Recommender for Network Operation Centers. IEEE Transactions on Network and Service Management. 2021;18(3):2702-13.
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Using machine learning in communication networks | 2018 | |||
| DOCKER: Lightweight Linux containers for consistent development and deployment | 2014 | |||
| Using ensemble methods to solve the problem of pulsar search | 2019 | |||
| A pro-active and adaptive mechanism for fast failure recovery in SDN data centers | 2018 | |||
| Machine learning based network status detection and fault localization | 2019 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| On the Use of Spatial Graphs for Performance Degradation Root-Cause Analysis Toward Self-Healing Mobile Networks | IEEE Access |
| 2024 | |
| Toward a Superintelligent Action Recommender for Network Operation Centers Using Reinforcement Learning | IEEE Access |
| 2023 | |
Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications | Security and Communication Networks |
| 2 | 2022 |
Citations Analysis
The category
Science: Science (General): Cybernetics: Information theory 3
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications and was published in 2022. The most recent citation comes from a 2024 study titled On the Use of Spatial Graphs for Performance Degradation Root-Cause Analysis Toward Self-Healing Mobile Networks. This article reached its peak citation in 2024, with 1 citations. It has been cited in 2 different journals, 50% of which are open access. Among related journals, the IEEE Access cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.