Hyper-Specific CoreDNS Sub-Field Selection & Research Topic Generation: Dynamic BGP Route Propagation Optimization via Reinforcement Learning-Driven Adaptive Weighting of Peer AS Metrics

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-17
DOI
https://doi.org/10.5281/zenodo.16890741
Primary Topic
Theoretical and Computational Physics
Type
article
Field-Weighted Citation Impact
0.00
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article

Hyper-Specific CoreDNS Sub-Field Selection & Research Topic Generation: Dynamic BGP Route Propagation Optimization via Reinforcement Learning-Driven Adaptive Weighting of Peer AS Metrics

Zenodo (CERN European Organization for Nuclear Research)
Theoretical and Computational Physics
article

Hyper-Specific CoreDNS Sub-Field Selection & Research Topic Generation: Dynamic BGP Route Propagation Optimization via Reinforcement Learning-Driven Adaptive Weighting of Peer AS Metrics

article en

Abstract

No abstract available for this paper.
Zenodo (CERN European Organization for Nuclear Research)
Freed–Hardeman University (US)
Openalex Percentile: Top 58%
Theoretical and Computational Physics
DOIOpen Access PDFOpenAlex
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Hyper-Specific CoreDNS Sub-Field Selection & Research Topic Generation: Dynamic BGP Route Propagation Optimization via Reinforcement Learning-Driven Adaptive Weighting of Peer AS Metrics · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS