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HBMO-RAS: A Hybrid Bio-Inspired and ML-Augmented Optimization Framework for URLLC-Grade Resource Allocation in 6G Digital Twin-Enabled IIoT

Computers, Materials & Continua · Kurunandan Jain, Prabu Pachiyannan, Prabhakar Krishnan et al. · 2026

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Quick facts

Author
Kurunandan Jain, Prabu Pachiyannan, Prabhakar Krishnan, Ramesh Chandra Poonia, Ahmed Zohair Ibrahim ref
Published
ref
Publisher
Tech Science Press ref
Journal
Computers, Materials & Continua ref

Questions

Who wrote HBMO-RAS: A Hybrid Bio-Inspired and ML-Augmented Optimization Framework for URLLC-Grade Resource Allocation in 6G Digital Twin-Enabled IIoT?
HBMO-RAS: A Hybrid Bio-Inspired and ML-Augmented Optimization Framework for URLLC-Grade Resource Allocation in 6G Digital Twin-Enabled IIoT was written by Kurunandan Jain, Prabu Pachiyannan, Prabhakar Krishnan, Ramesh Chandra Poonia.
When was HBMO-RAS: A Hybrid Bio-Inspired and ML-Augmented Optimization Framework for URLLC-Grade Resource Allocation in 6G Digital Twin-Enabled IIoT first published?
HBMO-RAS: A Hybrid Bio-Inspired and ML-Augmented Optimization Framework for URLLC-Grade Resource Allocation in 6G Digital Twin-Enabled IIoT was first published in 2026.

Sources

  1. Publisher record (DOI, via Crossref)

Last updated · facts checked 12 November 2026

Cite this page

"HBMO-RAS: A Hybrid Bio-Inspired and ML-Augmented Optimization Framework for URLLC-Grade Resource Allocation in 6G Digital Twin-Enabled IIoT". DigiNepal. Last updated 12 November 2026. https://diginpl.com/books/paper-cmc-2026-086903. Retrieved 14 November 2026.

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Open-access publication. Read it free at the publisher: https://doi.org/10.32604/cmc.2026.086903.

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