End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning.

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Bibliographic Details
Title: End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning.
Authors: Ur Rehman HMR; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea., Gul MJ; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea., Younas R; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea., Jhandir MZ; Department of Data Science, The Islamia University of Bahawalpur, Bahawalpur, Pakistan., Alvarez RM; Universidad Europea del Atlantico, Isabel Torres 21, 39011, Santander, Spain.; Universidad Internacional Iberoamericana, Arecibo, PR, 00613, USA.; Universidade Internacional do Cuanza, Cuito, Angola., Miro Y; Universidad Europea del Atlantico, Isabel Torres 21, 39011, Santander, Spain.; Universidad Internacional Iberoamericana, 24560, Campeche, Mexico.; Universidad de La Romana, La Romana, Dominican Republic., Ashraf I; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea. imranashraf@ynu.ac.kr.
Source: Scientific reports [Sci Rep] 2026 Jan 26; Vol. 16 (1), pp. 6226. Date of Electronic Publication: 2026 Jan 26.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2045-2322
DOI:10.1038/s41598-026-37191-w