Gast ::
LiBiNet: Lightweight boundary information enhanced network for crack segmentation
; ; ; ; ; ;
In
Explainable AI and Machine Learning for Water Resources Engineering: Enhancing Decision-Making and Sustainable Development through Interpretable Models in Civil Engineering and Spatial Sciences / Edited by Dr. Komali Kantamaneni, Professor Upaka Rathnayake, Assist. Prof. Sudhir Kumar Singh, Dr. Keval H Jodhani
In
Results in engineering 29, Seiten/Artikel-Nr.:109612
2026
Online
DOI: 10.1016/j.rineng.2026.109612
DOI: 10.18154/RWTH-2026-03214
URL: https://publications.rwth-aachen.de/record/1031965/files/1031965.pdf
Einrichtungen
Thematische Einordnung (Klassifikation)
DDC: 620
OpenAccess: 
PDF
Dokumenttyp
Journal Article/Contribution to a book
Format
online
Sprache
English
Anmerkung
Peer reviewed article
Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-105031171128
WOS Core Collection: WOS:001707139800005
Interne Identnummern
RWTH-2026-03214
Datensatz-ID: 1031965
Beteiligte Länder
Australia, Germany, Peoples R China
; 
; 
; 
; Article Processing Charges ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; Fees ; IF >= 5 ; JCR ; SCOPUS ; Web of Science Core Collection
|
The record appears in these collections: |
PDF