eprintid: 20636
rev_number: 3
eprint_status: archive
userid: 1
dir: disk0/00/02/06/36
datestamp: 2026-07-21 06:29:29
lastmod: 2026-07-21 06:29:29
status_changed: 2026-07-21 06:29:29
type: article
metadata_visibility: show
creators_name: Islam, Md Mahmodul
creators_name: Babikir, Ismailalwali
creators_name: Elsaadany, Mohamed
creators_name: Elkurdy, Sami
creators_name: Siddiqui, Numair A.
creators_name: Akinyemi, Oluwaseun Daniel
title: Application of a Pre-Trained CNN Model for Fault Interpretation in the Structurally Complex Browse Basin, Australia
ispublished: pub
note: Cited by: 9; All Open Access; Gold Open Access; Green Open Access
date: 2023
publisher: Multidisciplinary Digital Publishing Institute (MDPI)
official_url: https://www.scopus.com/pages/publications/85192362871?origin=resultslist
id_number: 10.3390/app132011300
full_text_status: none
publication: Applied Sciences (Switzerland)
volume: 13
number: 20
refereed: TRUE
issn: 20763417
citation:   Islam, Md Mahmodul and Babikir, Ismailalwali and Elsaadany, Mohamed and Elkurdy, Sami and Siddiqui, Numair A. and Akinyemi, Oluwaseun Daniel  (2023) Application of a Pre-Trained CNN Model for Fault Interpretation in the Structurally Complex Browse Basin, Australia.  Applied Sciences (Switzerland), 13 (20).   ISSN 20763417