eprintid: 20399
rev_number: 3
eprint_status: archive
userid: 1
dir: disk0/00/02/03/99
datestamp: 2025-11-06 06:22:44
lastmod: 2025-11-06 06:22:44
status_changed: 2025-11-06 06:22:44
type: article
metadata_visibility: show
creators_name: Radzman, Muhammad Iqmmal Rezzwan
creators_name: Mahamad, Abd Kadir Bin
creators_name: Muji, Siti Zarina Mohd
creators_name: Saon, Sharifah
creators_name: Ahmadon, Mohd Anuaruddin
creators_name: Yamaguchi, Shingo
creators_name: Setiawan, Muhammad Ikhsan
title: Pipe leakage detection system with artificial neural network
ispublished: pub
note: Cited by: 3; All Open Access, Gold Open Access, Green Open Access
date: 2022
publisher: Institute of Advanced Engineering and Science
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132394464&doi=10.11591%2fijai.v11.i3.pp977-985&partnerID=40&md5=00207ca516369e360c9f6edec5ba1749
id_number: 10.11591/ijai.v11.i3.pp977-985
full_text_status: none
publication: IAES International Journal of Artificial Intelligence
volume: 11
number: 3
pagerange: 977 – 985
refereed: TRUE
issn: 20894872
citation:   Radzman, Muhammad Iqmmal Rezzwan and Mahamad, Abd Kadir Bin and Muji, Siti Zarina Mohd and Saon, Sharifah and Ahmadon, Mohd Anuaruddin and Yamaguchi, Shingo and Setiawan, Muhammad Ikhsan  (2022) Pipe leakage detection system with artificial neural network.  IAES International Journal of Artificial Intelligence, 11 (3).  977 – 985.  ISSN 20894872