eprintid: 19621
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/96/21
datestamp: 2024-06-04 14:19:21
lastmod: 2024-06-04 14:19:21
status_changed: 2024-06-04 14:15:26
type: article
metadata_visibility: show
creators_name: Al-Selwi, S.M.
creators_name: Hassan, M.F.
creators_name: Abdulkadir, S.J.
creators_name: Muneer, A.
creators_name: Sumiea, E.H.
creators_name: Alqushaibi, A.
creators_name: Ragab, M.G.
title: RNN-LSTM: From applications to modeling techniques and beyond�Systematic review
ispublished: pub
note: cited By 0
abstract: Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN) algorithm known for its ability to effectively analyze and process sequential data with long-term dependencies. Despite its popularity, the challenge of effectively initializing and optimizing RNN-LSTM models persists, often hindering their performance and accuracy. This study presents a systematic literature review (SLR) using an in-depth four-step approach based on the PRISMA methodology, incorporating peer-reviewed articles spanning 2018�2023. It aims to address how weight initialization and optimization techniques can bolster RNN-LSTM performance. This SLR offers a detailed overview across various applications and domains, and stands out by comprehensively analyzing modeling techniques, datasets, evaluation metrics, and programming languages associated with these networks. The findings of this SLR provide a roadmap for researchers and practitioners to enhance RNN-LSTM networks and achieve superior results. © 2024 The Author(s)
date: 2024
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193947329&doi=10.1016%2fj.jksuci.2024.102068&partnerID=40&md5=2c87f8f56c9f765c907d979bbf351dc7
id_number: 10.1016/j.jksuci.2024.102068
full_text_status: none
publication: Journal of King Saud University - Computer and Information Sciences
volume: 36
number: 5
refereed: TRUE
citation:   Al-Selwi, S.M. and Hassan, M.F. and Abdulkadir, S.J. and Muneer, A. and Sumiea, E.H. and Alqushaibi, A. and Ragab, M.G.  (2024) RNN-LSTM: From applications to modeling techniques and beyond�Systematic review.  Journal of King Saud University - Computer and Information Sciences, 36 (5).