articleJournal of King Saud University - Computer and Information SciencesMay 21, 2024HYBRID OA

RNN-LSTM: From applications to modeling techniques and beyond—Systematic review

SMSafwan Mahmood Al-SelwiMFMohd Fadzil HassanSJSaid Jadid AbdulkadirAMAmgad MuneerEHEbrahim Hamid Sumiea

Universiti Teknologi Petronas

Indexed incrossrefdoaj

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…

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416
total citations
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130.37
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100%
References
219
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Authors

7

Topics & keywords

Topics
Keywords
  • Recurrent neural network
  • Computer science
  • Initialization
  • Artificial intelligence
  • Popularity
  • Machine learning
  • Process (computing)
  • Systematic review
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