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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 RNN-LSTM networks. The findings of this SLR provide a roadmap for researchers and practitioners to enhance RNN-LSTM networks and achieve superior results.
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Safwan Mahmood Al-Selwi
Mohd Fadzil Hassan
Said Jadid Abdulkadir
Journal of King Saud University - Computer and Information Sciences
Universiti Teknologi Petronas
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Al-Selwi et al. (Tue,) studied this question.
www.synapsesocial.com/papers/68e690f6b6db643587617aea — DOI: https://doi.org/10.1016/j.jksuci.2024.102068