Version 1
: Received: 11 August 2024 / Approved: 12 August 2024 / Online: 12 August 2024 (12:47:52 CEST)
How to cite:
Mienye, I. D.; Swart, T. G.; Obaido, G. Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications. Preprints2024, 2024080748. https://doi.org/10.20944/preprints202408.0748.v1
Mienye, I. D.; Swart, T. G.; Obaido, G. Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications. Preprints 2024, 2024080748. https://doi.org/10.20944/preprints202408.0748.v1
Mienye, I. D.; Swart, T. G.; Obaido, G. Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications. Preprints2024, 2024080748. https://doi.org/10.20944/preprints202408.0748.v1
APA Style
Mienye, I. D., Swart, T. G., & Obaido, G. (2024). Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications. Preprints. https://doi.org/10.20944/preprints202408.0748.v1
Chicago/Turabian Style
Mienye, I. D., Theo G. Swart and George Obaido. 2024 "Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications" Preprints. https://doi.org/10.20944/preprints202408.0748.v1
Abstract
Recurrent Neural Networks (RNNs) have significantly advanced the field of machine learning by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures such as Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Bidirectional LSTM (BiLSTM), and stacked LSTM. The study examines the application of RNNs in different domains, including natural language processing (NLP), speech recognition, financial time series forecasting, bioinformatics, autonomous vehicles, and anomaly detection. Additionally, the study discusses recent innovations, such as the integration of attention mechanisms and the development of hybrid models that combine RNNs with convolutional neural networks (CNNs) and transformer architectures. This review aims to provide machine learning researchers and practitioners with a comprehensive overview of the current state and future directions of RNN research.
Keywords
Deep learning; GRU; LSTM; Machine learning; NLP; RNN
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
