Recurrent Neural Networks - Architectures and Applications: Analyzing architectures and applications of recurrent neural networks (RNNs) for modeling sequential data and time-series prediction

Authors

  • Dr. Ngozi Oluwafemi Associate Professor of Artificial Intelligence, Covenant University, Nigeria Author

Keywords:

GRU, Time-Series Forecasting

Abstract

Recurrent Neural Networks (RNNs) have emerged as a powerful tool for modeling sequential data and time-series prediction due to their ability to capture temporal dependencies. This paper provides a comprehensive analysis of the architectures and applications of RNNs in various domains. We begin by discussing the basic architecture of RNNs and then delve into more advanced variants, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), highlighting their differences and advantages.

Next, we explore the diverse applications of RNNs, including natural language processing (NLP), speech recognition, machine translation, and time-series forecasting. We discuss how RNNs are used in each application, their performance, and challenges faced in real-world scenarios. Additionally, we review recent developments and trends in RNN research, such as attention mechanisms and transformer-based architectures, which have further improved the capabilities of RNNs.

Finally, we conclude with a discussion on future directions and potential research avenues for advancing RNN architectures and applications.

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References

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K. Joel Prabhod, “ASSESSING THE ROLE OF MACHINE LEARNING AND COMPUTER VISION IN IMAGE PROCESSING,” International Journal of Innovative Research in Technology, vol. 8, no. 3, pp. 195–199, Aug. 2021, [Online]. Available: https://ijirt.org/Article?manuscript=152346

Tatineni, Sumanth, and Sandeep Chinamanagonda. “Leveraging Artificial Intelligence for Predictive Analytics in DevOps: Enhancing Continuous Integration and Continuous Deployment Pipelines for Optimal Performance”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Feb. 2021, pp. 103-38, https://aimlstudies.co.uk/index.php/jaira/article/view/104.

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Published

30-12-2023

How to Cite

[1]
Dr. Ngozi Oluwafemi, “Recurrent Neural Networks - Architectures and Applications: Analyzing architectures and applications of recurrent neural networks (RNNs) for modeling sequential data and time-series prediction”, Australian Journal of Machine Learning Research & Applications, vol. 3, no. 2, pp. 239–249, Dec. 2023, Accessed: Nov. 07, 2024. [Online]. Available: https://sydneyacademics.com/index.php/ajmlra/article/view/61

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