Long Short-Term Memory (LSTM): Trends and Future Research Potential

Abstract

-One of the most widely used machine learning methods, Long Short-Term Memory (LSTM), is particularly useful for time series prediction. In this study, we carried out a bibliometric analysis against publications about LSTMs to identify trends and contributions of researchers in the development of machine learning technology. We collect bibliometric data from the Scopus database and use the bibliometric analysis method to analyze trends and contributions of researchers in publications about LSTM. Results of the bibliometric analysis show that LSTM is a lot used in related machine learning applications with time series data and is one the most popular technique for use in predictions. In addition, the use of LSTM is often combined with other deep learning methods, such as neural networks, to improve accuracy prediction. In addition, the results of the bibliometric analysis also show that the use of LSTM has spread to various fields, such as in handwriting recognition, processing Language experience, and recognition of a face. Implications from the results of this study are that the use of LSTM can provide solutions that are accurate and effective in solving prediction problems in various fields, especially in practical applications such as business, health, and transportation. The results of the LSTM bibliometric analysis can provide a broader view of trends and the contributions of researchers to the development of machine learning technology, as well as identify potential research areas for further development. Therefore, this research provides an important contribution to strengthening the results of previous research and showing that the use of LSTM has great potential in the development of future machine learning technology