A review on long short-term memory combination development

Riyadi, Ahmad and Rokhman, Nur and Heryawan, Lukman (2025) A review on long short-term memory combination development. IAES International Journal of Artificial Intelligence, 14 (6). 4427 - 4441. ISSN 20894872

[thumbnail of A REVIEW....pdf] Text
A REVIEW....pdf - Published Version
Restricted to Registered users only

Download (968kB) | Request a copy

Abstract

Long short-term memory (LSTM) has continued to develop since it was proposed in 1997. LSTM has optimized solutions to various problems. The LSTM cell, architecture, and memory model have been reviewed. A review of LSTM implementation has been carried out in various problem domains. There are combinations of LSTM with other methods to optimize solutions. However, there is no review on the development of LSTM combination (LC). This research reviews the development of the LC model on nine research questions, namely: development framework, data, preprocessing, learning process, tasks, optimization and evaluation, domain problems, trends, and challenges. The results show that the LC model is increasingly widespread in solving problems. The LC model has completed 26 types of tasks. Prediction, detection, forecasting, classification, and recognition are the most frequently performed tasks. LC model development trends show that LSTM is increasingly collaborative with other methods on a wider scope. The challenges identified include research areas, data, model developments, the area of implementation, performance, and efficiency.

Item Type: Article
Uncontrolled Keywords: Combination; Deep learning; Long short-term memory; Optimization; Systematic literature review
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Mathematics and Natural Sciences > Computer Science & Electronics Department
Depositing User: Wiyarsih Wiyarsih
Date Deposited: 19 Jun 2026 06:27
Last Modified: 19 Jun 2026 06:27
URI: https://ir.lib.ugm.ac.id/id/eprint/27595

Actions (login required)

View Item
View Item