{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:11:51Z","timestamp":1778778711874,"version":"3.51.4"},"reference-count":78,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In 1997, Long Short-Term Memory (LSTM) networks were proposed, which significantly changed the landscape of sequential data analysis by resolving the critical issue of the vanishing gradient problem in recurrent neural networks (RNNs). Over the last 25 years, LSTM has advanced from its inception as an innovative solution to its widespread adoption as an essential tool in various fields, including natural language processing (NLP), speech recognition, financial prediction, and healthcare analytics. The present study is a bibliometric review of the evolution of LSTMs. The evolution of LSTM is discussed in terms of its theoretical advancements, architectural developments, and its applications. The study is based on data obtained from the Scopus database, which is then analyzed to identify publication patterns, prominent authors, prominent institutions, and global contributions to the field. The present study is an insightful review of the evolution of LSTM, highlighting its developments and advancements, as well as its applications, to identify its future scope.<\/jats:p>","DOI":"10.3390\/a19050390","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T16:27:56Z","timestamp":1778776076000},"page":"390","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Long Short-Term Memory Networks Since Their Inception: Mapping 25 Years of Scientific Development via Bibliometric Analysis"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8861-8865","authenticated-orcid":false,"given":"Subhashree","family":"Mohapatra","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India"},{"name":"Faculty of Climate Change and Sustainability, Asian Institute of Technology, Klong Luang 12120, Pathumthani, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jai Govind","family":"Singh","sequence":"additional","affiliation":[{"name":"Faculty of Climate Change and Sustainability, Asian Institute of Technology, Klong Luang 12120, Pathumthani, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subham Pankaj","family":"Samantaray","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2160-4703","authenticated-orcid":false,"given":"Manohar","family":"Mishra","sequence":"additional","affiliation":[{"name":"Faculty of Climate Change and Sustainability, Asian Institute of Technology, Klong Luang 12120, Pathumthani, Thailand"},{"name":"Department of Electrical and Electronics Engineering, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys. 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