TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

A detailed examination of LSTM networks for large-scale text generation with optimizing sequence models

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pp. 1477–1486Vol. 45Issue 5July 2024DOI: 10.47974/JIOS-1769XML
Received:
09 Apr 2024
Published Online:
12 Aug 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1769
Pages:
1477–1486

Abstract

It discover long-range connections in direct information, Long Short-Term Memory (LSTM) systems have gotten to be valuable for making huge sums of content. We take a near see at LSTM systems in this think about, particularly how they can be utilized and moved forward for content era assignments. In this paper, we see into the structure of LSTM systems and appear their one of a kind capacity to memorize and keep in mind things over long arrangement of occasions. Our investigate moreover looks into the problems that come up after you attempt to prepare LSTM systems to make huge sums of content, such as over fitting and vanishing slants. To bargain with these problems, we see into distinctive optimization strategies, like dropout regularization and slope clipping, to form LSTM-based text creation models more steady and viable. In expansion, we see into ways to fine-tune hyper parameters to urge the most excellent comes about. We appear that LSTM systems can make consistent and valuable content in a assortment of settings by doing a parcel of tests on diverse content collections. These tests cover a wide extend of employments, such as dialect modelling, discussion era, and imaginative composing. Our finding about educate us a parcel approximately how to construct and progress LSTM-based grouping models for large-scale content era assignments. This opens the entryway to indeed more advance in AI and characteristic dialect preparing. 

Keywords

Subject Classifications

Primary 00A05Secondary 97P40

References

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