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Deep Learning Models of LSTM-Ann and Bilstm-ANN for Classification Accuracy

by M. Srisankar, K.P. Lochanambal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 3
Year of Publication: 2024
Authors: M. Srisankar, K.P. Lochanambal
10.5120/ijca2024923363

M. Srisankar, K.P. Lochanambal . Deep Learning Models of LSTM-Ann and Bilstm-ANN for Classification Accuracy. International Journal of Computer Applications. 186, 3 ( Jan 2024), 22-27. DOI=10.5120/ijca2024923363

@article{ 10.5120/ijca2024923363,
author = { M. Srisankar, K.P. Lochanambal },
title = { Deep Learning Models of LSTM-Ann and Bilstm-ANN for Classification Accuracy },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2024 },
volume = { 186 },
number = { 3 },
month = { Jan },
year = { 2024 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number3/33053-2024923363/ },
doi = { 10.5120/ijca2024923363 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:29:37.409838+05:30
%A M. Srisankar
%A K.P. Lochanambal
%T Deep Learning Models of LSTM-Ann and Bilstm-ANN for Classification Accuracy
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 3
%P 22-27
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers This paper has illustrated the deep integration of BiLSTM-ANN (Fully Connected Neural Network) and LSTM-ANN and manifested how these integration methods are performing better than single BiLSTM, LSTM and ANN models is discussed. This overview covers different classification methods, Sentiment analysis work, and existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in real-world problems are discussed.

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Index Terms

Computer Science
Information Sciences

Keywords

Tokenization LSTM-ANN Bi-LSTM-ANN

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