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An Optimized Hybrid Model for Classifying Bacterial Genus using an Integrated CNN-RF Approach on 16S rDNA Sequences

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Title An Optimized Hybrid Model for Classifying Bacterial Genus using an Integrated CNN-RF Approach on 16S rDNA Sequences
 
Creator Meharunnisa, M
Sornam, M
Ramesh, B
 
Subject Convolutional neural networks
Deep learning
Ensemble approach
Feature extraction
Hybrid model
 
Description 392-404
The classification of the bacterial genus based on 16S ribosomal DNA (rDNA) sequences is crucial in microbiology and
medical research. In recent years, deep learning techniques such as Convolutional Neural Networks (CNNs) have shown promising
results in this field. However, these models are limited by the need for large annotated datasets and can be prone to overfitting. On
the other hand, Random Forest (RF) algorithms are well known for their accuracy and robustness, but lack the ability to capture
complex patterns in sequences. In this study, we propose a hybrid CNN-RF model to address these limitations and improve the
classification of the bacterial genus based on 16S rDNA sequences. Our model combines the strengths of both approaches by using
CNNs to extract features from the sequences and RF to make the final classification decision. The proposed hybrid model was
evaluated on a 16S rDNA sequence dataset and showed improved performance compared to both standalone CNN and RF models.
Experimental results show that the proposed model outperforms the existing model in terms of accuracy. On the test set, the
proposed model achieved an accuracy of 98.93% while the standalone CNN and RF with an accuracy of 91.95% and 68.78%
respectively. This work demonstrates the effectiveness of the Integrated CNN-RF approach in bacterial genus classification and
highlights its potential for future applications in microbial research
 
Date 2024-04-09T11:24:20Z
2024-04-09T11:24:20Z
2024-04
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/63729
https://doi.org/10.56042/jsir.v83i4.2670
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.83(4) [April 2024]