Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019141" target="_blank" >RIV/62690094:18470/22:50019141 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-022-09744-2.pdf" target="_blank" >https://www.nature.com/articles/s41598-022-09744-2.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-022-09744-2" target="_blank" >10.1038/s41598-022-09744-2</a>

Result language

angličtina

Original language name

Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application

Original language description

Deep learning has recently been utilized with great success in a large number of diverse application domains, such as visual and face recognition, natural language processing, speech recognition, and handwriting identification. Convolutional neural networks, that belong to the deep learning models, are a subtype of artificial neural networks, which are inspired by the complex structure of the human brain and are often used for image classification tasks. One of the biggest challenges in all deep neural networks is the overfitting issue, which happens when the model performs well on the training data, but fails to make accurate predictions for the new data that is fed into the model. Several regularization methods have been introduced to prevent the overfitting problem. In the research presented in this manuscript, the overfitting challenge was tackled by selecting a proper value for the regularization parameter dropout by utilizing a swarm intelligence approach. Notwithstanding that the swarm algorithms have already been successfully applied to this domain, according to the available literature survey, their potential is still not fully investigated. Finding the optimal value of dropout is a challenging and time-consuming task if it is performed manually. Therefore, this research proposes an automated framework based on the hybridized sine cosine algorithm for tackling this major deep learning issue. The first experiment was conducted over four benchmark datasets: MNIST, CIFAR10, Semeion, and UPS, while the second experiment was performed on the brain tumor magnetic resonance imaging classification task. The obtained experimental results are compared to those generated by several similar approaches. The overall experimental results indicate that the proposed method outperforms other state-of-the-art methods included in the comparative analysis in terms of classification error and accuracy.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific reports

ISSN

2045-2322

e-ISSN

2045-2322

Volume of the periodical

12

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

20

Pages from-to

"Article Number: 6302"

UT code for WoS article

000783914800002

EID of the result in the Scopus database

2-s2.0-85128424342