FUZ-SMO: A fuzzy slime mould optimizer for mitigating false alarm rates in the classification of underwater datasets using deep convolutional neural networks

FUZ-SMO: A fuzzy slime mould optimizer for mitigating false alarm rates in the classification of underwater datasets using deep convolutional neural networks

Sonar sound datasets are of significant importance in the domains of underwater surveillance and marine research as they enable experts to discern intricate patterns within the depths of the water. Nevertheless, the task of classifying sonar sound datasets continues to pose significant challenges. I...

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Bibliographic Details
Main Authors: Dong liang Zhang, Zhiyong Jiang, Fallah Mohammadzadeh, Seyed Majid Hasani Azhdari, Laith Abualigah, Taher M. Ghazal
Format: Article
Language:English
Published: Elsevier 2024-04-01
Series:Heliyon
Subjects:
Fuzzy slime mould optimizer
LSTM
False alarm rates
Sonar
Dataset classification
Convolutional neural networks
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844024047121

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http://www.sciencedirect.com/science/article/pii/S2405844024047121

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