FUZ-SMO: A fuzzy slime mould optimizer for mitigating false alarm rates in the classification of underwater datasets using deep convolutional neural networks-Reference-Cited by-同舟云学术

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

Published:2024-04 Issue:7 Volume:10 Page:e28681
ISSN:2405-8440
Container-title:Heliyon
language:en
Short-container-title:Heliyon

Author:

liang Zhang Dong,Jiang Zhiyong,Mohammadzadeh Fallah^ORCID,Hasani Azhdari Seyed Majid,Abualigah Laith,Ghazal Taher M.

Funder

Key Scientific Research Project of Colleges and Universities in Henan Province

Publisher

Elsevier BV

Reference55 articles.

1. A new approach to system design optimization of underwater gliders;Yang;IEEE/ASME Trans. Mechatronics,2022

2. A laboratory and field universal estimation method for tire–pavement interaction noise (TPIN) based on 3D image technology;Wang;Sustainability,2022

3. Towards restoration of missing underwater forests;Campbell;PLoS One,2014

4. Sonar false alarm rate suppression using classification methods based on interior search algorithm;Ravakhah;IJCSNS,2017

5. Marine Propellers Design Using Slime Mould Optimization Algorithm to Improve the Efficiency and Reduce the Cavitation;karimi,2020

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