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

MLA

Dong liang Zhang, et al. “FUZ-SMO: A Fuzzy Slime Mould Optimizer for Mitigating False Alarm Rates in the Classification of Underwater Datasets Using Deep Convolutional Neural Networks.” Heliyon, vol. 10, no. 7, Apr. 2024. EBSCOhost, https://doi.org/10.1016/j.heliyon.2024.e28681.

APA

Dong liang Zhang, Zhiyong Jiang, Fallah Mohammadzadeh, Seyed Majid Hasani Azhdari, Laith Abualigah, & Taher M. Ghazal. (2024). FUZ-SMO: A fuzzy slime mould optimizer for mitigating false alarm rates in the classification of underwater datasets using deep convolutional neural networks. Heliyon, 10(7). https://doi.org/10.1016/j.heliyon.2024.e28681

Chicago

Dong liang Zhang, Zhiyong Jiang, Fallah Mohammadzadeh, Seyed Majid Hasani Azhdari, Laith Abualigah, and Taher M. Ghazal. 2024. “FUZ-SMO: A Fuzzy Slime Mould Optimizer for Mitigating False Alarm Rates in the Classification of Underwater Datasets Using Deep Convolutional Neural Networks.” Heliyon 10 (7). doi:10.1016/j.heliyon.2024.e28681.