Research Article Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System Ali Hosseini Nejad Takhti , 1 Abbas Saffari , 2 Diego Mart´ ın , 3 Mohammad Khishe , 4 and Mokhtar Mohammadi 5 1 Department of Information Technology, College of Engineering and Computer Science, Sari Branch, Islamic Azad University, Sari, Iran 2 Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran 3 ETSI Telecomunicaci´ on, Universidad Polit´ecnica de Madrid, Av. Complutense 30, Madrid 28040, Spain 4 Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran 5 Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq Correspondence should be addressed to Abbas Saffari; abbas.saffari@birjand.ac.ir Received 4 February 2022; Revised 11 September 2022; Accepted 27 September 2022; Published 18 October 2022 Academic Editor: Jianli Liu Copyright © 2022 Ali Hosseini Nejad Takhti et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest for researchers and activist fields. In this paper, first, an experimental data set was created using a designed scenario. e whale optimization algorithm (WOA) is then used to train the multilayer perceptron neural network (MLP-NN). However, due to the large size of the data, the algorithm has not determined a clear boundary between the exploration and extraction phases. Next, to support this shortcoming, the fuzzy inference is used as a new approach to developing and upgrading WOA called FWOA. Fuzzy inference by setting FWOA control parameters can well define the boundary between the two phases of exploration and ex- traction. To measure the performance of the designed categorizer, in addition to using it to categorize benchmark datasets, five benchmarking algorithms CVOA, WOA, ChOA, BWO, and PGO were also used for MLPNN training. e measured criteria are concurrency speed, ability to avoid local optimization, and the classification rate. e simulation results on the obtained data set showed that, respectively, the classification rate in MLPFWOA, MLP-CVOA, MLP-WOA, MLP-ChOA, MLP-BWO, and MLP- PGO classifiers is equal to 94.98, 92.80, 91.34, 90.24, 89.04, and 88.10. As a result, MLP-FWOA performed better than other algorithms. 1. Introduction e deep oceans make up 95% of the oceans’ volume, which is the largest habitat on Earth[1]. Creatures are continually being explored in the depths of the ocean with new ways of life [2, 3]. Much research has been conducted in the depths of the ocean, but unfortunately, this research is not enough, and many hidden secrets in the ocean remain unknown [4]. Various species of marine mammals, including whales and dolphins, live in the ocean. Underwater audio signal processing is the newest way to measure the presence, abundance, and migratory marine mammal patterns [5–7]. e use of human-based methods and intelligent methods is one method of recognizing whales and dolphins [8]. Initially, human operator-based methods were used to identify whales and dolphins. Its advantages include simplicity and ease of work. However, the main Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 3216400, 21 pages https://doi.org/10.1155/2022/3216400