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This study introduces a novel Fuzzy Whale Optimization Algorithm (FWOA) to improve marine mammal sound classification. The FWOA enhances the Multilayer Perceptron Neural Network (MLP-NN), achieving a 94.98% classification rate for endangered species identification.

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Area of Science:

  • Marine biology
  • Bioacoustics
  • Artificial Intelligence

Background:

  • Deep-sea soundscapes are complex, necessitating accurate marine mammal identification for conservation.
  • Existing algorithms like the Whale Optimization Algorithm (WOA) struggle with large datasets for marine mammal classification.

Purpose of the Study:

  • To develop an improved algorithm for classifying marine mammal sounds.
  • To enhance the performance of the Multilayer Perceptron Neural Network (MLP-NN) in identifying endangered marine species.

Main Methods:

  • An experimental dataset of deep-sea sounds was created.
  • The Whale Optimization Algorithm (WOA) was initially used to train an MLP-NN.
  • A Fuzzy Inference System was integrated with WOA to create the Fuzzy Whale Optimization Algorithm (FWOA) to refine exploration and extraction phases.

Main Results:

  • The MLP-FWOA classifier achieved a classification rate of 94.98% on the experimental dataset.
  • MLP-FWOA outperformed benchmark algorithms including CVOA, WOA, ChOA, BWO, and PGO.
  • Performance was evaluated based on concurrency speed, local optimization avoidance, and classification rate.

Conclusions:

  • The Fuzzy Whale Optimization Algorithm (FWOA) significantly improves the classification accuracy of marine mammal sounds when used with MLP-NN.
  • FWOA effectively addresses the limitations of WOA in handling large datasets and defining exploration-extraction boundaries.
  • This approach offers a promising tool for researchers and conservationists focused on identifying and protecting endangered marine species.