BAOS-CNN: A novel deep neuroevolution algorithm for multispecies seagrass detection.

ABSTRACT: Deep learning, a subset of machine learning that utilizes neural networks, has seen significant advancements in recent years. These advancements have led to breakthroughs in a wide range of fields, from natural language processing to computer vision, and have the potential to revolutionize many industries or organizations. They have also demonstrated exceptional performance in the identification and mapping of seagrass images. However, these deep learning models, particularly the popular Convolutional Neural Networks (CNNs) require architectural engineering and hyperparameter tuning. This paper proposes a Deep Neuroevolutionary (DNE) model that can automate the architectural engineering and hyperparameter tuning of CNNs models by developing and using a novel metaheuristic algorithm, named

SUBMITTER: Noman MK 

PROVIDER: S-EPMC11198790 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature