Dwarf Mongoose Optimization with Transfer Learning-Based Fish Behavior Classification Model

Abstract

Behavioral monitoring can be used to monitor aquatic ecosystems and water quality over time. Using precise and rapid fish performance detection, fishermen may make educated management decisions on recirculating aquaculture systems while decreasing labor. Sensors and procedures for recognizing fish behavior are often developed and prepared by researchers in big numbers. Deep learning (DL) techniques have revolutionized the capability to automatically analyze videos, which were utilized for behavior analysis, live fish detection, biomass estimation, water quality monitoring, and species classification. The benefit of DL is that it could automatically study the extraction of image features and reveals brilliant performance in identifying sequential actions. This paper focuses on the design of Dwarf Mongoose Optimization with Transfer Learning-based fish behavior classification (DMOTLB-FBC) model. The presented DMOTLB-FBC technique intends to effectively monitor and classify fish behaviors. Initially, the DMOTLB-FBC technique follows Gaussian filtering (GFI) technique for noise removal process. Besides, a transfer learning (TL)-based neural architectural search network (NASNet) model is used to produce a collection of feature vectors. For fish behavior classification, graph convolution network (GCN) model is employed in this work. To improve the fish behavior classification results of the DMOTLB-FBC technique, the DWO algorithm is applied as a hyperparameter optimizer of the GCN model. The experimentation analysis of the DMOTLB-FBC technique is tested on fish video dataset and the widespread comparison study reported the enhancements of the DMOTLB-FBC technique over other recent approaches.