In fishery acoustics, surveys using sensor systems such as sonars and echosounders have
been widely considered to be accurate tools for acquiring fish species data, fish species …

Artificial intelligence (AI) has become the future trend for prediction after the data is provided
to machine learning. This study uses data analysis to optimize the experiment, find the best …

Abstract Machine learning, a subfield of artificial intelligence, offers various methods that can
be applied in marine science. It supports data-driven learning, which can result in automated …

Abstract In 2020, the developing COVID-19 pandemic disrupted fisheries surveys to an
unprecedented extent. Many surveys were cancelled, including those for walleye pollock …

This paper reports on an exploratory study of the automatic detection of discrete scatterers in
the water column from underwater acoustic data with deep learning (DL) networks …

Classification of zooplankton to species with broadband echosounder data could increase
the taxonomic resolution of acoustic surveys and reduce the dependence on net and trawl …

This paper addresses the automatic identification of pelagic species in acoustic backscatter
data. Large quantities of data acquired during underwater acoustic surveys for …

Monitoring marine environments is a crucial part of understanding the impact of oceans on
global climate and their importance for biodiversity and ecological systems particularly in the …

Acoustic surveys are the standard approach for evaluating many fish stocks around the
world. The analysis of such survey data requires the accurate echo-classification of target …

Modern challenges in the increasing exploitation of aquatic ecosystems require efficient,
reliable, and noninvasive technologies to acquire biomass information on a large scale. For …