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The Application of Artificial Intelligence and Machine Learning in a...
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The Application of Artificial Intelligence and Machine Learning in a Marine Context - Edition II

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312).

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1610

Special Issue Editors

Prof. Dr. Fausto Pedro García Márquez

E-Mail Website
Guest Editor
Ingneium Research Group, Universidad de Castilla-La Mancha, Ciudad Real, Spain
Interests: artificial intelligence; maintenance; management; advanced analytics; data science
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Cococcioni

E-Mail Website
Guest Editor
Department of Information Engineering, University of Pisa, Via Girolamo Caruso, 16, 56122 Pisa, PI, Italy
Interests: artificial intelligence; genetic fuzzy systems; multiobjective optimization; decision support systems; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue covers research on the applications of artificial intelligence and machine learning methods using data from marine contexts. Studies exploring applications of these methods to different professional areas, such as fisheries, engineering, economy, and society, will be accepted. This Special Issue welcomes multi-disciplinary works combining marine, engineering and computer science approaches. This work relates to the broader contexts of digital twins of the ocean, big data, data mining, and open science for marine data; contributions are expected to relate to these areas.

Prof. Dr. Fausto Pedro García Márquez
Dr. Marco Cococcioni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • marine science
  • ecological modelling
  • ecological niche modelling
  • digital twins of the ocean
  • fisheries models
  • data mining
  • big data
  • open science

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

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Research

19 pages, 4962 KiB  
Article
A Prediction of the Shooting Trajectory for a Tuna Purse Seine Using the Double Deep Q-Network (DDQN) Algorithm
by Daeyeon Cho and Jihoon Lee
J. Mar. Sci. Eng. 2025, 13(3), 530; https://doi.org/10.3390/jmse13030530 - 10 Mar 2025
Viewed by 439
Abstract
The purse seine is a fishing method in which a net is used to encircle a fish school, capturing isolated fish by tightening a purse line at the bottom of the net. Tuna purse seine operations are technically complex, requiring the evaluation of [...] Read more.
The purse seine is a fishing method in which a net is used to encircle a fish school, capturing isolated fish by tightening a purse line at the bottom of the net. Tuna purse seine operations are technically complex, requiring the evaluation of fish movements, vessel dynamics, and their interactions, with success largely dependent on the expertise of the crew. In particular, efficiency in terms of highly complex tasks, such as calculating the shooting trajectory during fishing operations, varies significantly based on the fisher’s skill level. To address this challenge, developing techniques to support less experienced fishers is necessary, particularly for operations targeting free-swimming fish schools, which are more difficult to capture compared to those utilizing Fish Aggregating Devices (FADs). This study proposes a method for predicting shooting trajectories using the Double Deep Q-Network (DDQN) algorithm. Observation states, actions, and reward functions were designed to identify optimal scenarios for shooting, and the catchability of the predicted trajectories was evaluated through gear behavior analysis. The findings of this study are expected to aid in the development of a trajectory prediction system for inexperienced fishers and serve as foundational data for automating purse seine fishing systems. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context - Edition II)
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22 pages, 61756 KiB  
Article
Evaluation of Deep Learning Models for Polymetallic Nodule Detection and Segmentation in Seafloor Imagery
by Gabriel Loureiro, André Dias, José Almeida, Alfredo Martins and Eduardo Silva
J. Mar. Sci. Eng. 2025, 13(2), 344; https://doi.org/10.3390/jmse13020344 - 13 Feb 2025
Viewed by 587
Abstract
Climate change has led to the need to transition to clean technologies, which depend on an number of critical metals. These metals, such as nickel, lithium, and manganese, are essential for developing batteries. However, the scarcity of these elements and the risks of [...] Read more.
Climate change has led to the need to transition to clean technologies, which depend on an number of critical metals. These metals, such as nickel, lithium, and manganese, are essential for developing batteries. However, the scarcity of these elements and the risks of disruptions to their supply chain have increased interest in exploiting resources on the deep seabed, particularly polymetallic nodules. As the identification of these nodules must be efficient to minimize disturbance to the marine ecosystem, deep learning techniques have emerged as a potential solution. Traditional deep learning methods are based on the use of convolutional layers to extract features, while recent architectures, such as transformer-based architectures, use self-attention mechanisms to obtain global context. This paper evaluates the performance of representative models from both categories across three tasks: detection, object segmentation, and semantic segmentation. The initial results suggest that transformer-based methods perform better in most evaluation metrics, but at the cost of higher computational resources. Furthermore, recent versions of You Only Look Once (YOLO) have obtained competitive results in terms of mean average precision. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context - Edition II)
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