scholarly journals Towards sustainable demersal fisheries: NepCon image acquisition system for automatic Nephrops norvegicus detection

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252824
Author(s):  
Maria Sokolova ◽  
Fletcher Thompson ◽  
Patrizio Mariani ◽  
Ludvig Ahm Krag
Keyword(s):  
Learning Algorithm ◽  
Image Acquisition ◽  
Automatic Detection ◽  
Observation System ◽  
Fishing Gear ◽  
Learning Approaches ◽  
Nephrops Norvegicus ◽  
Trawl Fishery ◽  
Perception System ◽  
Catch Composition

Underwater video monitoring systems are being widely used in fisheries to investigate fish behavior in relation to fishing gear and fishing gear performance during fishing. Such systems can be useful to evaluate the catch composition as well. In demersal trawl fisheries, however, their applicability can be challenged by low light conditions, mobilized sediment and scattering in murky waters. In this study, we introduce a novel observation system (called NepCon) which aims at reducing current limitations by combining an optimized image acquisition setup and tailored image analyses software. The NepCon system includes a high-contrast background to enhance the visibility of the target objects, a compact camera and an artificial light source. The image analysis software includes a machine learning algorithm which is evaluated here to test automatic detection and count of Norway lobster (Nephrops norvegicus). NepCon is specifically designed for applications in demersal trawls and this first phase aims at increasing the accuracy of N. norvegicus detection at the data acquisition level. To find the best contrasting background for the purpose we compared the output of four image segmentation methods applied to static images of N. norvegicus fixed in front of four test background colors. The background color with the best performance was then used to evaluate computer vision and deep learning approaches for automatic detection, tracking and counting of N. norvegicus in the videos. In this initial phase we tested the system in an experimental setting to understand the feasibility of the system for future implementation in real demersal fishing conditions. The N. norvegicus directed trawl fishery typically has no assistance from underwater observation technology and therefore are largely conducted blindly. The demonstrated perception system achieves 76% accuracy (F-score) in automatic detection and count of N. norvegicus, which provides a significant elevation of the current benchmark.

scholarly journals Using vertical distribution to separate fish from crustaceans in a mixed species trawl fishery

2019 ◽  
Vol 76 (10) ◽  
pp. 1781-1794 ◽  
Author(s):  
Junita Diana Karlsen ◽  
Ludvig Ahm Krag ◽  
Bent Herrmann ◽  
Henrik Skaarup Lund
Keyword(s):  
Vertical Distribution ◽  
Separation Efficiency ◽  
Fishing Gear ◽  
Relative Height ◽  
Size Selectivity ◽  
Mixed Species ◽  
Nephrops Norvegicus ◽  
Trawl Fishery ◽  
Successful Separation ◽  
Commercial Species

A major challenge in mixed fisheries is achieving acceptable size selectivity for morphologically different species using the same fishing gear. Separator trawls can have different selective properties in the upper and lower compartments and provide successful separation of species. We used a horizontally divided codend with small square meshes (40 mm) and a simple frame to stimulate fish to swim into the upper compartment. The majority of the fish were separated successfully from Nephrops (Nephrops norvegicus), but their preference were uniform. Less than 10% of the Nephrops entered the upper compartment. Length-based analysis revealed three patterns of separation efficiency among nine commercial species: length-dependent separation and preference for the upper or lower compartments. The separation efficiency should be improved for small roundfish and flatfish. There was little diel effect on the separation efficiency. The preference of fish for a compartment, taking the relative height of that compartment into account, was established for this and similar previous studies to enable comparison of results. We recommend length-based analysis to account for the fished population when interpreting results.

scholarly journals Application of Métier-Based Approaches for Spatial Planning and Management: A Case Study on a Mixed Trawl Fishery in Taiwan

2021 ◽  
Vol 9 (5) ◽  
pp. 480
Author(s):  
Yi-Jou Lee ◽  
Nan-Jay Su ◽  
Hung-Tai Lee ◽  
William Wei-Yuan Hsu ◽  
Cheng-Hsin Liao
Keyword(s):  
Spatial Planning ◽  
Clustering Algorithm ◽  
Fishing Gear ◽  
Target Species ◽  
Trawl Fishery ◽  
Fishery Resources ◽  
Planning And Management ◽  
Catch Composition ◽  
Fleet Dynamics

Mixed fisheries refer to fishing activities that catch more than one species simultaneously, and a species may be fished using different gear. A trawl fishery shares these features to exploit multiple species simultaneously, with diverse fishing gear and strategies. The situation becomes more complex when interactions among fleet dynamics, fishing activities, and fishery resources are involved and influence each other. Information regarding the operational patterns may be hidden in a set of long-term big data. This study aims to investigate the fishery structure and fleet dynamics of trawl fisheries in Taiwan for spatial planning and management, based on a long-term dataset from a management system that collects information by using voyage data recorders (VDR) and dockside observers. We applied a two-step data mining process with a clustering algorithm to classify the main groups of fishery resources and then identified 18 catch métiers based on catch composition. The target species, operation pattern, and fishing season were determined for each métier, and associated with the relevant fishery resources and the fishing gear used. Additionally, fishing effects on target species were estimated using information on fishing grounds and trajectories from VDR. The métier-based approach was successfully applied to define the six major fishery resources targeted by trawlers. We examined the key features of fishing activity associated with catch composition and spatial-temporal fishing metrics, which could be used to provide suggestions for the spatial planning and management of the mixed trawl fishery in the offshore waters of Taiwan.

Fishers' description of changes in fishing gear and fishing practices in the Australian South East Trawl Fishery

2001 ◽  
Vol 52 (4) ◽  
pp. 411 ◽  
Author(s):  
Pascale Baelde
Keyword(s):  
Stock Assessment ◽  
Single Species ◽  
Fishing Gear ◽  
Qualitative Synthesis ◽  
Quota System ◽  
Fish Stocks ◽  
Trawl Fishery ◽  
Individual Transferable Quota ◽  
Catch Composition ◽  
Fishing Practices

Between the mid 1980s and early 1990s, the concurrence of three major events significantly altered the structure and dynamics of the demersal trawl sector in the Australian South-East Fishery (SEF). These events included marked technological improvement, severe decline of major fish stocks and introduction of an Individual Transferable Quota system. They have led to a switch from maximizing catch volume to maximizing catch composition and quotas, with important associated changes in fishing practices and catches. To better understand these changes and their effect on stock assessment and management, an industry survey asked SEF trawl fishers to describe their fishing gear and fishing practices in detail. This paper is a qualitative synthesis of current trends in fishing that most significantly affect the single-species, logbook-dependent assessment and management of the fishery. It demonstrates how effective collaboration between scientists and fishers can benefit fisheries research and management, by helping scientists make more informed analysis and interpretations of fisheries data.

scholarly journals Supervised learning for the detection of negation and of its scope in French and Brazilian Portuguese biomedical corpora

2020 ◽  
pp. 1-21 ◽  
Author(s):  
Clément Dalloux ◽  
Vincent Claveau ◽  
Natalia Grabar ◽  
Lucas Emanuel Silva Oliveira ◽  
Claudia Maria Cabral Moro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
State Of The Art ◽  
Automatic Detection ◽  
Brazilian Portuguese ◽  
Supervised Machine Learning ◽  
Biomedical Domain ◽  
Learning Approaches ◽  
Cross Domain ◽  
Automatic Methods

Abstract Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.

Selectivity experiments with escape windows in the North Sea Nephrops (Nephrops norvegicus) trawl fishery

1999 ◽  
Vol 42 (1-2) ◽  
pp. 167-181 ◽  
Author(s):  
N Madsen ◽  
T Moth-Poulsen ◽  
R Holst ◽  
D Wileman
Keyword(s):  
North Sea ◽  
Nephrops Norvegicus ◽  
Trawl Fishery ◽  
The North ◽  
The North Sea

SMILE: Mutual Information Learning for Integration of Single-cell Omics Data

2021 ◽  
Author(s):  
Yang Xu ◽  
Priyojit Das ◽  
Rachel Patton McCord
Keyword(s):  
Deep Learning ◽  
Mutual Information ◽  
Single Cell ◽  
Learning Algorithm ◽  
Cellular Systems ◽  
Supplementary Information ◽  
Omics Data ◽  
Learning Approaches ◽  
Rna Seq ◽  
Integrate Data

Abstract Motivation Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single cell omics data to be integrated across sources, types, and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). Results Using a unique cell-pairing design, SMILE successfully integrates multi-source single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also integrate data from two or more modalities, such as joint profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C, and ChIP data. When paired cells are known, SMILE can integrate data with unmatched feature, such as genes for RNA-seq and genome wide peaks for ATAC-seq. Integrated representations learned from joint profiling technologies can then be used as a framework for comparing independent single source data. Supplementary information Supplementary data are available at Bioinformatics online. The source code of SMILE including analyses of key results in the study can be found at: https://github.com/rpmccordlab/SMILE.

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

scholarly journals PENDEKATAN BIONOMI PADA PELUANG PENGEMBANGAN PERIKANAN TANGKAP DI KABUPATEN KEPULAUAN TALAUD

2012 ◽  
Vol 1 (2) ◽  
pp. 57
Author(s):  
Jeti Pulu ◽  
Mulyono S. Baskoro ◽  
Daniel R. Monintja ◽  
Budhi H. Iskandar ◽  
Akhmad Fauzi
Keyword(s):  
Open Access ◽  
Economic Value ◽  
Secondary Data ◽  
Primary Data ◽  
Fishing Gear ◽  
Fish Production ◽  
Resource Rent ◽  
Illegal Fishing ◽  
Fishing Gears ◽  
Catch Composition

The research is aimed to reveal opportunity development of the capture fisheries in Talaud Islands Regency by using bionomy approach with Gordon-Schaefer model (Fauzy, 2005) con-cerning the dominant of illegal fishing activities around the area. The research was started by co-llected some secondary data on fish production and number of fishing units. Primary data were collected on catch composition and types of fishing gear. Gordon-Schaefer methods was applied to evaluate the tuna and skipjack resources in the area. The troll and pole and line are indicated as the dominant fishing gears used to catch the skipjack and tuna. In case of open access condition, the production will end up to 25,09 tons, while the resource rent will be end up to zero. For the development, simulations were exercised in 3 scenarios: 1) scenario of enhancing domestic fleet, 2) scenario of illegal fishing, and 3) scenario of net surplus. With those scenarios, if the illegal fishing could be eliminated, the capture fisheries in this regency could render economic value to 10 billion rupiah.

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