AIMC Topic: Plankton
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Intelligent Detection and Recognition of Marine Plankton by Digital Holography and Deep Learning.
Sensors (Basel, Switzerland)
Apr 6, 2025
The detection, observation, recognition, and statistics of marine plankton are the basis of marine ecological research. In recent years, digital holography has been widely applied to plankton detection and recognition. However, the recording and reco...
Predicting thermodynamic adhesion energies of membrane fouling in planktonic anammox MBR via backpropagation neural network model.
Bioresource technology
Jun 18, 2024
Predicting thermodynamic adhesion energies was a critical strategy for mitigating membrane fouling. This study utilized a backpropagation (BP) neural network model to predict the thermodynamic adhesion energies associated with membrane fouling in a p...
In-domain versus out-of-domain transfer learning in plankton image classification.
Scientific reports
Jun 27, 2023
Plankton microorganisms play a huge role in the aquatic food web. Recently, it has been proposed to use plankton as a biosensor, since they can react to even minimal perturbations of the aquatic environment with specific physiological changes, which ...
Deep-Learning-Based Automated Tracking and Counting of Living Plankton in Natural Aquatic Environments.
Environmental science & technology
May 19, 2023
Plankton are widely distributed in the aquatic environment and serve as an indicator of water quality. Monitoring the spatiotemporal variation in plankton is an efficient approach to forewarning environmental risks. However, conventional microscopy c...
Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level.
Water research
Apr 23, 2022
Harmful algal blooms (HABs) have become a global issue, affecting public health and water industries in numerous countries. Because funds for monitoring HABs are limited, model development may be an alternative approach for understanding and managing...
Main-Chain Sulfonium-Containing Homopolymers with Negligible Hemolytic Toxicity for Eradication of Bacterial and Fungal Biofilms.
ACS macro letters
Dec 10, 2021
Antimicrobials against planktonic cells and established biofilms at low doses are in increasing demand to tackle antibiotic-resistant biofilm infections. As a promising alternative to antibiotics, cationic polymers can effectively kill planktonic mic...
Machine Learning for the Study of Plankton and Marine Snow from Images.
Annual review of marine science
Aug 30, 2021
Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit t...
Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye.
Sensors (Basel, Switzerland)
May 2, 2020
In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the metho...
Modelling the influence of environmental parameters over marine planktonic microbial communities using artificial neural networks.
The Science of the total environment
Apr 26, 2019
Guanabara Bay is a tropical estuarine ecosystem that receives massive anthropogenic impacts from the metropolitan region of Rio de Janeiro. This ecosystem suffers from an ongoing eutrophication process that has been shown to promote the emergence of ...
Inhibition of bacterial surface colonization by immobilized silver nanoparticles depends critically on the planktonic bacterial concentration.
Journal of colloid and interface science
Dec 23, 2015
Immobilization of antimicrobial silver nanoparticles (AgNPs) on surfaces has been proposed as a method to inhibit biofouling or as a possible route by which incidental releases of AgNPs may interfere with biofilms in the natural environment or in was...
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