AIMC Topic: Diatoms

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Integration of spectroscopic techniques and machine learning for optimizing Phaeodactylum tricornutum cell and fucoxanthin productivity.

Bioresource technology Dec 16, 2024
The development of sustainable and controlled microalgae bioprocesses relies on robust and rapid monitoring tools that facilitate continuous process optimization, ensuring high productivity and minimizing response times. In this work, we analyse the ...
Machine Learning Microalgae Xanthophylls Diatoms Spectrometry, Fluorescence Spectrum Analysis
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Fuzzy logic as a novel approach to predict biological condition gradient of various streams in Ceyhan River Basin (Turkey).

The Science of the total environment Jan 12, 2024
Creating a method to categorize the ecological status of streams according to their biological conditions and establishing scientifically defensible nutrient criteria to protect their biotic integrity poses significant challenges. Biomonitoring of le...
Diatoms Fuzzy Logic Turkey Seasons Rivers Environmental Monitoring
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Microscopic image recognition of diatoms based on deep learning.

Journal of phycology Nov 23, 2023
Diatoms are a crucial component in the study of aquatic ecosystems and ancient environmental records. However, traditional methods for identifying diatoms, such as morphological taxonomy and molecular detection, are costly, are time consuming, and ha...
Biodiversity Diatoms Ecosystem Deep Learning
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Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint.

PloS one Feb 24, 2023
Diatoms represent one of the morphologically and taxonomically most diverse groups of microscopic eukaryotes. Light microscopy-based taxonomic identification and enumeration of frustules, the silica shells of these microalgae, is broadly used in aqua...
Deep Learning Image Processing, Computer-Assisted Microscopy Diatoms
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Identification of diatom taxonomy by a combination of region-based full convolutional network, online hard example mining, and shape priors of diatoms.

International journal of legal medicine Jul 20, 2021
Diatom test is one of the commonly used diagnostic methods for drowning in forensic pathology, which provides supportive evidence for drowning. However, in forensic practice, it is time-consuming and laborious for forensic experts to classify and cou...
Diatoms Neural Networks, Computer Classification
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An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm.

International journal of legal medicine Jan 3, 2021
Seasonal or monthly databases of the diatom populations in specific bodies of water are needed to infer the drowning site of a drowned body. However, existing diatom testing methods are laborious, time-consuming, and costly and usually require specif...
Forensic Pathology Drowning Models, Animal Diatoms Rats Rats, Sprague-Dawley Seasons Rivers Lung China Sensitivity and Specificity Animals Artificial Intelligence Databases, Factual Neural Networks, Computer
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Learning Diatoms Classification from a Dry Test Slide by Holographic Microscopy.

Sensors (Basel, Switzerland) Nov 7, 2020
Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy o...
Holography Diatoms Machine Learning Neural Networks, Computer Microscopy
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Automated diatom searching in the digital scanning electron microscopy images of drowning cases using the deep neural networks.

International journal of legal medicine Aug 13, 2020
Forensic diatom test has been widely accepted as a way of providing supportive evidences in the diagnosis of drowning. The current workflow is primarily based on the observation of diatoms by forensic pathologists under a microscopy, and this process...
Deep Learning Microscopy, Electron, Scanning Diatoms Forensic Pathology Humans Drowning Neural Networks, Computer
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Digital whole-slide image analysis for automated diatom test in forensic cases of drowning using a convolutional neural network algorithm.

Forensic science international Aug 8, 2019
Diatom examinations have been widely used to perform drowning diagnosis in forensic practice. However, current methods for recognizing diatoms, which use light or electron microscopy, are time-consuming and laborious and often result in false positiv...
Humans Neural Networks, Computer Algorithms Deep Learning Sensitivity and Specificity Diatoms Lung Forensic Pathology Drowning
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Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll , Diatoms, Green Algae and Turbidity.

International journal of environmental research and public health Aug 30, 2018
Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters ar...
Chlorophyll A Spectrum Analysis Humic Substances Germany Environmental Monitoring Machine Learning Diatoms Water Quality Chlorophyll Ecosystem
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