Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional …

J Zhang, C Li, Y Yin, J Zhang, M Grzegorzek - Artificial Intelligence Review, 2023 - Springer
Microorganisms are widely distributed in the human daily living environment. They play an
essential role in environmental pollution control, disease prevention and treatment, and food …

Deep learning as a tool for ecology and evolution

ML Borowiec, RB Dikow, PB Frandsen… - Methods in Ecology …, 2022 - Wiley Online Library
Deep learning is driving recent advances behind many everyday technologies, including
speech and image recognition, natural language processing and autonomous driving. It is …

Implementation options for DNA-based identification into ecological status assessment under the European Water Framework Directive

D Hering, A Borja, JI Jones, D Pont, P Boets… - Water research, 2018 - Elsevier
Assessment of ecological status for the European Water Framework Directive (WFD) is
based on “Biological Quality Elements”(BQEs), namely phytoplankton, benthic flora, benthic …

Machine learning in marine ecology: an overview of techniques and applications

P Rubbens, S Brodie, T Cordier… - ICES Journal of …, 2023 - academic.oup.com
Abstract Machine learning covers a large set of algorithms that can be trained to identify
patterns in data. Thanks to the increase in the amount of data and computing power …

nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning

YZ Chen, ZZ Wang, Y Wang, G Ying… - Briefings in …, 2021 - academic.oup.com
Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational
modification and has been reported to be involved in various pathophysiological processes …

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

C Li, K Wang, N Xu - Artificial Intelligence Review, 2019 - Springer
Microorganisms such as protozoa and bacteria play very important roles in many practical
domains, like agriculture, industry and medicine. To explore functions of different categories …

Application of deep learning in aquatic bioassessment: Towards automated identification of non-biting midges

D Milošević, A Milosavljević, B Predić… - Science of the Total …, 2020 - Elsevier
Morphological species identification is often a difficult, expensive, and time-consuming
process which hinders the ability for reliable biomonitoring of aquatic ecosystems. An …

Benchmark database for fine-grained image classification of benthic macroinvertebrates

J Raitoharju, E Riabchenko, I Ahmad, A Iosifidis… - Image and Vision …, 2018 - Elsevier
Managing the water quality of freshwaters is a crucial task worldwide. One of the most used
methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in …

A deep learning approach to detect and identify live freshwater macroinvertebrates

S Jaballah, GF Garcia, F Martignac, N Parisey, S Jumel… - Aquatic Ecology, 2023 - Springer
The study of macroinvertebrates using computer vision is in its infancy and still faces
multiple challenges including destructive sampling, low signal-to-noise ratios, and the …

Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches

E Yousef Kalafi, WB Tan, C Town, SK Dhillon - BMC bioinformatics, 2016 - Springer
Abstract Background Monogeneans are flatworms (Platyhelminthes) that are primarily found
on gills and skin of fishes. Monogenean parasites have attachment appendages at their …