Machine learning and deep learning based computational approaches in automatic microorganisms image recognition: methodologies, challenges, and …

P Rani, S Kotwal, J Manhas, V Sharma… - … Methods in Engineering, 2022 - Springer
Microorganisms or microbes comprise majority of the diversity on earth and are extremely
important to human life. They are also integral to processes in the ecosystem. The process of …

A systematic review of deep learning microalgae classification and detection

DM Madkour, MI Shapiai, SE Mohamad, HH Aly… - IEEE …, 2023 - ieeexplore.ieee.org
Algae represent the majority of the diversity on Earth and are a large group of organisms that
have photosynthetic properties that are important to life. The species of algae are estimated …

[HTML][HTML] The application of fully unmanned robotic systems for inspection of subsea pipelines

AG Rumson - Ocean engineering, 2021 - Elsevier
This paper focuses on recent innovations in the methods used for external remote subsea
pipeline inspection. An unmanned method is revealed, in which an Autonomous Underwater …

[HTML][HTML] Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicability

CM Song - Journal of Hydrology, 2022 - Elsevier
A conceptual runoff model mathematically expresses hydrological phenomena originating
from spatial and temporal changes based on physical laws; thus, it can describe causal …

[HTML][HTML] Computer vision based deep learning approach for the detection and classification of algae species using microscopic images

Abdullah, S Ali, Z Khan, A Hussain, A Athar, HC Kim - Water, 2022 - mdpi.com
The natural phenomenon of harmful algae bloom (HAB) has a bad impact on the quality of
pure and freshwater. It increases the risk to human health, water bodies and overall aquatic …

Deep learning-based algal detection model development considering field application

J Park, J Baek, J Kim, K You, K Kim - Water, 2022 - mdpi.com
Algal blooms have various effects on drinking water supply systems; thus, proper monitoring
is essential. Traditional visual identification using a microscope is a time-consuming method …

[HTML][HTML] Algal morphological identification in watersheds for drinking water supply using neural architecture search for convolutional neural network

J Park, H Lee, CY Park, S Hasan, TY Heo, WH Lee - Water, 2019 - mdpi.com
An excessive increase in algae often has various undesirable effects on drinking water
supply systems, thus proper management is necessary. Algal monitoring and classification …

An improved algae-YOLO model based on deep learning for object detection of ocean microalgae considering aquacultural lightweight deployment

D Liu, P Wang, Y Cheng, H Bi - Frontiers in Marine Science, 2022 - frontiersin.org
Algae are widely distributed and have a considerable impact on water quality. Harmful algae
can degrade water quality and be detrimental to aquaculture, while beneficial algae are …

Regression modelling of spatiotemporal extreme US wildfires via partially-interpretable neural networks

J Richards, R Huser - arxiv preprint arxiv:2208.07581, 2022 - arxiv.org
Risk management in many environmental settings requires an understanding of the
mechanisms that drive extreme events. Useful metrics for quantifying such risk are extreme …

Comparative assessment of artificial intelligence (AI)-based algorithms for detection of harmful bloom-forming algae: an eco-environmental approach toward …

A Gaur, G Pant, AS Jalal - Applied Water Science, 2023 - Springer
Organic effluent enrichment in water may selectively promote algal growth, resulting in water
pollution and posing a threat to the aquatic ecosystem. Recent harmful algal blooms (HABs) …