FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3140319353> ?p ?o ?g. }

• W3140319353 endingPage "146956" @default.
• W3140319353 startingPage "146956" @default.
• W3140319353 abstract "The presence of harmful algal bloom in many reservoirs around the world, alongside the lack of sanitation law/ordinance regarding cyanotoxin monitoring (particularly in developing countries), create a scenario in which the local population could potentially chronically consume cyanotoxin-contaminated waters. Therefore, it is crucial to develop low cost tools to detect possible systems failures and consequent toxin release inferred by morphological changes of cyanobacteria in the raw water. This paper aimed to look for the best combination of convolutional neural network (CNN), optimizer and image segmentation technique to differentiate P. agardhii trichomes before and after chemical stress caused by the addition of hydrogen peroxide. This method takes a step towards accurate monitoring of cyanobacteria in the field without the need for a mobile lab. After testing three different network architectures (AlexNet, 3ConvLayer and 2ConvLayer), four different optimizers (Adam, Adagrad, RMSProp and SDG) and five different image segmentations methods (Canny Edge Detection, Morphological Filter, HP filter, GrabCut and Watershed), the combination 2ConvLayer with Adam optimizer and GrabCut segmentation, provided the highest median accuracy (93.33%) for identifying H2O2-induced morphological changes in P. agardhii. Our results emphasize the fact that the trichome classification problem can be adequately tackled with a limited number of learned features due to the lack of complexity in micrographs from before and after chemical stress. To the authors' knowledge, this is the first time that CNNs were applied to detect morphological changes in cyanobacteria caused by chemical stress. Thus, it is a significant step forward in developing low cost tools based on image recognition, to shield water consumers, especially in the poorest regions, against cyanotoxin-contaminated water." @default.
• W3140319353 created "2021-04-13" @default.
• W3140319353 creator A5001534233 @default.
• W3140319353 creator A5034848272 @default.
• W3140319353 creator A5035686740 @default.
• W3140319353 creator A5042755134 @default.
• W3140319353 date "2021-08-01" @default.
• W3140319353 modified "2023-09-27" @default.
• W3140319353 title "Detection of morphological changes caused by chemical stress in the cyanobacterium Planktothrix agardhii using convolutional neural networks" @default.
• W3140319353 cites W1603861165 @default.
• W3140319353 cites W1845916887 @default.
• W3140319353 cites W1968451927 @default.
• W3140319353 cites W1972711404 @default.
• W3140319353 cites W1976116913 @default.
• W3140319353 cites W2001332087 @default.
• W3140319353 cites W2015099600 @default.
• W3140319353 cites W2015462393 @default.
• W3140319353 cites W2034264779 @default.
• W3140319353 cites W2047035978 @default.
• W3140319353 cites W2064685255 @default.
• W3140319353 cites W2080643984 @default.
• W3140319353 cites W2081382164 @default.
• W3140319353 cites W2112796928 @default.
• W3140319353 cites W2124351162 @default.
• W3140319353 cites W2133059825 @default.
• W3140319353 cites W2136704614 @default.
• W3140319353 cites W2140411821 @default.
• W3140319353 cites W2145023731 @default.
• W3140319353 cites W2325370163 @default.
• W3140319353 cites W2341149581 @default.
• W3140319353 cites W2462487662 @default.
• W3140319353 cites W2743554502 @default.
• W3140319353 cites W2748937207 @default.
• W3140319353 cites W2767628865 @default.
• W3140319353 cites W2792681889 @default.
• W3140319353 cites W2800742934 @default.
• W3140319353 cites W2889134669 @default.
• W3140319353 cites W2895732650 @default.
• W3140319353 cites W2944433988 @default.
• W3140319353 cites W2955872377 @default.
• W3140319353 cites W4211010233 @default.
• W3140319353 doi "https://doi.org/10.1016/j.scitotenv.2021.146956" @default.
• W3140319353 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33894604" @default.
• W3140319353 hasPublicationYear "2021" @default.
• W3140319353 type Work @default.
• W3140319353 sameAs 3140319353 @default.
• W3140319353 citedByCount "4" @default.
• W3140319353 countsByYear W31403193532022 @default.
• W3140319353 countsByYear W31403193532023 @default.
• W3140319353 crossrefType "journal-article" @default.
• W3140319353 hasAuthorship W3140319353A5001534233 @default.
• W3140319353 hasAuthorship W3140319353A5034848272 @default.
• W3140319353 hasAuthorship W3140319353A5035686740 @default.
• W3140319353 hasAuthorship W3140319353A5042755134 @default.
• W3140319353 hasBestOaLocation W31403193531 @default.
• W3140319353 hasConcept C144024400 @default.
• W3140319353 hasConcept C149923435 @default.
• W3140319353 hasConcept C153180895 @default.
• W3140319353 hasConcept C154945302 @default.
• W3140319353 hasConcept C2779669040 @default.
• W3140319353 hasConcept C2780834024 @default.
• W3140319353 hasConcept C2781226839 @default.
• W3140319353 hasConcept C2908647359 @default.
• W3140319353 hasConcept C41008148 @default.
• W3140319353 hasConcept C523546767 @default.
• W3140319353 hasConcept C54355233 @default.
• W3140319353 hasConcept C81363708 @default.
• W3140319353 hasConcept C86803240 @default.
• W3140319353 hasConcept C89600930 @default.
• W3140319353 hasConceptScore W3140319353C144024400 @default.
• W3140319353 hasConceptScore W3140319353C149923435 @default.
• W3140319353 hasConceptScore W3140319353C153180895 @default.
• W3140319353 hasConceptScore W3140319353C154945302 @default.
• W3140319353 hasConceptScore W3140319353C2779669040 @default.
• W3140319353 hasConceptScore W3140319353C2780834024 @default.
• W3140319353 hasConceptScore W3140319353C2781226839 @default.
• W3140319353 hasConceptScore W3140319353C2908647359 @default.
• W3140319353 hasConceptScore W3140319353C41008148 @default.
• W3140319353 hasConceptScore W3140319353C523546767 @default.
• W3140319353 hasConceptScore W3140319353C54355233 @default.
• W3140319353 hasConceptScore W3140319353C81363708 @default.
• W3140319353 hasConceptScore W3140319353C86803240 @default.
• W3140319353 hasConceptScore W3140319353C89600930 @default.
• W3140319353 hasFunder F4320334627 @default.
• W3140319353 hasLocation W31403193531 @default.
• W3140319353 hasLocation W31403193532 @default.
• W3140319353 hasLocation W31403193533 @default.
• W3140319353 hasLocation W31403193534 @default.
• W3140319353 hasLocation W31403193535 @default.
• W3140319353 hasOpenAccess W3140319353 @default.
• W3140319353 hasPrimaryLocation W31403193531 @default.
• W3140319353 hasRelatedWork W2175746458 @default.
• W3140319353 hasRelatedWork W2613736958 @default.
• W3140319353 hasRelatedWork W2673946014 @default.
• W3140319353 hasRelatedWork W2732542196 @default.
• W3140319353 hasRelatedWork W2760085659 @default.
• W3140319353 hasRelatedWork W2795329967 @default.
• W3140319353 hasRelatedWork W3093612317 @default.

• next