A model of a digital biological ecosystem

Dhillon, Sarinder Kaur; Chiew, S. L.; Leow, Lee Kien; Sidhu, Amandeep S.; Shuhaimi, N. I.; Leong, Yee Mei; Chong, Ving Ching

doi:10.1080/14772000.2013.856962

Cited by 2 publications

(1 citation statement)

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“…In conclusion, the purpose of this study is to develop a fully automated identification system capable of identifying monogenean specimens. Eventually, this study will be integrated into a digital biological ecosystem published by the corresponding author [36]. This work will also be extended to a species classification pipeline which incorporates semantics using textual annotations and image attributes.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

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BackgroundMonogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts’ (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457–462, 2011), (J Zoolog Syst Evol Res 52(2): 95–99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods.ResultImages of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%.ConclusionsThe methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in extraction and selection of features will be implemented.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2016

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Conceptualizing Information Systems as Biological Ecosystems - A ”New” Vocabulary for Speaking of Information Systems

Sell,

Jeansson,

Helmefalk

et al. 2023

36th Bled eConference – Digital Economy and Society: The Balancing Act for Digital Innovation in Times of Instability: June 25

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Biological ecosystems and related concepts are well known and have been used for a long time, also outside of the ecology context. In this conceptual paper, we explore the use of biological ecosystem concepts as a new lens to understand and analyze information systems. We propose that applying a frame of reference from a different discipline enriches information systems analysis in several ways, firstly through broadening the perspective of information systems, secondly by offering connections to phenomena and areas that were previously outside of the scope of the information system, and lastly through offering a new viewpoint on actors, roles and functions within an information system. Further research is needed to deepen our understanding of the information system ecosystem and apply this approach to other business activities.

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A model of a digital biological ecosystem

Cited by 2 publications

References 8 publications

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

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

Conceptualizing Information Systems as Biological Ecosystems - A ”New” Vocabulary for Speaking of Information Systems

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