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rdf:type
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Description
| - Introduction: The progress in information technology has opened opportunities for the computer-assisted taxonomy. Methods: The use of ANN requires a training database in which specimens, correctly identified by experts, are included. For ANN inputs can be used digital images, optically sensed wing beat frequency spectra, near-infrared reflectance spectra, bioacoustic recordings, chemotaxonomy or morphometry. An ANN model is designed to find a relationship between the characters (=input) and species (=output). The quality of the training set is an essential prerequisite to obtaining reliable identifications. Results: Our case studies used morphometric data mostly. The high percentage of correctly identified specimens (about 97 %) is promising for a wider use of ANN. Conclusions: ANN is cheap and non-destructive suitable also for type material or permanently mounted slides. ANN have the potential to enhance the practice of routine identification with a non-expert as technical help.
- Introduction: The progress in information technology has opened opportunities for the computer-assisted taxonomy. Methods: The use of ANN requires a training database in which specimens, correctly identified by experts, are included. For ANN inputs can be used digital images, optically sensed wing beat frequency spectra, near-infrared reflectance spectra, bioacoustic recordings, chemotaxonomy or morphometry. An ANN model is designed to find a relationship between the characters (=input) and species (=output). The quality of the training set is an essential prerequisite to obtaining reliable identifications. Results: Our case studies used morphometric data mostly. The high percentage of correctly identified specimens (about 97 %) is promising for a wider use of ANN. Conclusions: ANN is cheap and non-destructive suitable also for type material or permanently mounted slides. ANN have the potential to enhance the practice of routine identification with a non-expert as technical help. (en)
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Title
| - Insect identification using Artificial Neural Networks (ANN)
- Insect identification using Artificial Neural Networks (ANN) (en)
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skos:prefLabel
| - Insect identification using Artificial Neural Networks (ANN)
- Insect identification using Artificial Neural Networks (ANN) (en)
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skos:notation
| - RIV/00216224:14310/08:00042029!RIV11-MSM-14310___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/00216224:14310/08:00042029
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - Artificial Neural Networks; ANN; Artificial Intelligence; Entomology; Identification (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
| - Fedor, Peter
- Havel, Josef
- Malenovský, Igor
- Muráriková, Natália
- Vaňhara, Jaromír
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http://linked.open...n/vavai/riv/zamer
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http://localhost/t...ganizacniJednotka
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