Detailed Information on Publication Record
2007
Artificial neural networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae)
VAŇHARA, Jaromír, Natália MURÁRIKOVÁ, Igor MALENOVSKÝ and Josef HAVELBasic information
Original name
Artificial neural networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae)
Name in Czech
Umělé neuronové sítě pro identifikaci dvoukřídlých: příkladová studie pro rody Tachina a Ectophasia (Diptera, Tachinidae
Authors
VAŇHARA, Jaromír (203 Czech Republic, guarantor, belonging to the institution), Natália MURÁRIKOVÁ (703 Slovakia, belonging to the institution), Igor MALENOVSKÝ (203 Czech Republic) and Josef HAVEL (203 Czech Republic, belonging to the institution)
Edition
Biologia, Bratislava, Versita, 2007, 1335-6372
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10600 1.6 Biological sciences
Country of publisher
Slovakia
Confidentiality degree
není předmětem státního či obchodního tajemství
RIV identification code
RIV/00216224:14310/07:00023693
Organization unit
Faculty of Science
UT WoS
000249986300013
Keywords in English
artificial neural networks; species identification; Diptera; Tachinidae; Tachina; Ectophasia; parasitoids
Tags
Tags
International impact, Reviewed
Změněno: 1/3/2012 15:12, Mgr. Igor Malenovský, Ph.D.
V originále
The classification methodology based on morphometric data and supervised artificial neural networks (ANN) was tested on five fly species of the parasitoid genera Tachina and Ectophasia (Diptera, Tachinidae). Objects were initially photographed, then digitalized; consequently the picture was scaled and measured by means of an image analyser. The 16 variables used for classification included length of different wing veins or their parts and width of antennal segments. The sex was found to have some influence on the data and was included in the study as another input variable. Better and reliable classification was obtained when data from both the right and left wings were entered, the data from one wing were however found to be sufficient. The prediction success (correct identification of unknown test samples) varied from 88 to 100% throughout the study depending especially on the number of specimens in the training set. Classification of the studied Diptera species using ANN is possible assuming a sufficiently high number (tens) of specimens of each species is available for the ANN training. The methodology proposed is quite general and can be applied for all biological objects where it is possible to define adequate diagnostic characters and create the appropriate database.
In Czech
Metoda determinace založená na morfometrických znacích a vyhodnocovaná pomoci umělých neuronových sítí (ANN)byla testována na 5 druzích dvoukřídlého hmyzu rodů Tachina and Ectophasia (Diptera, Tachinidae).
Links
| MSM0021622416, plan (intention) |
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