A fish detection approach based on BAT algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099050" target="_blank" >RIV/61989100:27240/16:86099050 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-26690-9_25" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-26690-9_25</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-26690-9_25" target="_blank" >10.1007/978-3-319-26690-9_25</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A fish detection approach based on BAT algorithm

Popis výsledku v původním jazyce

Fish detection and identification are important steps towards monitoring fish behavior. The importance of such monitoring step comes from the need for better understanding of the fish ecology and issuing conservative actions for keeping the safety of this vital food resource. The recent advances in machine learning approaches allow many applications to easily analyze and detect a number of fish species. The main competence between these approaches is based on two main detection parameters: the time and the accuracy measurements. Therefore, this paper proposes a fish detection approach based on BAT optimization algorithm (BA). This approach aims to reduce the classification time within the fish detection process. The performance of this system was evaluated by a number of well-known machine learning classifiers, KNN, ANN, and SVM. The approach was tested with 151 images to detect the Nile Tilapia fish species and the results showed that k-NN can achieve high accuracy 90 %, with feature reduction ratio close to 61 % along with a noticeable decrease in the classification time. (C) Springer International Publishing Switzerland 2016.

Název v anglickém jazyce

A fish detection approach based on BAT algorithm

Popis výsledku anglicky

Fish detection and identification are important steps towards monitoring fish behavior. The importance of such monitoring step comes from the need for better understanding of the fish ecology and issuing conservative actions for keeping the safety of this vital food resource. The recent advances in machine learning approaches allow many applications to easily analyze and detect a number of fish species. The main competence between these approaches is based on two main detection parameters: the time and the accuracy measurements. Therefore, this paper proposes a fish detection approach based on BAT optimization algorithm (BA). This approach aims to reduce the classification time within the fish detection process. The performance of this system was evaluated by a number of well-known machine learning classifiers, KNN, ANN, and SVM. The approach was tested with 151 images to detect the Nile Tilapia fish species and the results showed that k-NN can achieve high accuracy 90 %, with feature reduction ratio close to 61 % along with a noticeable decrease in the classification time. (C) Springer International Publishing Switzerland 2016.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 407

ISBN

978-3-319-26688-6

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

11

Strana od-do

273-283

Název nakladatele

Springer Verlag

Místo vydání

London

Místo konání akce

Beni Suef

Datum konání akce

28. 11. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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