Machine Learning in Classification of the Wax Structure of Breathing Openings on Leaves Affected by Air Pollution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F21%3A43921007" target="_blank" >RIV/60461373:22340/21:43921007 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/21:00347486

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-57802-2_19" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-57802-2_19</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-57802-2_19" target="_blank" >10.1007/978-3-030-57802-2_19</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning in Classification of the Wax Structure of Breathing Openings on Leaves Affected by Air Pollution

Popis výsledku v původním jazyce

Texture analysis and classification of image components be- long to common problems of the interdisciplinary area of digital sig- nal and image processing. The paper is devoted to the pattern matrix construction using features evaluated by the discrete Fourier transform (DFT) or the discrete wavelet transform (DWT) using the relative power in selected frequency bands or scale levels, respectively. Image features are then used to recognize groups of similar pattern vectors by self- organizing neural networks forming a mathematical tool for cluster anal- ysis. Further classification methods including the decision tree, support vector machine, nearest neighbour method and neural networks are then applied for construction of specific models and evaluation of their accu- racy and cross validation errors. The proposed algorithm is applied for analysis of given microscopic images representing wax structures cover- ing breathing openings on leaves affected by environmental pollution in different locations. The classification accuracy depends upon the method used and it is higher than 92% for all experiments.

Název v anglickém jazyce

Machine Learning in Classification of the Wax Structure of Breathing Openings on Leaves Affected by Air Pollution

Popis výsledku anglicky

Texture analysis and classification of image components be- long to common problems of the interdisciplinary area of digital sig- nal and image processing. The paper is devoted to the pattern matrix construction using features evaluated by the discrete Fourier transform (DFT) or the discrete wavelet transform (DWT) using the relative power in selected frequency bands or scale levels, respectively. Image features are then used to recognize groups of similar pattern vectors by self- organizing neural networks forming a mathematical tool for cluster anal- ysis. Further classification methods including the decision tree, support vector machine, nearest neighbour method and neural networks are then applied for construction of specific models and evaluation of their accu- racy and cross validation errors. The proposed algorithm is applied for analysis of given microscopic images representing wax structures cover- ing breathing openings on leaves affected by environmental pollution in different locations. The classification accuracy depends upon the method used and it is higher than 92% for all experiments.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LTAIN19007" target="_blank" >LTAIN19007: Vývoj pokročilých výpočetních algoritmů pro objektivní posouzení pooperační rehabilitace</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)

ISBN

978-3-030-57801-5

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

8

Strana od-do

199-206

Název nakladatele

Springer International Publishing Switzerland

Místo vydání

Cham

Místo konání akce

BURGOS

Datum konání akce

16. 9. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—