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Statements

Subject Item
n2:RIV%2F70883521%3A28140%2F10%3A63508853%21RIV11-GA0-28140___
rdf:type
skos:Concept n13:Vysledek
dcterms:description
In the view of global data growth in all industry fields, emphasis should be put on the quality and the information content as well as on the data optimization of processes which work with this data. This paper deals with a possibility to optimize by means of data mining a process of steganalysis solved by neural networks. One of the basic methods of data mining classification can reduce a dimension of data which is performed by NN later. To obtain a datamining model algorithm J48 was used and also several testing methods which classify instances with 64 attributes into two classes. For comparison with the originally published neural networks model, neural networks were used again to verify the data mining model. Conclusion is focused on holding an accuracy of classification and prediction of the model with additional refers to time savings caused by reduction of used data. In the view of global data growth in all industry fields, emphasis should be put on the quality and the information content as well as on the data optimization of processes which work with this data. This paper deals with a possibility to optimize by means of data mining a process of steganalysis solved by neural networks. One of the basic methods of data mining classification can reduce a dimension of data which is performed by NN later. To obtain a datamining model algorithm J48 was used and also several testing methods which classify instances with 64 attributes into two classes. For comparison with the originally published neural networks model, neural networks were used again to verify the data mining model. Conclusion is focused on holding an accuracy of classification and prediction of the model with additional refers to time savings caused by reduction of used data.
dcterms:title
Datamining Optimization of Steganalysis by means of Neural Network Datamining Optimization of Steganalysis by means of Neural Network
skos:prefLabel
Datamining Optimization of Steganalysis by means of Neural Network Datamining Optimization of Steganalysis by means of Neural Network
skos:notation
RIV/70883521:28140/10:63508853!RIV11-GA0-28140___
n3:aktivita
n11:Z n11:S n11:P
n3:aktivity
P(GA102/09/1680), S, Z(MSM7088352101)
n3:dodaniDat
n9:2011
n3:domaciTvurceVysledku
n12:5781264 n12:5707803 n12:2098474
n3:druhVysledku
n18:D
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
252980
n3:idVysledku
RIV/70883521:28140/10:63508853
n3:jazykVysledku
n17:eng
n3:klicovaSlova
Classification; Data mining; Optimization; Steganalysis; Artificial Neural Networks
n3:klicoveSlovo
n19:Artificial%20Neural%20Networks n19:Data%20mining n19:Steganalysis n19:Optimization n19:Classification
n3:kontrolniKodProRIV
[D76310C9D97B]
n3:mistoKonaniAkce
Zlín
n3:mistoVydani
Zlín
n3:nazevZdroje
Internet, bezpečnost a konkurenceschopnost organizací. Řízení procesů a využití moderních teerminálových technologií
n3:obor
n4:BD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n22:GA102%2F09%2F1680
n3:rokUplatneniVysledku
n9:2010
n3:tvurceVysledku
Procházka, Michal Hološka, Jiří Oplatková, Zuzana
n3:typAkce
n10:EUR
n3:zahajeniAkce
2010-01-01+01:00
n3:zamer
n16:MSM7088352101
s:numberOfPages
6
n15:hasPublisher
Univerzita Tomáše Bati ve Zlíně, Fakulta aplikované informatiky
n20:isbn
978-83-61645-16-0
n21:organizacniJednotka
28140