This HTML5 document contains 47 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n21http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n13http://purl.org/net/nknouf/ns/bibtex#
n12http://localhost/temp/predkladatel/
n9http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n18http://linked.opendata.cz/ontology/domain/vavai/
n20https://schema.org/
n11http://linked.opendata.cz/resource/domain/vavai/zamer/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
n19http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21230%2F08%3A03145519%21RIV09-MSM-21230___/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n15http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n7http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n16http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n5http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n17http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n10http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n6http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F08%3A03145519%21RIV09-MSM-21230___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
In the present time, biomedical data processing is an important step in the process of diagnostics, prevention and healthcare in the physicians work. The goal of this work is to help the physicians to cope with long-term biomedical data. In present time, many optimization, classification and data processing methods are inspired by nature. This paper reviews most recent advances on the field of ant colony inspired clustering. With constantly growing datasets needed to be processed, there is a significant need to study and implement robust and effective methods for processing such datasets. Thus, improved methods are presented and evaluated on long-term ECG data recordings. Paper also concentrates on the drawbacks and advantages of the methods. This paper also presents and evaluates novel method called ACO\_DTree for classification tree generation and their evolution inspired by natural processes. In the present time, biomedical data processing is an important step in the process of diagnostics, prevention and healthcare in the physicians work. The goal of this work is to help the physicians to cope with long-term biomedical data. In present time, many optimization, classification and data processing methods are inspired by nature. This paper reviews most recent advances on the field of ant colony inspired clustering. With constantly growing datasets needed to be processed, there is a significant need to study and implement robust and effective methods for processing such datasets. Thus, improved methods are presented and evaluated on long-term ECG data recordings. Paper also concentrates on the drawbacks and advantages of the methods. This paper also presents and evaluates novel method called ACO\_DTree for classification tree generation and their evolution inspired by natural processes. Účelem této práce je poskytnout pomoc lékařům, kteří pracují s dlouhodobými biomedicinskými záznamy. K dispozici je velké množství algoritmů inspirovaných přírodními procesy. Práce uvádí některé vylepšené metody a jejich aplikaci na dlouhodobé záznamy EKG. Uvádíme i novou metodu ACO_DTree, která slouží k vytváření rozhodovacích stromů a jejich optimalizaci.
dcterms:title
Veylepšené algoritmy inspirované mravenčími koloniemi při zpracování biomedicínských dat Improved Ant Colony Inspired Algorithms in Biomedical Data Processing Improved Ant Colony Inspired Algorithms in Biomedical Data Processing
skos:prefLabel
Veylepšené algoritmy inspirované mravenčími koloniemi při zpracování biomedicínských dat Improved Ant Colony Inspired Algorithms in Biomedical Data Processing Improved Ant Colony Inspired Algorithms in Biomedical Data Processing
skos:notation
RIV/68407700:21230/08:03145519!RIV09-MSM-21230___
n4:aktivita
n5:Z
n4:aktivity
Z(MSM6840770012)
n4:dodaniDat
n6:2009
n4:domaciTvurceVysledku
n9:1182943 n9:3558568 n9:9431446
n4:druhVysledku
n17:D
n4:duvernostUdaju
n7:S
n4:entitaPredkladatele
n19:predkladatel
n4:idSjednocenehoVysledku
371683
n4:idVysledku
RIV/68407700:21230/08:03145519
n4:jazykVysledku
n16:eng
n4:klicovaSlova
ACO_DTree; ant colony clustering; ant colony optimization; dynamic time warping
n4:klicoveSlovo
n15:ant%20colony%20clustering n15:ant%20colony%20optimization n15:ACO_DTree n15:dynamic%20time%20warping
n4:kontrolniKodProRIV
[F34AE1DC477D]
n4:mistoKonaniAkce
Lefkada
n4:mistoVydani
Singapore
n4:nazevZdroje
Advaced Topics in Scattering and Biomedical Engineering
n4:obor
n10:JC
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:rokUplatneniVysledku
n6:2008
n4:tvurceVysledku
Lhotská, Lenka Huptych, Michal Burša, Miroslav
n4:typAkce
n21:WRD
n4:zahajeniAkce
2007-09-28+02:00
n4:zamer
n11:MSM6840770012
s:numberOfPages
8
n13:hasPublisher
World Scientific
n20:isbn
978-981-281-484-5
n12:organizacniJednotka
21230