Neuro-fuzzy risk prediction model for computational grids

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099961" target="_blank" >RIV/61989100:27240/16:86099961 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/16:86099961

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-29504-6_13" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-29504-6_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-29504-6_13" target="_blank" >10.1007/978-3-319-29504-6_13</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Neuro-fuzzy risk prediction model for computational grids

Popis výsledku v původním jazyce

Prediction of risk assessment is demanding because it is one of the most important contributory factors towards grid computing. Hence, researchers were motivated for developing and deploying grids on diverse computers, which is responsible for spreading resources across administrative domains so that resource sharing becomes effective. Risk assessment in grid computing can analyses possible risks, that is, the risk of growing computational requirements of an organization. Thus, risk assessment helps in determining these risks. In this, we present an adaptive neuro-fuzzy inference system that can provide an insight of predicting the risk environment. The main goal of this paper is to obtain empirical results with an illustration of high performance and accurate results. We used data mining tools to determine the contributing attributes so that we can obtain the risk prediction accurately.

Název v anglickém jazyce

Neuro-fuzzy risk prediction model for computational grids

Popis výsledku anglicky

Prediction of risk assessment is demanding because it is one of the most important contributory factors towards grid computing. Hence, researchers were motivated for developing and deploying grids on diverse computers, which is responsible for spreading resources across administrative domains so that resource sharing becomes effective. Risk assessment in grid computing can analyses possible risks, that is, the risk of growing computational requirements of an organization. Thus, risk assessment helps in determining these risks. In this, we present an adaptive neuro-fuzzy inference system that can provide an insight of predicting the risk environment. The main goal of this paper is to obtain empirical results with an illustration of high performance and accurate results. We used data mining tools to determine the contributing attributes so that we can obtain the risk prediction accurately.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

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 427

ISBN

978-3-319-29503-9

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

10

Strana od-do

127-136

Název nakladatele

Springer Verlag

Místo vydání

London

Místo konání akce

Paříž

Datum konání akce

9. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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