All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Neuro-fuzzy risk prediction model for computational grids

The result's identifiers

  • Result code in 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>

  • Alternative codes found

    RIV/61989100:27740/16:86099961

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Neuro-fuzzy risk prediction model for computational grids

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    R - Projekt Ramcoveho programu EK

Others

  • Publication year

    2016

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Advances in Intelligent Systems and Computing. Volume 427

  • ISBN

    978-3-319-29503-9

  • ISSN

    2194-5357

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    127-136

  • Publisher name

    Springer Verlag

  • Place of publication

    London

  • Event location

    Paříž

  • Event date

    Sep 9, 2015

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article