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Neuro-fuzzy risk prediction model for computational grids

Abdelwahab, S., Ojha, V. ORCID: and Abraham, A. (2016) Neuro-fuzzy risk prediction model for computational grids. In: Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015, Sep 9, 2015 - Sep 11, 2015, Paris - Villejuif, France, pp. 127-136,

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To link to this item DOI: 10.1007/978-3-319-29504-6_13


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 analyze 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 predict 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 to obtain the risk prediction accurately.

Item Type:Conference or Workshop Item (Paper)
Divisions:Interdisciplinary Research Centres (IDRCs) > Centre for the Mathematics of Planet Earth (CMPE)
Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:93558
Publisher:Springer Science \mathplus Business Media


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