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Zalizah Awang Long
Universiti Kuala Lumpur (UniKL)
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Impact of big data congestion in IT: an adaptive knowledge-based bayesian network Soheli Farhana; Adidah Lajis; Zalizah Awang Long; Haidawati Nasir
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (514.921 KB) | DOI: 10.11591/ijece.v10i2.pp2031-2036

Abstract

Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.
Co-Authors Adidah Lajis Haidawati Nasir Soheli Farhana