2018
DOI: 10.21474/ijar01/6672
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The Ambient Scrutinize of Scheduling Algorithms in Big Data Territory.

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Cited by 7 publications
(4 citation statements)
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“…Shifting security perimeters that aren't clearly defined necessitate the addition of new levels of security via endpoint protection. To avoid the risks that can come from the use of remote devices, security must maintain better control over access points [86]. This enables businesses to defend their servers, workstations, and mobile devices from cyber-attacks both locally and remotely.…”
Section: Endpoint Securitymentioning
confidence: 99%
“…Shifting security perimeters that aren't clearly defined necessitate the addition of new levels of security via endpoint protection. To avoid the risks that can come from the use of remote devices, security must maintain better control over access points [86]. This enables businesses to defend their servers, workstations, and mobile devices from cyber-attacks both locally and remotely.…”
Section: Endpoint Securitymentioning
confidence: 99%
“…This layer also possesses query independent (Q.I) ranker to rank resources according to their inartificial order, independent from user queries. For example, page rank is a form of Q.I Ranking [37]. The depending on the timing between discovery and search activity, a WoTSE can push resources directly to the query resolution process, skipping the index and storage layer.…”
Section: The Architecture For Wotsementioning
confidence: 99%
“…The system must generalise its answers in order to respond appropriately in situations that are not represented in the training examples. By adding more training instances, the supervised learning system's performance improves [8]. The classification, object identification, picture captioning, regression, and labelling are a few examples of supervised learning issues.…”
Section: Introductionmentioning
confidence: 99%