2022
DOI: 10.4018/ijcac.295241
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Modelling of the Cloud Service Quality Factors Using ISM

Abstract: In today's world, it is appallingly hard to run a global business, spread over multiple continents without adopting cloud computing technology. Cloud computing makes it easy, saves bucks, reduces IT burden, and helps organisations to focus on customer requirements, market strategy, growth, revenue, and profit, etc. While the organisations are busy planning, selecting, migrating their core business data and applications to the cloud, at the same time it is pertinent to evaluate the service quality of cloud serv… Show more

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Cited by 4 publications
(6 citation statements)
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“…where N is the number of data sources used, r i is the rank ratio for ith ranking, and r = 0 0 . Since the complexity of this formula was factorial, Aerts et al 63 proposed a faster alternative formula with complexity O N ( ) 2 :…”
Section:   mentioning
confidence: 99%
See 1 more Smart Citation
“…where N is the number of data sources used, r i is the rank ratio for ith ranking, and r = 0 0 . Since the complexity of this formula was factorial, Aerts et al 63 proposed a faster alternative formula with complexity O N ( ) 2 :…”
Section:   mentioning
confidence: 99%
“…Over time and the emergence of new technologies such as cloud 1,2 IoT, 3 and fog computing, many text data have posed many data science challenges. We are beholding their overgrowing every moment.…”
Section: Introductionmentioning
confidence: 99%
“…Nowadays, research are proposing the use of machine learning to solve the real word problems related to cloud computing, 16,17 cyber security, 18 IoT, 19,20 fog computing, 21 and the healthcare sector. 22 Considering the importance of feature selection for intelligent systems, many different research have been proposed in this field.…”
Section: Related Workmentioning
confidence: 99%
“…The aforementioned objectives are in different ranges, which definitely affect the performance of the fitness function. Hence, the values are transferred into same range with Equations ( 16), (17), and (18), where Maximum information is the maximum information that can be obtained by selected features. Since the maximum information of each feature can be 1, therefore, the variable Maximum information is equal with the number of selected features.…”
Section: Feature Optimizermentioning
confidence: 99%
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