Distance impact on quality of video streaming services in cloud environments

Alomari, Amirah

doi:10.1504/ijssc.2017.088991

Cited by 6 publications

(3 citation statements)

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“…Wanget al(2019) specifiedthatthekeyfeatureofcloudcomputingistoprovidequalitycomputingservicestotheusers. Alomari(2017) Jammal(2017), Khanghahi&Ravanmehr(2013)andPaquetteet al(2010 accredited availability as an imperative factor for service quality evaluation of the cloud service providers.Uniquesellingpoint(USP)ofcloudserviceprovidersis100%uninterruptedavailability ofthecloudservicestothecustomers (Paquetteet al,2010). 2019)andLin&Chen( 2012)).…”

Section: Need For This Researchmentioning

confidence: 99%

Modelling of the Cloud Service Quality Factors Using ISM

Agarwal

Dhingra

2022

International Journal of Cloud Applications and Computing

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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 service providers. This will help organisations to adopt the right cloud service provider as per their business requirements. Eleven cloud service quality factors have been explored through an extensive review of the literature and then interpretive structural modelling (ISM) has been used to find out the driving and dependent factors of the cloud service quality and contextual relations among them. This study reveals six driving factors of the cloud service quality namely availability, reliability, scalability, security, service responsiveness, and usability.

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Section: Need For This Researchmentioning

confidence: 99%

Modelling of the Cloud Service Quality Factors Using ISM

Agarwal

Dhingra

2022

International Journal of Cloud Applications and Computing

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“…Recently, how to accurately monitor public opinion information 1–6 in large‐scale social media has become an important research hotspot of cyber security and cyber attacks. As a result, there have been many attempts to develop text mining techniques for accurately detecting topics and sentiments from large volumes of social contents 7,12–16 .…”

Section: Related Workmentioning

confidence: 99%

“…There are various public opinion monitoring tasks [1][2][3][4] and one of them is sentiment classification. In general, it refers to whether the semantic orientation of a text is positive, negative or neutral.…”

Section: Introductionmentioning

confidence: 99%

Topic sentiment analysis using words embeddings dependency in edge social system

Xie

Zhang

et al. 2019

Trans Emerging Tel Tech

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The topic sentiment analysis is really fundamental in detecting potential cyber threats and cyber attacks in edge social systems. We can detect potential cyber threats and cyber attacks by identifying the sentiment orientation and topics of public opinion information in the edge social system. Topic sentiment joint model is an extended model, which aims to deal with the problem of detecting sentiments and topics simultaneously from the online comment. Most existing topic sentiment joint models ignore the dependency among words so that they lose rich semantic information and the resulting distribution might be not very satisfactory. In this paper, we propose a novel topic sentiment joint model with word embeddings dependency based on recurrent neural network. The model introduces the dependency among word embedding and delivers topic information and sentiment information of words by a recurrent neural network. It fully extends the semantic information and redefines the topic sentiment‐word distribution. Moreover, we obtain more accurate topic detection and sentiment analysis. Experimental results on online review data set show that the proposed model significantly improves the sentiment classification accuracy and achieved better topic detection compared with previous methods.

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Residual Reconstruction Algorithm Based on Sub-pixel Multi-hypothesis Prediction for Distributed Compressive Video Sensing

Cao

Tong

et al. 2018

Advances in Intelligent Systems and Computing

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Distance impact on quality of video streaming services in cloud environments

Cited by 6 publications

References 0 publications

Modelling of the Cloud Service Quality Factors Using ISM

Modelling of the Cloud Service Quality Factors Using ISM

Topic sentiment analysis using words embeddings dependency in edge social system

Residual Reconstruction Algorithm Based on Sub-pixel Multi-hypothesis Prediction for Distributed Compressive Video Sensing

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