A graph-based method for ranking of cloud service providers

Trueman, Tina Esther; Narayanasamy, P.; Kumar, Jay

doi:10.1007/s11227-021-04156-x

Cited by 12 publications

(3 citation statements)

References 39 publications

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“…Another significant issue is regarding the consideration of user priority, which needs to be incorporated while recommending the optimal service. The proposed method introduces the efficient technique to select the most [25] _ Compute partial correlation between cloud service providers _ Graphical Lasso Regularization _ Ranks service providers through degree centrality optimal cloud service by fixing the existing gaps. To aggregate the C-DRM (i) tackle the space complexity by minimizing the search space through similarity ranking, (ii) priority of the user requirements are taken into account and converted into weights using entropy function (iii) provides the reliable ranking of the cloud services by handling the uncertainty in user preferences.…”

Section: Novelty Of the Proposed Method: C-drmmentioning

confidence: 99%

C-DRM: Coalesced P-TOPSIS Entropy Technique addressing Uncertainty in Cloud Service Selection

Nivitha¹,

Pabitha²

2022

ITC

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Cloud Computing is diversified with its services exponentially and lured large number of consumers towards the technology indefinitely. It has become a highly challenging problem to satiate the user requirements. Most of the existing system ingest large search space or provide inappropriate service; hence, there is a need for the reliable and space competent service selection/ranking in the cloud environment. The proposed work introduces a novel pruning method and Dual Ranking Method (DRM) to rank the services from n services in terms of space conserving and providing reliable service quenching the user requirements as well. Dual Ranking Method (DRM) is proposed focusing on the uncertainty of user preferences along with their priorities; converting it to weights with the use of Jensen-Shannon (JS) Entropy Function. The ranking of service is employed through Priority-Technique for Order of Preference by Similarity to Ideal Solution (P-TOPSIS) and space complexity is reduced by novel Utility Pruning method. The performance of the proposed work Clustering – Dual Ranking Method (C-DRM) is estimated in terms of accuracy, Closeness Index (CI) and space complexity have been validated through case study where results outperforms the existing approaches

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Section: Novelty Of the Proposed Method: C-drmmentioning

confidence: 99%

C-DRM: Coalesced P-TOPSIS Entropy Technique addressing Uncertainty in Cloud Service Selection

Nivitha¹,

Pabitha²

2022

ITC

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“…The best-t network models were selected based on an extended Bayesian information criterion (EBIC). Regularized models using LASSO with EBIC have high speci city and varying sensitivity based on sample size and true network structure 33 .…”

Section: Estimation Of Regularized Partial Correlation Networkmentioning

confidence: 99%

Unraveling pain experience and catastrophizing after cognitive behavioral therapy: A network model analysis

YOKOYAMA,

Adachi,

Yoshino

2024

Preprint

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Pain experiences are often complex with catastrophic cognitions, emotions, and behaviors. Cognitive behavioral therapists share the work of unraveling these complex experiences with their patients. However, the change process underlying the unraveling of the pain experience have not yet been quantified. We used an interrelationship-focused network model to examine the way an undifferentiated conceptualization between cognition and pain experience changed via group cognitive-behavioral therapy (CBT). Overall, 65 participants were included in the study; they attended the total of 12 weekly group CBT and filled the Short-Form McGill Pain Questionnaire and the pain catastrophizing questionnaire. Before treatment, there were no edges in the partial correlation-based network because of large covariation across items. After treatment, many edges appeared and, particularly strong couplings were found between items within the same subscale. The formative shift from a non-edged pre-treatment network to a mature post-treatment network indicates that patients were able to conceptualize these symbolic constructs better. These results are consistent with the fundamental monitoring process of CBT.

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“…We selected the best-fit network models using an extended Bayesian information criterion (EBIC). Regularized models using LASSO with EBIC have high specificity and varying sensitivity based on the sample size and true network structure [24].…”

Section: Network Estimationmentioning

confidence: 99%

Trace of depression: Network structure of depressive symptoms in different clinical conditions

et al. 2022

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A graph-based method for ranking of cloud service providers

Cited by 12 publications

References 39 publications

C-DRM: Coalesced P-TOPSIS Entropy Technique addressing Uncertainty in Cloud Service Selection

C-DRM: Coalesced P-TOPSIS Entropy Technique addressing Uncertainty in Cloud Service Selection

Unraveling pain experience and catastrophizing after cognitive behavioral therapy: A network model analysis

Trace of depression: Network structure of depressive symptoms in different clinical conditions

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