Empirical study based on machine learning approach to assess the QoS/QoE correlation

Mushtaq, M. Sajid; Augustin, Brice; Mellouk, Abdelhamid

doi:10.1109/noc.2012.6249939

Cited by 80 publications

(80 citation statements)

References 14 publications

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“…For the SVM model (SMO) the accuracy was 88.59% (Mobile phones), 89.38% (PDAs) and 91.45% (Laptops).They found from the results that both methods outperform the Discriminate Analysis method which was used in [29]. However, the error of these modelsis between 10% and 20%, as shown in [37].Work in [38] has also used DT and SVM for building an objective QoE model and then compares them with other Machine Learning methods including: Naive Bayes (NB), kNearest Neighbours (k-NN), Random Forest (RF) and Neural Networks (NNet). Results show that RF performs slightly better than the others examined.…”

Section: Qoemodels Based Onmachine Learning Methodsmentioning

confidence: 93%

“…al [38] -It is observed that DT has the best performance,in the case of mean absolute error rate, as compared to all other algorithms.…”

Section: Mushtaqetmentioning

confidence: 94%

“…Therefore, it is necessary to develop a sophisticated method to test the trustworthiness and quality of the test subjects. Work in [38] has also used the Crowdsourcing approach to collect QoE datasets.…”

Section: Qoe Model Using Crowdsourcing For Subjective Testsmentioning

confidence: 99%

“…al [33] × × × × Machado et al [37] × × × × Du et al [39] × × Kim et al [21] × × × Khan et al [42] × × Malinovski et al [44] × × × × Han et al [50] × × × × × × Laghari et al [51] × × × Ramos et al [28] × × × Koumaras et al [22] × × × Frank et al [40] × × × Calyam et al [41] × × × × × × Mok et al [23] × × Elkotob et al [32] × × × × × × Mushtaq et al [38] × × × × Hoßfeld et al [45] × × × ×…”

mentioning

confidence: 99%

See 3 more Smart Citations

Survey on Qoe\Qos Correlation Models Formultimedia Services

Alreshoodi¹,

Woods²

2013

IJDPS

101

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Section: Qoemodels Based Onmachine Learning Methodsmentioning

confidence: 93%

“…al [38] -It is observed that DT has the best performance,in the case of mean absolute error rate, as compared to all other algorithms.…”

Section: Mushtaqetmentioning

confidence: 94%

Section: Qoe Model Using Crowdsourcing For Subjective Testsmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Survey on Qoe\Qos Correlation Models Formultimedia Services

Alreshoodi¹,

Woods²

2013

IJDPS

101

View full text Add to dashboard Cite

show abstract

“…Method Native Bays (NB) [13] Decision Trees (DT) [15] Random Forest (RF) [10] Support Vector Machine (SVM) [6] Random Neural Network (RNN) [1] Adaptive Neuro Fuzzy Inference System (ANFIS) [14] a) Naive Bayes (NB) Bayes (NB) classifier is based on Naive Bayes theorem. It simplifies learning assuming that the features are independent for a given class.…”

Section: Qoe Estimation Used Methodsmentioning

confidence: 99%

A Hierarchical Classification Model of QoE Influence Factors

Amour

Souihi

Hoceini

et al. 2015

Lecture Notes in Computer Science

Self Cite

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Abstract. Quality of Service (QoS) optimization are not sufficient to ensure users needs. That's why, operators are investigating a new concept called Quality of Experience (QoE), to evaluate the real quality perceived by users. This concept becomes more and more important, but still hard to estimate. This estimation can be influenced by a lot of factors called : Quality of Experience Influence Factors (QoE IFs). In this work, we survey and review existing approaches to classify QoE IFs. Then, we present a new modular and extensible classification architecture. Finally, regarding the proposed classification, we evaluate some QoE estimation approaches to highlight the fact that categories do not affect in the same the user perception.

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Intelligent admission control manager for fog‐integrated cloud: A hybrid machine learning approach

Sham

Vidyarthi

2021

Concurrency and Computation

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Internet of Things (IoT) and other smart devices produce data that are large in volume, variety, and velocity. Cloud not only helps in data analysis, but also provides storage and computation facility to these data. It has been experienced that for many time-critical applications, by the time request traverses back and forth to the cloud for analysis/execution, the opportunity to act upon it may get over. Therefore, time sensitivity and priority for such applications greatly matter. Fog computing, an upcoming computing infrastructure, complements the cloud and overcomes this limitation by supporting time-sensitive and priority-based applications by provisioning the computation, bandwidth, and storage. However, adopting fog-integrated cloud introduces newer resource management challenges requiring a new request scheduling scheme with appropriate quality of service/experience (QoS/QoE). In this work, an intelligent admission control manager is being proposed for placing the request based on the parameters such as CPU, memory, storage besides few other categorical parameters, for example, job priority and time sensitivity. The proposed work applies machine intelligence techniques, clustering for labeling the applications' requests followed by a decision tree, using the labeled requests, to classify the incoming requests. The proposed methodology is demonstrated in terms of accuracy, execution time, precision, recall, variation in accuracy, and execution time by introducing noise in multiple size batches to avoid the generalization error and fault tolerance. A comparative study with few well-known classifiers has also been performed to ascertain the effectiveness of the proposed model. The proposed model is light enough to be placed appropriately on the fog node.

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Empirical study based on machine learning approach to assess the QoS/QoE correlation

Cited by 80 publications

References 14 publications

Survey on Qoe\Qos Correlation Models Formultimedia Services

Survey on Qoe\Qos Correlation Models Formultimedia Services

A Hierarchical Classification Model of QoE Influence Factors

Intelligent admission control manager for fog‐integrated cloud: A hybrid machine learning approach

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