Multifactor Influences on the Quality of Experience Service Users of Telecommunication Providers in the Republic of Srpska, Bosnia and Herzegovina

Banjanin, Milorad K.; Maričić, Goran; Stojčić, Mirko

doi:10.24874/ijqr17.02-05

Cited by 2 publications

(6 citation statements)

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“…Figure 2 graphically shows an interaction model between individual factors that affect the QoE i value. As can be concluded from the figure, there are two-way relations of influence according to the each with each principle of the paired factors and their corresponding input variables, including transitory subjective-user variables of satisfaction and/or dissatisfaction [15]. The value of QoE i as a dependent variable is obtained via a mathematical model, directly based on users' ratings of satisfaction and dissatisfaction indicators, while independent variables serve as inputs to QoE i prediction and classification models.…”

Section: An Interaction Model Of Paired Factors Affecting Qoe Imentioning

confidence: 99%

“…Regarding age structure, respondents between the age of 30 and 45 dominates with 55% of the total sample. The representation of users by other age intervals, in descending order, is as follows: interval 45 to 60 years with 29%, interval 20 to 30 with 8%, respondents older than 60 with 8%, respondents younger than 20 with a negligible 0.006 % [15].…”

Section: Research Sample Statisticsmentioning

confidence: 99%

“…When it comes to user satisfaction with the price of services, the achieved MOS value is 2.83, which can be considered as solid. Users rated the legal security in interactions with the provider in the area of contract-based service provision and bill payment by cost accounting with an average score of 2.97 (solid) [15].…”

Section: Research Sample Statisticsmentioning

confidence: 99%

“…The main goal of this research is to model the overall quality of user experience in interactions with telecommunication services that belong to the conversational, streaming, interactive and background classes of network traffic. For the case study, the three largest mobile operators, as providers of the aforementioned services, operating on the territory of the Republic of Srpska and Bosnia and Herzegovina were chosen: Mtel, BH Telekom and HT Eronet [15].…”

Section: Introductionmentioning

confidence: 99%

“…Subjective assessment is considered the most accurate approach to measuring QoE, where a discrete MOS scale of five levels is used to quantify factors affecting QoE: 1: bad, 2: poor; 3: fair, 4: good, 5: excellent [25,33]. In this research, the evaluation of user QoE i is carried out with a subjective approach and a survey method, based on which a research instrument was created-a QoE questionnaire with 11 questions [15], through which five reconstructed groups of paired factors affecting QoE i were presented.…”

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confidence: 99%

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Classification and Prediction of Sustainable Quality of Experience of Telecommunication Service Users Using Machine Learning Models

et al. 2022

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The quality of experience (QoE) of the individual user of telecommunication services is one of the most important criteria for choosing the service package of mobile providers. To evaluate the sustainability of QoE, this paper uses indicators of user satisfaction or dissatisfaction with the quality of network services (QoS), especially with conversational, streaming, interactive and background classes of traffic in networks. The importance of knowing the impact of selected combinations of paired legal–regulatory, technological–process, content-formatted and performative, contextual–relational and subjective user-influencing factors on QoE sustainability is investigated using a multiple linear regression model created in Minitab statistical software, machine learning model based on boosted decision trees created in the MATLAB software package and predictive models created by using an automatic modeling method. The classification of influence factors and their matching for the analysis of interaction fields of users and services aim to mark QoE as sustainable by determining the accuracy of the weight of subjective ratings of user satisfaction indicators as transitional variables in the predictive model of QoE. The hypothetical setting is that the individual user’s curiosity, creativity, communication, personality, courage, confidence, charisma, competence, common sense and memory are adequate transition variables in a sustainable QoE model. Using the applied methodology with an original research approach, data were collected on the evaluations of research variables from anonymous users of mobile operators in the geo-space of Republika Srpska and B&H. By treating the data with mathematical and machine learning models, the QoE assessment was performed at the level of an individual user, and after that, several models were created for the prediction and classification of QoEi. The results show that the relative error (RE) of the predictive models, created over the collected dataset, is insufficiently low, so the improvement of the prediction performance was achieved via data augmentation (DA). In this way, the relative prediction error is reduced to a value of RE = 0.247. The DA method was also applied for the creating a classification model, which at best demonstrated an accuracy of 94.048%.

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Section: An Interaction Model Of Paired Factors Affecting Qoe Imentioning

confidence: 99%

Section: Research Sample Statisticsmentioning

confidence: 99%

Section: Research Sample Statisticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Classification and Prediction of Sustainable Quality of Experience of Telecommunication Service Users Using Machine Learning Models

et al. 2022

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Predictive Modeling of Delay in an LTE Network by Optimizing the Number of Predictors Using Dimensionality Reduction Techniques

Stojčić,

Banjanin,

Vasiljević

et al. 2023

Applied Sciences

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Delay in data transmission is one of the key performance indicators (KPIs) of a network. The planning and design value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in a Long Term Evolution (LTE) network using three ML techniques: Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that the k-NN model has been selected as the best solution in all three optimization approaches. For the RReliefF optimization algorithm, the best model has six inputs and the minimum relative error (RE) RE = 0.109. For the Backward selection via the recursive feature elimination algorithm, the best model has four inputs and RE = 0.041. Finally, for the Pareto 80/20 rule, the best model has 11 inputs and RE = 0.049. The comparative analysis of the results concludes that, according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.

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Multifactor Influences on the Quality of Experience Service Users of Telecommunication Providers in the Republic of Srpska, Bosnia and Herzegovina

Cited by 2 publications

References 0 publications

Classification and Prediction of Sustainable Quality of Experience of Telecommunication Service Users Using Machine Learning Models

Classification and Prediction of Sustainable Quality of Experience of Telecommunication Service Users Using Machine Learning Models

Predictive Modeling of Delay in an LTE Network by Optimizing the Number of Predictors Using Dimensionality Reduction Techniques

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