Прегледај по Аутор "Banjanin, Milorad K."

Сада се приказује 1 - 3 од 3
  • Сличица
    Ставка
    Adaptive Modeling of Prediction of Telecommunications Network Throughput Performances in the Domain of Motorway Coverage
    (MDPI, 2021) Banjanin, Milorad K.; Stojčić, Mirko; Drajić, Dejan; Ćurguz, Zoran; Milanović, Zoran; Stjepanović, Aleksandar
    The main goal of this paper is to create an adaptive model based on multilayer perceptron (MLP) for prediction of average downlink (DL) data throughput per user and average DL data throughput per cell within an LTE network technology and in a geo-space that includes a segment of the Motorway 9th January with the access roads. The accuracy of model prediction is estimated based on relative error (RE). With multiple trainings and testing of 30 different variants of the MLP model, with different metaparameters the final model was chosen whose average accuracy for the Cell Downlink Average Throughput variable is 89.6% (RE = 0.104), while for the Average User Downlink Throughput variable the average accuracy is 88% (RE = 0.120). If the coefficient of determination is observed, the results showed that the accuracy of the best selected prediction model for the first variable is 1.4% higher than the accuracy of the prediction of the selected model for the second dependent variable. In addition, the results showed that the performance of the MLP model expressed over R2 was significantly better compared to the reference multiple linear regression (MLR) model used.
  • Сличица
    Ставка
    MULTIFACTOR INFLUENCES ON THE QUALITY OF EXPERIENCE SERVICE USERS OF TELECOMMUNICATION PROVIDERS IN THE REPUBLIC OF SRPSKA, BOSNIA AND HERZEGOVINA
    (University of Montenegro, 2023) Banjanin, Milorad K.; Maričić, Goran; Stojčić, Mirko
    The paper investigates multidisciplinary factors influencing the quality of experience (QoE) of service users of telecommunication provider as an open-structured stock-company of people who are creators of services and applications and/or proactive service users. The quality of user experience QoE is created and improved over time under multifactorial influences. The aim of this paper is to analyze legal-regulatory, socio-contextual, technological-process and stock-company ,factors as input independent variables and subjective-user factors as transition variables of influence on motivation, behavior and user satisfaction in the Model of influences factors (MIF). The output from MIF is dependent variable QoE. MIF was created on a perceptual and reference path with interactively related factors of all influences on QoE through subjective-user assessment. The quality of MIF was verified by statistical significance analysis among variables of paired factors influencing QoE using SPSS technology, regression analysis, and the Boosted Desedions Tree technique of machine learning method.
  • Сличица
    Ставка
    Predictive Modeling of Delay in an LTE Network by Optimizing the Number of Predictors Using Dimensionality Reduction Techniques
    (MDPI, 2023) Stojčić, Mirko; Banjanin, Milorad K.; Vasiljević, Milan; Nedić, Dragana; Stjepanović, Aleksandar; Danilović, Dejan; Puzić, Goran
    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.