Prediction of User Throughput in the Mobile Network Along the Motorway and Trunk Road

The main goal of this research is to create a machine learning model for predicting user throughput in the mobile 4G network of the network provider M:tel Banja Luka, Bosnia and Herzegovina. The geographical area of the research is limited to the section of Motorway "9th January" (M9J) Banja Luka - Doboj, between the node Johovac and the town of Prnjavor (P-J section), and the area of the section of trunk road M17, between the node Johovac and the town of Doboj (J-D section). Based on the set of collected data, several models based on machine learning techniques were trained and tested together with the application of the Correlation-based Feature Selection (CFS) method to reduce the space of input variables. The test results showed that the models based on k-Nearest Neighbors (k-NN) have the lowest relative prediction error, for both sections, while the model created for the trunk road section has significantly better performance

Factors affecting indoor radon variations: A case study in schools of eastern macedonia

The subject of this study is the radon concentrations variations, measured with a nuclear track detectors in a total of 58 premises in all 29 primary schools of 4 municipalities in the Eastern part of the Republic of Macedonia. Despite a relatively small territory, the variability of radon concentrations proved to be significant. The geometric means (geometric standard deviations) of radon concentrations in the examined municipalities were in the range from GM = 71 Bq/m 3 (GSD = 2.08) to GM = 162 Bq/m 3 (GSD = 2.69), while for the entire region it was: GM = 96 Bq/m 3 (GSD = 2.47). The influence of the geographical and geological features of the school site as well as the building characteristics on the radon variations were investigated. The analysis showed that type of municipality, building materials, basement and geology have significant effects and respectively describe 6%, 16%, 22%, 39% of the radon total variability

Factors affecting indoor radon variations: A case study in schools of eastern macedonia

The subject of this study is the radon concentrations variations, measured with a nuclear track detectors in a total of 58 premises in all 29 primary schools of 4 municipalities in the Eastern part of the Republic of Macedonia. Despite a relatively small territory, the variability of radon concentrations proved to be significant. The geometric means (geometric standard deviations) of radon concentrations in the examined municipalities were in the range from GM = 71 Bq/m 3 (GSD = 2.08) to GM = 162 Bq/m 3 (GSD = 2.69), while for the entire region it was: GM = 96 Bq/m 3 (GSD = 2.47). The influence of the geographical and geological features of the school site as well as the building characteristics on the radon variations were investigated. The analysis showed that type of municipality, building materials, basement and geology have significant effects and respectively describe 6%, 16%, 22%, 39% of the radon total variability

Prediction of Long-Term Indoor Radon Concentration Based on Short-Term Measurements

We present a method for the estimation of annual radon concentration based on short-term (three months) measurements. The study involves results from two independent sets of indoor radon concentration measurements performed in 16 cities of the Republic of Macedonia. The first data set contains winter and annual radon concentration obtained during the National survey in 2010 and the second, contains only the radon concentration measured during the winter of 2013. Both data sets pertain to radon concentration from the same cities and have been measured applying the same methodology in ground floor dwellings. The results appeared to be consistent and the dispersion of radon concentration was low. Linear regression analysis of the radon concentration measured in winter of 2010 and of the 2010 annual radon concentration revealed a high coefficient of determination R-2 = 0.92, with a relative uncertainty of 3%. Furthermore, this model was used to estimate the annual radon concentration solely from winter-term measurements performed in 2013. The geometrical mean of the estimated annual radon concentration of the 2013: radon concentration (A-2013) =98 Bqm(-3) was almost equal to the geometrical mean of the annual radon concentration from the 2010, radon concentration (A-2010) = 99 Bqm(-3). Analysis of the influence of building characteristics, such as presence/absence of a basement in the building, or the dominant building material on the estimated annual radon concentration is also reported. Our results show that a low number of relatively short-term radon measurements may produce a reasonable insight into a gross average obtained in a larger survey

Prediction of user throughput in the mobile network along the motorway and trunk road

The main goal of this research is to create a machine learning model for predicting user throughput in the mobile 4G network of the network provider M:tel Banja Luka, Bosnia and Herzegovina. The geographical area of the research is limited to the section of Motorway "9th January" (M9J) Banja Luka - Doboj, between the node Johovac and the town of Prnjavor (P-J section), and the area of the section of trunk road M17, between the node Johovac and the town of Doboj (J-D section). Based on the set of collected data, several models based on machine learning techniques were trained and tested together with the application of the Correlation-based Feature Selection (CFS) method to reduce the space of input variables. The test results showed that the models based on k-Nearest Neighbors (k-NN) have the lowest relative prediction error, for both sections, while the model created for the trunk road section has significantly better performance

Experimental Testing of Combustion Parameters and Emissions of Waste Motor Oil and Its Diesel Mixtures

The production of hydrocarbon fuel from waste engine oil is an excellent way to produce alternative fuels. The aim of the research in this paper is obtaining fuel with a mixture of waste engine oil (WMO) and diesel fuel that can be used as an alternative fuel for internal combustion engines and low power heat generators. With this goal in mind, tests were conducted to estimate the combustion parameters and emissions at a low heat output of 40 kW. Waste motor oils (WMO) and four of its diesel mixtures were used, varying in weight from 20% WMO to 50% WMO. Test results were analysed and compared with diesel fuel. Higher NO, CO and CO2 emissions were determined for WMO and its mixtures compared to diesel fuel. The flue gas temperature in the kiln was high for all WMO and diesel blends, which indicates the efficiency of the input energy. The absorption of flue gases in the scrubber with distilled water showed higher presence of sulphates, sulphides, nitrates and nitrites compared to allowable values

Adaptive Modeling of Prediction of Telecommunications Network Throughput Performances in the Domain of Motorway Coverage

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

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

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%

Active and Passive Radon Concentration Measurements and First-Step Mapping in Schools of Banja Luka, Republic of Srpska

Radon concentration measurements were performed in all 25 primary schools in Banja Luka city, the capital of Republic of Srpska, during 2011 and 2012, using both active RAD7 continual radon measuring instruments and CR-39 passive (commercially known as Gamma) detectors. The two complimentary methods were employed not only to obtain annual averages, but also to study the dynamics of radon concentration changes during the week. For each school, average and temporal variations of radon concentrations were analysed, taking into consideration local geology, building materials and meteorological conditions. The influence of forced ventilation, caused by frequent opening of doors and windows during working hours, with typical dawn and weekend peaks is evident in most but not all schools. Elevated levels of radon concentration ( GT 400 Bq m(-3)) were found in a few schools using both methods. Although high correlation factor of 0.8 between passive and active methods was found, still short-time (one-week) measurements cannot be used for annual estimation of radon activity but only as a screening one. Thus, the conclusion concerns only long time measurements as valid indicator of annual radon activity.1st East European Radon Symposium (FERAS), Sep 02-05, 2012, Cluj Napoca, Romani