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 QoE_i. 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%.

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.

This paper analyzes the problem of multi-criteria decision making methods (MCDM) when selecting the optimal type of sour cherry for planting. Choice of varieties in agriculture is a very complex problem and is usually characterized by the interaction of a large number of factors, including often limited resources and uncertain information (price, time). In this context, mathematical models can represent valuable support for farmers when deciding on the choice of crops and plants for planting. An integrated MCDM method is presented, along with expert knowledge and Fuzzy Interference System (FIS) for sour cherry varieties choosing. The proposed approach assists decision makers in complex calculations and diminishes the impact of personal subjectivity and perception in order to define the overall evaluation. Data is incorporated in proposed fuzzy system and validated by a numerical example.

Delay in data transmission is one of key performance indicators (KPIs) of a network. The planning and project 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 an Long Term Evolution (LTE) network using three ML techniques - Neural Networks (NN), 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, in all three optimization approaches, the k-NN model is selected as the best solution. For the RReliefF optimization algorithm, the best model has 6 inputs and minimum relative error (RE), RE=0.109; for the Backward selection via the recursive feature elimination algorithm, the best model has 4 inputs and RE=0.041; and 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.

In accordance with the Law on Insurance and the Law on Accounting and Audit, insurance companies in Serbia are required to prepare financial statements. These materials are submitted to the Agency of Business Registers and the National Bank of Serbia from 31st December of the reporting year according to the Law on Accounting and Audit. Insurance companies which undergo a change in a legal position, such as mergers, divisions, i.e. sales, have to submit financial statements with a cut-off on the day determined by the decision on the change of a legal position or on the day determined by the sales contract. Furthermore, financial reports are submitted in the cases of the insolvency proceedings or liquidation of an insurance company. The notion of audit in an insurance company has its importance from the Insurance Law itself, in which the entire Chapter 9 is dedicated to the audit of financial statements. It deals with the performance of the audit, prior consent to the selection of the audit company, liabilities of the audit company, and the check of the audit report and the notification of the body responsible for supervising the audit. The significance and role of audit in an insurance company is far broader, which will be explored later in this paper.

The economy based on the exploitation of fossil fuels has entered the phase of disappearance, and a new one is being formed under the influence of the third industrial revolution. The third industrial revolution is developing from the process of the resolution of serious problems that have appeared in the ecosystem, and demands a new platform of interaction between the world's governments, civil society and the business world [Stigson 2008]. The consumer society that was developing until the beginning of the 21st century started from the premise that we cannot expect charity from nature. Regardless of their renewability and speed of renewal, natural resources should have been used in the measure in which humanity needed it. Needs rose uncontrollably, damaging the basic principle by which the balance of the biosphere was maintained - the parity of community members' insignificance. The man with the technosphere prevailed over the biosphere. Since nothing is provided for free and nature knows best, the beginning of the 21st century is seeing renewed attempts to establish a co-evolution of nature and society. This necessitates radical changes in people's work, lifestyle and thinking. The problem of waste requires a total break with previous practices. The existing 6.6 billion people annually produce between 2.5 and 4 billion tons of waste (The Millennium Development Goals Report 2008). Developed countries such as, e.g., the US, annually produce about 700 kilograms of waste per person, while poor countries, such as, e.g., the urban parts of India, produce only 150 kilograms per person annually. Each resident of the EU leaves behind 500 kg of waste per year. Serbia has the same civilization problems. In addition, as a transition country, it is in a position to decide the extent to which this problem will be in the hands of state companies and how much will be left to private capital owners. Moreover, Serbia is creating a development strategy that also includes food production. The agricultural-food production sector, which is one of the biggest environmental polluters, has always made up a significant part of Serbia's GDP. This paper attempts to emphasize the complexity of the problem of waste and the necessity of cooperation between all organizations within the decision-making process.