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Copyright of Serbian Journal of Management is the property of Serbian Journal of Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The opportunity for large amounts of open-for-public and available data is one of the main drivers of the development of an information society at the beginning of the 21st century. In this sense, acquiring knowledge from these data using different methods of machine learning is a prerequisite for solving complex problems in many spheres of human activity, starting from medicine to education and the economy, including traffic as today's important economic branch. Having this in mind, this paper deals with the prediction of the risk of traffic incidents using both historical and real-time data for different atmospheric factors. The main goal is to construct an ensemble model based on the use of several machine learning algorithms which has better characteristics of prediction than any of those installed when individually applied. In global, a case-proposed model could be a multi-agent system, but in a considered case study, a two-agent system is used so that one agent solves the prediction task by learning from the historical data, and the other agent uses the real time data. The authors evaluated the obtained model based on a case study and data for the city of Niš from the Republic of Serbia and also described its implementation as a practical web citizen application. [ABSTRACT FROM AUTHOR]

Many methods exist for solving the problem of evaluating efficiency in different processes. They are divided into two basic groups, parametric and non-parametric methods, which can have significant differences in the results. In this study, the authors consider the process of assessing the business climate depending on realized foreign investments. Due to the expected difference in efficiency assessment using different approaches, the goal of this paper is to create an optimization model of an ensemble for efficiency assessment that uses both types of methods with the aim of creating a symmetrical approach that achieves better results than each type of method individually. The proposed solution simultaneously analyzes the impact of different factors on foreign investments in order to determine the most important factors and thus enable each local government to ensure the best possible efficiency in this process. The innovative idea of this study is in the inclusion of classification and feature selection methods of machine learning to fulfill the set goal. Our research, focused on a specific case study in various cities across the Republic of Serbia, evaluated the effectiveness of that process. This study extends previous research and confirms the published results, highlighting the advantages of the newly proposed model. [ABSTRACT FROM AUTHOR]

Research Question: This paper investigates into how machine learning can be applied for the purpose of detecting anomalies in the data describing transport component within the cellular network. Motivation: In the field of telecommunications, terabytes of data are generated each hour. This makes the manual analysis almost impossible to perform. There are thousands of components whose behaviour needs to be monitored, since anomalous behaviour could indicate a possible failure that can lead to network degradation, huge maintenance costs, and finally - a bad user experience. Our goal is to try to catch anomalous behaviour automatically, and thus help domain experts when performing drill down analysis of the degraded base stations and their key performance indicators (KPIs). Idea: The main idea of this paper is to empirically evaluate the application of machine learning for the problem of anomaly detection, in the field of telecommunications, specifically to long term evolution (LTE) networks. Data: Data used in the analysis contains information about base transceiver stations (BTS) behaviour through the time. The data are gathered from a cellular network provider located in Serbia. The data are collected on an hourly basis, for a period of two weeks, resulting in almost 700 thousand rows. The behaviour is assessed by 96 transport KPIs coming from BTS, describing the package losses, delays, transmission success rates, etc. Tools: Two main algorithms, ensemble-based Isolation Forest and autoencoder neural network, are elaborated and applied in order to identify patterns of anomalous behaviour. Findings: The results show that machine learning can be successfully applied in the field of LTE networks for the problem of anomaly detection. Machine learning can significantly reduce the time needed for the domain experts to identify anomalies within the network. In addition to time efficiency, one of the algorithms tested is able to identify anomalous KPIs separately, which is crucial when performing root cause analysis, by using drill-down approach, in order to identify which component is degraded. Contribution: This paper enriches existing research related to anomaly detection in LTE networks and provides an innovative approach to automated root-cause analysis of network degradation. [ABSTRACT FROM AUTHOR]

This paper presents a deep neural network (DNN) approach designed to estimate the milk yield of Holstein-Friesian cattle. The DNN comprised stacked dense (fully connected) layers, each hidden layer followed by a dropout layer. Various configurations of the DNN were tested, incorporating 2 and 3 hidden layers containing 8 to 54 neurons. The experiment involved testing the DNN with different activation functions such as the sigmoid, tanh, and rectified linear unit (ReLU). The dropout rates ranging from 0 to 0.3 were employed, with the output layer using a linear activation function. The DNN models were trained using the Adam, SGD, and RMSprop optimizers, with the root mean square error serving as the loss metric. The training dataset comprised information from a unique database containing records of dairy cows in the Republic of Serbia, totaling 3,406 cows. The input parameters (a total of 27) for the DNN included breeding and milk yield data from the cow's mother, as well as the father's ID, whereas the output parameters (a total of 8) consisted of milk yield parameters (a total of 3) and breeding parameters of the cow (a total of 5). Training iterations were conducted using a batch size of 8 over 500, 1000, and 5000 epochs. [ABSTRACT FROM AUTHOR]

With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills must be taught to students at all levels in an efficient and up-to-date manner. This paper gives an overview of the current state of data science and machine learning education globally and both at the high school and university levels, while outlining some illustrative and positive examples. Special focus is given to vocational education and training (VET), where the teaching of these skills is at its very beginning. Also presented and analysed are survey results concerning VET students in Slovenia, Serbia, and North Macedonia, and their knowledge, interests, and prerequisites regarding data science and machine learning. These results confirm the need for development of efficient and accessible curricula and courses on these subjects in vocational schools. [ABSTRACT FROM AUTHOR]

Technological development has brought humanity to the era of an information society in which information is the main driver. This implies existing large amounts of data from which knowledge should be extracted. In this sense, artificial intelligence represents a trend applied in many areas of human activity. This paper is focused on ensemble modeling based on the use of several machine learning algorithms, which enable the prediction of the risk to human health due to the state of atmospheric factors. The model uses two multi-agents as a technique of emergent intelligence to make a collective decision. The first agent makes a partial decision on the prediction task by learning from the available historical data. In contrast, the second agent does the same from the data available in real-time. The proposed prediction model was evaluated in a case study related to the city of Niš, Republic of Serbia, and showed a better result than each algorithm separately. It represents a reasonable basis for further upgrading both in the scope of different groups of the atmospheric parameters and in the methodological sense, as well as technically through implementation in a practical web citizen service. [ABSTRACT FROM AUTHOR]

In previous years, significant attempts have been made to enhance computer-aided diagnosis and prediction applications. This paper presents the results obtained using different machine learning (ML) algorithms and a special type of a neural network map to uncover previously unknown comorbidities associated with chronic diseases, allowing for fast, accurate, and precise predictions. Furthermore, we are presenting a comparative study on different artificial intelligence (AI) tools like the Kohonen self-organizing map (SOM) neural network, random forest, and decision tree for predicting 17 different chronic non-communicable diseases such as asthma, chronic lung diseases, myocardial infarction, coronary heart disease, hypertension, stroke, arthrosis, lower back diseases, cervical spine diseases, diabetes mellitus, allergies, liver cirrhosis, urinary tract diseases, kidney diseases, depression, high cholesterol, and cancer. The research was developed as an observational cross-sectional study through the support of the European Union project, with the data collected from the largest Institute of Public Health "Dr. Milan Jovanovic Batut" in Serbia. The study found that hypertension is the most prevalent disease in Sumadija and western Serbia region, affecting 9.8% of the population, and it is particularly prominent in the age group of 65 to 74 years, with a prevalence rate of 33.2%. The use of Random Forest algorithms can also aid in identifying comorbidities associated with hypertension, with the highest number of comorbidities established as 11. These findings highlight the potential for ML algorithms to provide accurate and personalized diagnoses, identify risk factors and interventions, and ultimately improve patient outcomes while reducing healthcare costs. Moreover, they will be utilized to develop targeted public health interventions and policies for future healthcare frameworks to reduce the burden of chronic diseases in Serbia. [ABSTRACT FROM AUTHOR]

In the present paper we propose a new artificial neural network model for the estimation of coal cutting resistance and excavator performance as a nonlinear relationship between the examined input (excavator movement angle in the left and right direction, slice height and thickness, coal unit weight, compressive and shear strength) and output factors (excavator effective capacity, maximum current/power/force/energy consumption, linear and areal cutting resistance). We analyze the dataset collected from three open-pit coal mines in Serbia: Field D, Tamnava Eastern Field and Tamnava Western Field (all part of the Kolubara coal basin). The model is developed using a multilayer feedforward neural network, with a Levenberg-Marquardt learning algorithm. Results of the preformed analysis indicate satisfying statistical accuracyof the developed model (R>0.9). Additionally, we analyze the individual effects of input factors on the properties of coal cutting resistance and performance of the excavator, by invokling the multiple linear regression. As a result, we single out the statististically significant and physically possible interactions between the individual controlling factors. [ABSTRACT FROM AUTHOR]

Accurate prediction of the prestressed steel amount is essential for a concrete-road bridge's successful design, construction, and long-term performance. Predicting the amount of steel required can help optimize the design and construction process, and also help project managers and engineers estimate the overall cost of the project more accurately. The prediction model was developed using data from 74 constructed bridges along Serbia's Corridor X. The study examined operationally applicable models that do not require indepth modeling expertise to be used in practice. Neural networks (NN) models based on regression trees (RT) and genetic programming (GP) models were analyzed. In this work, for the first time, the method of multicriteria compromise ranking was applied to find the optimal model for the prediction of prestressed steel in prestressed concrete bridges. The optival model based on GP was determined using the VIKOR method of multicriteria optimization; the accuracy of which is expressed through the MAPE criterion is 9.16%. A significant average share of 46.11% of the costs related to steelworks, in relation to the total costs, indicates that the model developed in the paper can also be used for the implicit estimation of construction costs. [ABSTRACT FROM AUTHOR]

The paper presents and analyzes the state-of-the-art machine learning techniques that can be applied as a decision-support system in the estimation of resource consumption in the construction of reinforced concrete and prestressed concrete road bridges. The formed database on the consumption of concrete in the construction of bridges, along with their project characteristics, was the basis for the formation of the assessment model. The models were built using information from 181 reinforced concrete bridges in the eastern and southern branches of Corridor X in Serbia, with a value of more than 100 million euros. The application of artificial neural network models (ANNs), models based on regression trees (RTs), models based on support vector machines (SVM), and Gaussian processes regression (GPR) were analyzed. The accuracy of each model is determined by multi-criterion evaluation against four accuracy criteria root mean square error (RMSE), mean absolute error (MAE), Pearson's linear correlation coefficient (R), and mean absolute percentage error (MAPE). According to all established criteria, the model based on GPR demonstrated the greatest accuracy in calculating the concrete consumption of bridges. According to the study, using automatic relevance determination (ARD) covariance functions results in the most accurate and optimal models and also makes it possible to see how important each input variable is to the model's accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Medunarodni Problemi is the property of Institute of International Politics & Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Higher education in the Republic of Serbia needs to be reformed. This paper presents a performance analysis of the changes that the authors assume are mandatory, presenting the research problem this article addresses. Cabinet research, performed by analyzing the theoretical building blocks of available knowledge and experience, is underway. Articles and studies from various publications, such as academic journals and institutes, were used as sources. In addition, academic articles and papers and studies about artificial intelligence, machine learning, and extended reality were also consulted. The authors consider that these technologies could be of great assistance in developing a new higher education strategy. Further, this research is exploratory given that information from the 100 Serbian students from selected higher education institutions was used to better understand if these technologies are welcomed by students. Based on SmartPls software, the research analysis proved that artificial intelligence (AI) and machine learning (ML) are appropriate technologies implemented in higher education institutions (HEI) to develop skills among students, a collaborative learning environment, and an accessible research environment. Additionally, extended reality (XR) facilitates increased motivation, engagement, and learning-by-doing activities between students, offering a realistic environment for learning. [ABSTRACT FROM AUTHOR]

This study proposes an innovative model that determines the importance of selected factors of a univariate problem. The proposed model has been developed based on the example of determining the impact of non-medical factors on the quality of inpatient treatment, but it is generally applicable to any process of binary classification. In addition, an ensemble stacking model that involves the asymmetric use of two different well-known algorithms is proposed to determine the importance of individual factors. This model is constructed so that the standard logistic regression is first applied as mandatory. Further, the classification algorithms are implemented if the defined conditions are met. Finally, feature selection algorithms, which belong to the optimization group of algorithms, are applied as a combinatorial algorithm. The proposed model is verified through a case study conducted using real data obtained from health institutions in the region connected to the city of Nis, Republic of Serbia. The obtained results show that the proposed model can achieve better results than each of the methods included in it and surpasses several state-of-the-art ensemble algorithms in the field of machine learning. The proposed solution has been implemented in the form of a modern mobile application. [ABSTRACT FROM AUTHOR]

Accurate load forecasting of the district heating network (DHN) is essential to guarantee effective energy production, distribution, and rational utilization. An improved Facebook-Prophet (FB-Prophet) model with additional positional encoding layers has been developed to forecast the DHN heat consumption. The accuracy of univariate and multivariate FB-Prophet models is evaluated; this paper also evaluates the optimum training dataset length. To explore the performance of the improved FB-Prophet model in heating load forecasting tasks, another seven machine learning models, namely FB-Prophet, DeepVAR, long-short term memory, extreme gradient boosting, multilayer perceptron, recurrent neural network, and support vector regression are used for comparison. The historical heating load, outside temperature, relative humidity, speed of wind, direction of wind, and weather type of a DHN in Serbia are utilized to extensively investigate the effectiveness of the improved FB-Prophet model. The prediction outcomes of all the models are thoroughly analyzed. The results indicate that the improved FB-Prophet model can generate the most precise and consistent predictions and it showed better results for sparse DHN data. The prediction curve is fitted to the trend of hourly DHN consumption change, which can play an effective guiding function in the distribution of heat. • An improved Facebook-Prophet model is proposed for heat load forecasting. • Positional encoding layers are added to build an accurate forecasting model. • Optimal dataset split ratio is evaluated for the proposed model to increase accuracy. • The proposed model is compared with another seven prediction models. • Results proved that the proposed model is superior in terms of heat load forecasting. [ABSTRACT FROM AUTHOR]

Featured Application: The primary application of this work is in environmental resource management, specifically in the detection and monitoring of vegetation patterns and changes. By employing a machine learning approach, specifically the Support Vector Machines (SVM) algorithm, the study demonstrates that including vegetation indices alongside multispectral bands significantly improves the accuracy of vegetation detection, achieving an overall classification accuracy of up to 99.01%. The study's findings underscore the potential of machine learning and remote sensing in vegetation detection and monitoring and highlight the importance of incorporating vegetation indices to enhance classification accuracy. The matter above has significant implications for decision-making processes in environmental resource management, particularly in regions with diverse forest ecosystems. The potential applications of this work extend beyond the specific geographical context of the study. The methodology and findings could be applied to other regions and ecosystems, providing valuable insights for the preservation and conservation of forest ecosystems globally. Future research could further explore the applicability of these findings in different geographical regions and investigate other vegetation indices to improve the accuracy of forest detection and monitoring processes. Vegetation plays an active role in ecosystem dynamics, and monitoring its patterns and changes is vital for effective environmental resource management. This study explores the possibility of machine learning techniques and remote sensing data to improve the accuracy of forest detection. The research focuses on the southeastern part of the Republic of Serbia as a case study area, using Sentinel-2 multispectral bands. The study employs publicly accessible satellite data and incorporates different vegetation indices to improve classification accuracy. The main objective is to examine the practicability of expanding the input parameters for forest detection using a machine learning approach. The classification process is performed by employing support vector machines (SVM) algorithm and utilising the SVM module in the scikit-learn package. The results demonstrate that including vegetation indices alongside the multispectral bands significantly improves the accuracy of vegetation detection. A comprehensive assessment reveals an overall classification accuracy of up to 99.01% when the selected vegetation indices (MCARI, RENDVI, NDI45, GNDVI, NDII) are combined with the Sentinel-2 bands. This research highlights the potential of machine learning and remote sensing in forest detection and monitoring. The findings underscore the importance of incorporating vegetation indices to enhance classification accuracy using the Python programming language. The study's outcomes provide valuable insights for environmental resource management and decision-making processes, particularly in regions with diverse forest ecosystems. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the population characteristics of spirlin, Alburnoides bipunctatus, in Serbia, since this small fish species is facing a severe decline in its abundance and its natural habitats in Europe. We investigated the spirlin population dynamics, including size, age structure, growth pattern, mortality, and exploitation rate. Additionally, we used the Uniform Manifold Approximation and Projection approach with the Decision Tree algorithm to investigate the influence of different environmental parameters on the population parameters to unveil which factors shape the abundance and distribution of spirlin. The results showed that the highest values of production, abundance, and biomass were estimated in sites with low temperature, optimal pH, and well-oxygenated water, even though we found them in heavily polluted waters with extremely high values of conductivity. Moreover, we observed a pattern of migratory behavior, in which spirlin migrate upstream to sites at a higher altitude in early summer and autumn. Despite the putative vulnerability and high sensitivity of spirlin populations, our results showed that the species was abundant, occurring in altered habitats (due to pollution, climate change, anthropogenic pressure, etc.). [ABSTRACT FROM AUTHOR]

Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children. [ABSTRACT FROM AUTHOR]

This paper is dedicated to modeling extreme TEC (Total Electron Content) values at the territory of Serbia. For the extreme TEC values, we consider the maximum values from the peak of the 11-year cycle of solar activity in the years 2013, 2014 and 2015 for the days of the winter and summer solstice and autumnal and vernal equinox. The average TEC values between 10 and 12 UT (Universal Time) were treated. As the basic data for all processing, we used GNSS (Global Navigation Satellite System) observation obtained by three permanent stations located in the territory of Serbia. Those data, we accept as actual, i.e. as a "true TEC values". The main objectives of this research were to examine the possibility to use two machine learning techniques: neural networks and support vector machine. In order to emphasize the quality of applied techniques, all results are adequately compared to the TEC values obtained by using International Reference Ionosphere global model. In addition, we separately analyzed the quality of techniques throughout temporal and spatial-temporal approach. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) refer to a group of several hundred compounds, among which 16 are identified as priority pollutants, due to their adverse health effects, frequency of occurrence, and potential for human exposure. This study is focused on benzo(a)pyrene, being considered an indicator of exposure to a PAH carcinogenic mixture. For this purpose, we have applied the XGBoost model to a two-year database of pollutant concentrations and meteorological parameters, with the aim to identify the factors which were mostly associated with the observed benzo(a)pyrene concentrations and to describe types of environments that supported the interactions between benzo(a)pyrene and other polluting species. The pollutant data were collected at the energy industry center in Serbia, in the vicinity of coal mining areas and power stations, where the observed benzo(a)pyrene maximum concentration for a study period reached 43.7 ng m − 3 . The metaheuristics algorithm has been used to optimize the XGBoost hyperparameters, and the results have been compared to the results of XGBoost models tuned by eight other cutting-edge metaheuristics algorithms. The best-produced model was later on interpreted by applying Shapley Additive exPlanations (SHAP). As indicated by mean absolute SHAP values, the temperature at the surface, arsenic, PM 10 , and total nitrogen oxide (NOx) concentrations appear to be the major factors affecting benzo(a)pyrene concentrations and its environmental fate. [ABSTRACT FROM AUTHOR]

This study was carried out to observe, measure and analyze the effects of using software visualization in teaching programming with participants from two institutions of higher educations in Serbia. Basic programming learning is notorious for complex for many novice students at university level. The visualizations of examples of program code or programming tasks could help students to grasp programming structures more easily. This paper describes an investigation about the possibilities of enhancement of learning Java using the visualization software Jeliot. An analysis of 400 students' test results indicates that a significant percentage of students had achieved better results when they were using a software visualization tool. In the experience of the authors Jeliot may yield the best results if implemented in with students who are new to the art of programming. [ABSTRACT FROM AUTHOR]

Considering the great importance of groundwater quality for water supply, in the last decade, significant scientific attention has been devoted to nitrate reduction transformation pathways and nitrogen conservation in groundwaters in the form of ammonium. To evaluate and assess the ability of machine learning models to predict the ammonium concentration, four machine learning models were applied: a three-layer neural network (NN), a deep neural network (DNN), and two variants of support vector regression (SVR) models: with linear and with Gaussian radial basis function kernel. A dataset of 322 samples with 13 predictor variables representing selected parameters responsible for oxidative/reductive nitrogen transformations in shallow alluvial groundwater was acquired from measurements in 55 monitoring wells during a 6-year monitoring period (2011–2016) in Serbia. Applied principal component analysis and cluster analysis gave an insight into conditionality and relations between the selected parameters, distinguishing four main factors, which explained 70.97% of total variance, and classifying examined objects by similarity. Extracted factors correlated the concentration patterns, implying the main nitrogen transformations in examined groundwater. The machine learning models were successfully applied for predicting the ammonium concentration with high determination coefficients (R2) in tests: 0.84 for DNN and 0.64 for NN, while the SVR did not prove to be adequate with the best R2 of 0.24. [ABSTRACT FROM AUTHOR]

The use of various advanced traffic data collection systems on one hand, and the development of Big Data technologies for the storage and processing of large amounts of data on the other hand, have enabled the application of various non-parametric methods for traffic volume prediction. In this research, the possibilities of application of supervised machine learning, as a method of Big Data analytics, with the aim to predict various indicators of the traffic volume were investigated. The research was conducted through two case studies. In both studies, for training and testing predictive models, traffic data generated by selected automatic traffic counters on the roads in the Republic of Serbia, in the period from 2011 to 2018, were used. Prediction models were trained, tested and applied using Weka software tool. The most basic data preparation was performed using macros for MS Excel written in VBA (Visual Basic for Applications). In the first case study, the goal was to predict the total volume of traffic by days, on selected sections of state roads in the Republic of Serbia. The datasets used for training and testing of machine learning models in the first case study were prepared using MS Access database, and the prediction results were presented using Excel Pivot Charts. In the second case study, we selected one counting point and performed prediction of the hourly vehicle flow, by directions and in total for both directions. The preparation of data sets, as well as the visualization of the results of the Big Data analysis in the second case study, was performed using programs written in the Python programming language. On the prepared data sets, using Weka software tool, different regression prediction models were trained and tested in both case studies. In the first case study, the best results were received by models based on regression decision trees, while in the second study, models based on Lazy IBk, Random Forest, Random Committee and Random Tree algorithms were among best. In each of the case studies, the best prediction model was selected by comparing model performance measures, such as: correlation coefficient, mean absolute error, and square root of mean square error. The model based on the M5P algorithm has shown the best performance in the first study, while the Lazy IBk algorithm gave the best results in the second study. Using the best predictive models, the prediction of daily or hourly traffic for 2020 was made at selected traffic counting points. Supervised machine learning has proven to be an effective method in predicting the volume of traffic flow. [ABSTRACT FROM AUTHOR]