As manufacturing technologies advance, the integration of artificial neural networks in machining high-hardness materials and optimization of multi-objective parameters is becoming increasingly prevalent. By employing modeling and optimization strategies during the machining of such materials, manufacturers can improve surface roughness and tool life while minimizing cutting time, tool vibrations, and cutting forces. In this paper, the aim was to analyze the impact of input parameters (cutting speed, feed rate, depth of cut, and insert radius) on surface roughness and cutting forces during the machining of 90MnCrV7 using feed-forward neural network models and SHAP analysis. Afterward, multi-criteria optimization was applied to determine the optimal parameter levels to achieve minimum surface roughness and cutting forces using the modified PSI-TOPSIS method. According to the SHAP analysis, the insert radius has the most significant impact on the surface roughness and passive force, while in the multi-criteria analysis, according to ANOVA results, the insert radius has the most significant impact on all considered outputs. The results show that an insert radius of 0.8 mm, a cutting speed of 260 m/min, a feed rate of 0.08 mm, and a depth of cut of 0.5 mm are the optimal combination of input parameters.

The specific geographic location of Sarajevo, which is located in a valley surrounded by mountains, provides the opportunity to analyze the relation between the concentration of $P{M}_{10}$ and meteorological parameters with and without temperature inversion. The main aim of this paper was to develop forecasting models of the hourly average of $P{M}_{10}$ values in the Sarajevo urban area based on meteorological parameters measured in Sarajevo and on the Bjelasnica mountain with and without temperature inversion by using principal component regression (PCR). Also, this research explored and analyzed the differences in the values of the meteorological parameters and $P{M}_{10}$ in Sarajevo with and without temperature inversion, and the difference in temperatures between Sarajevo and Bjelasnica with temperature inversion using statistical hypothesis testing with a total of 240 hypothesis tests performed. The measurements of meteorological parameters were taken from 2020 to 2022 for both Sarajevo (630 m) and the Bjelasnica mountain (2067 m), which allowed for the identification of time periods with and without temperature inversion, while measurements of $P{M}_{10}$ were taken only in Sarajevo. Data were collected during the heating season (November, December, January, February and March). Since analyses have shown that only January and November had time periods with and without temperature inversion during each hour of the day, a total of seven cases were identified: two cases with and five cases without temperature inversion. For each case, three PCR models were developed using all principal components, backward elimination and eigenvalue principal component elimination criteria ($\lambda <1$). A total of 21 models were developed. The performance of the models were evaluated based on the coefficient of determination $R^2$ and the standard error $SE$. The backward elimination models were shown to have high performances with the highest value of $R^2=$ 97.19 and the lowest value of $SE=1.32$. The study showed that some principal components with eigenvalues $\lambda <1$ were significantly related to the independent variable $P{M}_{10}$ and thus were retained in the PCR models. In the study, it was shown that backward elimination PCR was an adequate tool to develop $P{M}_{10}$ forecasting models with high performances and that it could be useful for authorities for early warnings or other action to protect citizens from very harmful pollution. Hypothesis tests showed different relations of meteorological parameters and $P{M}_{10}$ with and without temperature inversion.

This study investigates the use of deep learning algorithms to predict the discharge coefficient (Cd) of contaminated multi-hole orifice flow meters with circular opening. Datasets (MHO1 and MHO2) were obtained from computational fluid dynamic simulations for two circular multi-hole orifice flow meters of different geometries. To evaluate the performance and generalization capabilities of different models, three distinct scenarios, each involving different dataset configurations and normalization techniques were designed. For each scenario, three deep learning models (feedforward neural networks, convolutional neural network, and recurrent neural network) were implemented and evaluated based on their performance metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). For all three scenarios eight models for each neural network model were developed (FFNN – four models, CNN – two models, RNN – two models). The same structure of models was used across all scenarios to ensure consistency in the evaluation process. Key input parameters include geometrical and flow variables such as β – parameter, contamination thickness, radial distance, Reynolds number, and orifice diameters. Results demonstrate the effectiveness of deep learning in accurately predicting discharge coefficient for different contamination conditions and different geometries. This study showed that deep learning models can be used for prediction of discharge coefficients for multi-hole orifice flow meters of similar geometry, based on data obtained from one orifice flow meter for different contamination parameters.

Background Determining human identity has always been important in forensic investigations. Forensic dentistry has developed significantly having a key role in determining gender and age. One of the methods that is important in forensic dentistry is the analysis of orthopantomograms, which are X-rays of the complete upper and lower jaw, including the surrounding anatomical structures. The uniqueness of the dental features recorded in orthopantomograms makes them useful for individual identification, more specifically for the assessment of gender and age. This study was conducted to evaluate the application of convolutional neural networks in automating the process of gender and age estimation based on orthopantomograms, to improve accuracy and efficiency in forensic dentistry. Methodology Convolutional neural networks are powerful tools in the field of artificial intelligence for image processing and analysis because their convolutional layers extract specific features that are characteristic of a certain class. A total of 3716 orthopantomograms collected from the database of the University of Sarajevo - Faculty of Dentistry with the Dental Clinical Center were used to create convolutional neural network models for predicting gender and age. The orthopantomograms were taken in the period from January to December 2022 for the needs of doctors and providing services to patients at four polyclinics: Clinic for Dental Diseases and Endodontics, Clinic for Oral Diseases and Periodontology, Clinic for Oral Surgery, and Clinic for Pediatric and Preventive Dentistry. Results The results derived from three developed models confirm that the developed convolutional neural networks have high accuracy. The first model estimated gender, while the second and the third models estimated age within certain age ranges, the second from 12 to 24 years, and the third from 20 to 70 years. After training on the training dataset, all models achieved high accuracy on the validation dataset. The models demonstrated high accuracy without signs of overfitting, with the first model achieving 95.98%, the second model achieving 97.90%, and the third model achieving 96.12% accuracy. Conclusion This research concluded that the developed convolutional neural networks for gender and age estimation from orthopantomograms showed high accuracy. Models' predictions of gender and two age groups exceeded 95% accuracy. Therefore, convolutional neural networks can be considered useful tools for gender and age determination in forensic dentistry and can facilitate and speed up the processes of assessment and determination of essential characteristics.

Introduction: This study investigates and compare the students’ entrepreneurial mindset dimensions and intentions from EU member countries Italy, Austria, Sweden, and Greece, and an EU candidate country Bosnia and Herzegovina, which are important for fostering start-ups, economic development, and job creation. By analyzing students’ entrepreneurial mindset dimensions, demographic and academic characteristics, and availability of resources, the research aims to identify factors that impact students’ entrepreneurial intentions. Findings provide valuable insights into how these factors vary across different educational, economic, and social contexts with guidance for enhancing education to better support students’ entrepreneurial aspirations.Methods: Machine learning Random Forest was used to analyze the impact of entrepreneurial mindset dimensions, resources, and demographic and academic characteristics on students’ entrepreneurial intentions of students from EU member countries and Bosnia and Herzegovina. SHapley Additive exPlanations (SHAP) values were utilized to analyze feature importances and contributions to the model’s predictions. Statistical hypothesis tests were also conducted to compare differences of students’ entrepreneurial mindset dimensions, intentions and availability of resources between the EU member countries and Bosnia and Herzegovina.Results: High values of entrepreneurial mindset dimensions have positive impact on entrepreneurial intentions in both EU member countries and Bosnia and Herzegovina. The availability of resources and orientation to innovations were the most impactful features for students in EU and Bosnia and Herzegovina, respectively. Gender and academic characteristics showed minimal influence. There are no significant differences in all dimensions between EU member countries and Bosnia and Herzegovina, except for confidence dimension and entrepreneurial intentions, which are significantly greater in Bosnia and Herzegovina.Discussion: Findings suggest that tailored educational interventions focusing on key entrepreneurial mindset dimensions and resource access could significantly enhance entrepreneurial intentions among students. For policymakers and educators, this study provides a foundation for developing targeted strategies that align with the specific contexts of both EU member countries and Bosnia and Herzegovina. In this way higher education institutions can better support students’ entrepreneurial aspirations, contributing to broader economic development and job creation. This research offers recommendations for improving entrepreneurship education across diverse educational, economic, and social contexts and more balanced and inclusive economic development in Europe.

In this paper five neural network models were developed using NARX-SP neural network type in order to predict air pollutants concentrations (SO2, PM10, NO2, O3 and CO ) for the 72nd hour ahead for Sarajevo. Hourly values of air pollutants concentrations and meteorological parameters (air temperature, pressure and humidity, wind speed and direction) for Sarajevo were used. Optimal model was selected based on the values of R2, MSE and the complexity of models. Optimal neural network model can predict air pollutants concentrations for the 72nd hour ahead with high accuracy, as well as for all hours up to 72nd hour.