Financial literacy has emerged as a crucial factor in promoting economic stability and resilience, particularly in Montenegro. With the increasing complexity of financial products and the growing need for individuals to make sound financial decisions, the importance of financial literacy cannot be overstated. This study employs a quantitative, survey-based approach to explore the association between financial literacy levels and measures of economic stability, including savings rates, active debt management, and access to financial products. Data were collected through a representative, two-stage stratified sample of 1000 Montenegrin adults aged 18–79, ensuring comprehensive geographic coverage across all Montenegrin municipalities and balanced representation by gender and age. This stratification enables a detailed analysis of financial literacy trends across the population. Correlation analysis reveals that higher levels of financial literacy are associated with better financial practices, such as increased savings and responsible credit use, thereby enhancing economic resilience at the household level. Moreover, improved financial literacy contributes to sustainability by fostering long-term financial stability, reducing inequalities, and promoting inclusive economic growth. The findings suggest that financial literacy can mitigate the impact of economic shocks, emphasizing the need for policies that promote financial education as a tool for sustainable development. This study contributes to the literature on financial literacy in emerging economies and offers actionable insights for policymakers in Montenegro and similar contexts, highlighting financial education as a pathway to individual and national economic resilience.

Compared to traditional statistical models, Machine Learning (ML) algorithms provide the ability to interpret, understand and summarize patterns and regularities in observed data for making predictions in an advanced and more sophisticated way. The main reasons for the advantage of ML methods in making predictions are a small number of significant predictors of the statistical models, which means limited informative capability, and pseudo-correct regular statistical patterns, used without previous understanding of the used data causality. Also, some ML methods, like Artificial Neural Networks, use non-linear algorithms, considering links and associations between parameters. On the other hand, statistical models use one-step-ahead linear processes to improve only short-term prediction accuracy by minimizing a cost function. Although designing an optimal ML model can be a very complex process, it can be used as a potential solution for making improved prediction models compared to statistical ones. However, ML models will not automatically improve prediction accuracy, so it is necessary to evaluate and analyze several statistical and ML methods, including some artificial neural networks, through accuracy measures for prediction purposes in various fields of applications. A couple of techniques for improving suggested ML methods and artificial neural networks are proposed to get better accuracy results

Traditional statistical models as tools for summarizing patterns and regularities in observed data can be used for making predictions. However, statistical prediction models contain small number of important predictors, which means limited informative capability. Also, predictive statistical models that provide some type of pseudo-correct regular statistical patterns, are used without previous understanding of the used data causality. Machine Learning (ML) algorithms as area in Artificial Intelligence (AI) provide the ability to interpret and understand data in more sophisticated way. Artificial Neural Networks as kind of ML methods use non-linear algorithms, considering links and associations between parameters, while statistical use one-step-ahead linear processes to improve only short-term prediction's accuracy by minimizing cost function. Disregarding that designing an optimal artificial neural network is very complex process, they are considered as potential solution for overcoming main flaws of statistical prediction models. However, they will not automatically improve predictions accuracy, so several artificial neural networks and traditional statistical methods are evaluated and analyzed through accuracy measures for prediction purposes in various fields of applications. Based on gained results, couple of techniques for improving artificial neural networks are proposed to get better accuracy results than statistical predictive methods.

This chapter is written in order to give students general summary of basic statistical methods, techniques, indicators and procedures. However emphasis is on the application of those that are frequently used in biomedical research. Statistics is science about data, but data that represent numbers with their context. Therefore, in this chapter the topics of population, samples, types of variables, descriptive statistics and statistical inference will be briefly covered. At the end, correlation and regression analysis will be briefly presented. The aim of this chapter is to give a very short overview of the main principles; techniques and procedures that should be used in order to obtain and understand analyzed biomedical data.

This study aimed to investigate the application of machine learning techniques for disease prediction. Three popular machine learning algorithms, Random Forest, Support Vector Machines and Naive Bayes, were employed and their performance was evaluated. Results showed that the best performing model was based on Random Forest algorithm with the average accuracy of 87%. This model has been additionally tuned in order to achieve even better performance, which resulted with 90% accuracy. This study highlights the potential of AI in disease prediction and provides insights into the importance of algorithm selection and tuning for optimal performance.

Small- and medium-sized enterprises (SMEs) are central to sustainable development in transition economies, yet their financial fragility often limits resilience and the capacity to invest in innovation and responsible practices. Despite growing interest in SME, financial health and its role in sustainability, empirical evidence from small transition economies like Montenegro remains scarce, particularly on how liquidity and profitability dynamics underpin conditions for SDG-aligned growth. This study addresses this gap by analyzing how core financial indicators—cash position, capital structure, and working capital efficiency—affect liquidity and profitability among 345 Montenegrin SMEs across manufacturing, services, and trade. Using OLS and robust regression models, results reveal that a higher cash-to-revenue ratio and moderate leverage significantly enhance both short-term solvency and profitability, while working capital efficiency shows nuanced effects and sector-specific patterns emerge in capital-intensive industries. These findings highlight financial management as a foundation for SME resilience, creating financial preconditions for innovation and digital investments in aligned with SDGs (goals 8, 9, 12). Policy recommendations focus on improving finance access and financial literacy to foster innovation-driven, sustainable SME models aligned with the 2030 Agenda.