In the age of digitization and the ever-present use of artificial intelligence (AI), it is essential to develop methodologies that enable the systematic evaluation and ranking of different AI algorithms. This paper investigated the application of the PIPRECIA-S model as a methodological framework for the multi-criteria ranking of AI algorithms. Analyzing relevant criteria such as efficiency, flexibility, ease of implementation, stability and scalability, the paper provided a comprehensive overview of existing algorithms and identified their strengths and weaknesses. The research results showed that the PIPRECIA-S model enabled a structured and objective assessment, which facilitated decision-making in selecting the most suitable algorithms for specific applications. This approach not only advances the understanding of AI algorithms but also contributes to the development of strategies for their implementation in various industries.

The selection of an appropriate operating system (OS) in educational environments is a critical decision that impacts the functionality, user experience, and overall efficiency. Financial factors, along with the availability and functionality of tools used on these systems, play a crucial role in this selection. Furthermore, the OS affects the user experience, security, and adaptability to learning. Previous research in this area is scarce, and this paper contributes to a better understanding of the OS selection process in education. This paper proposes a novel multi-criteria decision-making approach to evaluate and select the most suitable OS for educational institutions. The methodology integrates the PIPRECIA method for assessing weighted criteria, alongside utility analysis (NWA), enabling a balanced decision-making process between institutional management and IT experts. The evaluation considered the following criteria: performance, cost, security, usability, implementation, support, and documentation. The study compared three popular operating systems: Microsoft Windows 11, GNU Linux Ubuntu 22.04 LTS, and Apple macOS 12 using the proposed approach. The results, based on the integrated evaluation of all criteria, indicate that GNU Linux Ubuntu 22.04 LTS (0.562) ranked highest, followed by Microsoft Windows 11 (0.553) and Apple macOS 12 (0.543). This paper emphasizes the importance of objective, group-based decision-making in selecting an OS for education, providing practical insights and guidelines for effective technology integration in academic settings.

The increasing demand for energy-efficient and interconnected smart buildings, particularly in the tourism sector, has driven the adoption of advanced wireless technologies. IoT technologies are crucial in this evolution, improving modern buildings’ functionality and operational efficiency. This study investigates the utilization of various wireless technologies within Wireless Personal Area Networks (WPANs), including Bluetooth BLE 4.2, Bluetooth BLE 5.0, ZigBee, and Z-Wave, in smart buildings. A multiple-criteria decision-making (MCDM) approach, specifically the PIPRECIA-S model, was applied to evaluate these technologies based on criteria such as device connectivity, mobility, low energy consumption, scalability, flexibility, and interoperability. Simulations using the PIPRECIA-S model were conducted to assess technology performance across various real-world scenarios. The results indicate that ZigBee (0.2942) and Bluetooth BLE 5.0 (0.2602) provide superior performance in terms of energy efficiency and scalability, followed by Z-Wave (0.2550) and Bluetooth BLE 4.2 (0.1906). These findings provide decision-makers with data-driven recommendations for selecting the most suitable wireless technologies for smart buildings.

It is hard to envisage any business activity nowadays without the use of computers. The users who in their work utilize computers or some other displays for viewing electronic contents are exposed to the computer vision syndrome effects. This paper defines the computer vision syndrome and explains occurrences of the syndrome, setting out also its consequences. Analysis has been made of the viewpoints of users from the developed EU countries regarding the computer vision syndrome. Comparison has been made of the obtained data with the data showing viewpoints of the users in the Republic of Serbia. The aim of this analysis is to form a comprehensive picture of the computer vision syndrome issues. At the end of the paper, solutions are depicted for reducing the consequences and preventing the computer vision syndrome occurrences.

The development of information technologies enabled interactive teaching and the application of various software in education

This paper investigates the development and application of an integrated framework for the quantitative assessment of cyber resilience of network architectures in educational institutions. Resilience assessment criteria were identified and mapped to the NIST Cybersecurity Framework to cover key functional areas of cybersecurity, while the ISO 31000 standard was applied to assess disruption scenarios, including cyber-attacks and network infrastructure failures. The PIPRECIA-S method enables precise weighting of criteria based on expert assessments, and the H-SCRM methodology allows quantitative evaluation of network alternatives according to defined criteria and scenarios. The results show that the hybrid cloud-managed network architecture achieves the highest level of cyber resilience, while software-defined networking and traditional LAN architecture achieve lower global resilience index values. Sensitivity analysis confirms the stability of the ranking of alternatives and the robustness of the proposed model. The paper provides practical guidelines for improving network security and decision-making in educational institutions through an integrated approach to risk and resilience management.