In research aimed at determining the level of interest of high school students in enrolling in colleges, predictive analysis models and comparisons are rarely applied during the classification and processing of various data. All of this leads to significant fluctuations in college admissions, where certain schools are unable to admit a large number of students who show interest in a specific field. On the other hand, high school students lose interest in certain schools, leading to the discontinuation of specific directions essential for today's job market needs. Institutions largely fail to conduct a comparison and linkage of teaching and non-teaching activities when analyzing the talents and interests of high school students from different fields. The goal of this paper is to use programming language classifiers to predict student enrollments in colleges based on the results students demonstrate during regular attendance in high schools through participation in innovation fairs.

Despite the fact that technology is improving day by day and that the medical devices (MDs) are being constantly upgraded, their malfunction is not a rare occurrence. The aim of this research is to develop an expert system that can predict whether the device will satisfy functional and safety requirements during a regular inspection. This expert system can be seen as part of Industry 4.0 that is revolutionizing medical device management. In order to develop the system, five machine learning algorithms that are representative of each classifier group, were used: (1) Random Forest, (2) Decision Tree, (3) Support Vector Machine, (4) Naive Bayes, (5) k-Nearest Neighbour. The Decision Tree outperformed other classifiers achieving the classification accuracy of 100% with and without attribute selection applied on the dataset. This study showed that machine learning algorithms can be used in order to predict MDs performance and potential failures in order to make the process of maintenance of medical devices more convenient and sophisticated and it is one step in modernizing medical device management systems by utilizing artificial intelligence.

Solar Particle Events (SPEs) generate cosmic radiation of different magnitude in a time span of several hours or even days. This contributes to an increased probability of higher magnitude Single-Event Upsets (SEUs) occurrence in space applications. It is critical to establish early detection of SEU rate or Soft Error Rate (SRE) changes to enable timely radiation hardening measures. This research paper focuses on the high-accuracy detection of SPEs using the manually collected space data. Additionally, the prediction of SRE increase or decrease was established with the seven widely used supervised machine learning algorithms. Excellent performance of 97.82%, including a high F1-score, was achieved during the presence of SPE using $k$-Nearest Neighbor algorithms.

This research paper deals with the problem of Metal-Oxide Surge Arrester (MOSA) condition monitoring and a new methodology in surge arrester monitoring and diagnostics is presented. A machine learning algorithm (back propagation regression) is used to estimate the non-linearity coefficient of the surge arrester, based on operating voltage and leakage current of the arrester. Using a simulated system, this research investigates the possibility of application and efficiency of machine learning. It is shown that the applied learning algorithm results are competitive with the model results parameters calculated as R2 = 0.999 and mean absolute real error computed as 0.005 which has shown that the proposed model can be used for MOSA monitoring and diagnostic purposes.

The fast pace of scientific and technological developments and the pressing need for a flexible skilled workforce and innovative products in the more competitive than ever world markets require robust but flexible mechanisms for the development, implementation, monitoring and assessment of undergraduate and graduate curricula and courses. In our research, we focus on the quality assurance process designed to achieve the desired Learning Outcomes (LOs) for new courses and education programs. We propose tools and techniques used to determine the extent to which the stated learning outcomes are achieved. More specifically, we present the Quality Assurance approach developed in the ERASMUS+ CBHE project Electrical Energy Markets and Engineering Education (ELEMEND). The approach for developing the LOs is based on the European Qualification Framework (EQF) which defines professional levels in terms of learning outcomes, i.e. knowledge, skills and autonomy-responsibility, and the ENQA European Standards and Guidelines for determining the quality assurance procedures and metrics. As a case study, the methodology is applied to the LOs of ELEMEND courses and the results are discussed. Additionally, this paper reflects the unique experience of collaboration between EU universities, HEIs of West Balkans, enterprises, and professional associations in order to create up to date curricula in smart grid related topics with sustainable links to the related industry and businesses.