The European Entrepreneurship Competence (EntreComp) framework has been proposed by the European Commission in order to build consensus around a common understanding of entrepreneurship competence (knowledge, skills, and attitude). This study reports a bibliometric review of the research to date, addressing the EntreComp framework, based on documents published during the period from 2016 to June 2022. The research dataset was collected from Web of Science Core Collection (WoS) and included 37 articles. The bibliometric analysis includes citation, cooccurence and network analysis to represent the between countries and interorganizational collaboration, to identify relationships between documents topics. Along with the bibliometric analysis, a detailed description of characteristics for each article (study design and objectives, instruments, research findings, etc.) was also presented. Overall, this study provides insights regarding research addressing EntreComp framework, that could guide future research directions of practitioners/policy makers/researchers/etc. in the entrepreneurial competencies field and other interconnected domains.

Recent progress in machine learning (ML) has fueled the emergence of intelligent clinical decision support systems (CDSSs) designed to optimize diagnostic and prognostic accuracy through the analysis of complex and heterogeneous medical data. The analysis provides a comprehensive perspective on the use of machine learning in the medical field by integrating a bibliometric assessment of the recent literature and a detailed examination of the algorithms used in current studies. The bibliometric component highlights the evolution of publications, the thematic distribution of research and emerging directions within various medical specialties. In addition, the evaluation of selected articles sheds light on the concrete ways of applying ML algorithms, as well as the methodological limitations encountered in clinical practice. Random forest and gradient boosting are commonly used in internal medicine and cardiology, while convolutional neural networks (CNNs) dominate neuroimaging in neurology and image-based analyses in oncology and radiology.

In this paper, we provide an overview of Human-Computer Interaction (HCI) in the context of Artificial Intelligence (AI), focusing on Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANN), and applications of these techniques in the field of Healthcare. This review highlights the critical role of the human factor in AI-driven systems with discussions on AI ethics and expectations. Artificial Intelligence techniques, including ML and DL enhance gesture and speech recognition in HCI, while ANN models are particularly effective for hand gesture recognition. In Human-Computer Interaction, AI techniques bring value and understanding to Healthcare in real life. Despite the benefits that Artificial Intelligence brings to HCI, challenges remain. The future promises new applications and perspectives in HCI, where AI with ML, DL, and ANN have an effective impact.

Some students' expectations and points of view related to the Artificial Intelligence course are explored and analyzed in this study. We anonymous collected answers from 58 undergraduate students out of 200 enrolled in the Computer Science specialization. The answers were analysed and interpreted using thematic analysis to find out their interests and attractive and unattractive aspects related to the Artificial Intelligence study topic. We concluded that students are interested in Artificial Intelligence due to its trendiness, applicability, their passion and interest in the subject, the potential for future growth, and high salaries. However, the students' expectations were mainly related to achieving medium knowledge in the Artificial Intelligence field, and men seem to be more interested in acquiring high-level skills than women. The most common part that wasn't enjoyed by the students was the mathematical aspect used in Artificial Intelligence. Some of them (a small group) were also aware of the Artificial Intelligence potential which could be used in an unethical manner for negative purposes. Our study also provides a short comparison to the Databases course, in which students were not that passionate or interested in achieving medium knowledge, their interest was related to DB usage and basic information.

This study explores and analyses the expectations of second-year students enrolled in different lines of study related to Database course, as their interest in having a carrier path in a database related domain and how it reflects the job demands from the market. The participants in the study provided two sets of answers, anonymously collected (in the begging and in the middle of the course), thus allowing us to track how their interests changed as long as they found out more about the subject. We asked for their experience and initial knowledge, we found out that they are aware of the SQL and usability and importance of databases, but they appreciated the database knowledge will be used occasionally. Even if it was not the original scope of the paper, we also found out that men are more interested in learning in depth (acquiring security, performance, complexity database related information) than women do. In terms of the participants set, there were 87 answers from 191 enrolled students that were analyzed and interpreted using thematic analysis.