The aim of the paper is to diagnose the entrepreneurship competency levels among students to identify differences in competencies and their levels regarding gender, material status, and professional situation. In addition, the goal of the analysis is to indicate the competencies that need to be strengthened among individual groups of students. The research was conducted using a questionnaire by The European Entrepreneurship Competence (EntreComp) framework that was sent to students at the Pedagogical University of Cracow and the Rzeszow University. The rule induction method enabled us to discover dependencies that were not obvious among different competencies of respondents and their status. The research revealed that the surveyed women had completely different competencies than men. Good financial status has a positive impact on the self-assessment of competencies and worse-cause difficulties in assessing business ideas. Unemployed students need stimulation to take action, seek funding, share ideas, and protect them. Students running their businesses are able to identify market needs. The results revealed the following implications: It is important to verify the EntreComp methodology to examine how different groups are evaluating their entrepreneurial competencies; the data mining technique enables discover of new knowledge based on regularities hidden in data; and the results can be used to tailor special teaching programs for developing skills that individual subgroups lack.

Considering that the population is aging rapidly, the demand for technology for aging-at-home, which can provide reliable, unobtrusive monitoring of human activity, is expected to expand. This research focuses on improving the solution of the posture detection problem, which is a part of fall detection system. Fall detection, using depth maps obtained by the Microsoft Kinect sensor, is a two-stage method. We concentrate on the first stage of the system, that is, pose recognition from a depth map. For lying pose detection, a new hybrid FRSystem is proposed. In the system, two rule sets are investigated, the first one created based on a domain knowledge and the second induced based on the rough set theory. Additionally, two inference aggregation approaches are considered with and without the knowledge measure. The results indicate that the new axiomatic definition of knowledge measures, which we propose has a positive impact on the effectiveness of inference and the rule induction method reducing the number of rules in a set maintains it.

We compare four discretization methods, all based on entropy: the original C4.5 approach to discretization, two globalized methods, known as equal interval width and equal frequency per interval, and a relatively new method for discretization called multiple scanning using the C4.5 decision tree generation system. The main objective of our research is to compare the quality of these four methods using two criteria: an error rate evaluated by ten-fold cross-validation and the size of the decision tree generated by C4.5. Our results show that multiple scanning is the best discretization method in terms of the error rate and that decision trees generated from datasets discretized by multiple scanning are simpler than decision trees generated directly by C4.5 or generated from datasets discretized by both globalized discretization methods.

This study explores the concept of environmental responsibility and assesses the attitudes and perceptions of young adults towards environmental challenges. Applying a hybrid approach based on feature selection, machine learning methods (classification and regression trees (CART) and recursive feature elimination (RFE)) and statistical methods (chi-squared tests), we analyzed survey data from 500 students across three universities. The results reveal that 82% of students rate their climate knowledge as moderate or good, while 92% perceive climate change as a serious threat. Women are more likely than men to report engagement in pro-environmental initiatives. Students’ environmental orientation weakens in the middle years of study but re-emerges in the final year, possibly reflecting greater maturity and a stronger sense of responsibility before graduation. The willingness to establish sustainable enterprises does not always correspond to a high level of knowledge or daily environmental practices. While undergraduates report high levels of climate awareness, they often fail to translate this into concrete actions, indicating a gap between knowledge, motivation, and practice. The insights from the research can inform environmental education strategies, institutional practices, and youth engagement programs within higher education.