This paper presents the LANA Adaptive Labeling Framework (ALF) as an advanced framework for dynamic method labeling and selecting optimal data processing methods in multiple multicriteria intelligent software systems, focusing on business processes in higher education institutions (HEIs). Earlier approaches to method labeling relied on static hierarchical structures. In contrast, LANA ALF introduces adaptability through continuous learning from user feedback, automatic balancing of criteria based on historical data and current task requirements, and multidimensional labels for comprehensive method evaluation. Each query is represented with a set of labels, while neural networks evaluate the optimal method by balancing criteria such as performance, cost, reliability, and accuracy. User feedback is stored in dynamic tables (e.g., user satisfaction), automatically adapting their structure to new tasks and data types. The results demonstrate that LANA ALF enables intelligent agents to autonomously make decisions without the need for direct involvement of data science experts, thereby increasing accuracy, reliability, and user satisfaction. This framework provides a foundation for further application of ALF in various domains

Education and employment stakeholders worldwide have increasingly acknowledged the need to teach students soft skills to improve their academic performance and long-term prospects. Soft skills are transferable across jobs and industries and related to personal and social competencies. Their development aims to empower and increase personal growth and learning participation and improve job opportunities. Given their central role in shaping students’ educational experiences, teachers must be well-versed in the value of cultivating soft skills and awareness of the necessity to incorporate their study into various curricular frameworks. As a result, this article investigates whether business schools adequately prepare their students for the soft skills demanded by today’s labor market. Business teachers in Bosnia and Herzegovina were the subjects of the survey. The findings indicate that teachers recognize the value of teaching students soft skills but that current curricula may be strengthened in this area.

There is a generally accepted opinion that young people, born in the era of intensive use of ICT and the Internet, are much better at handling new technologies and using Internet resources than older generations. In support of this claim, it is stated that different digital technologies and the Internet have been a natural environment for these generations since birth. This paper aims to check to what extent the above statements apply to University of Mostar (SUM) students. For this purpose, the authors researched SUM students to determine how they self-assess their knowledge and use of Internet resources. On the other hand, it was necessary to use Internet resources to pass exams in certain subjects. In this paper, the authors compared the results obtained by surveying students with actual exam results. The results of the research suggest that the students have relatively good knowledge and coping skills with the tasks they solve within the individual courses of their studies. However, Insufficient mastery of the Internet and its information is indicated by lower ratings of the ability to evaluate found materials and ratings of the ability to use the advanced functions of the Google search engine.

Today's extensive requirements for the storage, management, and analysis of complex, dynamic, evolving, distributed, and heterogeneous data from different sources and platforms, e.g., Big data, generate enormous challenges for IT, especially database applications. That is why the demand for data reduction is increasingly coming from the world of databases, intending to reduce the costs of storing, processing, and querying Big data. There is a large number of different techniques for Big data reduction that can cause confusion and complicate this process. Because of that, the authors proposed a Big data reduction framework to structure and present both data reduction techniques and necessary components essential for a better understanding of the process. The importance and the components of the proposed framework are explained in this paper.