JUCS - Journal of Universal Computer Science 28(5): 443-444, doi: 10.3897/jucs.86654
expand article infoChristian Gütl
‡ Graz University of Technology, Graz, Austria
Open Access

Dear Readers,

I am very happy to announce the fifth regular issue of 2022. In this issue, a variety of topical research aspects of computer science are covered in four articles by 14 authors from 7 countries. Allow me to express my appreciation to all authors for their sound research and to the editorial board for the highly valuable reviews and comments for improvement. This continuous stream of relevant and novel contributions, along with the generous support of the consortium members, sustains the quality of our journal.

In the ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. Please consider yourself and encourage your colleagues to submit high-quality articles or special issues for our journal.

In the fifth regular issue, I am very pleased to introduce the following 4 accepted articles: María Castañeda, Mercedes G. Merayo, Juan Boubeta-Puig, and Iván Calvo from Spain introduce MODELFY, a novel model-driven solution for designing a decision-making process based on fuzzy automata that allows users to abstract from technical complexities. Natasa Koceska and Saso Koceski from North Macedonia present in their work the design and validation of a low-cost mobile robot system that can assist elderly people and professional caregivers in everyday activities. In a collaborative research between Palestine, the USA, Saudi Arabia and Libya, Thaer Thaher, Mohammed Awad, Mohammed Aldasht, Alaa Sheta, Hamza Turabieh and Hamouda Chantar have developed an enhanced evolutionary-based approach to feature selection using the Grey Wolf Optimizer for classification of high-dimensional biological data. Nikola Zornić and Aleksandar Marković from Serbia present a methodological framework for building a hybrid agent-based model that integrates machine learning algorithms aimed at overcoming some of the elaborated problems related to the use of a utility function.