Editorial

• Published in: JUCS - Journal of Universal Computer Science
• Main Author: Christian Gütl
• Format: Article in Journal/Newspaper
• Language: English
• Published: Graz University of Technology 2023
• Subjects: Electronic computers. Computer science; QA75.5-76.95; sami
• Online Access: https://doi.org/10.3897/jucs.114450; https://doaj.org/article/9befe4992c544b6ca17c413d73740bf7

Dear Readers, It gives me great pleasure to announce the tenth regular issue of 2023. In this issue, various topical aspects of computer science are covered by 18 authors from 8 countries in 6 articles. As always, I would like to thank all the authors for their sound research and the editorial board for their highly valuable review effort and suggestions for improvement. These contributions, together with the generous support of the consortium members, sustain the quality of our journal. In an 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 receiving 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 issue proposals for our journal. As we want to secure the financial support also for the years to come, we are looking for institutions and libraries to financially support our diamond open access journal as consortium members, who will then benefit from the research community, international visibility, and the opportunity to manage special issues and focused topics within the journal. Please think about the possibility of such financial participation by your institution, we would be very grateful for any kind of support. In this regular issue, I am very pleased to introduce the following 6 accepted articles: In a collaboration between researchers from Palestine and Jordan, Rasha R. Atallah, Ahmad Sami Al-Shamayleh, and Mohammed A. Awadallah look into the impact of plastic surgery on face recognition models and propose a model based on an artificial neural network with model-agnostic meta-learning (ANN-MAML) for plastic surgery face recognition which results in an accuracy of 90% in all evaluation experiments. In another research collaboration between colleagues from Tunisia and the Kingdom of ...