JUCS - Journal of Universal Computer Science 28(10): 1001-1002, doi: 10.3897/jucs.96515
expand article infoChristian Gütl
‡ Graz University of Technology, Graz, Austria
Open Access

Dear Readers,

It gives me great pleasure to announce the eighth regular issue of 2022. In this issue, various topical aspects of computer science are covered by 18 authors from 5 countries in 5 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.

In this regular issue, I am very pleased to introduce the following 5 accepted articles: Raoua Abdelkhalek, Imen Boukhris, and Zied Elouedi from Tunisia present their research on more trustworthy predictions based on the uncertain framework of belief function theory to support the representation, quantification and management of imperfect evidence. In a research collaboration between Jordan and the USA, Abd Al-Rahman Al-Nounou, Osama Al-Khaleel, Fadi Obeidat, and Mohammad Al-Khaleel present a methodology for designing binary multipliers, in which different sizes of customized partial products generation cells are designed and used as smaller building blocks. Venilton FalvoJr, Anderson da Silva Marcolino, Nemesio Freitas Duarte Filho, Edson OliveiraJr, and Ellen Francine Barbosa present the design, development and experimental evaluation of a software product line for mobile learning applications. Ghazala Hcini, Imen Jdey, and Hela Ltifi from Tunesia introduce their research on a new and robust deep learning model for automatically classifying malaria cells as infected or uninfected, built on a convolutional neural network. And last but not least, Sercan Yalçın, Musa Eşit, and Mehmet İshak Yüce from Turkiye discuss their approach to estimating climatological parameters based on artificial intelligence techniques with particle swarm optimization and deep neural networks.