Editorial

Abstract

Dear Readers, It gives me great pleasure to announce the eighth regular issue of 2024. In this issue, 6 papers by 20 authors from 9 countries – Algeria, Brazil, China, Germany, Iraq, Ireland, Pakistan, Turkey, United Kingdom – cover various topical and novel aspects of computer science. As always, I would like to thank all the authors for their sound research and the editorial board and guest reviewers for their extremely valuable review effort and suggestions for improvement. I also want to thank the readers for their interest in our articles, which is reflected in the increasing number of accesses and PDF downloads. These contributions, together with the generous support of the consortium members, sustain the quality of our journal. In a continuous 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. In the eighth regular issue, I am very pleased to introduce the following 6 accepted articles: In a joint research work between Iraq, Algeria and the UK, Rewayda Razaq Abo-Alsabeh, Meryem Cheraitia and Abdellah Salhi discuss their results on a plant propagation algorithm for the bin packing problem. Ildevana Poltronieri, Avelino Francisco Zorzo, Maicon Bernardino and Edson Oliveira Jr from Brazil introduce Usa-DSL, a usability evaluation process for domain-specific languages (DSLs) that aims to assist DSL designers in evaluating their languages in terms of ease and quality of use without requiring deep knowledge of usability evaluation. Carina Heßeling, Sebastian Litzinger and Jörg Keller from Germany report on their research on the archive-based covert channel in sensor streaming data. This is an approach in which the covert sender and receiver first build an archive of values that occur in the stream in a certain time interval, and then encode bits of the secret message via sensor stream values belonging to the class of seen values or not. In another collaborative effort between researchers from Pakistan and Ireland, Anwar Ahmed Khan, Shama Siddiqui and Indrakshi Dey present a novel risk prediction approach, namely Association Rule Mining for Risk Prediction (ARMR), which integrates an IoMT framework with the emerging machine learning technique known as Association Rule Mining (ARM). Furkan Berk Seyrek and Halil Yiğit from Turkey discuss their study, which focuses on the classification of lung images from computed tomography (CT) scans into cancerous and non-cancerous categories by employing prevalent deep learning models, transfer learning, and rigorous evaluation metrics. And last but not least, Yu Zhong, Bo Shen, Tao Wang, Jinglin Zhang and Yun Liu from China address the interaction and fusion of rich textual information for document-level relation extraction that simultaneously considers multiple types of nodes.Enjoy Reading!Best regards, Christian Gütl, Managing EditorGraz University of Technology, Graz, Austria