Editorial

Abstract

Dear Readers,  I am very happy to announce the fifth regular issue of 2023. In this issue, 5 articles by 22 authors from 5 countries cover a variety of topical research aspects in computer science. 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 five accepted articles: César Domínguez Pérez, Jónathan Heras, Eloy Mata, Vico Pascual, Lucas Fernández-Cedrón, Marcos Martínez-Lanchares, Jon Pellejero-Espinosa, Antonio Rubio-Loscertales, and Carlos Tarragona-Perez from Spain report on their deep learning approach for semi-supervised semantic segmentation to identify irrelevant objects in a waste recycling plant. In a research collaboration between Australia and Iraq, Mitchell Jensen, Khamael Al-Dulaimi, Khairiyah Saeed Abduljabbar and Jasmine Banks are focusing on their work on autoimmune disease detection in humans, more specifically on automating the classification procedure of HEp-2 stained cells from microscopic images and improving the accuracy of computer-aided diagnosis. Kashif Mehboob Khan, Warda Haider, Najeed Ahmed Khan, and Darakhshan Saleem from Pakistan address their research on big data provenance using blockchain for qualitative analytics through machine learning. Abubakhari Sserwadda, Alper Ozcan, and Yusuf Yaslan from Türkiye present their research on a novel end-to-end unified topological similarity and centrality driven hybrid deep learning model for temporal link prediction. And last but not least, Tahseen A. Wotaifi, and Ban N. Dhannoon from Iraq aim in their research to use deep learning, pre-trained models, and machine learning based on Convolution Neural Networks to predict Arabic and English fake news based on three public and available datasets: the Fake-or-Real dataset, the AraNews dataset, and the Sentimental LIAR dataset. Enjoy Reading! Cordially,  Christian Gütl, Managing Editor