Academic Editor
Contributions by role
Editor 1
Bernard De Baets
Ghent University, Belgium
Summary
Bernard De Baets is a Senior Full Professor and Head of Department at the Department of Data Analysis and Mathematical Modelling of the Faculty of Bioscience Engineering of Ghent University. He is leading the Knowledge-based Systems research unit KERMIT. As a trained mathematician, computer scientist and knowledge engineer, he has developed a passion for multi- and interdisciplinary research. While being deeply involved in fundamental research in three interlaced research threads (knowledge-based, predictive and spatio-temporal modelling), he also aims at innovative applications in the applied biological sciences. He is a prolific writer, with a bibliography comprising over 550 peer-reviewed journal papers (bestowed with various best paper awards), accumulating close to 25000 Google scholar citations. He actively serves the research community, in particular as editor-in-chief of Fuzzy Sets and Systems and as a member of the editorial board of several other journals. Bernard is an Honorary Professor of Budapest Tech (Hungary), a Doctor Honoris Causa of the University of Turku (Finland), a Profesor Invitado of the Universidad Central “Marta Abreu” de las Villas (Cuba) and a Professor Extraordinarius of the University of South Africa. He is a fellow of IFSA (International Fuzzy Systems Association), a recipient of the EUSFLAT Scientific Excellence Award and a nominee of the Ghent University Prometheus Award for Research.
Edited Journals
IECE Contributions

Free Access | Review Article | 04 January 2025
A Machine Learning-Based Scientometric Evaluation for Fake News Detection
IECE Transactions on Intelligent Systematics | Volume 2, Issue 1: 38-48, 2025 | DOI:10.62762/TIS.2024.564569
Abstract
In the modern world, disseminating false information is a problem that must be addressed, and algorithms based on machine learning are used to spot and stop the spread of incorrect information. Due to the current unregulated development of false news fabrication and dissemination, democracy is continuously under threat. Fake news may mislead individuals while influencing them because of its persuasiveness and life sciences. Using data from the Web of Science, this study undertakes a bibliometric analysis of research on the application of machine learning for fake news identification. The research underscores the need for a streamlined approach to analyze data exclusively from the Web of Scie... More >

Graphical Abstract
A Machine Learning-Based Scientometric Evaluation for Fake News Detection