Submission Deadline: 30 April 2026 View: 130 Submit to Special Issue
Dr. Amir Mosavi
Email: amir.mosavi@nik.uni-obuda.hu
Affiliation: John von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, Hungary
Research Interests: machine learning, deep learning, ensemble and hybrid models, applied mathematics, soft computing, deep reinforcement learning, machine learning for big data, mathematical IT, hydropower modeling, prediction models, time series prediction, climate models, machine learning for remote sensing, hazard models, extreme events, atmospheric models, forecasting models, predictive analytics, Internet of Things
Prof. Dr. Felde Imre
Email: felde.imre@uni-obuda.hu
Affiliation: John von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, Hungary
Research Interests: deep learning, heat transfer
We invite contributions to a special issue on Applied Machine Learning, focused on the practical use of machine learning, deep learning, interactive, and generative AI. AI now plays an active role in many areas of daily life, including healthcare, education, finance, agriculture, and industry. We are looking for research that shows how AI moves beyond theory, where models are not only developed but also tested and used in real-world settings. We also welcome insights into how researchers and professionals navigate the challenges that come with applying AI in complex and fast-changing environments.
This issue highlights the growing importance of FAIR and responsible modelling. Responsible AI means building systems that are fair, transparent, and accountable, qualities that matter when AI systems influence decisions that impact people's lives. These principles should guide the entire development process and not appear only at the final stage. We value work that shows how responsibility becomes part of the design from the start.
We also place strong emphasis on the use of FAIR data, which means data that is Findable, Accessible, Interoperable, and Reusable. FAIR data supports collaboration and reproducibility, which are key to progress in science and technology. It allows researchers to build on each other's work, share insights, and develop solutions that are more open, reliable, and ethical. We welcome both original research and thoughtful reviews. We especially encourage submissions that follow the PRISMA guideline, which help map the current landscape, uncover gaps, and point to future directions in applied AI. This special issue aims to collect work that shows AI in action, research grounded in real needs, shaped by ethical thinking, and built for impact. We hope to bring together a community committed to making AI not only smarter but also more human-centered and trustworthy.
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