CFP - MAKE Special Issue on Surgical Intelligence
Special Issue Information
"AI methods and applications are transforming engineering research at an unprecedented rate. It is most apparent when we contemplate on decades of research and the contributions of eminent colleagues. Machine learning and knowledge extraction are increasingly transforming the way complex dynamical systems are modeled, monitored, predicted and controlled. Their combination with systems engineering and domain knowledge is especially important in safety-critical and data-rich applications, where performance must be balanced with robustness, interpretability and uncertainty awareness. Key applications include biomedical engineering and applied industrial settings. This Special Issue celebrates the 50th birthday of Professor Levente Kovács and his contributions to physiological modeling and control, personalized and robust control, artificial pancreas systems, model-based cancer therapy, biomedical informatics and interdisciplinary computational methods.
In line with the scope of MAKE, our Special Issue focuses on work in which machine learning, knowledge extraction or data-driven inference constitutes a substantive methodological and applied contribution in multi-disciplinary applications. It is also aimed to connect with the machine-learning-relevant themes of the jubilee IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI 2027, for more information about the event, please visit the following link: https://saci2027.uni-obuda.hu/, including computational intelligence, intelligent mechatronics, systems engineering, intelligent manufacturing systems, intelligent robotics and informatics, inviting the best paper authors from the conference to contribute.
We invite original research articles, reviews and perspectives on hybrid model-based and data-driven learning; system identification and time-series prediction; reinforcement learning and learning-based control; physiological and biomedical data analytics; multimodal and image-based learning; trustworthy and explainable AI; uncertainty-aware and robust learning; digital twins and predictive models; anomaly detection and predictive maintenance; knowledge representation and extraction for decision support and machine learning for robotics, mechatronics and intelligent manufacturing. Particular interest is given to reproducible methods that integrate prior knowledge with data, generalize across operating conditions and support safe and interpretable decisions, applied to multi-disciplinary problems.
By bringing together researchers from machine learning, advanced control theory, biomedical engineering, robotics and informatics, this Special Issue aims to highlight emerging applications at the interface of learning, knowledge and intelligent systems.
Prof. Dr. Péter Galambos
Prof. Dr. Tamás Haidegger
Prof. Dr. Radu-Emil Precup
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions."
Keywords
- machine learning
- knowledge extraction
- data-driven modeling
- physiological modeling and biomedical AI
- learning-based control
- reinforcement learning
- trustworthy and explainable AI
- uncertainty-aware learning
- digital twins
- computational intelligence
- intelligent cyber–physical systems
Source: MAKE SI
Comments