Conventional methods for modeling complex systems that rely on statistical modeling and physical processes typically require a lot of work by researchers with little expertise. Machine learning and its applications are becoming increasingly relevant to the research community. Using less prior knowledge, sophisticated machine learning algorithms can approximate extremely complex systems with high accuracy. Machine learning (ML) techniques are widely used in many applications, including autonomous systems, robotics, agriculture, image and audio recognition, medicine, and many other fields.
This special issue focuses on articles that describe the development of new analytical frameworks that advance practical ML techniques. We would like to encourage, in particular, all participants of the IEEE 2024 International Conference on Control, Automation and Diagnosis (ICCAD’24), held in Paris (France) (https://www.iccad-conf.com/), to submit extended versions of their presented papers. Contributions arising from papers given at a conference should be substantially extended and should cite the conference paper where appropriate.
We also focus on contributions that describe the application of machine learning methods to real-world problems and the theoretical and/or experimental studies that yield new insights into the design of ML systems. It is open to all researchers of this area.
