Finalidades e escopo
The Journal of Machine Learning for Modeling and Computing (JMLMC) focuses on the study of machine learning methods for modeling and scientific computing. The scope of the journal includes, but is not limited to, research of the following types: (1) the use of machine learning techniques to model real-world problems such as physical systems, social sciences, biology, etc.; (2) the development of novel numerical strategies, in conjunction of machine learning methods, to facilitate practical computation; and (3) the fundamental mathematical and numerical analysis for understanding machine learning methods.
Call for Papers
The Journal of Machine Learning for Modeling and Computing (JMLMC) is
seeking submissions from leaders in the field. If you would like to
contribute, please submit your articles in Begell House submission site at
Begell House Submission System
Please feel free to contact Editor-in-Chief Dongbin Xiu at firstname.lastname@example.org if
you have any questions or need any assistance. Begell House can also be
contacted at email@example.com.
Author instructions for the Journal of Machine Learning for Modeling and
Computing can be found at:
As part of the community reciprocation that furthers research in any field,
authors who submit articles to JMLMC acknowledge that they may be asked to
review other articles for the journal.
Enquiries can be directed to Editor-in-Chief Dongbin Xiu at firstname.lastname@example.org