Machine Learning with Applications

Machine Learning with Applications (MLWA) is a peer reviewed, open access journal focused on research related to machine learning. The journal encompasses all aspects of research and development in ML, including but not limited to data mining, computer vision, natural language processing (NLP), intelligent systems, neural networks, AI-based software engineering, bioinformatics and their applications in the areas of engineering, medicine, biology, education, business and social sciences. It covers a broad spectrum of applications in the community, from industry, government, and academia.

The journal publishes research results in addition to new approaches to ML, with a focus on value and effectiveness. Application papers should demonstrate how ML can be used to solve important practical problems. Research methodology papers should demonstrate an improvement to the way in which existing ML research is conducted.

Submissions must be novel, technically sound, and clearly presented. MLWA accepts both regular papers and technical notes (technical notes are limited to a maximum of 10 pages). In addition, survey articles and discussion papers on ML are welcome.

Submissions meeting journal criteria will undergo a single-blind review process, utilizing a minimum of two (2) external referees. Our dedicated editorial team, together with active researchers from all areas of ML, ensure that papers move through the evaluation and review as fast as possible without compromising on the quality of the process.

The journal audience comprises academia, industry, and practitioners. Authors are strongly encouraged to make their datasets publicly accessible via a repository of their choosing. Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our Support Center.

Reproducibility Badge Initiative and Software Publication

Reproducibility Badge Initiative (RBI) is a collaboration with Code Ocean (CO), a cloud based computational reproducibility platform that helps the community by enabling sharing of code and data as a resource for non-commercial use. CO verifies the submitted code (and data) and certifies its reproducibility. Code submission will be verified by the Code Ocean team for computational reproducibility by making sure it runs, delivers results and it is self-contained. For more information please visit this help article. Note that an accepted paper will be published independently of the CO application outcome. However, if the paper receives the Reproducibility badge, it will be given additional exposure by having an attached R Badge, and by being citable at the CO website with a DOI.

We invite you to convert your open source software into an additional journal publication in Software Impacts, a multi-disciplinary open access journal. Software Impacts provides a scholarly reference to software that has been used to address a research challenge. The journal disseminates impactful and re-usable scientific software through Original Software Publications which describe the application of the software to research and the published outputs.

For more information contact us at: [email protected]