Journal overview and metrics

Machine Learning and Convergence Science (MLCS) is an international, peer-reviewed, open-access journal that publishes research in which advances in machine learning and substantive application domains inform and strengthen one another. We provides a scholarly forum for researchers, domain specialists, and practitioners working across machine learning methodology, health and clinical sciences, food systems and agriculture, development and public policy, environment, energy, infrastructure, and trustworthy intelligent systems. The journal prioritizes research that combines methodological rigor with meaningful scientific, practical, or societal contribution. MLCS particularly welcomes studies that address real-world conditions, use appropriate validation strategies, report findings transparently, and provide insights that extend beyond the application of established methods to new datasets.

All research manuscripts undergo editorial screening followed by double-anonymous peer review by at least two independent reviewers. MLCS maintains editorial independence and applies established standards of publication ethics and research integrity. The journal also supports research relevant to the United Nations Sustainable Development Goals (SDGs), particularly SDG 1 (No Poverty), SDG 2 (Zero Hunger), SDG 3 (Good Health and Well-being), and SDG 9 (Industry, Innovation and Infrastructure).

Why Publish with MLCS?

• Convergence-Oriented Scope: MLCS connects advances in machine learning with substantive questions in science, health, society, and industry.
• Rigorous Peer Review: Each manuscript that proceeds to external review is evaluated through a double-anonymous process by at least two independent reviewers with relevant expertise.
• Open Access: MLCS provides immediate open access to published articles under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
• Research Transparency: The journal encourages reproducible methods, appropriate validation, transparent reporting, and responsible interpretation of computational results.
• No Author Fees: MLCS does not charge submission or article processing fees. [Use this statement only if this is the journal's established policy.]
• Interdisciplinary Audience: The journal brings together computer scientists, data scientists, domain researchers, and practitioners working on problems that require both computational and substantive expertise.

Submission Requirements

Manuscripts submitted to MLCS must present original work that has not been published previously and is not under consideration by another journal.

The journal conducts similarity screening as part of its editorial assessment. Editors evaluate any identified textual overlap in context and may take further action when concerns regarding plagiarism, duplicate publication, inappropriate attribution, or other forms of publication misconduct arise.

Authors must ensure that their manuscripts comply with applicable requirements for research ethics, authorship, conflicts of interest, data integrity, and responsible reporting.

For detailed requirements, please consult the [Author Guidelines], [Peer Review Process], and [Publication Ethics and Malpractice Statement].