The Babylonian Journal of Machine Learning (BJML) is an online, peer-reviewed, academic journal that adheres to the highest standards of scholarly publishing. BJML invites submissions from researchers worldwide, focusing on theoretical and practical advancements in the field of machine learning and its interdisciplinary applications.

Manuscript Preparation

Authors should prepare their manuscripts using the BJML Word Template or LaTeX Template. Submissions must follow the BJML format to proceed with the peer-review process.

Originality

Submissions to BJML must be original and not previously published or under review elsewhere. Authors are required to ensure the novelty of their work and adhere to ethical standards in research and publishing.

Review Process

All submissions are rigorously peer-reviewed by experts. The review process assesses originality, clarity of presentation, and significance to the field of machine learning. BJML aims to complete the review process within four weeks. For delays, authors may contact the editorial office.

The review process considers:

• Originality: Novel contributions to machine learning.
• Clarity: High-quality presentation and articulation.
• Significance: Relevance and impact on the field.

Open Access

BJML is an open-access journal, ensuring that all accepted manuscripts are freely accessible to the global research community. There are no fees for submission or publication.

Conflict of Interest

Authors, reviewers, and editors must disclose any potential conflicts of interest to ensure transparency and maintain the integrity of the review and publication process. Please refer to the Conflict of Interest Policy.