About the Journal
Journal Overview
The European Journal of Artificial Intelligence and Machine Learning (EJAIML) is a United Kingdom–based, international, peer-reviewed, and open-access scholarly journal dedicated to advancing global research and innovation in Artificial Intelligence (AI) and Machine Learning (ML).
EJAIML provides a respected platform for researchers, academicians, and professionals to publish original, high-quality, and unplagiarised manuscripts that expand theoretical understanding, algorithmic design, and real-world applications of intelligent systems.
Committed to academic integrity, transparency, and innovation, the journal bridges the gap between research and practice, facilitating the global exchange of scientific knowledge in the field of AI and ML.
The journal publishes twelve issues annually (monthly) and maintains a selective acceptance ratio of approximately 32%, ensuring that only original, relevant, and technically sound research is published.
However, this acceptance ratio is dynamic — it continuously changes depending on the overall quality, depth, and contribution of manuscripts received in each issue.
Call for Papers
The European Journal of Artificial Intelligence and Machine Learning (EJAIML) invites researchers, academicians, and professionals to submit their original and unpublished manuscripts for its upcoming monthly issue.
As a UK-based, peer-reviewed, and open-access international journal, EJAIML publishes high-quality research in Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning, NLP, and related intelligent systems.
All submissions undergo a rigorous double-blind peer review to ensure academic integrity and global relevance. The acceptance ratio (around 32%) varies according to the quality and contribution of submitted papers.
The journal follows the COPE guidelines for ethical publication and operates under the Creative Commons International License, making all articles free to access, use, and download worldwide.
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Publication Frequency: Monthly
Open Access | COPE Compliant | Creative Commons Licensed | Peer-Reviewed