About the Journal
The Journal of Artificial Intelligence & Machine Learning Studies (JAIMLS) is a scholarly, peer-reviewed, open-access journal that serves as a platform for innovative contributions across the full spectrum of AI and machine learning. Established in 2017 and published annually from Sweden, JAIMLS advances foundational theories, novel algorithmic models, and interdisciplinary applications—from neural networks and NLP to real-world intelligent systems. The journal maintains a high editorial standard through double-blind peer review, ensuring academic rigor and real-world relevance.
Journal Snapshot
Journal Name: Journal of Artificial Intelligence & Machine Learning Studies (JAIMLS)
ISSN: 2784-6590
Impact Factor: 6.3 (By ResearchBib)
Journal Initials: JAIMLS
Research Scope: Artificial Intelligence, Deep Learning, Neural Networks, Natural Language Processing, Reinforcement Learning, Predictive Analytics, Computational Intelligence
Publication Mode: Digital (On this Website)
Frequency: Annual (1 Volume per Year)
Launch Year: 2017
Review Mode: Double Blind Peer Review
Plagiarism Allowed: 10% (as per Turnitin)
Coverage: Global
Language: English
Current Issue
Welcome to this year’s edition of the Journal of Artificial Intelligence & Machine Learning Studies (JAIMLS). This annual volume features original research, critical analyses, and practical implementations across AI and machine learning disciplines. From advanced learning models to ethical implications and applied algorithms, each contribution reflects the journal’s commitment to responsible, high-impact innovation. We continue to serve as a global platform for interdisciplinary dialogue, academic excellence, and industry relevance—empowering readers with knowledge to shape the intelligent systems of tomorrow.
