Welcome to European Studies in AI & Analytics

Advancing scientific knowledge and supporting the global research community through open-access journals and professional publishing.

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About Us

European Studies in AI & Analytics is a European-based academic publishing platform dedicated to advancing scientific knowledge across various disciplines. We provide open-access, peer-reviewed journals that serve as reputable outlets for researchers, academicians, and industry professionals to disseminate their findings.

Our Mission

Our mission is to foster innovation and collaboration by offering high-quality publication avenues that bridge the gap between research and practical applications. We aim to support the global scientific community by ensuring that groundbreaking research is accessible to all.

Our Journals

EAJSE
European Advanced Journal for Science & Engineering (EAJSE) - p-ISSN 3050-9696 & e-ISSN 3050-970X

Focuses on publishing high-quality research across all domains of science, technology, and engineering.

EAJET
European Advanced Journal for Emerging Technologies (EAJET) - p-ISSN 3050-9734 & e-ISSN 3050-9742

Dedicated to publishing cutting-edge research in emerging and disruptive technologies.

EJAAI
European Journal of Analytics and Artificial Intelligence (EJAAI) - p-ISSN 3050-9556 & e-ISSN 3050-9564

A premier journal advancing research in analytics, artificial intelligence, and their interdisciplinary applications.

EDSJ
European Data Science Journal (EDSJ) - p-ISSN 3050-9572 & e-ISSN 3050-9580

The European Data Science Journal (EDSJ) offers a platform for high-quality research in data science, including data mining, data analysis, and big data technologies.

Editorial Excellence

Our journals are managed by expert editorial boards comprising scholars from leading universities and research institutions. We uphold rigorous peer-review standards to ensure the integrity and quality of published work.

Open Access Commitment

All our journals are open access, allowing researchers worldwide to read and cite work without barriers. This model promotes wider dissemination and impact of research findings.

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