Introduction

Epistora Journal of Artificial Intelligence and Intelligent Systems (EJAIS) is a premier peer-reviewed, open access journal dedicated to advancing the frontiers of artificial intelligence and intelligent systems. Established to bridge theoretical AI research with real-world applications, EJAIS serves as a global platform for AI researchers, data scientists and engineers to disseminate transformative discoveries

The journal embraces the full spectrum of AI - from foundational machine learning algorithms and neural network architectures to computer vision, natural language processing, robotics and ethical AI frameworks. With a rigorous double-blind peer review process and rapid publication workflow, we ensure that high-impact research reaches the global community without delay. All articles are published under the CC BY 4.0 license, enabling unrestricted access, reuse and maximum societal impact

Our commitment extends beyond traditional publishing. We actively promote algorithmic transparency, reproducibility and responsible AI innovation. By combining scholarly excellence with modern publishing innovations - including audio article summaries and digital preservation - EJAIS is shaping the future of artificial intelligence research communication

Aim & Scope

Aim: To accelerate responsible AI innovation and intelligent systems research by publishing high-quality, rigorously peer-reviewed research across all domains of artificial intelligence. We aim to foster interdisciplinary dialogue, support early-career researchers and provide an inclusive open access home for transformative AI science

Scope: The journal welcomes original research articles, comprehensive reviews, short communications, technical notes, perspectives and data papers covering, but not limited to:

Machine Learning Deep Learning Computer Vision Natural Language Processing Robotics Reinforcement Learning Generative AI Large Language Models Explainable AI Federated Learning Edge AI AI in Healthcare Autonomous Systems Neural Networks Probabilistic Reasoning Knowledge Graphs AI Safety Algorithmic Fairness Multi-Agent Systems Cognitive Computing Swarm Intelligence Evolutionary Computation Bayesian Inference AI Ethics & Governance Transfer Learning Few-Shot Learning Neurosymbolic AI AI for Science Foundation Models

Article Types

  • Original Research - Full-length reports of novel algorithms, architectures and empirical results. Up to 8,000 words with comprehensive methodology and evaluation
  • Review Articles - Systematic surveys, comprehensive reviews and meta-analyses with future research directions. Up to 6,000 words
  • Short Communications - Urgent findings, preliminary results and time-sensitive breakthroughs requiring rapid dissemination. Up to 2,500 words
  • Technical Notes - Methodological innovations, benchmark datasets, reproducibility studies and implementation advancements
  • Perspectives - Expert viewpoints on emerging trends, policy implications, ethical considerations and future challenges in AI
  • Data Papers - Comprehensive description of high-quality datasets, including curated training data and benchmark resources for the AI community

Mission & Values

Accelerate responsible AI innovation through trusted open access. Core principles: scientific rigour, algorithmic transparency, constructive peer review, rapid decisions, global accessibility without financial barriers. Bridging theoretical advances with real-world intelligent systems - healthcare, autonomous vehicles, climate AI and human-centered computing

Indexing & Archiving

Google Scholar Research Gate CrossRef DOI ISSN Academia R Discovery

Published content is maintained through structured digital preservation and accessibility practices designed to support long-term scholarly availability and continued research access