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Open Access Policy

Open Access Policy

Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML)

eISSN: 2978-3704 | International Peer-Reviewed Journal | Open Access

1. Introduction & Core Vision

Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML) is an international, peer-reviewed, Open Access journal committed to the unrestricted, permanent, and global dissemination of high-quality artificial intelligence and machine learning research. The journal covers key domains including foundational AI, deep learning, computer vision, natural language processing, reinforcement learning, robotics, cognitive computing, and cross-disciplinary AI applications.

FEAIML operates on the foundational principle that scientific, computational, and machine learning knowledge should be freely accessible to everyone without financial, legal, or technical barriers. By promoting open scholarship, the journal seeks to accelerate global AI innovation, elevate research visibility, bridge academic research with real-world technological application, and foster reproducible, ethical AI solutions worldwide.

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2. Open Access Statement

FEAIML provides immediate, permanent, and barrier-free access to all published articles under the Gold Open Access model:

  • Immediate Availability: All research articles are published open access and made freely available online instantly upon publication.

  • Zero Embargo Period: The journal enforces no paywalls, subscription requirements, or delayed access periods (embargoes).

  • Universal Reach: Readers worldwide can view, download, print, copy, distribute, and share full-text PDFs without subscription fees, user registrations, or prior administrative approvals.

3. Copyright Retention & Author Rights

In accordance with modern scholarly publishing standards, DOAJ guidelines, and Scopus requirements, authors retain full, unrestricted copyright of their work published in FEAIML.

  • Non-Exclusive License: Authors grant FEAIML a non-exclusive license to publish, reproduce, distribute, display, archive, and preserve the final Version of Record as part of the permanent scientific record.

  • Re-use Rights: Authors retain the absolute right to reuse, distribute, translate, and reproduce their published work in future academic endeavors, books, patents, course materials, lectures, code repositories, or institutional archives without seeking formal permission or paying fees to the publisher.

4. Creative Commons Attribution 4.0 International License (CC BY 4.0)

All articles published in FEAIML are licensed and distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Under the terms of this license, researchers, AI engineers, data scientists, students, institutions, and the general public are free to:

  • Share: Copy, distribute, and redistribute the published material in any medium or format.

  • Adapt: Remix, transform, build upon, and adapt the material for any lawful purpose, including commercial applications, software integration, and model training adaptations.

  • Mandatory Attribution Requirement: Credit must be properly attributed to the original author(s) and FEAIML as the original place of publication, a link to the CC BY 4.0 license must be provided, and any modifications or adaptations made must be clearly indicated.

5. Rights of Readers & Reuse Guidelines

Consistent with the Budapest Open Access Initiative (BOAI) and the CC BY 4.0 license, readers, software developers, research labs, and industrial practitioners are permitted to:

  • Search, read, download, print, link, or crawl full-text articles for indexing, text mining, automated model evaluation, and data mining purposes.

  • Pass published content, raw experimental datasets, benchmark results, and source codes to computational tools, LLM pipelines, and analytical software without seeking extra authorization or paying royalties.

  • Cite and incorporate published AI findings into research, literature reviews, technical reports, white papers, patents, and software frameworks.

Proper Citation Standard

Any reuse or distribution of published work must explicitly include:

  1. Complete author attribution.

  2. Full journal citation (Frontiers in Emerging Artificial Intelligence and Machine Learning).

  3. The official Digital Object Identifier (DOI) assigned to the article.

  4. A clear indication if any adaptations, translations, or modifications were made.

6. Self-Archiving & Repository Policy

FEAIML actively supports self-archiving and open repository practices (Green Open Access). Authors are encouraged to deposit and share the final Publisher's PDF (Version of Record) immediately upon publication across various platforms without any embargo period:

  • Institutional & Subject Repositories: University repositories, AI/CS archives (e.g., arXiv), and digital databases.

  • Code & Data Repositories: GitHub, GitLab, Hugging Face, Kaggle, Zenodo, and open data archives.

  • Academic & Professional Profiles: ORCID, ResearchGate, Academia.edu, Google Scholar, LinkedIn, and personal/faculty websites.

  • Funding Agency Archives: Open access repositories required by national or international research funders.

Requirement: All self-archived versions must contain complete bibliographic citation details, a direct link to the official article DOI, and explicit recognition of FEAIML as the original publisher.

7. Article Processing Charge (APC) & Publishing Sustainability

To maintain high-quality Open Access operations, professional copyediting, technical typesetting, Crossref DOI registration, permanent digital archiving, and web infrastructure maintenance, FEAIML follows a transparent cost model:

  • Submission Fee: $0 (Free)

  • Peer Review Fee: $0 (Free)

  • Article Processing Charge (APC): An APC applies only after a manuscript successfully clears formal double-blind peer review and receives official editorial acceptance.

  • Waivers & Support: FEAIML offers partial or full APC waivers for deserving researchers, early-career AI scientists, and authors from low-income countries demonstrating genuine financial hardship, ensuring financial constraints never impede quality research publication.

8. Alignment with Global Open Science Standards

FEAIML aligns its publishing operations, licensing, and Open Access policies with established international frameworks and standards:

  • DOAJ: Principles of Transparency and Best Practice in Scholarly Publishing.

  • COPE: Committee on Publication Ethics guidelines for transparent licensing and archiving.

  • BOAI: Budapest Open Access Initiative definition and standards for open access publishing.

  • Scopus & Web of Science: Standards for article accessibility, permanent preservation, open metadata indexing, and computational reproducibility.

9. Policy Review & Governance

This policy is reviewed annually by the Editorial Board to maintain alignment with evolving open science directives, international copyright standards, and academic publishing best practices. The latest version will always remain publicly available on the journal portal.

10. Contact Information

For inquiries regarding Open Access licensing, copyright retention, self-archiving rights, or APC waiver requests, please contact the Editorial Office: