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Indexing

Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML)

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

The Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML) is committed to maximizing the visibility, accessibility, discoverability, and long-term preservation of published scholarly content across global academic databases, search engines, computational platforms, and digital repositories.

Journal Highlights

  • eISSN: 2978-3704

  • Frequency: Continuous / Periodic Publication (Peer-Reviewed Open Access Journal)

  • Permanent Identifiers: Persistent DOI Assignment via Crossref for Every Published Article

  • Licensing: Creative Commons Attribution 4.0 International (CC BY 4.0)

  • Editorial Standards: COPE, DOAJ, and Scopus Compliant Governance

  • Global Reach: International Editorial Board & Unrestricted Open Access Model

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Major Academic Databases & Scholarly Platforms

  • Google Scholar

    Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML) is fully discoverable through Google Scholar. All published articles are permanently archived with structured metadata in the journal’s open access repository, enabling seamless automated crawling, indexing, citation tracking, and algorithmic discovery by Google Scholar to maximize research reach.

  • Crossref DOI Registration

    FEAIML is a registered member of Crossref. Every accepted and published article is assigned a persistent Digital Object Identifier (DOI), ensuring permanent citation link stability, automated cross-publisher referencing, metadata dissemination, and long-term digital record integrity across machine learning research networks.

  • Index Copernicus International (ICI World of Journals)

    Index Copernicus is an international database evaluating scientific journals worldwide. FEAIML’s inclusion in the ICI World of Journals enhances its international credibility, discoverability, and academic standing in the fields of artificial intelligence, computer vision, and machine learning.

  • Neliti

    Neliti is a global academic repository and indexing platform. Indexing in Neliti ensures that FEAIML’s artificial intelligence literature and benchmark evaluations are easily discoverable and accessible to researchers, data scientists, software architects, and technical institutions worldwide.

  • Scilit

    Powered by MDPI, Scilit is a comprehensive, real-time database of scholarly literature. Scilit automatically aggregates metadata from Crossref and open access sources, significantly increasing the citation potential, computational reproducibility, and search visibility of FEAIML publications.

  • EuroPub

    EuroPub provides global academic indexing and exposure for peer-reviewed journals. Inclusion in EuroPub expands FEAIML's reach across European and international academic, industrial, and AI research circles.

  • ResearchBib (Academic Resource Index)

    ResearchBib serves as a high-impact academic resource index. It aids computer scientists, AI engineers, and researchers in discovering topically relevant technical journals, tracking calls for papers, and accessing published research articles.

  • Zenodo / OpenAIRE

    Through Zenodo and the OpenAIRE ecosystem, FEAIML supports Open Science principles. Published research outputs, computational models, and associated metadata are preserved in secure European open-access repository infrastructure, supporting long-term digital preservation and open data standards.

  • ISSN International Centre

    The journal is officially registered with the International Standard Serial Number (ISSN) portal (eISSN: 2978-3704), verifying its status as an internationally recognized, continuous digital serial publication in computing and artificial intelligence.

Open Access, Article Processing & Editorial Independence

To ensure maximum impact and adherence to DOAJ and Scopus transparency standards:

  • Open Access Infrastructure: FEAIML operates under the Gold Open Access model, making all technical research immediately free to read, download, and analyze without subscription barriers.

  • Article Processing Charges (APC): The journal charges $0 submission and review fees. An Article Processing Charge (APC) applies only upon formal double-blind peer review completion and official acceptance to support Crossref DOI registration, technical typesetting, server hosting, and digital archiving.

  • APC Waivers: Waiver options are available for deserving authors, early-career AI researchers, and scholars from low-income nations or those with documented financial hardship, ensuring indexing access is purely merit-based.

  • Editorial Insulation: Editorial decisions are completely autonomous and independent of publishing fee payments.

Global Visibility & Discoverability Statement

Through continuous integration with international indexing databases, academic search engines, repository infrastructures, metadata aggregators, and scholarly networking services, FEAIML ensures that published articles remain:

  • Globally Searchable: Easy to locate through academic search engines, AI repositories, and technical databases.

  • Permanently Citable: Backed by persistent Crossref DOIs and structured open metadata.

  • Preserved Long-Term: Archived across multiple digital repositories and open access platforms.

  • Fully Accessible: Free of subscription barriers under the CC BY 4.0 open access model.

Important Notice Regarding Indexing Status

Indexing coverage, database inclusion schedules, automated metadata harvesting, and article discoverability may vary depending on individual database processing schedules, evaluation cycles, and platform-specific harvesting protocols. Authors and researchers are encouraged to verify individual article indexing directly through the respective platform portals.

Contact Information

For inquiries regarding indexing coverage, metadata distribution, or repository partnerships, please contact the Editorial Office: