Peer-reviewed • Open Access • Academic + Modern

Journal of Data-Driven Intelligence in Engineering Applications

An international peer-reviewed journal focused on data-driven intelligence, AI, machine learning, analytics, intelligent systems, IoT, control, optimization, and real-world engineering applications.

Why Publish with JDDIEA

A practical, author-friendly publication experience with strong research visibility and clean editorial workflow.
Peer Reviewed
Structured peer-review workflow with transparent editorial handling.
Open Access
Research remains freely accessible to readers, improving reach and citations.
Multidisciplinary
Engineering applications of AI, ML, analytics, IoT systems, control, and optimization.
Widely Discoverable
Indexing roadmap supported by metadata quality and responsible publishing practices.

About JDDIEA

Journal of Data-Driven Intelligence in Engineering Applications (JDDIEA) is an international peer-reviewed, open access journal focused on research that translates data-driven intelligence into real engineering impact.

JDDIEA welcomes original research and high-quality reviews in artificial intelligence, machine learning, data analytics, intelligent systems, IoT-enabled engineering, control and automation, optimization, and applied computational methods. The journal is positioned for authors who want academic credibility along with a modern, clean publication experience.

We encourage interdisciplinary submissions that connect theory with implementation, including real datasets, simulations, prototypes, and engineering case studies. Submissions are processed through a structured editorial workflow, enabling transparent handling from initial screening to peer review and final publication.

JDDIEA follows a quality-first approach with ethical publishing practices, plagiarism screening before review, and clear communication at every stage. Authors can access guidelines and templates from the Downloads section and track their manuscript progress through the submission system.

Peer Review Double-anonymized
Access Open
Identifiers ISSN / E-ISSN: to be assigned
DOI Implementation planned
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    Publish research across engineering and applied AI domains with a strong focus on real-world applications.
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    Open access publishing to improve visibility and practical reuse of research outcomes.
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    Persistent-identifier and metadata infrastructure is part of the journal's publication-development roadmap.
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    Indexing and metadata quality roadmap aligned with major research databases and repositories.
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    Fast and structured review workflow with clear editorial communication and process tracking.
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    Email notifications to authors at each process stage (submission, review, decision, publication).
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    Quick support through email and official contact channels for author and reviewer assistance.
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    HTTPS-secured access and a clean journal interface for reading, downloading, and submission.