JIET OPEN ACCESS
Journal of Intelligence and Engineering Technology
ISSN:3136-0939 (print) | ISSN:3136-0947 (online) | Publication Frequency: Quarterly
Articles15Editors &
Reviewers0
About the Journal
Journal of Intelligence and Engineering Technology (JIET) is a scholarly journal published by SUAS. It employs editor-invited peer review and editorial assessment through a standardized scoring framework evaluating submissions on originality, transparency, ethical integrity, and domain relevance. Quarterly publication with Online First. It covers domains including Intelligent Systems, Smart Manufacturing, Cyber–Physical Engineering, Engineering Data Science, Sustainable Infrastructure, Intelligent Materials, Human-Centered AI, Engineering Cybersecurity, and Emerging Convergent Engineering Domains; assigns DOIs and ARKs; and is indexed in WorldCat, OpenAIRE, Scilit, and BASE.
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Call for Editorial Board Members
2026 February 12
Journal of Intelligence and Engineering Technology (JIET) invites distinguished scholars to serve on its Editorial Board. Board members guide scope, review manuscripts, and uphold our standardized scoring framework.
2026 February 12
Journal of Intelligence and Engineering Technology (JIET) invites experienced scholars to join its reviewer pool. All reviewers conduct editor-invited peer review and editorial assessment using a standardized scoring framework evaluating originality, transparency, ethical integrity, and domain relevance.
Current Issue
All articles published in this issue have undergone a thorough peer review process, and stringent checks for repetition rates have been implemented to ensure the integrity of the content.
Total number of articles in this issue: 1
Total number of pages in this issue: 5
For inquiries regarding the content of specific articles, please feel free to contact the respective authors via their provided email addresses. For questions related to the journal itself, please reach out directly to SUAS Press.
Year
2026
Volume
1
Number
3
Status
Archived
Published
2026 July 26
Articles
Feature Mining and Node Prediction for Complex Correlation Datasets Based on Graph Neural Networks
Wenhao Xu.Complex correlation datasets widely exist in social networks, citation networks, and biological systems, where traditional machine learning methods fail to effectively capture implicit topological cor...
Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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