imiens

About the Journal

Welcome to Intelligent Methods in Engineering Sciences (IMIENS)

IMIENS is an international, interdisciplinary, peer-reviewed journal dedicated to advancing intelligent systems and applications across all fields of engineering. Our mission is to bridge the gap between theory and practice, fostering innovations in diverse areas such as nanotechnology, renewable energy, biomedical engineering, robotics, aerospace, industrial manufacturing, and more.

As an Open Access Journal, IMIENS ensures global accessibility to all published articles without subscription fees. This commitment accelerates the dissemination of knowledge and enhances the visibility and impact of your research.

  • ISSN: 2979-9236
  • DOI: 58190/imiens
  • Explore our articles on Google Scholar.

Join us in shaping the future of intelligent systems in engineering.

Announcements

IMIENS Indexed in ICI Journals Master List for 2024

2025-09-18

We wish to inform our readers, authors, and editors that the journal, "Intelligent Methods In Engineering Sciences" (ISSN: 2979-9236), has passed the evaluation process for the Index Copernicus International (ICI) Journals Master List and is now indexed for the year 2024.

Following a parametric evaluation of the journal's operations in 2024, an Index Copernicus Value (ICV) of 100.00 has been assigned.

We acknowledge the contributions of our authors, reviewers, and the editorial board in maintaining the standards required for this achievement.

Sincerely, The Editorial Board

Read more about IMIENS Indexed in ICI Journals Master List for 2024

Current Issue

Vol. 5 No. 1 (2026)
View Vol. 5 No. 1 (2026)

The Intelligent Methods in Engineering Sciences (Vol. 5, No. 1, 2026) presents a collection of innovative studies demonstrating the growing impact of intelligent systems and machine learning techniques across engineering and healthcare applications. This issue features research on deep learning and image processing approaches for plant disease detection, breast cancer diagnosis using ensemble-based machine learning methods, skin lesion classification through deep feature extraction with SqueezeNet architectures, and hybrid financial prediction models integrating deep learning with worst Omega optimization. Together, these contributions emphasize the effectiveness of artificial intelligence and computational intelligence methods in solving complex classification, prediction, and decision-making problems in real-world domains.

   
Published: 2026-04-30

Research Articles

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