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The Journal of Kryon is a peer-reviewed scholarly platform dedicated to advancing research in artificial intelligence, computational sciences, and emerging digital technologies. The journal provides a space for researchers, academicians, and practitioners to publish high-quality work that contributes to the global technology and innovation ecosystem. It encourages the dissemination of groundbreaking ideas, interdisciplinary approaches, and practical applications that shape the future of digital transformation.
The Journal of Kryon is a peer-reviewed scholarly platform dedicated to advancing research in artificial intelligence, computational sciences, and emerging digital technologies. The journal provides a space for researchers, academicians, and practitioners to publish high-quality work that contributes to the global technology and innovation ecosystem. It encourages the dissemination of groundbreaking ideas, interdisciplinary approaches, and practical applications that shape the future of digital transformation.
The Journal of Kryon is a peer-reviewed scholarly platform dedicated to advancing research in artificial intelligence, computational sciences, and emerging digital technologies. The journal provides a space for researchers, academicians, and practitioners to publish high-quality work that contributes to the global technology and innovation ecosystem. It encourages the dissemination of groundbreaking ideas, interdisciplinary approaches, and practical applications that shape the future of digital transformation.
Editorial Board
Chief Editor
Dr. A. Krishnaveni, PhD
Professor | Department of Computer Science and Engineering
Chennai Institute of Technology, India
Associate Editors
Dr. R. Manoj Kumar – Data Analytics & Decision Sciences
Dr. L. Saraswathi – AI-based Educational Research
Dr. Jonathan Reeves – Publishing Digital Transformation
Dr. Yue Ling – Human–Computer Interaction & Systems Engineering
Most Downloaded Article
This demonstration article showcases a complete JATS JournalPublishing v1.1 structure using unpublished, copyright‑free text created solely for testing. We include complex MathML with matrices, piecewise definitions, integrals, and coupled systems to exercise equation rendering and cross‑referencing. Figures and tables are provided with captions and callouts from the narrative, and we illustrate links to equations such as Variant D: this copy intentionally varies minor wording while preserving structure for pipeline regression tests.
Volume 1, Issue 1, Pages: 45-56, 26 Nov 2025