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About the Journal
Title: International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
Edited by: Elena Verdú, Universidad Internacional de la Rioja (Spain)
Published by: Universidad Internacional de la Rioja (Spain)
ISSN: 1989-1660 |DOI: 10.9781/ijimai
Periodicity: quarterly
Content access policy: open access
Editors & Editorial Board | Scientific Committee | Reviewers of the 2024 issues
Subjects: AI theories, methodologies, systems, and architectures that integrate multiple technologies, as well as applications combining AI with interactive multimedia techniques
The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI, ISSN 1989-1660) is a quarterly open-access journal that serves as an interdisciplinary forum for scientists and professionals to share research results and novel advances in artificial intelligence. The journal publishes contributions on AI theories, methodologies, systems, and architectures that integrate multiple technologies, as well as applications combining AI with interactive multimedia techniques.
Current Issue
Vol. 9 No. 4 (2025): IJIMAI 2025 - Regular Issue.
Artificial Intelligence (AI) is a scientific discipline that aims to drive disruptive scenarios for science-based technical developments that solve complex problems. The IJIMAI journal’s scope is precisely to demonstrate how the combination of two factors — technical foundations and sought-after applications — must guide future AI developments to find solutions to complex real-world problems. This IJIMAI publication opens with an article that considers the current framework for AI fundamentals: how can we improve AI technology to find solutions to real-unsolved problems? The initial answer seems to be related with a desired self-consistent procedure: let machines learn from our experience. In the article by Alotaibi et al., the analysis of neural networks in terms of the parameters used, how they work, and how do they respond to the problem itself led the authors to a rationale for decision-making regarding the performance of different neural models. The immediate question that arises is whether there are any universal and fundamental criteria that can be used to define the models that guide AI methods. Apparently, there are not such universal methods, and we are faced with a challenging open problem. Subsequent manuscripts will provide readers with more in-depth insights into this issue.
Published:
2025-09-01
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