Intelligent Information Processing Methods in Interdisciplinary
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: 31 May 2026
Special Issue Editor
Interests: information geometry; Riemannian geometry; radar/sonar signal processing; underwater environmental modelling; image processing; machine learning; target detection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In an era marked by an unprecedented pace of development in intelligent processing methods, its applications have permeated and transformed diverse fields including as healthcare, education, sports, and biology. This Special Issue on "Intelligent Information Processing Methods in Interdisciplinary" serves as an open and inclusive platform for researchers and practitioners to delve into the transformative potential of intelligent processing methods across these domains. This curated collection spotlights cutting-edge developments in AI-powered medical innovations, intelligent educational frameworks, data-driven sports analytics, and next-generation biotechnological solutions. By examining the integration of machine learning, deep learning, and other AI methodologies, the Special Issue not only showcases technical advancements but also emphasizes ethical considerations and real-world impact. The collection of peer-reviewed articles aspires to foster meaningful interdisciplinary dialog, bridge theoretical and applied research, and accelerate the translation of intelligent processing method innovations into actionable strategies that address complex, multifaceted challenges in our increasingly interconnected world.
This Special Issue focuses on recent advances in intelligent processing methods in interdisciplinary research, including a wide range of new processing techniques and experimental advances. Topics of interest include, but are not limited to, the following:
- AI-driven medical technologies;
- AI-driven biomedical signal processing;
- AI-driven sensor signal processing;
- AI-driven methods in sport and education;
- Machine learning methods and their applications in interdisciplinary research;
- Large language models and their applications in interdisciplinary research;
- Few-shot learning and its applications in interdisciplinary research;
- Federated learning and its applications in interdisciplinary research.
Dr. Xiaoqiang Hua
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI-driven medical technologies
- AI-driven biomedical signal processing
- AI-driven sensor signal processing
- AI-driven methods in sport and education
- machine learning methods and their applications in interdisciplinary research
- large language models and their applications in interdisciplinary research
- few-shot learning and its applications in interdisciplinary research
- federated learning and its applications in interdisciplinary research
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