Mathematical Methods in Machine Learning, Knowledge Graphs and Computer Vision
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 10 June 2025 | Viewed by 81
Special Issue Editor
Special Issue Information
Dear Colleagues,
The integration of mathematical methods in machine learning with knowledge graphs and computer vision has opened up a new frontier in the field of Artificial Intelligence. Knowledge graphs, which are structured semantic knowledge bases representing entities and their relationships, have become indispensable in various AI applications. Mathematical methods play a crucial role in these applications, including optimization and algorithm design.
This Special Issue is dedicated to exploring the confluence of mathematical methods and multimodal knowledge graphs in machine learning and computer vision. Our aim is to highlight the important role of mathematical methods in the advancement of these fields, especially in the era of big data and complex models.
We invite submissions that address the following areas, emphasizing the synergy between knowledge graphs and multimodal data in machine learning and computer vision: mathematical theories and algorithms that enhance the construction and querying of knowledge graphs; multimodal learning approaches that integrate knowledge graphs with various data types, including text, images, and sensory data; applications of knowledge graphs to improve computer vision tasks such as object detection, image segmentation, and scene understanding; innovative uses of mathematical methods for the analysis and interpretation of multimodal knowledge graphs; etc.
We are particularly interested in manuscripts that present novel methodologies, theoretical insights, and practical implementations that are at the intersection of these areas. Our aim is to encourage research that can lead to more efficient, more accurate, and more interpretable AI systems that are able to effectively process data and learn from them.
Dr. Qing Cheng
Guest Editor
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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
- knowledge graphs
- multimodal data
- machine learning
- deep learning
- computer vision
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