Data Mining, Optimization Algorithms and Applications in the Era of Foundation Models
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 8
Special Issue Editors
2. Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun 130024, China
Interests: artificial intelligence; data mining; optimization solving
Interests: artificial intelligence; combinatorial optimization problem solving; path planning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Foundation models—including large language models, vision transformers, and multi-modal architectures—have reshaped AI applications across language, image, and cross-modal tasks. However, in many domain-specific or low-resource scenarios—such as ancient script analysis, biomedical imaging, and scientific visualization—available data are often limited, highly structured, or weak in statistical regularity, making it difficult for large-scale pretraining to deliver reliable performance. Even the construction of vertical-domain foundation models faces challenges due to data scarcity and adaptation costs. These gaps underscore the continued relevance of classical and domain-adapted models—including CNNs, graph models, and other task-specific architectures—alongside optimization-driven approaches and data-centric learning. This Special Issue invites research that tackles complex, sparse, or structured data using innovative data mining techniques, optimization algorithms, and hybrid modeling strategies. We especially welcome studies that integrate algorithmic rigor with modern AI paradigms to enhance learning in domains where foundation models fall short or must be complemented.
Prof. Dr. Minghao Yin
Dr. Shuli Hu
Guest Editors
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Keywords
- data mining
- optimization algorithms
- domain-specific applications
- low-resource scenarios
- hybrid modeling and adaptation
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