Computational Intelligence in Addressing Data Heterogeneity
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".
Deadline for manuscript submissions: 31 August 2024 | Viewed by 457
Special Issue Editor
Interests: deep neural networks; interpretable machine learning; domain adaptation; nonlinear programming; computer-aided diagnosis; medical image analysis; neurological disorder diagnosis; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Data heterogeneity, characterized by the presence of diverse data sources with varying formats, structures, and semantics, has become a pervasive challenge in the era of big data. The issue focuses on the theoretical and computational challenges and advancements in leveraging computational intelligence techniques to tackle the complexities posed by heterogeneous data sources. This collection encompasses a diverse range of methodologies, algorithms, theories, applications, and case studies that highlight the role of computational intelligence in harmonizing and deriving valuable insights from heterogeneous data sources.
Topics include, but are not limited to:
- Deep learning;
- Data mining;
- Statistical learning;
- Robust machine learning;
- Decision support;
- Robust optimization;
- Curse of dimensionality;
- Interpretable machine learning;
- Uncertainty quantification;
- Federated learning;
- Computer vision and image processing;
- Anomaly detection;
- Applications with heterogeneous data.
Dr. Haifeng Wang
Guest Editor
Manuscript Submission Information
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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. Mathematics is an international peer-reviewed open access semimonthly 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 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
- robust machine learning
- robust optimization
- curse of dimensionality
- uncertainty quantification
