Multimodal Data Fusion

Multimodal Data Fusion is the systematic integration of heterogeneous data streams from multiple sensory or phenotypic modalities into a unified representation that enhances the accuracy, robustness, and interpretability of biological or physiological inferences in heat detection and multimodal phenotyping.

856

Publications

73.9K

Citations

4.2K

Authors

1.2K

Institutions

Publications per year

2017–2026

715

Authors

4.2K

  1. VD
    Vince D. Calhoun

    Mind Research Network, United States

    18 publications · 1.1K citations

    14

    h-index

  2. LM
    Louis‐Philippe Morency

    Carnegie Mellon University, United States

    7 publications · 2.4K citations

    7

    h-index

  3. DH
    Danfeng Hong

    Chinese Academy of Sciences, China

    7 publications · 1.3K citations

    7

    h-index

  4. BL
    Baiying Lei

    Shenzhen University, China

    5 publications · 280 citations

    5

    h-index

  5. DS
    Dinggang Shen

    University of North Carolina at Chapel Hill, United States

    5 publications · 184 citations

    5

    h-index

1–5 of 4.2K

Venues

  1. IEEE Access

    26 publications · 1.6K citations

    22

    h-index

  2. arXiv (Cornell University)

    Repository · 31 publications · 1.7K citations

    20

    h-index

  3. Information Fusion

    16 publications · 1.8K citations

    16

    h-index

  4. Sensors

    15 publications · 819 citations

    14

    h-index

  5. 14

    h-index

1–5