Deep Learning For Big Data

Deep Learning for Big Data is a computational framework within machine learning that leverages deep neural architectures to automatically learn hierarchical representations from large-scale, high-dimensional, and often heterogeneous datasets, distinguishing itself by its capacity to process massive volumes of data with minimal human intervention while uncovering complex patterns across distributed data sources.

849

Publications

91.8K

Citations

3.2K

Authors

1.2K

Institutions

Publications per year

2017–2026

739

Authors

3.2K

  1. LT
    Laurence T. Yang

    St. Francis Xavier University, Canada

    11 publications · 2.6K citations

    11

    h-index

  2. ZC
    Zhikui Chen

    Dalian University of Technology, China

    11 publications · 2.4K citations

    11

    h-index

  3. QZ
    Qingchen Zhang

    St. Francis Xavier University, Canada

    9 publications · 1.1K citations

    9

    h-index

  4. TM
    Taghi M. Khoshgoftaar

    Florida Atlantic University, United States

    8 publications · 3.5K citations

    8

    h-index

  5. PL
    Peng Li

    Dalian University of Technology, China

    7 publications · 581 citations

    7

    h-index

1–5 of 3.2K

Venues

  1. arXiv (Cornell University)

    Repository · 44 publications · 5.4K citations

    26

    h-index

  2. IEEE Access

    32 publications · 3.3K citations

    24

    h-index

  3. Future Generation Computer Systems

    FGCS · 9 publications · 996 citations

    9

    h-index

  4. IEEE Transactions on Industrial Informatics

    9 publications · 894 citations

    9

    h-index

  5. Sustainability

    8 publications · 496 citations

    8

    h-index

1–5