Machine Learning For Localization

Machine Learning for Localization is a computational methodology that employs data-driven models to infer spatial positions within indoor environments by learning patterns from sensor observations, distinguished from conventional approaches through its reliance on adaptive algorithms that generalize from training data rather than fixed physical or geometrical assumptions.

762

Publications

36.1K

Citations

2.5K

Authors

811

Institutions

Publications per year

2017–2026

521

Authors

2.5K

  1. QY
    Qiang Yang

    Hong Kong University of Science and Technology, Hong Kong SAR China

    12 publications · 1.2K citations

    12

    h-index

  2. SJ
    Sinno Jialin Pan

    Nanyang Technological University, Singapore

    8 publications · 642 citations

    8

    h-index

  3. HR
    Hamada Rizk

    Tanta University, Egypt

    7 publications · 340 citations

    7

    h-index

  4. WX
    Wendong Xiao

    University of Science and Technology Beijing, China

    7 publications · 311 citations

    7

    h-index

  5. MY
    Moustafa Youssef

    Egypt-Japan University of Science and Technology, Egypt

    7 publications · 340 citations

    7

    h-index

1–5 of 2.5K

Venues

  1. Sensors

    59 publications · 2.3K citations

    30

    h-index

  2. IEEE Access

    35 publications · 2.2K citations

    27

    h-index

  3. IEEE Sensors Journal

    27 publications · 1.1K citations

    20

    h-index

  4. IEEE Internet of Things Journal

    18 publications · 731 citations

    16

    h-index

  5. IEEE Communications Letters

    12 publications · 423 citations

    12

    h-index

1–5