Statistical Anomaly Detection

Statistical Anomaly Detection is a methodological framework within statistical analysis that identifies rare or irregular patterns in electoral data deviating significantly from expected behavior under normal voting processes, thereby signaling potential instances of election fraud.

178

Publications

7.9K

Citations

562

Authors

267

Institutions

Publications per year

2017–2026

72

Authors

562

  1. AE
    Ahmed E. Kosba

    University of Maryland, College Park, United States

    5 publications · 435 citations

    5

    h-index

  2. MY
    Moustafa Youssef

    Egypt-Japan University of Science and Technology, Egypt

    5 publications · 435 citations

    5

    h-index

  3. AS
    Ahmed Saeed

    Karolinska Institutet, Sweden

    4 publications · 414 citations

    4

    h-index

  4. DK
    Dmitry Kobak

    University of Tübingen, Germany

    3 publications · 49 citations

    3

    h-index

  5. PK
    Peter Klimek

    Medical University of Vienna, Austria

    3 publications · 179 citations

    3

    h-index

1–5 of 562

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 200 citations

    8

    h-index

  2. IEEE Access

    5 publications · 358 citations

    5

    h-index

  3. Sensors

    2 publications · 156 citations

    2

    h-index

  4. Future Generation Computer Systems

    FGCS · 2 publications · 521 citations

    2

    h-index

  5. PLoS ONE

    2 publications · 55 citations

    2

    h-index

1–5