Anomaly Inspection

Anomaly Inspection is a methodological procedure in exploratory data analysis that systematically identifies, characterizes, and evaluates anomalous observations or patterns within a dataset to distinguish them from normal behavior.

17

Publications

949

Citations

63

Authors

30

Institutions

Publications per year

2017–2026

8

Authors

63

  1. JD
    John Devlin

    La Trobe University, Australia

    2 publications · 33 citations

    2

    h-index

  2. AM
    Andrew Martchenko

    La Trobe University, Australia

    2 publications · 33 citations

    2

    h-index

  3. PH
    Phat Huynh

    La Trobe University, Australia

    2 publications · 33 citations

    2

    h-index

  4. RR
    Robert Ross

    La Trobe University, Australia

    2 publications · 33 citations

    2

    h-index

  5. SZ
    Shengxi Zhou

    Northwestern Polytechnical University, China

    1 publications · 24 citations

    1

    h-index

1–5 of 63

Venues

  1. IEEE Access

    2 publications · 72 citations

    2

    h-index

  2. AI Magazine

    1 publications · 14 citations

    1

    h-index

  3. 1

    h-index

  4. Remote Sensing

    1 publications · 25 citations

    1

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 103 citations

    1

    h-index

1–5