Rule-based Detection Systems

Rule-Based Detection Systems is a detection methodology that belongs to the class of rule-based systems and is distinguished by its reliance on predefined logical rules derived from domain knowledge to identify suspicious patterns indicative of fraudulent activities in Medicare claims or money laundering transactions.

56

Publications

3.1K

Citations

211

Authors

103

Institutions

Publications per year

2017–2026

20

Variants

Rule-based Detection System

Authors

211

  1. VB
    Vinayak Borkar

    University of California, Irvine, United States

    2 publications · 267 citations

    2

    h-index

  2. SS
    Sunita Sarawagi

    Indian Institute of Technology Bombay, India

    2 publications · 267 citations

    2

    h-index

  3. KD
    Kaustubh Deshmukh

    University of Washington, United States

    2 publications · 267 citations

    2

    h-index

  4. IS
    I. S. A. Pereira

    Universidade Federal do Espírito Santo, Brazil

    2 publications · 52 citations

    2

    h-index

  5. CT
    Claude Turner

    Bowie State University, United States

    1 publications · 24 citations

    1

    h-index

1–5 of 211

Venues

  1. Expert Systems with Applications

    3 publications · 224 citations

    3

    h-index

  2. IEEE Access

    2 publications · 128 citations

    2

    h-index

  3. Computers & Security

    2 publications · 87 citations

    2

    h-index

  4. IEEE Transactions on Fuzzy Systems

    TFS · 1 publications · 12 citations

    1

    h-index

  5. 1

    h-index

1–5