Explainable Detection Models
Explainable Detection Models is a detection framework in the domain of money laundering detection that belongs to the class of machine learning models designed for anomaly identification, distinguished by its intrinsic capacity to provide transparent, interpretable, and human-comprehensible justifications for its detection decisions.
56
Publications
5.3K
Citations
243
Authors
116
Institutions
Publications per year
2017–2026
50
Parents
56
Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
Daniel J. Arp, Michael Spreitzenbarth, Hugo Gascón et al. · 2014 · 2.3K citations
1–5 of 56
243
2
h-index
2
h-index
2
h-index
2
h-index
2
h-index
1–5 of 243
116
AGH University of Krakow
Krakow, Poland
2 h-index
7 publications
600 citations

University of Electronic Science and Technology of China
Chengdu, China
2 h-index
5 publications
141 citations

University of Southern Denmark
Odense, Denmark
2 h-index
8 publications
145 citations
Nanyang Technological University
Singapore, Singapore
2 h-index
3 publications
419 citations

Bydgoszcz University of Science and Technology
Bydgoszcz, Poland
2 h-index
7 publications
600 citations

King Saud University
Riyadh, Saudi Arabia
2 h-index
2 publications
221 citations
National Center for Biotechnology Information
Bethesda, United States
1 h-index
3 publications
39 citations
Cloud Computing Center
Dongguan, China
1 h-index
4 publications
320 citations
1–8 of 116
5 publications · 546 citations
4
h-index
Repository · 3 publications · 103 citations
3
h-index
3 publications · 155 citations
2
h-index
2 publications · 54 citations
2
h-index
2 publications · 412 citations
2
h-index
1–5