The week's pick
Reseach Article
Unsupervised Hybrid Algorithm to Detect Anomalies for Predicting Terrorists Attacks
by
Francesco Curia
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 35 |
| Year of Publication: 2020 |
| Authors: Francesco Curia |
10.5120/ijca2020920432
|
Francesco Curia . Unsupervised Hybrid Algorithm to Detect Anomalies for Predicting Terrorists Attacks. International Journal of Computer Applications. 176, 35 ( Jul 2020), 1-8. DOI=10.5120/ijca2020920432
@article{
10.5120/ijca2020920432,
author = {
Francesco Curia
},
title = { Unsupervised Hybrid Algorithm to Detect Anomalies for Predicting Terrorists Attacks },
journal = {
International Journal of Computer Applications
},
issue_date = { Jul 2020 },
volume = { 176 },
number = { 35 },
month = { Jul },
year = { 2020 },
issn = { 0975-8887 },
pages = {
1-8
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume176/number35/31424-2020920432/
},
doi = { 10.5120/ijca2020920432 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:44:13.374852+05:30
%A Francesco Curia
%T Unsupervised Hybrid Algorithm to Detect Anomalies for Predicting Terrorists Attacks
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 35
%P 1-8
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This work presents a hybrid approach for unsupervised algorithms (UHA), in order to extract information and patterns from data concerning terrorist attacks. The reference data are those of the Global Terrorism Database. The work presents an approach based on autoencoders and k-modes type clustering. The results obtained are examined through some metrics presented in the article and it is also considered methodologically how to determine a robust threshold for anomaly detection problems.
References
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Index Terms
Computer Science
Information Sciences
Keywords