GDL

Journal from gdlhub / 2021-08-24 11:59:52
Oleh : Dewi Rahmawati, Riyanarto Sarno, King Saud University
Dibuat : 2021-08-24, dengan 0 file

Keyword : Anomaly, Control flow patterns, Fuzzy regression, Multiple linear regression
Url : http://www.sciencedirect.com/science/article/pii/S1319157818305822
Sumber pengambilan dokumen : Web

Deviations in port container handling can be detected by many factors. One of them is anomalies in the process model. Several studies have proposed anomaly detection methods. However, these methods do not accommodate verbal judgments of experts. These methods treat instances with low deviation as containing anomalies while in reality not all instances with low deviation contain anomalies. Considering this, a method was developed for detecting anomalies in port container handling using fuzzy regression in order to accommodate verbal expert judgments on the rate of anomaly (ROA). First, a control flow pattern is built to form an anomaly pattern that will be used for detecting wrong patterns. Then, five anomaly attributes were declared, i.e. skip sequence, wrong throughput time (max), wrong throughput time (min), wrong patterns and wrong decisions. In the experiment, the rate of anomaly was found using three methods, namely fuzzy regression (FR), support vector regression (SVR) with radial basis function (RBF) kernel, and multiple linear regression (MLR). The results showed that fuzzy regression was better at detecting anomalies than multiple linear regression and support vector regression. The experimental validation showed that fuzzy regression combined with control flow pattern was able to reduce false positives and false negatives. The sensitivity, specificity and accuracy of the proposed method were 96%, 97% and 99%, respectively.

Deskripsi Alternatif :

Deviations in port container handling can be detected by many factors. One of them is anomalies in the process model. Several studies have proposed anomaly detection methods. However, these methods do not accommodate verbal judgments of experts. These methods treat instances with low deviation as containing anomalies while in reality not all instances with low deviation contain anomalies. Considering this, a method was developed for detecting anomalies in port container handling using fuzzy regression in order to accommodate verbal expert judgments on the rate of anomaly (ROA). First, a control flow pattern is built to form an anomaly pattern that will be used for detecting wrong patterns. Then, five anomaly attributes were declared, i.e. skip sequence, wrong throughput time (max), wrong throughput time (min), wrong patterns and wrong decisions. In the experiment, the rate of anomaly was found using three methods, namely fuzzy regression (FR), support vector regression (SVR) with radial basis function (RBF) kernel, and multiple linear regression (MLR). The results showed that fuzzy regression was better at detecting anomalies than multiple linear regression and support vector regression. The experimental validation showed that fuzzy regression combined with control flow pattern was able to reduce false positives and false negatives. The sensitivity, specificity and accuracy of the proposed method were 96%, 97% and 99%, respectively.

Beri Komentar ?#(0) | Bookmark

Properti	Nilai Properti
ID Publisher	gdlhub
Organisasi	King Saud University
Nama Kontak	Herti Yani, S.Kom
Alamat	Jln. Jenderal Sudirman
Kota	Jambi
Daerah	Jambi
Negara	Indonesia
Telepon	0741-35095
Fax	0741-35093
E-mail Administrator	elibrarystikom@gmail.com
E-mail CKO	elibrarystikom@gmail.com