Identification of vessel anomaly behavior using support vector machines and Bayesian networks
In this work, a model based on Support Vector Machines (SVMs) classification to identify vessel anomaly behavior have been proposed and implemented, and the result is compared to Bayesian Networks (BNs). The works have been done using the real world Automated Identification System (AIS) ve...
| Main Authors: | , , |
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| Format: | Proceeding Paper |
| Language: | English English English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/38408/ http://irep.iium.edu.my/38408/1/p.1080.ICCCE.2014.pdf http://irep.iium.edu.my/38408/4/Sessions.pdf http://irep.iium.edu.my/38408/7/38408_Identification%20of%20vessel%20anomaly%20behavior_Scopus.pdf |
| _version_ | 1848781601135132672 |
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| author | Dwi Handayani, Dini Oktarina Sediono, Wahju Shah, Asadullah |
| author_facet | Dwi Handayani, Dini Oktarina Sediono, Wahju Shah, Asadullah |
| author_sort | Dwi Handayani, Dini Oktarina |
| building | IIUM Repository |
| collection | Online Access |
| description | In this work, a model based on Support Vector
Machines (SVMs) classification to identify vessel anomaly
behavior have been proposed and implemented, and the result is
compared to Bayesian Networks (BNs). The works have been
done using the real world Automated Identification System (AIS)
vesselreporting data. SVMs can achieve higher accuracy
compared to BNs in both memory-test and blind-test. The effect
of holdout method which is partitioned size of training and
testing data set on the accuracy result were also investigated in
this study. The proposed classifier demonstrated to be a viable
tool for identifying the vessel anomaly behavior by its high
accuracy. |
| first_indexed | 2025-11-14T15:52:09Z |
| format | Proceeding Paper |
| id | iium-38408 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-14T15:52:09Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-384082017-09-23T03:01:07Z http://irep.iium.edu.my/38408/ Identification of vessel anomaly behavior using support vector machines and Bayesian networks Dwi Handayani, Dini Oktarina Sediono, Wahju Shah, Asadullah TK7885 Computer engineering In this work, a model based on Support Vector Machines (SVMs) classification to identify vessel anomaly behavior have been proposed and implemented, and the result is compared to Bayesian Networks (BNs). The works have been done using the real world Automated Identification System (AIS) vesselreporting data. SVMs can achieve higher accuracy compared to BNs in both memory-test and blind-test. The effect of holdout method which is partitioned size of training and testing data set on the accuracy result were also investigated in this study. The proposed classifier demonstrated to be a viable tool for identifying the vessel anomaly behavior by its high accuracy. 2014-09 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/38408/1/p.1080.ICCCE.2014.pdf application/pdf en http://irep.iium.edu.my/38408/4/Sessions.pdf application/pdf en http://irep.iium.edu.my/38408/7/38408_Identification%20of%20vessel%20anomaly%20behavior_Scopus.pdf Dwi Handayani, Dini Oktarina and Sediono, Wahju and Shah, Asadullah (2014) Identification of vessel anomaly behavior using support vector machines and Bayesian networks. In: International Conference on Computer and Communication Engineering (ICCCE 2014), 23-25 Sep 2014, Kuala Lumpur. http://www.iium.edu.my/iccce/14/ |
| spellingShingle | TK7885 Computer engineering Dwi Handayani, Dini Oktarina Sediono, Wahju Shah, Asadullah Identification of vessel anomaly behavior using support vector machines and Bayesian networks |
| title | Identification of vessel anomaly behavior using support vector machines and Bayesian networks |
| title_full | Identification of vessel anomaly behavior using support vector machines and Bayesian networks |
| title_fullStr | Identification of vessel anomaly behavior using support vector machines and Bayesian networks |
| title_full_unstemmed | Identification of vessel anomaly behavior using support vector machines and Bayesian networks |
| title_short | Identification of vessel anomaly behavior using support vector machines and Bayesian networks |
| title_sort | identification of vessel anomaly behavior using support vector machines and bayesian networks |
| topic | TK7885 Computer engineering |
| url | http://irep.iium.edu.my/38408/ http://irep.iium.edu.my/38408/ http://irep.iium.edu.my/38408/1/p.1080.ICCCE.2014.pdf http://irep.iium.edu.my/38408/4/Sessions.pdf http://irep.iium.edu.my/38408/7/38408_Identification%20of%20vessel%20anomaly%20behavior_Scopus.pdf |