Identification of vessel anomaly behavior using support vector machines and Bayesian networks

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...

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Bibliographic Details
Main Authors: Dwi Handayani, Dini Oktarina, Sediono, Wahju, Shah, Asadullah
Format: Conference or Workshop Item
Language:English
English
English
Published: 2014
Subjects:
TK7885 Computer engineering
Online Access: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
id iium-38408
recordtype eprints
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 Conference or Workshop Item 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/
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
English
topic TK7885 Computer engineering
spellingShingle TK7885 Computer engineering
Dwi Handayani, Dini Oktarina
Sediono, Wahju
Shah, Asadullah
Identification of vessel anomaly behavior using support vector machines and Bayesian networks
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.
format Conference or Workshop Item
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
title 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_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_sort identification of vessel anomaly behavior using support vector machines and bayesian networks
publishDate 2014
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
first_indexed 2023-09-18T20:55:11Z
last_indexed 2023-09-18T20:55:11Z
_version_ 1777410256736878592

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