Cover Image

An improved artificial dendrite cell algorithm for abnormal signal detection

In dendrite cell algorithm (DCA), the abnormality of a data point is determined by comparing the multi-context antigen value (MCAV) with anomaly threshold. The limitation of the existing threshold is that the value needs to be determined before mining based on previous information and the existing M...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohamad Mohsin, Mohamad Farhan, Abu Bakar, Azuraliza, Hamdan, Abdul Razak, Abdul Wahab, Mohd Helmy
Format: Article
Language:English
Published: Universiti Utara Malaysia, UUM Press 2018
Subjects:
QA71-90 Instruments and machines
Online Access:http://eprints.uthm.edu.my/2898/1/AJ%202019%20%2862%29.pdf
http://eprints.uthm.edu.my/2898/
http://www.jict.uum.edu.my/index.php/previous-issues/153-vol17no12018#A3
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uthm.eprints.2898
record_format eprints
spelling my.uthm.eprints.28982021-11-16T04:01:43Z http://eprints.uthm.edu.my/2898/ An improved artificial dendrite cell algorithm for abnormal signal detection Mohamad Mohsin, Mohamad Farhan Abu Bakar, Azuraliza Hamdan, Abdul Razak Abdul Wahab, Mohd Helmy QA71-90 Instruments and machines In dendrite cell algorithm (DCA), the abnormality of a data point is determined by comparing the multi-context antigen value (MCAV) with anomaly threshold. The limitation of the existing threshold is that the value needs to be determined before mining based on previous information and the existing MCAV is inefficient when exposed to extreme values. This causes the DCA fails to detect new data points if the pattern has distinct behavior from previous information and affects detection accuracy. This paper proposed an improved anomaly threshold solution for DCA using the statistical cumulative sum (CUSUM) with the aim to improve its detection capability. In the proposed approach, the MCAV were normalized with upper CUSUM and the new anomaly threshold was calculated during run time by considering the acceptance value and min MCAV. From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy. Universiti Utara Malaysia, UUM Press 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/2898/1/AJ%202019%20%2862%29.pdf Mohamad Mohsin, Mohamad Farhan and Abu Bakar, Azuraliza and Hamdan, Abdul Razak and Abdul Wahab, Mohd Helmy (2018) An improved artificial dendrite cell algorithm for abnormal signal detection. Journal of Information and Communication Technology (JICT), 17 (1). pp. 33-54. ISSN 1675-414X http://www.jict.uum.edu.my/index.php/previous-issues/153-vol17no12018#A3
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic QA71-90 Instruments and machines
spellingShingle QA71-90 Instruments and machines
Mohamad Mohsin, Mohamad Farhan
Abu Bakar, Azuraliza
Hamdan, Abdul Razak
Abdul Wahab, Mohd Helmy
An improved artificial dendrite cell algorithm for abnormal signal detection
description In dendrite cell algorithm (DCA), the abnormality of a data point is determined by comparing the multi-context antigen value (MCAV) with anomaly threshold. The limitation of the existing threshold is that the value needs to be determined before mining based on previous information and the existing MCAV is inefficient when exposed to extreme values. This causes the DCA fails to detect new data points if the pattern has distinct behavior from previous information and affects detection accuracy. This paper proposed an improved anomaly threshold solution for DCA using the statistical cumulative sum (CUSUM) with the aim to improve its detection capability. In the proposed approach, the MCAV were normalized with upper CUSUM and the new anomaly threshold was calculated during run time by considering the acceptance value and min MCAV. From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy.
format Article
author Mohamad Mohsin, Mohamad Farhan
Abu Bakar, Azuraliza
Hamdan, Abdul Razak
Abdul Wahab, Mohd Helmy
author_facet Mohamad Mohsin, Mohamad Farhan
Abu Bakar, Azuraliza
Hamdan, Abdul Razak
Abdul Wahab, Mohd Helmy
author_sort Mohamad Mohsin, Mohamad Farhan
title An improved artificial dendrite cell algorithm for abnormal signal detection
title_short An improved artificial dendrite cell algorithm for abnormal signal detection
title_full An improved artificial dendrite cell algorithm for abnormal signal detection
title_fullStr An improved artificial dendrite cell algorithm for abnormal signal detection
title_full_unstemmed An improved artificial dendrite cell algorithm for abnormal signal detection
title_sort improved artificial dendrite cell algorithm for abnormal signal detection
publisher Universiti Utara Malaysia, UUM Press
publishDate 2018
url http://eprints.uthm.edu.my/2898/1/AJ%202019%20%2862%29.pdf
http://eprints.uthm.edu.my/2898/
http://www.jict.uum.edu.my/index.php/previous-issues/153-vol17no12018#A3
_version_ 1738581054350950400
score 13.211869

Similar Items