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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Utara Malaysia, UUM Press
2018
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/2898/ http://eprints.uthm.edu.my/2898/1/AJ%202019%20%2862%29.pdf |
| _version_ | 1848887871819218944 |
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| 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 |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T20:01:17Z |
| format | Article |
| id | uthm-2898 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:01:17Z |
| publishDate | 2018 |
| publisher | Universiti Utara Malaysia, UUM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-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 |
| 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 |
| title | 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_short | An improved artificial dendrite cell algorithm for abnormal signal detection |
| title_sort | improved artificial dendrite cell algorithm for abnormal signal detection |
| topic | QA71-90 Instruments and machines |
| url | http://eprints.uthm.edu.my/2898/ http://eprints.uthm.edu.my/2898/ http://eprints.uthm.edu.my/2898/1/AJ%202019%20%2862%29.pdf |