Automated recognition of Ficus deltoidea using ant colony optimization technique
Improving in the fields of soft computing and artificial intelligence, the branch study of automated herb recognition among plenty of weeds has become challenging issue due to their applications in medicine, food and industry. This paper presents innovative method to improve the accuracy of classi...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/27373/1/ID%2027373.pdf |
| _version_ | 1825947114755063808 |
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| author | Ishak, Asnor Juraiza Che Soh, Azura Marhaban, Mohammad Hamiruce Khamis, Shamsul Ghasab, Mohammad Ali Jan |
| author_facet | Ishak, Asnor Juraiza Che Soh, Azura Marhaban, Mohammad Hamiruce Khamis, Shamsul Ghasab, Mohammad Ali Jan |
| author_sort | Ishak, Asnor Juraiza |
| collection | UPM |
| description | Improving in the fields of soft computing and artificial
intelligence, the branch study of automated herb recognition
among plenty of weeds has become challenging issue due to their applications in medicine, food and industry. This paper presents innovative method to improve the accuracy of classification as well the efficiency, such that irrelevant features that make computational complexity are ignored by feature subset selection that is proposed by means of ant colony optimization algorithm (ACO). At first, through image processing specified features are extracted from the Ficus deltoidea leaves such as vein, morphology and texture features and they construct a search space to be chosen for the optimal subset features that is selected by ACO algorithm as support vector machine (SVM) classify them. The experimental results have shown that the proposed method not only optimize feature subset, but also has a remarkable positive impact on accuracy. |
| first_indexed | 2024-03-06T08:08:15Z |
| format | Conference or Workshop Item |
| id | upm.eprints-27373 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:08:15Z |
| publishDate | 2013 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-273732019-04-19T03:13:17Z http://psasir.upm.edu.my/id/eprint/27373/ Automated recognition of Ficus deltoidea using ant colony optimization technique Ishak, Asnor Juraiza Che Soh, Azura Marhaban, Mohammad Hamiruce Khamis, Shamsul Ghasab, Mohammad Ali Jan Improving in the fields of soft computing and artificial intelligence, the branch study of automated herb recognition among plenty of weeds has become challenging issue due to their applications in medicine, food and industry. This paper presents innovative method to improve the accuracy of classification as well the efficiency, such that irrelevant features that make computational complexity are ignored by feature subset selection that is proposed by means of ant colony optimization algorithm (ACO). At first, through image processing specified features are extracted from the Ficus deltoidea leaves such as vein, morphology and texture features and they construct a search space to be chosen for the optimal subset features that is selected by ACO algorithm as support vector machine (SVM) classify them. The experimental results have shown that the proposed method not only optimize feature subset, but also has a remarkable positive impact on accuracy. IEEE 2013 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/27373/1/ID%2027373.pdf Ishak, Asnor Juraiza and Che Soh, Azura and Marhaban, Mohammad Hamiruce and Khamis, Shamsul and Ghasab, Mohammad Ali Jan (2013) Automated recognition of Ficus deltoidea using ant colony optimization technique. In: 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA), 19-21 June 2013, Melbourne, Australia. (pp. 296-300). 10.1109/ICIEA.2013.6566383 |
| spellingShingle | Ishak, Asnor Juraiza Che Soh, Azura Marhaban, Mohammad Hamiruce Khamis, Shamsul Ghasab, Mohammad Ali Jan Automated recognition of Ficus deltoidea using ant colony optimization technique |
| title | Automated recognition of Ficus deltoidea using ant colony optimization technique |
| title_full | Automated recognition of Ficus deltoidea using ant colony optimization technique |
| title_fullStr | Automated recognition of Ficus deltoidea using ant colony optimization technique |
| title_full_unstemmed | Automated recognition of Ficus deltoidea using ant colony optimization technique |
| title_short | Automated recognition of Ficus deltoidea using ant colony optimization technique |
| title_sort | automated recognition of ficus deltoidea using ant colony optimization technique |
| url | http://psasir.upm.edu.my/id/eprint/27373/1/ID%2027373.pdf |
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