Nudibranch Suborders Classification based on Densely Connected Convolutional Networks
Nudibranchs, often called sea slugs, are a group of soft-bodied marine gastropod mollusks that shed their shells after their larval stage. With their body structure that is very similar between one suborder and another, sometimes it is hard to tell apart the suborder of a nudibranch. In this work,...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Andalas University
2024-03-01
|
| Series: | JITCE (Journal of Information Technology and Computer Engineering) |
| Subjects: | |
| Online Access: | http://10.250.30.20/index.php/JITCE/article/view/201 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823864564223049728 |
|---|---|
| author | Timothy Christyan Safitri Yuliana Utama Bagus Tri Yulianto Darmawan Faisal Dharma Adhinata |
| author_facet | Timothy Christyan Safitri Yuliana Utama Bagus Tri Yulianto Darmawan Faisal Dharma Adhinata |
| author_sort | Timothy Christyan |
| collection | DOAJ |
| description |
Nudibranchs, often called sea slugs, are a group of soft-bodied marine gastropod mollusks that shed their shells after their larval stage. With their body structure that is very similar between one suborder and another, sometimes it is hard to tell apart the suborder of a nudibranch. In this work, we make an Image Classification model for determining the suborder of a nudibranch using deep learning algorithms DenseNet and EfficientNet. The experiment is conducted using Google Colaboratory environment. For DenseNet, we use 121, 169, and 201 layers; meanwhile, we only use the baseline algorithm for EfficientNet. The dataset for research is randomly taken from marine fauna forums on the internet. DenseNet with 201 layers shows a better generalization than other classifiers (accuracy of DenseNet 121, 169, 201, and baseline EfficientNet, respectively 53%, 41%, 73%, and 47%). The research produces a decent system for classifying the suborder of the Nudibranch. Usage of image recognition or background blurring systems in future research can improve the system's accuracy.
|
| format | Article |
| id | doaj-art-5d69fcfb3a5e41b09092e25af274867c |
| institution | Kabale University |
| issn | 2599-1663 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Andalas University |
| record_format | Article |
| series | JITCE (Journal of Information Technology and Computer Engineering) |
| spelling | doaj-art-5d69fcfb3a5e41b09092e25af274867c2025-02-08T21:26:00ZengAndalas UniversityJITCE (Journal of Information Technology and Computer Engineering)2599-16632024-03-018110.25077/jitce.8.1.30-37.2024Nudibranch Suborders Classification based on Densely Connected Convolutional NetworksTimothy Christyan0Safitri Yuliana Utama1Bagus Tri Yulianto Darmawan2Faisal Dharma Adhinata3Universitas PertaminaUniversitas Negeri YogyakartaPoliteknik Negeri JakartaInstitut Teknologi Telkom Purwokerto Nudibranchs, often called sea slugs, are a group of soft-bodied marine gastropod mollusks that shed their shells after their larval stage. With their body structure that is very similar between one suborder and another, sometimes it is hard to tell apart the suborder of a nudibranch. In this work, we make an Image Classification model for determining the suborder of a nudibranch using deep learning algorithms DenseNet and EfficientNet. The experiment is conducted using Google Colaboratory environment. For DenseNet, we use 121, 169, and 201 layers; meanwhile, we only use the baseline algorithm for EfficientNet. The dataset for research is randomly taken from marine fauna forums on the internet. DenseNet with 201 layers shows a better generalization than other classifiers (accuracy of DenseNet 121, 169, 201, and baseline EfficientNet, respectively 53%, 41%, 73%, and 47%). The research produces a decent system for classifying the suborder of the Nudibranch. Usage of image recognition or background blurring systems in future research can improve the system's accuracy. http://10.250.30.20/index.php/JITCE/article/view/201Artificial IntelligenceImage ClassificationNudibranchDenseNetEfficientNet |
| spellingShingle | Timothy Christyan Safitri Yuliana Utama Bagus Tri Yulianto Darmawan Faisal Dharma Adhinata Nudibranch Suborders Classification based on Densely Connected Convolutional Networks JITCE (Journal of Information Technology and Computer Engineering) Artificial Intelligence Image Classification Nudibranch DenseNet EfficientNet |
| title | Nudibranch Suborders Classification based on Densely Connected Convolutional Networks |
| title_full | Nudibranch Suborders Classification based on Densely Connected Convolutional Networks |
| title_fullStr | Nudibranch Suborders Classification based on Densely Connected Convolutional Networks |
| title_full_unstemmed | Nudibranch Suborders Classification based on Densely Connected Convolutional Networks |
| title_short | Nudibranch Suborders Classification based on Densely Connected Convolutional Networks |
| title_sort | nudibranch suborders classification based on densely connected convolutional networks |
| topic | Artificial Intelligence Image Classification Nudibranch DenseNet EfficientNet |
| url | http://10.250.30.20/index.php/JITCE/article/view/201 |
| work_keys_str_mv | AT timothychristyan nudibranchsubordersclassificationbasedondenselyconnectedconvolutionalnetworks AT safitriyulianautama nudibranchsubordersclassificationbasedondenselyconnectedconvolutionalnetworks AT bagustriyuliantodarmawan nudibranchsubordersclassificationbasedondenselyconnectedconvolutionalnetworks AT faisaldharmaadhinata nudibranchsubordersclassificationbasedondenselyconnectedconvolutionalnetworks |