ACADSTAFF UGM
| Title | : | Determining Optimal Architecture of CNN using Genetic Algorithm for Vehicle Classification System |
| Author | : |
Wahyono, Ph.D. (1) Joko Hariyono (2) |
| Date | : | 31 2019 |
| Keyword | : | convolutional neural network (CNN),CNN architecture,evolutionary computing,genetic algorithm,classification system,vehicle type classification convolutional neural network (CNN),CNN architecture,evolutionary computing,genetic algorithm,classification system,vehicle type classification |
| Abstract | : | Convolutional neural network is a machine learning that provides a good accuracy for many problems in the field of computer vision, such as segmentation, detection, recognition, as well as classification systems. However, the results and performance of the system are affected by the CNN architecture. In this paper, we propose the utilization of evolutionary computation using genetic algorithm to determine the optimal architecture for CNN with transfer learning strategy from parent network. Furthermore, the optimal CNN produced is used as a model for the case of the vehicle type classification system. To evaluate the effectiveness of the utilization of evolutionary computing to CNN, the experiment will be conducted using vehicle classification datasets. |
| Group of Knowledge | : | Ilmu Komputer |
| Original Language | : | English |
| Level | : | Nasional |
| Status | : |
Published
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| No | Title | Action |
|---|---|---|
| 1 |
2019-IJCCS-Full Dokumen.pdf
Document Type : [PAK] Full Dokumen
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| 2 |
2019-IJCCS-Bukti Korespondensi.pdf
Document Type : [PAK] Bukti Korespondensi Penulis
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| 3 |
Indonesian Journal of Computing and Cybernetics Systems.pdf
Document Type : Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian)
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| 4 |
PEER REVIEW_wahyono 11.pdf
Document Type : [PAK] Peer Review
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| 5 |
PEER REVIEW_wahyono R11.pdf
Document Type : [PAK] Peer Review
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