Training of Convolutional Neural Network using Transfer Learning for Aedes Aegypti Larvae
| Main Authors: | Mohd Fuad, Mohamad Aqil; Universiti Teknikal Malaysia Melaka, Ab Ghani, Mohd Ruddin; Universiti Teknikal Malaysia Melaka, Ghazali, Rozaimi; Universiti Teknikal Malaysia Melaka, Izzuddin, Tarmizi Ahmad; Universiti Teknikal Malaysia Melaka, Sulaima, Mohamad Fani; Universiti Teknikal Malaysia Melaka, Jano, Zanariah; Universiti Teknikal Malaysia Melaka, Sutikno, Tole; Universitas Ahmad Dahlan |
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| Other Authors: | Universiti Teknikal Malaysia Melaka, Ministry of Higher Education Malaysia |
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Ahmad Dahlan
, 2018
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| Subjects: | |
| Online Access: |
http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/8744 http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/8744/5466 |
Daftar Isi:
- The flavivirus epidemiology has reached an alarming rate which haunts the world population including Malaysia. World Health Organization has proposed and practised various methods of vector control through environmental management, chemical and biological orientations. However, from the listed control vectors, the most crucial part to be heeded are non-accessible places like water storage and artificial container. The objective of the study was to acquire and compare various accuracies and cross-entropy errors of the training sets within different learning rates in water storage tank environment which was essential for detection. This experiment performed transfer learning where Inception-V3 was implemented. About 534 images were trained to classify between Aedes Aegypti larvae and float valve within 3 different learning rates. For training accuracy and validation accuracy, learning rates were 0.1; 99.98%, 99.90% and 0.01; 99.91%, 99.77% and 0.001; 99.10%, 99.93%. Cross-entropy errors for training and validation for 0.1 were 0.0021, 0.0184 whereas for 0.01 were 0.0091, 0.0121 and 0.001; 0.0513, 0.0330. Various accuracies and cross-entropy errors of the training sets within the different learning rates were successfully acquired and compared.