Perfecting a Video Game with Game Metrics
Vol 16, No 4: August 2018

Training of Convolutional Neural Network using Transfer Learning for Aedes Aegypti Larvae

Mohamad Aqil Mohd Fuad (Universiti Teknikal Malaysia Melaka)
Mohd Ruddin Ab Ghani (Universiti Teknikal Malaysia Melaka)
Rozaimi Ghazali (Universiti Teknikal Malaysia Melaka)
Tarmizi Ahmad Izzuddin (Universiti Teknikal Malaysia Melaka)
Mohamad Fani Sulaima (Universiti Teknikal Malaysia Melaka)
Zanariah Jano (Universiti Teknikal Malaysia Melaka)
Tole Sutikno (Universitas Ahmad Dahlan)



Article Info

Publish Date
01 Aug 2018

Abstract

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.

Copyrights © 2018






Journal Info

Abbrev

TELKOMNIKA

Publisher

Subject

Computer Science & IT

Description

Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of ...