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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Mohamad Aqil Mohd Fuad
Universiti Teknikal Malaysia Melaka
Computer Science & IT

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Training of Convolutional Neural Network using Transfer Learning for Aedes Aegypti Larvae Mohamad Aqil Mohd Fuad; Mohd Ruddin Ab Ghani; Rozaimi Ghazali; Tarmizi Ahmad Izzuddin; Mohamad Fani Sulaima; Zanariah Jano; Tole Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.8744

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.
A Review on Methods of Identifying and Counting Aedes Aegypti Larvae using Image Segmentation Technique Mohamad Aqil Mohd Fuad; Mohd Ruddin Ab Ghani; Rozaimi Ghazali; Mohamad Fani Sulaima; Mohd Hafiz Jali; Tole Sutikno; Tarmizi Ahmad Izzuddin; Zanariah Jano
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i3.6422

Abstract

Aedes aegypti mosquitoes are a small slender fly insect that spreads the arbovirus from flavivirus vector through its sucking blood. An early detection of this species is very important because once these species turn into adult mosquitoes a population control becomes more complicated. Things become worse when difficult access places like water storage tank becomes one of the breeding favorite places for Aedes aegypti mosquitoes. Therefore, there is a need to help the field operator during the routine inspection for an automated identification and detection of Aedes aegypti larvae, especially at difficult access places. This paper reviews different methodologies that have been used by various researchers in identifying and counting Aedes aegypti. The objective of the review was to analyze the techniques and methods in identifying and counting the Aedes Aegypti larvae of various fields of study from 2008 and above by taking account their performance and accuracy. From the review, thresholding method was the most widely used with high accuracy in image segmentation followed by hidden Markov model, histogram correction and morphology operation region growing.
Co-Authors Mohamad Fani Sulaima Mohd Hafiz Jali Mohd Ruddin Ab Ghani Rozaimi Ghazali Tarmizi Ahmad Izzuddin Tole Sutikno Zanariah Jano