The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm

The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm

Fish Hunger behaviour is essential in determining the fish feeding routine, particularly for fish farmers. The inability to provide accurate feeding routines (under-feeding or over-feeding) may lead the death of the fish and consequently inhibits the quantity of the fish produced. Moreover, the exce...

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Main Authors: Taha, Zahari, Razman, Mohd Azraan, Abdul Ghani, Ahmad Shahrizan, P.P. Abdul Majeed, Anwar, Musa, Rabiu Muazu, A. Adnan, Fatihah, Sallehudin, Muhammad Firdaus, Mukai, Yukinori
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Physics Publishing 2018
Subjects:
QA Mathematics
Online Access:http://irep.iium.edu.my/65175/
http://irep.iium.edu.my/65175/
http://irep.iium.edu.my/65175/
http://irep.iium.edu.my/65175/1/65175_The%20classification%20of%20hunger%20behaviour%20of%20Lates%20Calcarifer.pdf
http://irep.iium.edu.my/65175/2/65175_The%20classification%20of%20hunger%20behaviour%20of%20Lates%20Calcarifer_SCOPUS.pdf
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spelling iium-651752018-08-17T10:18:45Z http://irep.iium.edu.my/65175/ The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm Taha, Zahari Razman, Mohd Azraan Abdul Ghani, Ahmad Shahrizan P.P. Abdul Majeed, Anwar Musa, Rabiu Muazu A. Adnan, Fatihah Sallehudin, Muhammad Firdaus Mukai, Yukinori QA Mathematics Fish Hunger behaviour is essential in determining the fish feeding routine, particularly for fish farmers. The inability to provide accurate feeding routines (under-feeding or over-feeding) may lead the death of the fish and consequently inhibits the quantity of the fish produced. Moreover, the excessive food that is not consumed by the fish will be dissolved in the water and accordingly reduce the water quality through the reduction of oxygen quantity. This problem also leads the death of the fish or even spur fish diseases. In the present study, a correlation of Barramundi fish-school behaviour with hunger condition through the hybrid data integration of image processing technique is established. The behaviour is clustered with respect to the position of the school size as well as the school density of the fish before feeding, during feeding and after feeding. The clustered fish behaviour is then classified through k-Nearest Neighbour (k-NN) learning algorithm. Three different variations of the algorithm namely cosine, cubic and weighted are assessed on its ability to classify the aforementioned fish hunger behaviour. It was found from the study that the weighted k-NN variation provides the best classification with an accuracy of 86.5%. Therefore, it could be concluded that the proposed integration technique may assist fish farmers in ascertaining fish feeding routine. Institute of Physics Publishing 2018-04-06 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/65175/1/65175_The%20classification%20of%20hunger%20behaviour%20of%20Lates%20Calcarifer.pdf application/pdf en http://irep.iium.edu.my/65175/2/65175_The%20classification%20of%20hunger%20behaviour%20of%20Lates%20Calcarifer_SCOPUS.pdf Taha, Zahari and Razman, Mohd Azraan and Abdul Ghani, Ahmad Shahrizan and P.P. Abdul Majeed, Anwar and Musa, Rabiu Muazu and A. Adnan, Fatihah and Sallehudin, Muhammad Firdaus and Mukai, Yukinori (2018) The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm. In: International Conference on Innovative Technology, Engineering and Sciences 2018, iCITES 2018, 1st-2nd March 2018, Pekan, Pahang. http://iopscience.iop.org/article/10.1088/1757-899X/342/1/012017 10.1088/1757-899X/342/1/012017
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
topic QA Mathematics
spellingShingle QA Mathematics
Taha, Zahari
Razman, Mohd Azraan
Abdul Ghani, Ahmad Shahrizan
P.P. Abdul Majeed, Anwar
Musa, Rabiu Muazu
A. Adnan, Fatihah
Sallehudin, Muhammad Firdaus
Mukai, Yukinori
The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm
description Fish Hunger behaviour is essential in determining the fish feeding routine, particularly for fish farmers. The inability to provide accurate feeding routines (under-feeding or over-feeding) may lead the death of the fish and consequently inhibits the quantity of the fish produced. Moreover, the excessive food that is not consumed by the fish will be dissolved in the water and accordingly reduce the water quality through the reduction of oxygen quantity. This problem also leads the death of the fish or even spur fish diseases. In the present study, a correlation of Barramundi fish-school behaviour with hunger condition through the hybrid data integration of image processing technique is established. The behaviour is clustered with respect to the position of the school size as well as the school density of the fish before feeding, during feeding and after feeding. The clustered fish behaviour is then classified through k-Nearest Neighbour (k-NN) learning algorithm. Three different variations of the algorithm namely cosine, cubic and weighted are assessed on its ability to classify the aforementioned fish hunger behaviour. It was found from the study that the weighted k-NN variation provides the best classification with an accuracy of 86.5%. Therefore, it could be concluded that the proposed integration technique may assist fish farmers in ascertaining fish feeding routine.
format Conference or Workshop Item
author Taha, Zahari
Razman, Mohd Azraan
Abdul Ghani, Ahmad Shahrizan
P.P. Abdul Majeed, Anwar
Musa, Rabiu Muazu
A. Adnan, Fatihah
Sallehudin, Muhammad Firdaus
Mukai, Yukinori
author_facet Taha, Zahari
Razman, Mohd Azraan
Abdul Ghani, Ahmad Shahrizan
P.P. Abdul Majeed, Anwar
Musa, Rabiu Muazu
A. Adnan, Fatihah
Sallehudin, Muhammad Firdaus
Mukai, Yukinori
author_sort Taha, Zahari
title The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm
title_short The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm
title_full The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm
title_fullStr The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm
title_full_unstemmed The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm
title_sort classification of hunger behaviour of lates calcarifer through the integration of image processing technique and k-nearest neighbour learning algorithm
publisher Institute of Physics Publishing
publishDate 2018
url http://irep.iium.edu.my/65175/
http://irep.iium.edu.my/65175/
http://irep.iium.edu.my/65175/
http://irep.iium.edu.my/65175/1/65175_The%20classification%20of%20hunger%20behaviour%20of%20Lates%20Calcarifer.pdf
http://irep.iium.edu.my/65175/2/65175_The%20classification%20of%20hunger%20behaviour%20of%20Lates%20Calcarifer_SCOPUS.pdf
first_indexed 2023-09-18T21:32:28Z
last_indexed 2023-09-18T21:32:28Z
_version_ 1777412602143440896

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