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 overfeeding) may lead the death of the fish and consequently inhibits the quantity of the fish produced. Moreover, the exces...

Full description

Bibliographic Details
Main Authors: Zahari, Taha, M. A. M., Razman, Ahmad Shahrizan, Abdul Ghani, A. P. P., Abdul Majeed, R. M., Musa, F. A., Adnan, M. F., Sallehudin, Y., Mukai
Format: Conference or Workshop Item
Language:English
Published: IOP Publishing Ltd 2018
Subjects:
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/21439/
http://umpir.ump.edu.my/id/eprint/21439/
http://umpir.ump.edu.my/id/eprint/21439/7/The%20classification%20of%20hunger%20behaviour%20of%20Lates%20Calcarifer.pdf
id ump-21439
recordtype eprints
spelling ump-214392019-12-09T04:18:33Z http://umpir.ump.edu.my/id/eprint/21439/ The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm Zahari, Taha M. A. M., Razman Ahmad Shahrizan, Abdul Ghani A. P. P., Abdul Majeed R. M., Musa F. A., Adnan M. F., Sallehudin Y., Mukai TS Manufactures 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 overfeeding) 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. IOP Publishing Ltd 2018-04 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/21439/7/The%20classification%20of%20hunger%20behaviour%20of%20Lates%20Calcarifer.pdf Zahari, Taha and M. A. M., Razman and Ahmad Shahrizan, Abdul Ghani and A. P. P., Abdul Majeed and R. M., Musa and F. A., Adnan and M. F., Sallehudin and Y., Mukai (2018) The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm. In: IOP Conference Series: Materials Science and Engineering, International Conference on Innovative Technology, Engineering and Sciences 2018 (iCITES 2018), 1-2 March 2018 , Universiti Malaysia Pahang (UMP) Pekan Campus Library, Malaysia. pp. 1-5., 342 (012017). ISSN 1757-8981 https://doi.org/10.1088/1757-899X/342/1/012017
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TS Manufactures
spellingShingle TS Manufactures
Zahari, Taha
M. A. M., Razman
Ahmad Shahrizan, Abdul Ghani
A. P. P., Abdul Majeed
R. M., Musa
F. A., Adnan
M. F., Sallehudin
Y., Mukai
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 overfeeding) 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 Zahari, Taha
M. A. M., Razman
Ahmad Shahrizan, Abdul Ghani
A. P. P., Abdul Majeed
R. M., Musa
F. A., Adnan
M. F., Sallehudin
Y., Mukai
author_facet Zahari, Taha
M. A. M., Razman
Ahmad Shahrizan, Abdul Ghani
A. P. P., Abdul Majeed
R. M., Musa
F. A., Adnan
M. F., Sallehudin
Y., Mukai
author_sort Zahari, Taha
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 IOP Publishing Ltd
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/21439/
http://umpir.ump.edu.my/id/eprint/21439/
http://umpir.ump.edu.my/id/eprint/21439/7/The%20classification%20of%20hunger%20behaviour%20of%20Lates%20Calcarifer.pdf
first_indexed 2023-09-18T22:31:27Z
last_indexed 2023-09-18T22:31:27Z
_version_ 1777416313547784192

Similar Items