The identification of oreochromis niloticus feeding behaviour through the integration of photoelectric sensor and logistic regression classifier
Oreochromis niloticus or tilapia is the second major freshwater aqua- culture bred after catfish in Malaysia. By understanding the feeding behaviour, fish farmers will able to identify the best feeding routine. In the present investi- gation, photoelectric sensors are used to identify the movement,...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Universiti Malaysia Pahang
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/24587/ http://umpir.ump.edu.my/id/eprint/24587/1/35.%20The%20identification%20of%20oreochromis%20niloticus%20feeding%20behaviour.pdf http://umpir.ump.edu.my/id/eprint/24587/2/35.1%20The%20identification%20of%20oreochromis%20niloticus%20feeding%20behaviour.pdf |
| _version_ | 1848822070607085568 |
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| author | Mohamad Radzi, Mohd Sojak Mohd Azraai, Mohd Razman Anwar, P. P. Abdul Majeed Rabiu Muazu, Musa Ahmad Shahrizan, Abdul Ghani Ismed, Iskandar |
| author_facet | Mohamad Radzi, Mohd Sojak Mohd Azraai, Mohd Razman Anwar, P. P. Abdul Majeed Rabiu Muazu, Musa Ahmad Shahrizan, Abdul Ghani Ismed, Iskandar |
| author_sort | Mohamad Radzi, Mohd Sojak |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Oreochromis niloticus or tilapia is the second major freshwater aqua- culture bred after catfish in Malaysia. By understanding the feeding behaviour, fish farmers will able to identify the best feeding routine. In the present investi- gation, photoelectric sensors are used to identify the movement, speed and posi- tion of the fish. The signals acquired from the sensors are converted into binary data. The hunger behaviour classes are determined through k-means clustering algorithm, i.e., satiated and unsatiated. The Logistic Regression (LR) classifier was employed to classify the aforesaid hunger state. The model was trained by means of 5-fold cross-validation technique. It was shown that the LR model is able to yield a classification accuracy for tested data during the day at three dif- ferent time windows (4 hours each) is 100%, 88.7% and 100%, respectively, whilst the for-night data it was shown to demonstrate 100% classification accu- racy. |
| first_indexed | 2025-11-15T02:35:24Z |
| format | Conference or Workshop Item |
| id | ump-24587 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:35:24Z |
| publishDate | 2018 |
| publisher | Universiti Malaysia Pahang |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-245872019-03-27T06:58:31Z http://umpir.ump.edu.my/id/eprint/24587/ The identification of oreochromis niloticus feeding behaviour through the integration of photoelectric sensor and logistic regression classifier Mohamad Radzi, Mohd Sojak Mohd Azraai, Mohd Razman Anwar, P. P. Abdul Majeed Rabiu Muazu, Musa Ahmad Shahrizan, Abdul Ghani Ismed, Iskandar TS Manufactures Oreochromis niloticus or tilapia is the second major freshwater aqua- culture bred after catfish in Malaysia. By understanding the feeding behaviour, fish farmers will able to identify the best feeding routine. In the present investi- gation, photoelectric sensors are used to identify the movement, speed and posi- tion of the fish. The signals acquired from the sensors are converted into binary data. The hunger behaviour classes are determined through k-means clustering algorithm, i.e., satiated and unsatiated. The Logistic Regression (LR) classifier was employed to classify the aforesaid hunger state. The model was trained by means of 5-fold cross-validation technique. It was shown that the LR model is able to yield a classification accuracy for tested data during the day at three dif- ferent time windows (4 hours each) is 100%, 88.7% and 100%, respectively, whilst the for-night data it was shown to demonstrate 100% classification accu- racy. Universiti Malaysia Pahang 2018-12 Conference or Workshop Item NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24587/1/35.%20The%20identification%20of%20oreochromis%20niloticus%20feeding%20behaviour.pdf pdf en http://umpir.ump.edu.my/id/eprint/24587/2/35.1%20The%20identification%20of%20oreochromis%20niloticus%20feeding%20behaviour.pdf Mohamad Radzi, Mohd Sojak and Mohd Azraai, Mohd Razman and Anwar, P. P. Abdul Majeed and Rabiu Muazu, Musa and Ahmad Shahrizan, Abdul Ghani and Ismed, Iskandar (2018) The identification of oreochromis niloticus feeding behaviour through the integration of photoelectric sensor and logistic regression classifier. In: The 6th International Conference On Robotics Intelligence And Applications 2018 , 15-19 Disember 2018 , Putrajaya, Malaysia. pp. 1-7.. (Unpublished) (Unpublished) |
| spellingShingle | TS Manufactures Mohamad Radzi, Mohd Sojak Mohd Azraai, Mohd Razman Anwar, P. P. Abdul Majeed Rabiu Muazu, Musa Ahmad Shahrizan, Abdul Ghani Ismed, Iskandar The identification of oreochromis niloticus feeding behaviour through the integration of photoelectric sensor and logistic regression classifier |
| title | The identification of oreochromis niloticus feeding behaviour through the integration of photoelectric sensor and logistic regression classifier |
| title_full | The identification of oreochromis niloticus feeding behaviour through the integration of photoelectric sensor and logistic regression classifier |
| title_fullStr | The identification of oreochromis niloticus feeding behaviour through the integration of photoelectric sensor and logistic regression classifier |
| title_full_unstemmed | The identification of oreochromis niloticus feeding behaviour through the integration of photoelectric sensor and logistic regression classifier |
| title_short | The identification of oreochromis niloticus feeding behaviour through the integration of photoelectric sensor and logistic regression classifier |
| title_sort | identification of oreochromis niloticus feeding behaviour through the integration of photoelectric sensor and logistic regression classifier |
| topic | TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/24587/ http://umpir.ump.edu.my/id/eprint/24587/1/35.%20The%20identification%20of%20oreochromis%20niloticus%20feeding%20behaviour.pdf http://umpir.ump.edu.my/id/eprint/24587/2/35.1%20The%20identification%20of%20oreochromis%20niloticus%20feeding%20behaviour.pdf |