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 (Unpublished)
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Abstract
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.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Uncontrolled Keywords: | Photoelectric sensor; Logistic Regression; Oreochromis niloticus; fish hunger behaviour |
| Subjects: | T Technology > TS Manufactures |
| Faculty/Division: | Faculty of Manufacturing Engineering |
| Depositing User: | Pn. Hazlinda Abd Rahman |
| Date Deposited: | 27 Mar 2019 06:58 |
| Last Modified: | 27 Mar 2019 06:58 |
| URI: | http://umpir.ump.edu.my/id/eprint/24587 |
| Download Statistic: | View Download Statistics |
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