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008			201014t2 2 m a|||fram|| 000 0 eng d
020			_aTHE0008908(Local) _qhardback
040			_aUMP _beng _cUMP _erda
090			_aFTKPM .A97 2019 r Thesis
100	0		_aMohd Azraai Mohd Razman, _eauthor.
245	1	0	_aHunger behaviour classification of lates calcarifer using machine learning for automatic demand feeder through image processing / _cMohd Azraai Mohd Razman
264		1	_aKuantan, Pahang : _bUMP, _c2019
264		4	_c© 2019
300			_axv, 140 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-ROM
336			_atext _2rdacontent
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aFaculty of Manufacturing and Mechatronic Engineering Technology
502			_aThesis (Doctor of Philosophy in Manufacturing and Mechatronic Engineering Technology) -- Universiti Malaysia Pahang – 2019
504			_aIncludes bibliographical references
520	3		_aThe understanding and identification of fish hunger behaviour are non-trivial in the aquaculture industry. This thesis aims at classifying the hunger state of Lates Calcarifer via the integration of computer vision and machine learning. Prior to the classification of the hunger states, the hunger state of the fish is identified through the k-means clustering technique and it was established that the hunger state could be demarcated into either ‘Hungry’ or ‘Satiated’. Upon the identification of the hunger state, significant features that could contribute towards the accurate classification of the states are investigated. The aforesaid features are analysed by the box plot analysis and the Principal Component Analysis (PCA). The established features are COG x, COG y and the moving summation of the pixel. Different machine learning models were investigated by incorporating the identified features, i.e., Discriminant Analysis (DA), Support Vector Machine (SVM) and k-Nearest Neighbours (k-NN) and it was demonstrated that the SVM trained model is able to classify up to 99.00%, suggesting that the developed system is viable for fish farming. A supplementary analysis was further carried out to understand the circadian rhythm of the fish by evaluating the time-series features. Different window sizes ranging from 0.5 min, 1.0 min, 1.5 min and 2.0 min coupled with the mean, maximum, minimum and variance for each of the distinctive temporal window sizes are investigated. PCA and PCA varimax rotation was employed in order to identify the best features through classifying it via SVM and k-NN. It was shown that the mean and variance of all temporal sizes are significant. In addition, the efficacy of different models based on the identified secondary features, namely DA, SVM, k-NN, Decision Tree (Tree), Logistic Regression (LR), Random Forest Tree (RF) and Neural Network (NN) are evaluated. It was found that the k-NN yielded the highest classification accuracy with 96.47% from the test sets. In order to further refine the k-NN model developed, hyperparameter optimization by means of Bayesian Optimization was carried out. Through the optimization process, the best hyperparameters that could attain a classification accuracy of 97.16% are the Standardized Euclidean distance metric with a k value of one.
610	2	0	_aFaculty of Manufacturing and Mechatronic Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses
942			_2lcc _cTHESIS