000			03253ntm a2200277 a 4500
001			vtls000099681
003			KUKTEM
005			20251117113332.0
008			170502t2017 my da f am 000 0 eng d
020			_aTHE0005176(Local)
039		9	_a201905141025 _bhanafiah _c201705161011 _dnazri _c201705151703 _dnazri _c201705151554 _dnazri _y201705021052 _znazri
040			_aUMP
090			_aFKEE .F38 2016 r Thesis
100	0		_aFathimah Abdul Halim
245	1	0	_aClassification of ammonia from water based on odor-profile using K-NN and CBR / _cFathimah Abdul Halim
260			_aKuantan, Pahang : _bUMP, _c2016
300			_axiii, 115 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM
500			_aFaculty of Electrical and Electronics Engineering
502			_aThesis (Doctor of Philosophy (Instrumentation Engineering)) -- Universiti Malaysia Pahang – 2016
504			_aBibliography : p. 92-101
520	3		_aThis project presents the classification of ammonia concentration in water. High concentration of ammonia in the water possess negative effect toward the environment, especially marine’s life. Thus, monitoring and supervising the concentration of ammonia is crucial in order to maintain water quality. Classifying ammonia concentration from the water using electronic nose (E-nose) based on classification technique and signal processing approach is one of the popular method. E-nose consists of four gas sensors where the sensor array are used to produce a unique profile of an odor. The aim of this project is to establish classification method based on odor-profile of ammonia using E-nose. Two groups of ammonia concentration; high (20, 25 ppm) and low (5, 10, 15 ppm) have been measured by using E-nose. Before the ammonia concentration were measured by E-nose, the samples were validated using volatile organic compound (VOC) detector MiniRAE 3000. The confirmed concentrations from VOC detector then were proceeded using E-nose. The data from the E-nose measurements have been pre-processed and normalized using normalization technique in order to obtain ammonia 2D and 3D odor-profile pattern. From the odor-profile pattern, the mean feature was extracted. The extracted feature has been statistically validated using the box plot, proximity matrix and regression analysis. The significant selected normalization and mean features were applied as an input features to k-nearest neighbor (k-NN) and case-based reasoning (CBR) techniques in order to classify the features either as a low or high concentration. The classification results of the k-NN and CBR were evaluated based on performance measures evaluation methods. It was found that the k-NN classifier and the CBR classification techniques were able to classify the different concentration of ammonia from the water. K-NN was used as validation classifier system for CBR. Based on the k-NN and CBR performance measures results, it has been observed that the classification rate of k-NN and CBR are 100%.
610	2	0	_aFaculty of Electrical and Electronics Engineering _xDissertations
650		0	_aUniversities and Colleges _xDissertations
650		0	_aTheses
999			_aVIRTUA40 _c6931 _d6937
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5000*5020*5040*5200*6100*6500*6501*9992