03253ntm a2200277 a 4500 vtls000099681 KUKTEM 20251117113332.0 170502t2017 my da f am 000 0 eng d THE0005176(Local) 201905141025 hanafiah 201705161011 nazri 201705151703 nazri 201705151554 nazri 201705021052 nazri UMP FKEE .F38 2016 r Thesis Fathimah Abdul Halim Classification of ammonia from water based on odor-profile using K-NN and CBR / Fathimah Abdul Halim Kuantan, Pahang : UMP, 2016 xiii, 115 p. : ill. (some col.) ; 30 cm. + 1 CD-ROM Faculty of Electrical and Electronics Engineering Thesis (Doctor of Philosophy (Instrumentation Engineering)) -- Universiti Malaysia Pahang – 2016 Bibliography : p. 92-101 This 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%. Faculty of Electrical and Electronics Engineering Dissertations Universities and Colleges Dissertations Theses 0 0 lcc 0 1 20000 20000 2019-09-04 0 FKEE .F38 2016 r Thesis 0000117774 2019-09-04 00:00:00 1 2019-09-04 THESIS 0 0 lcc 0 1 20000 20000 2019-09-04 0 CD 10745 | FKEE .F38 2016 r Thesis 0000117775 2019-09-04 00:00:00 1 2019-09-04 THESIS VIRTUA40 6931 6937 VTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5000*5020*5040*5200*6100*6500*6501*9992