Texture-based of Naja Kaouthia snake recognition using K-Nearest Neighbour (KNN) / Nurul Hafeeza Aswani Aziz

Snake is one of the dangers animal and afraid by people. Conventionally, the method to recognize snake's species is done manually by collecting the data from the patients itself. However it is very hard to use these data as a reference as the information collected are uncertain due to incorrect...

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Main Author:	Aziz, Nurul Hafeeza Aswani
Format:	Thesis
Language:	English
Published:	2017
Online Access:	http://ir.uitm.edu.my/id/eprint/18258/ http://ir.uitm.edu.my/id/eprint/18258/2/TD_NURUL%20HAFEEZA%20ASWANI%20AZIZ%20CS%2017_5.pdf

id	uitm-18258
recordtype	eprints
spelling	uitm-182582019-02-28T02:41:59Z http://ir.uitm.edu.my/id/eprint/18258/ Texture-based of Naja Kaouthia snake recognition using K-Nearest Neighbour (KNN) / Nurul Hafeeza Aswani Aziz Aziz, Nurul Hafeeza Aswani Snake is one of the dangers animal and afraid by people. Conventionally, the method to recognize snake's species is done manually by collecting the data from the patients itself. However it is very hard to use these data as a reference as the information collected are uncertain due to incorrect impression of the snake's species. Thus this study proposed a prototype of recognition that specifically to recognise Naja Kaouthia species. There are three phases involved in this study which are data collection, processing (i.e extraction and recognition) and post-processing. A total of 20 images have been captured at Taman Rama dan Reptilia, Malacca and each images produced 10 data of extraction. In the processing phase, mean variance moving window was used for the extraction process. The part of snake that has been used for this study is the internasal. Therefore the region of interest method will only be focussed at this part of snake where the texture of the internasal is mean, standard deviation and magnitude. As for the recognition, The K-Nearest Neighbour had been used. The Naja Kaouthia using K-Nearest Neighbour algorithm is identified as a promising method in snake recognition which produced 100% accuracy rate for training data and 100% for testing data. 2017 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/18258/2/TD_NURUL%20HAFEEZA%20ASWANI%20AZIZ%20CS%2017_5.pdf Aziz, Nurul Hafeeza Aswani (2017) Texture-based of Naja Kaouthia snake recognition using K-Nearest Neighbour (KNN) / Nurul Hafeeza Aswani Aziz. Degree thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
description	Snake is one of the dangers animal and afraid by people. Conventionally, the method to recognize snake's species is done manually by collecting the data from the patients itself. However it is very hard to use these data as a reference as the information collected are uncertain due to incorrect impression of the snake's species. Thus this study proposed a prototype of recognition that specifically to recognise Naja Kaouthia species. There are three phases involved in this study which are data collection, processing (i.e extraction and recognition) and post-processing. A total of 20 images have been captured at Taman Rama dan Reptilia, Malacca and each images produced 10 data of extraction. In the processing phase, mean variance moving window was used for the extraction process. The part of snake that has been used for this study is the internasal. Therefore the region of interest method will only be focussed at this part of snake where the texture of the internasal is mean, standard deviation and magnitude. As for the recognition, The K-Nearest Neighbour had been used. The Naja Kaouthia using K-Nearest Neighbour algorithm is identified as a promising method in snake recognition which produced 100% accuracy rate for training data and 100% for testing data.
format	Thesis
author	Aziz, Nurul Hafeeza Aswani
spellingShingle	Aziz, Nurul Hafeeza Aswani Texture-based of Naja Kaouthia snake recognition using K-Nearest Neighbour (KNN) / Nurul Hafeeza Aswani Aziz
author_facet	Aziz, Nurul Hafeeza Aswani
author_sort	Aziz, Nurul Hafeeza Aswani
title	Texture-based of Naja Kaouthia snake recognition using K-Nearest Neighbour (KNN) / Nurul Hafeeza Aswani Aziz
title_short	Texture-based of Naja Kaouthia snake recognition using K-Nearest Neighbour (KNN) / Nurul Hafeeza Aswani Aziz
title_full	Texture-based of Naja Kaouthia snake recognition using K-Nearest Neighbour (KNN) / Nurul Hafeeza Aswani Aziz
title_fullStr	Texture-based of Naja Kaouthia snake recognition using K-Nearest Neighbour (KNN) / Nurul Hafeeza Aswani Aziz
title_full_unstemmed	Texture-based of Naja Kaouthia snake recognition using K-Nearest Neighbour (KNN) / Nurul Hafeeza Aswani Aziz
title_sort	texture-based of naja kaouthia snake recognition using k-nearest neighbour (knn) / nurul hafeeza aswani aziz
publishDate	2017
url	http://ir.uitm.edu.my/id/eprint/18258/ http://ir.uitm.edu.my/id/eprint/18258/2/TD_NURUL%20HAFEEZA%20ASWANI%20AZIZ%20CS%2017_5.pdf
first_indexed	2023-09-18T23:00:07Z
last_indexed	2023-09-18T23:00:07Z
_version_	1777418116558487552

Texture-based of Naja Kaouthia snake recognition using K-Nearest Neighbour (KNN) / Nurul Hafeeza Aswani Aziz

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