Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim

Identifying DNA sequences is very useful in forensic area. Currently, there are a lot of computational biology approaches (bioinformatics) in solving the molecular biology. The variation, complexity, and incompletely-understood nature of sequences make it impractical to hand-code algorithm by app...

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Main Author:	Abdul Rahim, Raifiza
Format:	Student Project
Published:	Faculty of Information Technology & Quantitative Sciences 2003
Online Access:	http://ir.uitm.edu.my/id/eprint/945/

id	uitm-945
recordtype	eprints
spelling	uitm-9452018-01-14T04:24:13Z http://ir.uitm.edu.my/id/eprint/945/ Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim Abdul Rahim, Raifiza Identifying DNA sequences is very useful in forensic area. Currently, there are a lot of computational biology approaches (bioinformatics) in solving the molecular biology. The variation, complexity, and incompletely-understood nature of sequences make it impractical to hand-code algorithm by applying the human ability and laboratory equipments in identifying the sequences. Artificial neural network (ANN), which is one of the commonly used machine learning technique, might be preferable to form its own descriptions of genetic concepts. Thus, it is applied in developing a prototype to identify the living organism whether it is human (Malay or India) or non-human. A multi-layer backpropagation algorithm of one hidden layer with 5 neurons was used. It is the constant representation whereby it produces one output. The training set was composed of 7 types of organisms from randomly selected DNA nucleotide sequences. The result of this prototype shows that it successfully can train the sequence of non-human. The reason it cannot train the human sequence probably because the way of massaging the data. By using the different sequences from the same types of organisms, the network successfully can identify. The training epoch and time can be accelerated if the network is included with the momentum. Faculty of Information Technology & Quantitative Sciences 2003 Student Project NonPeerReviewed Abdul Rahim, Raifiza (2003) Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim. [Student Project] (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
description	Identifying DNA sequences is very useful in forensic area. Currently, there are a lot of computational biology approaches (bioinformatics) in solving the molecular biology. The variation, complexity, and incompletely-understood nature of sequences make it impractical to hand-code algorithm by applying the human ability and laboratory equipments in identifying the sequences. Artificial neural network (ANN), which is one of the commonly used machine learning technique, might be preferable to form its own descriptions of genetic concepts. Thus, it is applied in developing a prototype to identify the living organism whether it is human (Malay or India) or non-human. A multi-layer backpropagation algorithm of one hidden layer with 5 neurons was used. It is the constant representation whereby it produces one output. The training set was composed of 7 types of organisms from randomly selected DNA nucleotide sequences. The result of this prototype shows that it successfully can train the sequence of non-human. The reason it cannot train the human sequence probably because the way of massaging the data. By using the different sequences from the same types of organisms, the network successfully can identify. The training epoch and time can be accelerated if the network is included with the momentum.
format	Student Project
author	Abdul Rahim, Raifiza
spellingShingle	Abdul Rahim, Raifiza Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim
author_facet	Abdul Rahim, Raifiza
author_sort	Abdul Rahim, Raifiza
title	Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim
title_short	Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim
title_full	Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim
title_fullStr	Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim
title_full_unstemmed	Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim
title_sort	identifying living organisms by using artificial neural networks approach / raifiza abdul rahim
publisher	Faculty of Information Technology & Quantitative Sciences
publishDate	2003
url	http://ir.uitm.edu.my/id/eprint/945/
first_indexed	2023-09-18T22:45:14Z
last_indexed	2023-09-18T22:45:14Z
_version_	1777417180511469568

Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim

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