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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| Format: | Student Project |
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Faculty of Information Technology & Quantitative Sciences
2003
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| Online Access: | https://ir.uitm.edu.my/id/eprint/945/ |
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| _version_ | 1833057994391158784 |
|---|---|
| author | Abdul Rahim, Raifiza |
| author_facet | Abdul Rahim, Raifiza |
| author_sort | Abdul Rahim, Raifiza |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.uitm.ir-945 |
| institution | Universiti Teknologi Mara |
| publishDate | 2003 |
| publisher | Faculty of Information Technology & Quantitative Sciences |
| record_format | eprints |
| spelling | my.uitm.ir-9452018-01-14T04:24:13Z https://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 Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim. (2003) [Student Project] (Unpublished) |
| spellingShingle | Abdul Rahim, Raifiza Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim |
| title | 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_short | 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 |
| url | https://ir.uitm.edu.my/id/eprint/945/ |
| url_provider | http://ir.uitm.edu.my/ |