Classification of DNA sequences using deep convolutional neural network
Also available in printed version
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| Format: | Bachelor thesis |
| Language: | English |
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Universiti Teknologi Malaysia
2025
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| Online Access: | https://utmik.utm.my/handle/123456789/54219 |
| Abstract | Abstract here |
| _version_ | 1854975094494855168 |
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| author | Nurul Amerah Kassim |
| author2 | Afnizanfaizal Abdullah, supervisor |
| author_facet | Afnizanfaizal Abdullah, supervisor Nurul Amerah Kassim |
| author_sort | Nurul Amerah Kassim |
| description | Also available in printed version |
| format | Bachelor thesis |
| id | utm-123456789-54219 |
| institution | Universiti Teknologi Malaysia |
| language | English |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| record_pdf | Abstract |
| spelling | utm-123456789-542192025-08-21T08:13:23Z Classification of DNA sequences using deep convolutional neural network Nurul Amerah Kassim Afnizanfaizal Abdullah, supervisor Computing Also available in printed version Extraction of meaningful information from the Deoxyribonucleic Acid (DNA) is a key elements in bioinformatics research and DNA sequence classification has a wide range of presentations such as genomic analysis, and biomedical data analysis. Nowadays, deep learning approach has become an attention to many researcher. This models contains multiple of non-linear transforming layers which practice to represent a data at successively high-level abstractions. With many hidden layer, this innovative model are expected to be able to elucidate any complex problems. Thus, Convolutional Neural Network approach is proposed to classify the whole genomic sequences of an organisms. As the purpose of this research is to evaluate the performance of the proposed model by implementing convolutional neural network approach, the research framework is focused to identify genetic marker for liver cancer from Hepatitis B Virus DNA sequences using deep learning principle. The results show that convolutional neural network have more than 90 percent accuracy in training the data sets. Moreover, this research also analysed different size of sequence length to observe the performance of the proposed model. The overall outcome in this research achieve within the expected results fahmimoksen UTM 92 p. Project Paper (Sarjana Muda Sains Komputer (Bioinformatik)) - Universiti Teknologi Malaysia, 2017 2025-03-17T02:33:05Z 2025-03-17T02:33:05Z 2017 Bachelor thesis https://utmik.utm.my/handle/123456789/54219 vital:109937 valet-20180503-085030 ENG Closed Access UTM Complete Unpublished Completion application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Computing Nurul Amerah Kassim Classification of DNA sequences using deep convolutional neural network |
| thesis_level | Other |
| title | Classification of DNA sequences using deep convolutional neural network |
| title_full | Classification of DNA sequences using deep convolutional neural network |
| title_fullStr | Classification of DNA sequences using deep convolutional neural network |
| title_full_unstemmed | Classification of DNA sequences using deep convolutional neural network |
| title_short | Classification of DNA sequences using deep convolutional neural network |
| title_sort | classification of dna sequences using deep convolutional neural network |
| topic | Computing |
| url | https://utmik.utm.my/handle/123456789/54219 |
| work_keys_str_mv | AT nurulamerahkassim classificationofdnasequencesusingdeepconvolutionalneuralnetwork |