Please use this identifier to cite or link to this item:
https://ptsldigital.ukm.my/jspui/handle/123456789/457923Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Soltane Mohamed | |
| dc.date.accessioned | 2023-09-12T09:15:38Z | - |
| dc.date.available | 2023-09-12T09:15:38Z | - |
| dc.date.issued | 2005 | |
| dc.identifier.other | ukmvital:1696 | |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/457923 | - |
| dc.language.iso | eng | |
| dc.publisher | UKM, Bangi | |
| dc.relation | Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina | |
| dc.rights | UKM | |
| dc.subject | Neural networks (Computer science) | |
| dc.title | Artificial Neural Networks (ANN) approach to PPG signal classification | |
| dc.type | theses | |
| dc.identifier.callno | QA76.87.S657 2005 | |
| Appears in Collections: | Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_1696+ABSTRACT+ABSTRACT.0.PDF Restricted Access | 1.26 MB | Adobe PDF |  View/Open | |
| ukmvital_1696+Source+Source.0.PDF Restricted Access | 7.59 MB | Adobe PDF |  View/Open |
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