Arabic language script and encoding identification with support vector machines and rough set theory
Arabic is ranking sixth among the world’s spoken languages with more than 230 million speakers around the Arabic world. There are different flavors and dialects of Arabic; the most common one is the Egyptian Arabic which has the largest number of users (more than 50 millions). Although, only a sma...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2007
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/6795/ http://eprints.utm.my/6795/1/MohamedOuldMohamedSidyaMFSKSM2007.pdf |
| _version_ | 1848891346883969024 |
|---|---|
| author | Mohamed Sidya, Mohamed Ould |
| author_facet | Mohamed Sidya, Mohamed Ould |
| author_sort | Mohamed Sidya, Mohamed Ould |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | Arabic is ranking sixth among the world’s spoken languages with more than 230 million speakers around the Arabic world. There are different flavors and dialects of Arabic; the most common one is the Egyptian Arabic which has the largest number of users (more than 50 millions). Although, only a small number Arabic speakers use the internet, still it constitutes a considerable share to the internet community. Unfortunately, so far, there has been no research to automatically distinguish between the Arabic language and the other languages that use the same script. This project deals with identifying the Arabic language from the Persian language; both languages are written in the Arabic script. The data for this project has been collected from the internet, the BBC website in particular. Many operations have been applied to this data, including stop word removal and stemming. This project is established to compare the performance of Support Vector Machines with Rough Set Theory in Identifying the Arabic language. The results show that both methods perform well but the Support Vector Machines outperform the Rough Set Theory. |
| first_indexed | 2025-11-15T20:56:31Z |
| format | Thesis |
| id | utm-6795 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:56:31Z |
| publishDate | 2007 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-67952018-08-03T08:49:15Z http://eprints.utm.my/6795/ Arabic language script and encoding identification with support vector machines and rough set theory Mohamed Sidya, Mohamed Ould QA75 Electronic computers. Computer science Arabic is ranking sixth among the world’s spoken languages with more than 230 million speakers around the Arabic world. There are different flavors and dialects of Arabic; the most common one is the Egyptian Arabic which has the largest number of users (more than 50 millions). Although, only a small number Arabic speakers use the internet, still it constitutes a considerable share to the internet community. Unfortunately, so far, there has been no research to automatically distinguish between the Arabic language and the other languages that use the same script. This project deals with identifying the Arabic language from the Persian language; both languages are written in the Arabic script. The data for this project has been collected from the internet, the BBC website in particular. Many operations have been applied to this data, including stop word removal and stemming. This project is established to compare the performance of Support Vector Machines with Rough Set Theory in Identifying the Arabic language. The results show that both methods perform well but the Support Vector Machines outperform the Rough Set Theory. 2007-11 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/6795/1/MohamedOuldMohamedSidyaMFSKSM2007.pdf Mohamed Sidya, Mohamed Ould (2007) Arabic language script and encoding identification with support vector machines and rough set theory. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:62506 |
| spellingShingle | QA75 Electronic computers. Computer science Mohamed Sidya, Mohamed Ould Arabic language script and encoding identification with support vector machines and rough set theory |
| title | Arabic language script and encoding identification with support vector machines and rough set theory |
| title_full | Arabic language script and encoding identification with support vector machines and rough set theory |
| title_fullStr | Arabic language script and encoding identification with support vector machines and rough set theory |
| title_full_unstemmed | Arabic language script and encoding identification with support vector machines and rough set theory |
| title_short | Arabic language script and encoding identification with support vector machines and rough set theory |
| title_sort | arabic language script and encoding identification with support vector machines and rough set theory |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/6795/ http://eprints.utm.my/6795/ http://eprints.utm.my/6795/1/MohamedOuldMohamedSidyaMFSKSM2007.pdf |