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Arifah Che Alhadi
Universiti Malaysia Terengganu
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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The development of an ontology model for early identification of children with specific learning disabilities Rosmayati Mohemad; Nur Fadila Akma Mamat; Noor Maizura Mohamad Noor; Arifah Che Alhadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (741.278 KB) | DOI: 10.11591/ijece.v9i6.pp5486-5494

Abstract

Ontology-based knowledge representation is explored in special education environment as not much attention has been given to the area of specific learning disabilities such as dyslexia, dysgraphia and dyscalculia. Therefore, this paper aims to capture the knowledge in special education domain, represent the knowledge using ontology-based approach and make it efficient for early identification of children who might have specific learning disabilities. In this paper, the step-by-step development process of the ontology is presented by following the five phases of ontological engineering approach, which consists of specification, conceptualization, formalization, implementation, and maintenance. The details of the ontological model’s content and structure is built and the applicability of the ontology for early identification and recommendation is demonstrated.
A computational analysis of short sentences based on ensemble similarity model Arifah Che Alhadi; Aziz Deraman; Masita Masila Abdul Jalil; Wan Nural Jawahir Wan Yussof; Rosmayati Mohemad
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (268.997 KB) | DOI: 10.11591/ijece.v9i6.pp5386-5394

Abstract

The rapid development of Internet along with the wide use of social media applications produce huge volume of unstructured data in short text form such as tweets, text snippets and instant messages. This form of data rarely contains repeated word. It presents challenge in sentences similarity analysis as the standard text similarity models merely rely on the number of word occurrence, often resulting unreliable similarity value. Besides, the use of abbreviation, acronyms, slang, smiley, jargon, symbol or non-standard short form also contributes to the difficulty in similarity analysis. Thus, an extended ensemble similarity model approach is proposed. An experimental study has been conducted using datasets of English short sentences. The findings are very encouraging in improving the similarity value for short sentences.
Co-Authors Aziz Deraman Masita Masila Abdul Jalil Noor Maizura Mohamad Noor Nur Fadila Akma Mamat Rosmayati Mohemad Wan Nural Jawahir Wan Yussof