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All Journal International Journal of Electrical and Computer Engineering
Rosmayati Mohemad
Universiti Malaysia Terengganu
Computer Science & IT Electrical & Electronics 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.
Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents Rosmayati Mohemad; Nazratul Naziah Mohd Muhait; Noor Maizura Mohamad Noor; Zulaiha Ali Othman
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5014-5026

Abstract

Few studies on text clustering for the Malay language have been conducted due to some limitations that need to be addressed. The purpose of this article is to compare the two clustering algorithms of k-means and k-medoids using Euclidean distance similarity to determine which method is the best for clustering documents. Both algorithms are applied to 1000 documents pertaining to housebreaking crimes involving a variety of different modus operandi. Comparability results indicate that the k-means algorithm performed the best at clustering the relevant documents, with a 78% accuracy rate. K-means clustering also achieves the best performance for cluster evaluation when comparing the average within-cluster distance to the k-medoids algorithm. However, k-medoids perform exceptionally well on the Davis Bouldin index (DBI). Furthermore, the accuracy of k-means is dependent on the number of initial clusters, where the appropriate cluster number can be determined using the elbow method.
Co-Authors Arifah Che Alhadi Aziz Deraman Masita Masila Abdul Jalil Nazratul Naziah Mohd Muhait Noor Maizura Mohamad Noor Nur Fadila Akma Mamat Wan Nural Jawahir Wan Yussof Zulaiha Ali Othman