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All Journal Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Noor Azah Samsudin
Universiti Tun Hussein Onn Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Multi-label classification approach for quranic verses labeling Abdullahi Adeleke; Noor Azah Samsudin; Mohd Hisyam Abdul Rahim; Shamsul Kamal Ahmad Khalid; Riswan Efendi
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp484-490

Abstract

Machine learning involves the task of training systems to be able to make decisions without being explicitly programmed. Important among machine learning tasks is classification involving the process of training machines to make predictions from predefined labels. Classification is broadly categorized into three distinct groups: single-label (SL), multi-class, and multi-label (ML) classification. This research work presents an application of a multi-label classification (MLC) technique in automating Quranic verses labeling. MLC has been gaining attention in recent years. This is due to the increasing amount of works based on real-world classification problems of multi-label data. In traditional classification problems, patterns are associated with a single-label from a set of disjoint labels. However, in MLC, an instance of data is associated with a set of labels. In this paper, three standard MLC methods: binary relevance (BR), classifier chain (CC), and label powerset (LP) algorithms are implemented with four baseline classifiers: support vector machine (SVM), naïve Bayes (NB), k-nearest neighbors (k-NN), and J48. The research methodology adopts the multi-label problem transformation (PT) approach. The results are validated using six conventional performance metrics. These include: hamming loss, accuracy, one error, micro-F1, macro-F1, and avg. precision. From the results, the classifiers effectively achieved above 70% accuracy mark. Overall, SVM achieved the best results with CC and LP algorithms.
A survey on local binary pattern and gabor filter as texture descriptors of smart profiling systems Shihab Hamad Khaleefah; Salama A. Mostafa; Aida Mustapha; Noor Azah Samsudin; Mohammad Faidzul Nasrudin; Abdullah Baz
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1379-1387

Abstract

With the dramatic expansion of image information nowadays, image processing and computer visions are playing a significant role in terms of several applications such as image classification, image segmentation, pattern recognition, and image retrieval. One of the important features that have been used in many image applications is texture. The texture is the characteristic of a set of pixels that formed the image. Therefore, analyzing such texture would have a significant impact on segmenting the image or detecting important portions of such image. This paper aims to overview the feature extraction and description techniques depicted in the literature to characterize regions for images. In particular, two of popular descriptors will be examined including local binary pattern (LBP) and gabor filter. The key characteristic behind such investigation lies in how the features of an image would contribute toward the process of recognition and image classification. In this regard, an extensive discussion is provided on both LBP and Gabor descriptors along with the efforts that have been intended to combine them. The reason behind investigating these descriptors is that they are considered the most common local and global descriptors used in the literature. The overall aim of this survey is to show current trends on using, modifying and adapting these descriptors in the domain of image processing.
Co-Authors Abdullah Baz Abdullahi Adeleke Adeleke Abdullahi Aida Mustapha Anatun Nadrah Rosman Azizan Ismail Fawwaz Zamir Mansor Mohamad Izzuddin Rahman Mohammad Faidzul Nasrudin Mohd Hisyam Abdul Rahim Mohd Rasidi Ibrahim Muhammad Syariff Aripin Nor Amirul Amri Nordin Nurul Shafiqah Che Dan Rd. Rohmat Saedudin Riswan Efendi Roshidi Din Salama A. Mostafa Shamsul Kamal Ahmad Khalid Shihab Hamad Khaleefah Zainuri Saringat Zulaiha Ali Othman