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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Abdul Rahim, Siti Khatijah Nor
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Computer Science & IT Engineering

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Semi-supervised spectral clustering using shared nearest neighbor for data with different shape and density YouSheng, Gao; Abdul Rahim, Siti Khatijah Nor; Hamzah, Raseeda; Ang, Li; Aminuddin, Raihah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2283-2290

Abstract

In the absence of supervisory information in spectral clustering algorithms, it is difficult to construct suitable similarity graphs for data with complex shapes and varying densities. To address this issue, this paper proposes a semisupervised spectral clustering algorithm based on shared nearest neighbor (SNN). The proposed algorithm combines the idea of semi-supervised clustering, adding SNN to the calculation of the distance matrix, and using pairwise constraint information to find the relationship between two data points, while providing a portion of supervised information. Comparative experiments were conducted on artificial data sets and University of California Irvine machine learning repository datasets. The experimental results show that the proposed algorithm achieves better clustering results compared to traditional K-means and spectral clustering algorithms.
Investing in Malaysian healthcare using technique for order preference by similarity to ideal solution Azhar, Farah Waheeda; Rahim, Zati Halwani Abd; Fahami, Norasyikin Abdullah; Abdul Rahim, Siti Khatijah Nor; Karim, Hilwana Abd
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1723-1730

Abstract

The purpose of this research is to assess the financial performance of Malaysian Healthcare companies using the multi-criteria and decision-making method, namely technique for order preference by similarity to ideal solution (TOPSIS). The financial data of 20 companies in 2019 are retrieved from Datastream. For many years, ratios of financial aspects have been employed to analyse the companies’ financial performance. However, some studies indicate that the traditional ratio analysis is insufficient to measure a firm's financial performance. Thus, this paper employs the technique for order preference by similarity to ideal solution, or simply TOPSIS, to gain a more comprehensive result. The TOPSIS approach involves seven steps, utilizing significant ratios in financial aspect such as debt ratio, debt to equity ratio, current ratio, return on equity (ROE), acid-test ratio, earnings per share (EPS), and return on asset (ROA), as the criteria to evaluate the companies' financial performances. The result of this study ranks 20 healthcare companies in Malaysia and makes recommendations for investment-worthy companies to the investors, allowing the maximization of investment benefits. The results from this research are crucial for investors, companies, market participants, public and private policymakers to enhance their investment decision-making.
DualVitOA: A dual vision transformer-based model for osteoarthritis grading using x-ray images Ruiyun, Qiu; Abdul Rahim, Siti Khatijah Nor; Jamil, Nursuriati; Hamzah, Raseeda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp925-932

Abstract

Knee osteoarthritis (OA) is a primary factor contributing to reduced activity and physical impairment in older individuals. Early identification and treatment of knee OA can assist patients in delaying the advancement of the condition. Currently, knee OA is detected early using X-ray images and assessed based on the Kellgren-Lawrence (KL) grading system. Doctors' assessments are subjective and can vary among different doctors. The automatic knee OA grading and diagnosis can assist doctors and help doctors reduce their workload. A new novel network called dual-vision transformer (ViT) OA is proposed to automatically diagnose knee OA. The network utilizes pre-processing technologies to process the data before doing classification operations using the Dual-ViT network. The suggested network outperformed neural networks like ResNet, DenseNet, visual geometry group (VGG), inception, and ViT in terms of accuracy and mean absolute error (MAE), and achieved an accuracy of 78.4 and MAE of 0.471, demonstrating its effectiveness.
Co-Authors Aminuddin, Raihah Azhar, Farah Waheeda Fahami, Norasyikin Abdullah Hamzah, Raseeda Jamil, Nursuriati Karim, Hilwana Abd Li Ang, Li Rahim, Zati Halwani Abd Ruiyun, Qiu YouSheng, Gao