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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Intelligent Informatics
Salahuddin, Lizawati
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Evaluation of texture feature based on basic local binary pattern for wood defect classification Ibrahim, Eihab Abdelkariem Bashir; Hashim, Ummi Raba'ah; Salahuddin, Lizawati; Ismail, Nor Haslinda; Choon, Ngo Hea; Kanchymalay, Kasturi; Zabri, Siti Normi
International Journal of Advances in Intelligent Informatics Vol 7, No 1 (2021): March 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i1.393

Abstract

Wood defects detection has been studied a lot recently to detect the defects on the wood surface and assist the manufacturers in having a clear wood to be used to produce a high-quality product. Therefore, the defects on the wood affect and reduce the quality of wood. This research proposes an effective feature extraction technique called the local binary pattern (LBP) with a common classifier called Support Vector Machine (SVM). Our goal is to classify the natural defects on the wood surface. First, preprocessing was applied to convert the RGB images into grayscale images. Then, the research applied the LBP feature extraction technique with eight neighbors (P=8) and several radius (R) values. After that, we apply the SVM classifier for the classification and measure the proposed technique's performance. The experimental result shows that the average accuracy achieved is 65% on the balanced dataset with P=8 and R=1. It indicates that the proposed technique works moderately well to classify wood defects. This study will consequently contribute to the overall wood defect detection framework, which generally benefits the automated inspection of the wood defects.
Socio-technical factors influencing big data analytics adoption in healthcare Wolseley, Nik Nurdini; Salahuddin, Lizawati; Mohd Aboobaider, Burhanuddin; Raja Ikram, Raja Rina; Hashim, Ummi Rabaah; Abdul Rahim, Fiza
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4745-4758

Abstract

The purpose of this study is to determine the key socio-technical factors influencing big data analytics adoption in healthcare services. A systematic literature review was conducted using peer-reviewed scholarly publications spanning from 2013 to 2023 to illuminate the influencing factors. Twelve papers focused on the factors influencing big data analytics (BDA) adoption in healthcare services were included for review. The factors were divided into four major groups namely i) person, ii) technology, iii) organization, and iv) environment. Analytical skills define a person, whereas technology is characterized by system quality and information quality. Organization support, organization resources, training, data governance, and evidence-based decision-making are all associated with the organization. Finally, government regulations are allocated to the environment. This review presents evidence of the socio-technical factors that influence big data analytics adoption in healthcare services. The findings from this review recommend future big data analytics adoption in healthcare services to carefully evaluate the factors identified in this study.
A review of recent deep learning applications in wood surface defect identification Ali, Martina; Hashim, Ummi Raba’ah; Kanchymalay, Kasturi; Wibawa, Aji Prasetya; Salahuddin, Lizawati; Rahiddin, Rahillda Nadhirah Norizzaty
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1696-1707

Abstract

Wood is widely used in construction, art, and home applications due to its aesthetic appeal and favorable mechanical properties. However, environmental factors significantly affect the growth and preservation of wood, often leading to defects that can reduce its performance and ornamental value. Researchers have introduced machine vision and deep learning methods to address the challenges of high labor costs and inefficiencies in identifying wood defects. Deep learning has shown great success in image recognition tasks, yielding impressive results. This paper reviews previous work on deep-learning strategies for identifying wood surface defects. It also discusses data augmentation techniques to address limited defect data and explores transfer learning to enhance classification accuracy on small datasets. Finally, the paper examines the potential limitations of deep learning for defect identification and suggests future research directions.
Co-Authors Abdul Rahim, Fiza Aji Prasetya Wibawa Ali, Martina Choon, Ngo Hea Hashim, Ummi Raba'ah Hashim, Ummi Raba’ah Ibrahim, Eihab Abdelkariem Bashir Ismail, Nor Haslinda Kasturi Kanchymalay, Kasturi Mohd Aboobaider, Burhanuddin Rahiddin, Rahillda Nadhirah Norizzaty Raja Ikram, Raja Rina Ummi Rabaah Hashim, Ummi Rabaah Wolseley, Nik Nurdini Zabri, Siti Normi