Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization
Ab. Aziz, Nor Azlina
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Transfer learning for improved electrocardiogram diagnosis of cardiac disease: exploring the potential of pre-trained models Sayed Ismail, Sharifah Noor Masidayu; Abdul Razak, Siti Fatimah; Ab. Aziz, Nor Azlina
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7053

Abstract

Predicting the onset of cardiovascular disease (CVD) has been a hot topic for researchers for years, and recently, the concept of transfer learning has been gaining traction in this field. Transfer learning (TL) is a process that involves transferring information gained from one task or domain to another related task or domain. This paper comprehensively reviews recent advancements in pre-trained TL models for CVD, focusing on electrocardiogram (ECG) signals. Forty-three articles were chosen from Scopus and Google Scholar sources and reviewed, focusing on the type of CVD detected, the database used, the ECG input format, and the pre-training model used for transfer learning. The results show that more than 80% of the studies utilize 2-dimensional (2D) ECG input from the two most utilized available ECG datasets: MIT-BIH arrhythmia (ARR) and MIT-BIH normal sinus rhythm. alexnet, visual geometry group (VGG), and residual network (ResNet) are among the pre-trained TL models with the highest number used among reviewed articles. Additionally, the development of pre-trained TL models over time has made it possible to detect CVD with ECG signals. It can also address limited data problems, promote the development of more dependable and resilient detection systems, and aid medical professionals in diagnosing CVD and other diseases.
Datasets for Artificial Intelligence-based Spine Analysis: A Scoping Review Zaim Ahmad, Muhammad Shahrul; Ab. Aziz, Nor Azlina; Siong, Lim Heng; Ghazali, Anith Khairunnisa
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3042

Abstract

The advancement of artificial intelligence (AI) and intense learning is key to automating the diagnosis and inspection of spinal-related pathologies. This automation reduces the need for human manual analysis. Reducing the burden on the healthcare system and the risk of human error. Spine medical images have several modalities, such as X-rays, computed tomography (CT), and magnetic resonance imaging (MRI). Each modality captured the vertebral features differently. The choice of modality affects the performance of the applied algorithms. It is also important to note that a large amount of data is better for training AI algorithms, profound learning algorithms. However, medical images are often limited owing to privacy concerns and the lack of open-source databases. Therefore, it is essential to identify data sources to ensure the success of AI projects for spine analysis. This review discusses available datasets and their characteristics, such as modality, size, and labels.  Additionally, the demographics and applications of the data were also discussed. The platform utilized to obtain related literature in this study is Lens. A scoping review was used in this study to extract information from related literature. The number of literature included in this study is 39. A total of 43 datasets, which include 32 private and 11 public datasets, are discussed in this review. This work will benefit researchers and developers developing an AI-based spinal analysis system.
Co-Authors Abdul Razak, Siti Fatimah Ghazali, Anith Khairunnisa Sayed Ismail, Sharifah Noor Masidayu Siong, Lim Heng Zaim Ahmad, Muhammad Shahrul