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p-Index From 2021 - 2026
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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS)
Lee-Yeng Ong
Multimedia University
Computer Science & IT Engineering Health Professions

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COVID-19 awareness among undergraduates in a private university Yi-Fan Tan; Lee-Yeng Ong; Meng-Chew Leow
International Journal of Public Health Science (IJPHS) Vol 11, No 1: March 2022
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v11i1.21132

Abstract

Since the first declaration of the coronavirus disease 2019 (COVID-19) outbreak in December 2019, the pandemic has brought some significant lifestyle changes among the people across the globe. There might be some underestimation in the severity level of COVID-19 among younger people, specifically around the age group of undergraduates. This study evaluated the awareness level of Malaysian undergraduates towards COVID-19 in Malaysia via an online survey hosted in a COVID-19 awareness roadshow event. This study highlighted the importance of high awareness level of the pandemic among undergraduates and their impact towards managing the spread of the pandemic to the vulnerable population. The respondents were information technology (IT) undergraduates from Multimedia University Melaka Campus, Malaysia. The overall awareness level of the undergraduates is high. Most respondents had a clear understanding on the effects of personal hygiene and personal protective equipment to avoid getting infected with COVID-19, as well as where to go if they got infected with the coronavirus. Most respondents also showed decent knowledge in identifying the basic symptoms of COVID-19. These findings give an insight into the COVID-19 awareness level among undergraduates and may help the policymakers and university managements to control the spread of COVID-19 and other emerging infections.
COVID-19 vaccine acceptance among Malaysians Yi-Fan Tan; Meng-Chew Leow; Lee-Yeng Ong
International Journal of Public Health Science (IJPHS) Vol 12, No 2: June 2023
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v12i2.22080

Abstract

Since the first declaration of the coronavirus (COVID-19) outbreak, massive number of efforts have been taken to develop and deploy the COVID-19 vaccines. However, there might be hesitation towards the vaccines as there were reports of side effects. This study evaluates the COVID-19 vaccination acceptance of the Malaysian public via an online survey hosted in a COVID-19 vaccination acceptance roadshow event. This study gives an insight to the level of vaccination acceptance of the Malaysian public, while at the same time highlights the possible reasons that vaccination rejection may occur in perspectives that are specific to Malaysians. The overall vaccination acceptance of the Malaysian public is high, as most of them either prefer to get vaccinated or already been vaccinated. Most of them have good knowledge on the safety of COVID-19 vaccines and the importance of vaccination. However, the respondents may have differing opinions on their confidence level towards vaccines by specific manufacturers. These findings give an insight into the COVID-19 vaccination acceptance level of the Malaysian public and may possibly aid in effort for vaccination acceptance should there be any form of pandemic as severe as the COVID-19 pandemic occurring in the future.
Effects of sparse datasets on time interval-aware self-attention sequential recommendation models Weishan Ooi; Lee-Yeng Ong; Meng-Chew Leow
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2761-2773

Abstract

Recommendation models serve as crucial filters in managing information, yet they face a few crucial challenges, such as capturing user-item interaction behaviors in sparse datasets. Data sparsity refers to an issue where there is a lack of interactions or missing values in the recommendation dataset. A sparse dataset with a massive number of missing values and interactions leads to more dynamic user behaviors, which suffers a poor recommendation quality. The self-attention mechanism from Transformer can alleviate the effects of data sparsity in datasets by assigning weights to items of interaction behaviors. This allows the model to capture the user dependencies in complex user behavior, which is beneficial for sparse datasets with patterns that are not immediately apparent. This approach has shown its capability to handle large and sparse datasets, as seen in time interval-aware self-attention sequential recommendation model (TiSASRec). It utilized the self-attention mechanism, considering the timestamp and absolute positions of items to estimate the higher attention weights to show the importance of recent items. Thus, this study aims to investigate the effects of sparse datasets by comparing the performance of TiSASRec model with self-attention based sequential recommendation model (SASRec), which excludes time interval-awareness.
Co-Authors Meng-Chew Leow Weishan Ooi Yi-Fan Tan