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All Journal International Journal of Public Health Science (IJPHS) Bulletin of Electrical Engineering and Informatics
Rizauddin Saian
Universiti Teknologi MARA
Electrical & Electronics Engineering Health Professions

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Cyberbullying victimization during COVID-19: psychological effects and the legal measures Zeti Zuryani Mohd Zakuan; Rizauddin Saian
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.v11i1.21047

Abstract

March 2020 has seen thousands of people across the globe have been infected with coronavirus disease 2019 (COVID-19). Apart from that, the pandemic has affected the learning of billions of students worldwide. As a result, educational institutions throughout the world have turned to online learning using online platforms. Online platforms not only cater to virtual learning, but the students will also have unrestricted access to their social media accounts and online games. Due to this situation, there has been an increase in violence and hate online, which includes bullying. The study reviewed articles to identify cyberbullying victimization during COVID-19. Articles published during COVID-19 pandemic from January 2020 to April 2021 were identified for review. The article search was conducted in April 2020 using the search engine on the National Centre for Biotechnology and Information (NCB) website and Google Scholar. Nineteen articles were chosen as being appropriate for the study's scope. The review highlights the presence and the psychological effects of cyberbullying during COVID-19. The current preventive measure in place is the legal measures. However, the legal measures are found not to be effective in combating cyberbullying.
ACM for segmenting intensity inhomogeneity images corrupted by a mixture of AGMG noise Nurhuda Ismail; Abdul Kadir Jumaat; Rizauddin Saian; Akmal Shafiq Badarul Azam
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.12549

Abstract

In practice, active contour models (ACMs) are effective in segmenting images, which are mostly applied in computer vision and medical imaging. However, the result may be challenged when the images contain noise and intensity inhomogeneity. Images are commonly deteriorated by a mixture of noise involving additive Gaussian and multiplicative Gamma (AGMG). To the best of our knowledge, this is the first study to formulate an ACM for segmenting intensity inhomogeneity images corrupted by AGMG noise. Our proposed mathematical model was developed using a combination of dual denoising terms to recover the noisy image and Laplacian of Gaussian (LoG) terms to smooth the targeted object. The model is solved using calculus of variations and finite difference methods. The performance of the suggested model for segmenting images was evaluated in MATLAB software and compared with current models using low and severe AGMG noise levels. Quantitative evaluations demonstrated that the proposed model outperformed existing models, achieving an average Jaccard similarity coefficient (JSC) improvement of roughly 2.5% and a 16.00% reduction in average relative error. Consequently, the proposed model proves significantly more effective for the segmentation of images degraded by intensity inhomogeneity and mixed AGMG noise.
Co-Authors Abdul Kadir Jumaat Akmal Shafiq Badarul Azam Nurhuda Ismail Zeti Zuryani Mohd Zakuan