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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Salah, Kamal
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Computer Science & IT Engineering

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Spatial domain noise removal filtering for low-resolution digital images Salah, Zaher; Al-Sit, Waleed T.; Salah, Kamal; Elsoud, Esraa
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1627-1642

Abstract

In this research work, six different filters are applied on a low resolution 8 b/pixel gray-scale images, which operate on small sub-images (windows of 3×3 to 11×11 pixels). The enhanced images are used to compare the efficiency of the different six filters using the peak signal to noise ratio (PSNR) image quality measure. Noise peak elimination filter (PSNR)=36.63) outperforms others, such as median filter (PSNR=36.61), while corruption estimation (PSNR=36.03) significantly cuts processing time by only processing the corrupted pixels while maintaining image details. Mean filter (PSNR=34.05) is sensitive to outliers, which cause the image's sharpness and fine features to be lost. By avoiding averaging across edges, bimodal-averaging filter (PSNR=35.30), which improves on the mean filter, chooses the mean of the biggest population. The median-mean filtering (PSNR=36.32), which combines median and mean filters and determines the output pixel by averaging the median and some nearby pixels, is another improvement above averaging.
Exploring patient-patient interactions graphs by network analysis Salah, Zaher; Abu Elsoud, Esraa; Salah, Kamal
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.pp1752-1762

Abstract

Understanding how patient demographics and shared experiences impact interactions is essential for strengthening pa/tient support networks and optimizing health outcomes as personalized healthcare becomes more and more important. To this end, this study explores the patient-patient interactions (PPIs) graph as a network and applies selected network analysis approaches to examine the PPIs network of accutane drug. Two main research questions are addressed by gaining deeper insight at the hidden patterns of reactivity and connectivity among interchanging nodes. There was a negative response to the first research question, which asked if patients react to others that have similar gender and/or age profiles in a consistent way. Patients tended to interact with people of different genders and ages, indicating a high degree of heterogeneity in the network. Negative responses were likewise given to the second research question, which asked if communities inside the network could identify patients based on gender or age profile. Network analysis approaches for community detection failed to distinguish between groups with similar demographic characteristics. Rather, groups seemed to emerge based on other factors, like similarity in patient opinions. The results imply that gender and age do not have a major influence on community membership. Future research will concentrate on applying more sophisticated graph mining techniques to expand these approaches to cover more and larger PPIs networks.
Co-Authors Abu Elsoud, Esraa Al-Sit, Waleed T. Elsoud, Esraa Salah, Zaher