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All Journal International Journal of Public Health Science (IJPHS) Civil Engineering Journal
Ibrahim, Khalid
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Civil Engineering, Building, Construction & Architecture Health Professions

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Examining stigma dynamics: a scoping review of social network analysis in communicable disease contexts Baharuddin, Izyan Hazwani; Ismail, Nurhuda; Patterson, Megan S.; Yasin, Siti Munira; Naing, Nyi Nyi; Ibrahim, Khalid
International Journal of Public Health Science (IJPHS) Vol 14, No 1: March 2025
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v14i1.24202

Abstract

The global COVID-19 pandemic has brought attention to the profound impact of stigma on individuals, communities, and societies. Social network analysis (SNA), based on network theory, offers a transformative approach to investigate the complex interplay of social structures, relationships, and information dissemination in communicable disease contexts. This scoping review aims to examine the utilization of SNA in studying stigma dynamics related to communicable diseases, assess the current research landscape, identify gaps, and highlight key findings. Three databases (Scopus, Web of Science, and PubMed) were searched for studies on SNA and stigma in communicable diseases. From the identified studies, three eligible articles were selected for review, providing insights into the role of stigma as a barrier to social integration, thereby impacting network centrality. The review also explores patterns of stigma communication on social media and examines the impact of interventions on individuals’ social networks. Overall, this review emphasizes the value of SNA in comprehending the intricate relationships between social networks and stigma in communicable disease contexts.
Ensemble Learning Models for Prediction of Punching Shear Strength in RC Slab-Column Connections Habibi, Omid; Youssef, Tarik; Naseri, Hamed; Ibrahim, Khalid
Civil Engineering Journal Vol 10 (2024): Special Issue "Sustainable Infrastructure and Structural Engineering: Innovations in
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-SP2024-010-01

Abstract

In reinforced concrete (RC) structures, accurate prediction of the punching shear strength (PSS) of slab-column connections is imperative for ensuring safety. The existing equations in the literature show variability in defining parameters influencing PSS. They neglect potential variable interactions and rely on a limited dataset. This study aims to develop an accurate and reliable model to predict the PSS of slab-column connections. An extensive dataset, including 616 experimental results, was collected from earlier studies. Six robust ensemble machine learning techniques—random forest, gradient boosting, extreme gradient boosting, adaptive boosting, gradient boosting with categorical feature support, and light gradient boosting machines—are employed to predict the PSS. The findings indicate that gradient boosting stands out as the most accurate method compared to other prediction models and existing equations in the literature, achieving a coefficient of determination of 0.986. Moreover, this study utilizes techniques to explain machine learning predictions. A feature importance analysis is conducted, wherein it is observed that the reinforcement ratio and compressive strength of concrete demonstrate the highest influence on the PSS output. SHapley Additive exPlanation is conducted to represent the influence of variables on PSS. A graphical user interface for PSS prediction was developed for users’ convenience. Doi: 10.28991/CEJ-SP2024-010-01 Full Text: PDF
Co-Authors Baharuddin, Izyan Hazwani Habibi, Omid Ismail, Nurhuda Naseri, Hamed Nyi Nyi Naing Patterson, Megan S. Yasin, Siti Munira Youssef, Tarik