Conference Proceedingshttp://hdl.handle.net/20.500.14131/5502024-01-29T03:33:27Z2024-01-29T03:33:27ZMapping the Research Landscape of Social and Cultural Impacts on Smart CitiesIbrahim, AsmaaBrahimi, Tayebhttp://hdl.handle.net/20.500.14131/13742024-01-21T10:15:27Z2024-01-01T00:00:00ZMapping the Research Landscape of Social and Cultural Impacts on Smart Cities
Ibrahim, Asmaa; Brahimi, Tayeb
The integration of digital technology and innovation in the creation of smart cities has significantly improved the quality of life for citizens. However, while there have been extensive studies on the technological capabilities of smart cities, there is a notable gap in research concerning their cultural and social aspects. To address this concern, this study aims to comprehensively examine the social and cultural impacts of smart cities through bibliometric analysis. By analyzing 1160 published articles from the Scopus database, the study highlights the importance of prioritizing the creation of inclusive, safe, resilient, and sustainable cities, aligning with the United Nations Sustainable Development Goal 11. The study identifies China, the United States, Italy, India, and the United Kingdom as the top contributing countries, with the Norwegian University of Science and Technology being the most active institution in this area. Moreover, this research explores the intersection of social and cultural impacts within the broader context of Innovation 5.0 and Industry 5.0, providing valuable insights for future researchers and practitioners. Nevertheless, it is crucial to acknowledge certain limitations, such as the reliance on Scopus data, which may exclude relevant publications from other sources. Additionally, the analysis based on bibliometric data may not capture the full extent of social and cultural impacts associated with smart cities.
2024-01-01T00:00:00ZDetection of Hydrogen Leakage Using Different Machine Learning TechniquesEl-Amin, Mohamed F.http://hdl.handle.net/20.500.14131/8502023-05-21T12:28:43Z2023-04-11T00:00:00ZDetection of Hydrogen Leakage Using Different Machine Learning Techniques
El-Amin, Mohamed F.
When employing pure hydrogen, its leakage poses a serious safety risk since it can cause fire or explode if it comes into contact with the air. In this study, hydrogen leakage in a form of a buoyant jet is investigated using machine learning approaches. As the experiments used to explore hydrogen leaks are extremely dangerous, and there is a limitation of data, we instead construct an artificial dataset using a traditional numerical model. The dataset was produced using a combined empirical-analytical-numerical model. Investigations into dataset preparation, feature significance, correlation, and hyperparameter adjustment are conducted. Artificial neural networks, random forests, gradient boosting regression, and decision trees are the machine-learning approaches that have been used to forecast the distribution of hydrogen leaks in the atmosphere. Different error metrics and R 2 correlation have been used to assess the prediction accuracy. The RF method was found to be the most effective approach for forecasting the dispersion of hydrogen leaking into the air.
2023-04-11T00:00:00ZMachine Learning Prediction for Nanoparticles Behavior in Hydrocarbon ReservoirsEl-Amin, Mohamed F.Al Wateed, Mohamed F.http://hdl.handle.net/20.500.14131/8452023-05-21T12:23:31Z2023-04-11T00:00:00ZMachine Learning Prediction for Nanoparticles Behavior in Hydrocarbon Reservoirs
El-Amin, Mohamed F.; Al Wateed, Mohamed F.
The use of machine learning to forecast how nanoparticles would migrate through porous material is covered in this research. We employed the random forest, decision tree, artificial neural network, and gradient boosting regression machine learning techniques. Since there are not many experimental datasets available, it is easier to create artificial datasets using verified numerical simulators. Additionally, covered in the paper are data preprocessing, correlations, the importance of features, and hyperparameter adjustment. Moreover, different error metrics and R 2 -correlation are used to gauge how well the predictive models perform. Finally, examples of the findings are presented. The decision tree model is determined to have the highest accuracy, the best performance, and the lowest root mean squared error.
2023-04-11T00:00:00ZData Mining and Visualization of Space Technology Research Trends in the Arab WorldBrahimi, Tayebhttp://hdl.handle.net/20.500.14131/7522023-04-30T01:46:45Z2023-04-11T00:00:00ZData Mining and Visualization of Space Technology Research Trends in the Arab World
Brahimi, Tayeb
Space exploration has become a crucial field in recent years, with many countries investing heavily in research and development to enhance their capabilities. Nevertheless, there has been a lack of research on the trends and advancements in aerospace research in the Arab world. This study aims to address this gap by conducting a bibliometric analysis of scientific publications using the Scopus database. The analyzed data covers the period from 1980 to 2022 and focuses on identifying the historical foundations, evolution, and emergence of space programs through citation, occurrence, collaboration, and clustering. The United Arab Emirates is found to be the most active country in terms of publications, followed by Saudi Arabia and Egypt. The results indicate that the majority of the publications are centered on remote sensing and the use of optical systems in space exploration. This study provides valuable insights into technological innovation in the aerospace sector in the Arab world and highlights potential research directions for future studies.
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