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Faizah Mohammed Bashir on Muck Rack

Faizah Mohammed Bashir

As seen in: Nature, MDPI
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Articles

A transferable machine learning framework integrating multi-dimensional vegetation thresholds to predict green infrastructure cooling efficacy in arid urban areas

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By Abdulrahman Abdulaziz Majrashi, Faizah Mohammed Bashir, Mohammed Alzahrani, Zahra Andleeb, Matusal Lamaro Lagebo, Yohannes Mehari Andiye, Nadia Hasen Rassas
| Nature Verified
Abstract Urban overheating, which affects more than two billion people, is a growing problem for cities in arid and semi-arid regions. The majority of current research relies on two-dimensional satellite indices like Normalized Difference Vegetation Index (NDVI), which do not capture the vertical vegetation structures that shape thermal comfort at street level, despite the fact that urban greening has demonstrated promise as a cooling strategy.
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Optimizing the sustainability assessment, mechanical properties and drying shrinkage of concrete containing groundnut shell ash using RSM modeling

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By Naraindas Bheel, Waleligne Molla Salilew, Faizah Mohammed Bashir, Zahra Andleeb, Zeineb Ben Houria
| Nature Verified
Abstract The past decade has seen increased research interest on the possible alternatives to cement in concrete and mortar, with materials that are environmentally friendly, economical, and socially inherent. However, recycling, reusing, and regenerating methods may be used to harness the potential benefits of agricultural/industrial wastes as alternatives.
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AI driven coupled optical thermal electrical optimization of climate responsive colored bipvt façades for energy efficient buildings

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By Khaled Almazam, Mohammed Awad Abuhussain, Faizah Mohammed Bashir, Yakubu Aminu Dodo, Otoma Orkaido Garo, Matusal Lamaro Lagebo, Eman Nasrallah
| Nature Verified
Abstract This study investigates the optimization of colored BIPV/T facades using a coupled optical-thermal-electrical model and artificial intelligence (AI) in two climates of Addis Ababa (humid-temperate) and Riyadh (hot-arid). In this study, a nonlinear model for predicting electrical and thermal efficiency was presented that combines EnergyPlus simulations with the Transformer model.
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