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Ayman Mohamed Mostafa on Muck Rack

Ayman Mohamed Mostafa

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Articles

High-optimized crash severity prediction: a calibrated, leakage-aware ensemble stacking framework for deployment-ready decision making

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By Mona Saleh Alzahrani, Eslam Hamouda, Mayada Tarek, Ayman Mohamed Mostafa
| Nature Verified
Abstract Predicting traffic crash severity is essential for emergency management, roadside infrastructure deployment and safety policy. The current machine learning (ML) approaches concentrate on accuracy, while overlooking the operational costs of false negatives (severe crash incidents missed) versus false positives (false alarms) and the associated higher societal cost of false negatives.
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A hybrid NSGA-II and random forest framework for multi-objective credit risk assessment

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By Alaa Alaerjan, Ayman Mohamed Mostafa, Mayada Tarek, Eslam Hamouda
| Nature Verified
Abstract Credit risk assessment is an important way to measure the likelihood of loan default. It helps institutions stay solvent and manage their capital effectively, which supports strong risk management in the financial sector. This study presents a new hybrid predictive framework that combines the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a Random Forest (RF) classifier. This approach minimizes the number of features while maximizing classification accuracy.
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Advancing Arabic automated essay scoring through cross-encoder BERT models and interpretable explanations

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By Ayman Mohamed Mostafa, Meshrif Alruily, Hisham Allahem, Azzah Allahim, Abdulaziz Shehab, Menwa Alshammeri, Mohammed Alnusayri, Mohamed Ezz
| Nature Verified
Abstract Automated Essay Scoring (AES) in Arabic remains underdeveloped due to the language’s diglossia, rich morphology, and limited annotated resources, reflecting inherent linguistic asymmetry. This study advances Arabic AES by introducing a BERT-based sentence-pair cross-encoder framework that leverages the structural symmetry of bidirectional attention and combines it with an Integrated Gradients (IG) explanation pipeline.
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