Mostafa Ezzat
Cairo University
17 Papers
2 Citations
Mostafa Ezzat is an academic researcher from Cairo University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 1, co-authored 9 publications.
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Papers
Improving flight delays prediction by developing attention-based bidirectional LSTM network
TL;DR: This study proposes an attention-based bidirectional LSTM network (ATT-BI-LSTM) for accurate flight delay prediction, outperforming existing models with 88% training accuracy and 83-94% testing accuracy in two scenarios, mitigating multi-billion-dollar losses in the aviation sector.
16
Expression of lncRNAs NEAT1 and lnc-DC in Serum From Patients With Behçet’s Disease Can Be Used as Predictors of Disease
Shereen Rashad Mohammed,Omayma O Abdelaleem,Fatma Ahmed,A. Abdelaziz,H A Hussein,Hanaa M. Eid,M. Kamal,Mostafa Ezzat,Marwa Ali +8 more
TL;DR: ROC curves showed that NEAT1 and lnc-DC levels in serum could be used as predictors of BD with high specificity and fair sensitivity.
Locking versus non-locking plates in fixation of extra-articular distal humerus fracture: a randomized controlled study.
TL;DR: The aim of this study was to compare elbow functional outcomes between locking and nonlocking plates in fixation of distal humerus fractures, and found both implants yield similar results, with locking plates showing slightly better clinical scores.
8
Airport resource allocation using machine learning techniques
TL;DR: A comparison between the most used machine learning techniques implemented in many different fields for demand prediction and resource allocation is presented, showing that even for variations accuracy in resource prediction in different scenarios; the Support Vector Machine techniquecan produce a good performance as resource allocation in the airport.
Optimized Planning of Resources Demand Curve in Ground Handling based on Machine Learning Prediction
TL;DR: A model for building a resource demand curve for future flight schedules based on machine learning is proposed, which has proven good accuracy when using one day of flights and measuring deviation between the proposed model predict demand curve when flights did not include the location feature and the actual demand Curve when flights include location.