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Neamat El Gayar
Researcher at Cairo University
Publications - 34
Citations - 1020
Neamat El Gayar is an academic researcher from Cairo University. The author has contributed to research in topics: Support vector machine & Time series. The author has an hindex of 9, co-authored 33 publications receiving 799 citations. Previous affiliations of Neamat El Gayar include Nile University & Heriot-Watt University.
Papers
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Journal ArticleDOI
An empirical comparison of machine learning models for time series forecasting
TL;DR: A large scale comparison study for the major machine learning models for time series forecasting, applying the models on the monthly M3 time series competition data to reveal significant differences between the different methods.
Journal ArticleDOI
Forecasting hotel arrivals and occupancy using Monte Carlo simulation
TL;DR: A new Monte Carlo simulation approach is proposed for the arrivals and occupancy forecasting problem, which simulation the hotel reservations process forward in time, and these future Monte Carlo paths will yield forecast densities.
Book ChapterDOI
A study of the robustness of KNN classifiers trained using soft labels
TL;DR: This work attempts to compare between learning using soft and hard labels to train K-nearest neighbor classifiers and proposes a new technique to generate soft labels based on fuzzy-clustering of the data and fuzzy relabelling of cluster prototypes.
Journal ArticleDOI
An integrated framework for advanced hotel revenue management
Neamat El Gayar,Mohamed Saleh,Amir F. Atiya,Hisham El-Shishiny,Athanasius Zakhary,Heba Abdel Aziz Mohammed Habib +5 more
TL;DR: In this paper, an integrated framework for hotel revenue room maximization is presented, which extends existing optimization techniques for hotel management to address group reservations and uses "forecasted demand" arrivals generated from real data.
Proceedings ArticleDOI
Fraud Detection using Machine Learning and Deep Learning
TL;DR: This paper aims to benchmark multiple machine learning methods such as k-nearest neighbor (KNN), random forest and support vector machines (SVM), while the deep learning methodssuch as autoencoders, convolutional neural networks (CNN), restricted boltzmann machine (RBM) and deep belief networks (DBN) are benchmarked.