Ayman G. Fayoumi

King Abdulaziz University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Anura P. Jayasumana

Colorado State University

Sherif Sakr

University of Tartu

Naif Radi Aljohani

King Abdulaziz University

Saeed‐Ul Hassan

Manchester Metropolitan University

Ahmed Barnawi

King Abdulaziz University

Ayman I. Madbouly

King Abdulaziz University

Ahmed M. Khedra

King Abdulaziz University

Abdul Hamid M. Ragab

King Abdulaziz University

Abdullah Alghamdi

King Saud University

Amin Y. Noaman

King Abdulaziz University

Cooperative Institutions

King Abdulaziz University

UNSW Sydney

Cairo University

Colorado State University

King Abdul Aziz University Hospital

Information Technology University

Princess Nourah bint Abdulrahman University

University of Tartu

Hodeidah University

Manchester Metropolitan University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Large scale graph processing systems: survey and an experimental evaluation

Cluster Computing (2015)

Omar Batarfi Radwa El Shawi Ayman G. Fayoumi Reza Nouri Amin Beheshti

10.1007/s10586-015-0472-6

Cite

Citations (112)

Transfer Learning-Based Classification of Maxillary Sinus Using Generative Adversarial Networks

Applied Sciences (2024)

Mohammad Alhumaid Ayman G. Fayoumi

Paranasal sinus pathologies, particularly those affecting the maxillary sinuses, pose significant challenges in diagnosis and treatment due to the complex anatomical structures and diverse disease manifestations. The aim of this study is to investigate the use of deep learning techniques, particularly generative adversarial networks (GANs), in combination with convolutional neural networks (CNNs), for the classification of sinus pathologies in medical imaging data. The dataset is composed of images obtained through computed tomography (CT) scans, covering cases classified into “Moderate”, “Severe”, and “Normal” classes. The lightweight GAN is applied to augment a dataset by creating synthetic images, which are then used to train and test the ResNet-50 and ResNeXt-50 models. The model performance is optimized using random search to perform hyperparameter tuning, and the evaluation is conducted extensively for various metrics like accuracy, precision, recall, and the F1-score. The results demonstrate the effectiveness of the proposed approach in accurately classifying sinus pathologies, with the ResNeXt-50 model achieving superior performance with accuracy: 91.154, precision: 0.917, recall: 0.912, and F1-score: 0.913 compared to ResNet-50. This study highlights the potential of GAN-based data augmentation and deep learning techniques in enhancing the diagnosis of maxillary sinus diseases.

10.3390/app14073083

Cite

Citations (0)

Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System

Sustainability (2023)

Mahmoud Ragab Hesham A. Abdushkour Louai A. Maghrabi Dheyaaldin Alsalman Ayman G. Fayoumi

Traffic flow monitoring plays a crucial role in Intelligent Transportation Systems (ITS) by dealing with real-time data on traffic situations and allowing effectual traffic management and optimization. A typical approach used for traffic flow monitoring frequently depends on collection and analysis of the data through a manual process that is not only resource-intensive, but also a time-consuming process. Recently, Artificial Intelligence (AI) approaches like ensemble learning demonstrate promising outcomes in numerous ITS applications. With this stimulus, the current study proposes an Improved Artificial Rabbits Optimization with Ensemble Learning-based Traffic Flow Monitoring System (IAROEL-TFMS) for ITS. The primary intention of the proposed IAROEL-TFMS technique is to employ the feature subset selection process with optimal ensemble learning so as to predict the traffic flow. In order to accomplish this, the IAROEL-TFMS technique initially designs the IARO-based feature selection approach to elect a set of features. In addition, the traffic flow is predicted using the ensemble model that comprises a Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), and Bidirectional Gated Recurrent Unit (BiGRU). Finally, the Grasshopper Optimization Algorithm (GOA) is applied for the adjustment of the optimum hyperparameters of all three DL models. In order to highlight the improved prediction results of the proposed IAROEL-TFMS algorithm, an extensive range of simulations was conducted. The simulation outcomes imply the supremacy of the IAROEL-TFMS methodology over other existing approaches with a minimum RMSE of 16.4539.

Ensemble Learning

Hyperparameter

Ensemble forecasting

10.3390/su151612601

Cite

Citations (7)

Efficient key distribution for secure and energy-optimized communication in wireless sensor network using bioinspired algorithms

Alexandria Engineering Journal (2024)

Adil O. Khadidos Nawaf Alhebaishi Alaa O. Khadidos Mohammed Altwijri Ayman G. Fayoumi

Wireless Sensor Networks (WSNs) play a major part in numerous applications such as smart agriculture, healthcare, and environmental monitoring. Safeguarding protected communication in this network is dominant. Securing data transmission in WSNs needs a strong key distribution device to defend against malicious attacks as well as illegal access. Traditional techniques like pre-shared or centralized key management are often unreasonable owing to resource limitations, particularly in large-scale sensor systems. To overcome this challenge, a lightweight key distribution technique is employed for safeguarding the security and privacy of data transmission streamlining processes decreasing computational overhead as well as energy consumption. By optimizing and simplifying key distribution devices, we propose to improve the complete efficacy and trustworthiness of WSNs that aid safe communication while preserving valuable energy resources. Therefore, this article designs an Efficient Key Distribution for Secure and Energy-Optimized Communication using Bioinspired Algorithms (EKD-SOCBA) for WSN. The purpose of the EKD-SOCBA technique is to accomplish security and energy efficiency in WSNs. Initially, the EKD-SOCBA technique applies a golden jackal optimization (GJO) based clustering approach to cluster the nodes and select cluster heads (CHs). Also, a lightweight Dynamic Step-wise Tiny Encryption Algorithm (DS-TEA) is applied to secure data transmission in the network. Finally, a lightweight key management phase is employed to protect the encryption key and decrease energy utilization and overhead costs. To exhibit the enhanced act of the EKD-SOCBA model, a comprehensive set of imitations was involved. Extensive results stated enhanced presentation of EKD-SOCBA methodology over other models on WSN.

10.1016/j.aej.2024.02.064

Cite

Citations (2)

Comparative Study of Classification Algorithms Using Big Data Analytics for Flight Delays Prediction

Lecture notes in networks and systems (2025)

Mona Hassan Asiri Abdullah Alghamdi Ayman G. Fayoumi Mahmoud Ragab

Data Analysis

10.1007/978-981-97-7178-3_25

Cite

Citations (0)

Photonic Network Communications (2003)

Ayman G. Fayoumi

Metropolitan area network

Ranging

Deflection routing

10.1023/a:1023634724313

Cite

Citations (1)

Performance Analysis of Multi-Fiber Synchronous Photonic Share-per-link Packet Switches

Ayman G. Fayoumi Fahad Ahmed Al-Zahrani A.A. Habiballa Anura P. Jayasumana

A performance model is presented for an optical packet switch architecture in which the wavelength converters are shared per output link and each output link consists of multiple fibers. Symmetry of the switch is exploited to derive the packet loss probability for the case where traffic is destined to different output ports with equal probability. The architecture performance is evaluated by means of an analytical model and confirmed by simulations under different switch parameter configurations. Wavelength converters are shown to improve the packet loss probability of the switch. The study shows that synchronous switches equipped with full conversion would have the least conversion utilization rate indicating that the use of a switch with less converter count, i.e., partial conversion, would offer better switch resources utilization and comparable packet loss performance

Packet Switching

Packet loss

Fast packet switching

Optical burst switching

10.1109/lcn.2005.104

Cite

Citations (7)

The Family of MapReduce and Large Scale Data Processing Systems

arXiv (Cornell University) (2013)

Sherif Sakr Anna Liu Ayman G. Fayoumi

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.

Data Processing

Source

Cite

Citations (7)

PERFORMANCE EVALUATION OF A CLOUD BASED LOAD BALANCER SEVERING PARETO TRAFFIC

Ayman G. Fayoumi Saudi Arabia

Cloud resources represent an unforeseeable breakthr ough in ITC industry. Load balancer is a key elemen t in resource provisioning for high available cloud s olutions, and yet its performance depends on the tr affic offered load. We develop a discrete event simulatio n to evaluate the performance with respect to the different load points. The performance metrics were the average waiting time inside the balance as wel l as the number of tasks. The performance study includes evaluating the chance of immediate serving or rejecting incoming tasks. Pareto traffic was consid ered for the offered traffic.

Provisioning

Source

Cite

Citations (19)

Webometrics: evolution of social media presence of universities

Scientometrics (2021)

Raheem Sarwar Afifa Zia Raheel Nawaz Ayman G. Fayoumi Naif Radi Aljohani

Webometrics

10.1007/s11192-020-03804-y

Cite

Citations (33)