Mohammed El-Abd

American University of Kuwait

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Co-Authors

Mohamed S. Kamel

Helwan University

Mounib Khanafer

American University of Kuwait

Iyad Abu Doush

American University of Kuwait

Diego Oliva

Universidad de Guadalajara

Issam Damaj

American University of Kuwait

Karam M. Sallam

University of Sharjah

Maryam Bin-Jassem

American University of Kuwait

Ali Darwiche

American University of Kuwait

Ossama Ahmed

Fraunhofer Institute for Material Flow and Logistics

Seyed Jalaleddin Mousavirad

Mid Sweden University

Cooperative Institutions

American University of Kuwait

Mansoura University

Zagazig University

University of Waterloo

Kuwait University

Constantine 1 University

Al-Balqa Applied University

Tanta University

Renewable Energy Development Center

University of Constantine 3

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Research Field

Parallel hardware implementation of the brain storm optimization algorithm using FPGAs

Microprocessors and Microsystems (2020)

Ahmed Hassanein Mohammed El-Abd Issam Damaj Haseeb Ur Rehman

Benchmark (surveying)

Initialization

Implementation

Brainstorming

10.1016/j.micpro.2020.103005

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Citations (17)

MPPT algorithm based on metaheuristic techniques (PSO & GA) dedicated to improve wind energy water pumping system performance

Scientific Reports (2024)

Abdelhak Bouchakour Laid Zarour Noureddine Bessous Mohcene Bechouat Abdelhalim Borni

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work.

10.1038/s41598-024-68584-4

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Citations (7)

PSO_Bounds: A New Hybridization Technique of PSO and EDAs

Studies in computational intelligence (2009)

Mohammed El-Abd Mohamed S. Kamel

EDAS

Benchmark (surveying)

10.1007/978-3-642-01085-9_17

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Citations (6)

A hybrid ABC-SPSO algorithm for continuous function optimization

Mohammed El-Abd

In this paper we investigate the hybridization of two swarm intelligence algorithms; namely, the Artificial Bee Colony Algorithm (ABC) and Particle Swarm Optimization (PSO). The hybridization technique is a component-based one where the PSO algorithm is augmented with an ABC component to improve the personal bests of the particles. Two different hybrid algorithms are tested in this work based on the method in which the ABC component is applied to the different particles. All the algorithms are applied to the well-known CEC05 benchmark functions and compared based on three different metrics.

Benchmark (surveying)

Component (thermodynamics)

Swarm intelligence

10.1109/sis.2011.5952576

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Citations (38)

IMODEII: an Improved IMODE algorithm based on the Reinforcement Learning

2022 IEEE Congress on Evolutionary Computation (CEC) (2022)

Karam M. Sallam Ali Wagdy Mohamed Mohammed El-Abd Ali Wagdy

The success of differential evolution algorithm depends on its offspring breeding strategy and the associated control parameters. Improved Multi-Operator Differential Evolution (IMODE) proved its efficiency and ranked first in the CEC2020 competition. In this paper, an improved IMODE, called IMODEII, is introduced. In IMODEII, Reinforcement Learning (RL), a computational methodology that simulates interaction-based learning, is used as an adaptive operator selection approach. RL is used to select the best-performing action among three of them in the optimization process to evolve a set of solution based on the population state and reward value. Different from IMODE, only two mutation strategies have been used in IMODEII. We tested the performance of the proposed IMODEII by considering 12 benchmark functions with 10 and 20 variables taken from CEC2022 competition on single objective bound constrained numerical optimisation. A comparison between the proposed IMODEII and the state-of-the-art algorithms is conducted, with the results demonstrating the efficiency of the proposed IMODEII.

Benchmark (surveying)

Differential Evolution

Action selection

Operator (biology)

10.1109/cec55065.2022.9870420

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Citations (5)

Pipeline Leak Identification Emergency Robot Swarm (PLIERS)

Ayman Kandil Ali Darwiche Reem Qasem Fatima Matook Ahmad Younis

As cities continue to expand, there has been a substantial increase in demand for factories and sewage systems, which in turn necessitated the need for maintaining the pipeline systems involved. These are typically difficult, dangerous, and expensive to investigate manually by humans, and therefore the use of automated robots is crucial to identifying cracks. Therefore, we propose PLIERS (Pipeline Leak Identification Emergency Robot Swarm). PLIERS is a system consisting of a swarm of robots that are used to investigate pipes for any cracks while communicating with one another as well as a server system. The robots are mounted with cameras to collect images of these cracks, which are then analyzed using a machine learning algorithm by the server to detect and confirm the cracks and their severity.

Identification

10.1109/secon58729.2023.10287512

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Citations (1)

Guest Editorial Special Issue on Project-Based, Senior Design, and Capstone Courses in Engineering Education

IEEE Transactions on Education (2020)

Issam Damaj Mohammed El-Abd Ashley Ater Kranov Jennifer DeBoer

Senior design and capstone courses are an integral part of any engineering curriculum around the world. These courses perhaps provide the only opportunity for students to apply the theoretical knowledge and technical skills they have acquired throughout the engineering degree. Moreover, they are often the (sole) environment in which students get some of the professional and transversal skills too. In addition, such courses present a great degree of nonuniformity since the students engage in projects of different nature (e.g., research oriented versus application oriented). Furthermore, not only do these courses prepare students for real-life engineering practice, they also serve as a major component in measuring program outcomes. The survey conducted over two decades ago by Todd et al. [item 1) in the Appendix], which collected responses from 360 departments across 173 schools, highlighted many of the issues related with capstone courses that are still relevant today. Such issues include course format, different degrees of faculty involvement, and project completion requirements. Indeed, all points highlighted thus far emphasize the importance of adequately designing and assessing capstone design courses.

10.1109/te.2020.2976266

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Citations (3)

An automated metaheuristic-optimized approach for diagnosing and classifying brain tumors based on a convolutional neural network

Results in Engineering (2024)

Mansourah Aljohani Waleed M. Bahgat Hossam Magdy Balaha Yousry AbdulAzeem Mohammed El-Abd

Brain tumors must be classified to determine their severity and appropriate therapy. Applying Artificial Intelligence to medical imaging has enabled remarkable developments. The presented framework classifies patients with brain tumors with high accuracy and efficiency using CNN, pre-trained models, and the Manta Ray Foraging Optimization (MRFO) algorithm on X-ray and MRI images. Additionally, the CNN and Transfer Learning (TL) hyperparameters will be optimized through MRFO, resulting in improved performance of the pre-trained model. Two public datasets from Kaggle were used to build the models. The first dataset consists of two X-ray classes, while the 2nd dataset includes three contrast-enhanced T1-weighted MRI classes. First, a patient should be diagnosed as "Healthy" (or "Tumor"). When the scan returns the result "Healthy," the patient has no abnormalities in their brain. If a scan reveals that the patient has a tumor, an MRI will be performed on them. After that, the type of tumor (i.e., meningioma, pituitary, and glioma) will be identified using the second proposed classifier. A comparative analysis of the models used in the two-class dataset showed that VGG16's pre-trained model outperformed other models. Besides, the Xception pre-trained model achieved the best results in the three-class dataset. A manual review of misclassifications was conducted to determine the reasons for the misclassifications and correct them. The evaluation of the suggested architecture yielded an accuracy of 99.96% for X-rays and 98.64% for T1-weighted contrast-enhanced MRIs. The proposed deep learning framework outperformed most current deep learning models.

10.1016/j.rineng.2024.102459

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Citations (14)

Hybrid cooperative co-evolution for the CEC15 benchmarks

2022 IEEE Congress on Evolutionary Computation (CEC) (2015)

Mohammed El-Abd

In this paper, we test the performance of hybrid cooperative co-evolution (hCC) on the CEC15 benchmarks. In its initial stage, the method applies the recently introduced differential grouping to learn the problem variables' inter-dependencies and separate the variables into groups of separable and non-separable ones. In its second stage, the method adopts different algorithms within the cooperative co-evolution (CC) framework to simultaneously optimize the generated groups. Results are reported for all required problem sizes.

Differential Evolution

10.1109/cec.2015.7257006

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Citations (7)

A cooperative approach to The Artificial Bee Colony algorithm

Mohammed El-Abd

The Artificial Bee Colony (ABC) algorithm is a powerful continuous optimization tool that has been proposed in the past few years. Many studies have shown the ABC superiority in terms of performance when compared to other well-known optimization algorithms. In this paper, the implementation of a Cooperative ABC (CABC) algorithm that is based on the explicit space decomposition approach is investigated. Both the ABC algorithm and its cooperative versions are applied to a well-known set of classical benchmark functions.

Benchmark (surveying)

Optimization algorithm

10.1109/cec.2010.5586007

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Citations (47)