Abdelhameed Ibrahim

Abdelhameed Ibrahim

Mansoura University

H-index: 61

Africa-Egypt

Professor Information

University

Mansoura University

Position

Associate Professor of Computer Engineering Faculty of Engineering

Citations(all)

8195

Citations(since 2020)

7955

Cited By

875

hIndex(all)

61

hIndex(since 2020)

61

i10Index(all)

120

i10Index(since 2020)

113

Email

University Profile Page

Mansoura University

Research & Interests List

Artificial intelligence

Machine Learning

Engineering Optimization

Metaheuristics

Swarm

Top articles of Abdelhameed Ibrahim

Greylag goose optimization: Nature-inspired optimization algorithm

Nature-inspired metaheuristic approaches draw their core idea from biological evolution in order to create new and powerful competing algorithms. Such algorithms can be divided into evolution-based and swarm-based algorithms. This paper proposed a new nature-inspired optimizer called the Greylag Goose Optimization (GGO) algorithm. The proposed algorithm (GGO) belongs to the class of swarm-based algorithms and is inspired by the Greylag Goose. Geese are excellent flyers and during their seasonal migrations, they fly in a group and can cover thousands of kilometers in a single flight. While flying, a group of geese forms themselves as a “V” configuration. In this way, the geese in the front can minimize the air resistance of the ones in the back. This allows the geese to fly around 70% farther as a group than they could individually. The GGO algorithm is first validated by being applied to nineteen datasets …

Authors

El-Sayed M El-kenawy,Nima Khodadadi,Seyedali Mirjalili,Abdelaziz A Abdelhamid,Marwa M Eid,Abdelhameed Ibrahim

Journal

Expert Systems with Applications

Published Date

2024/3/15

A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm (vol 11, 1221032, 2023)

The rising popularity of electric vehicles (EVs) can be attributed to their positive impact on the environment and their ability to lower operational expenses. Nevertheless, the task of determining the most suitable EV types for a specific site continues to pose difficulties, mostly due to the wide range of consumer preferences and the inherent limits of EVs. This study introduces a new voting classifier model that incorporates the Al-Biruni earth radius optimization algorithm, which is derived from the stochastic fractal search. The model aims to predict the optimal EV type for a given location by considering factors such as user preferences, availability of charging infrastructure, and distance to the destination. The proposed classification methodology entails the utilization of ensemble learning, which can be subdivided into two distinct stages: pre-classification and classification. During the initial stage of classification, the process of data preprocessing involves converting unprocessed data into a refined, systematic, and well-arranged format that is appropriate for subsequent analysis or modeling. During the classification phase, a majority vote ensemble learning method is utilized to categorize unlabeled data properly and efficiently. This method consists of three independent classifiers. The efficacy and efficiency of the suggested method are showcased through simulation experiments. The results indicate that the collaborative classification method performs very well and consistently in classifying EV populations. In comparison to similar classification approaches, the suggested method demonstrates improved performance in terms of assessment metrics …

Authors

Mohammed A Saeed,El-Sayed M. El-Kenawy,Abdelhameed Ibrahim,Abdelaziz A Abdelhamid,Marwa M Eid,M El-Said,Laith Abualigah,Amal H Alharbi,Doaa Sami Khafaga

Journal

Frontiers in Energy Research

Published Date

2023/11/3

Corrigendum: A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

Frontiers | Corrigendum: A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm Skip to main content Download Article Download Article Download PDF ReadCube EPUB XML (NLM) Share on Export citation EndNote Reference Manager Simple TEXT file BibTex View article impact View altmetric score SHARE ON TABLE OF CONTENTS Abstract Publisher’s note Export citation EndNote Reference Manager Simple TEXT file BibTex Check for updates People also looked at CORRECTION article Front. Energy Res., 17 January 2024 Sec. Energy Storage Volume 12 - 2024 | https://doi.org/10.3389/fenrg.2024.1366244 Corrigendum: A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm www.frontiersin.org Mohammed A. Saeed 1 www.frontiersin.org El-Sayed M. El-Kenawy 2 * …

Authors

Mohammed A Saeed,El-Sayed M El-Kenawy,Abdelhameed Ibrahim,Abdelaziz A Abdelhamid,Marwa M Eid,M El-Said,Laith Abualigah,Amal H Alharbi,Doaa Sami Khafaga

Journal

Frontiers in Energy Research

Published Date

2024/1/17

A novel version of whale optimization algorithm for solving optimization problems

The hunting behavior of humpback whales served as the basis for developing a previously invented swarm-based optimization algorithm known as the whale optimization algorithm (WOA). To achieve more precise solutions with higher reliability and faster convergence, this research endeavors to improve upon the initial WOA formulation. The new technique, called the advanced whale optimization algorithm (AWOA), is put to the test in engineering optimization problems. The AWOA, along with other metaheuristic techniques presented in previous literature, was tested in four optimization problems. Based on the numerical results, it was concluded that the AWOA was more efficient compared to the WOA.

Authors

Nima Khodadadi,El-Sayed M El-kenawy,Sepehr Faridmarandi,Mansoureh Shahabi Ghahfarokhi,Abdelhameed Ibrahim,Seyedali Mirjalili

Published Date

2024/1/1

Guided whale optimization algorithm (guided WOA) with its application

Biologically-inspired metaheuristic techniques are novel approaches that derive their central concept from the process of biological evolution. The end goal of these techniques is to develop powerful new algorithms that can compete with existing ones. The evolution-based algorithms and the swarm-based algorithms are two subcategories that may be found inside the bio-based algorithm category. Swarm intelligence powers the modern Whale Optimization Algorithm (WOA). Humpback whale hunting habits influenced it. The WOA optimization technique simulates humpback whale actions by hunting for a target, surrounding the prey, and attacking with a bubble net. The WOA algorithm has solved many optimization and feature selection challenges in recent years. The Guided WOA has undergone a number of modifications from the conventional WOA format. An enhanced method that can quickly direct the whales …

Authors

Abdelhameed Ibrahim,El-Sayed M El-kenawy,Nima Khodadadi,Marwa M Eid,Abdelaziz A Abdelhamid

Published Date

2024/1/1

Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean …

Authors

El-Sayed M El-kenawy,Nadjem Bailek,Kada Bouchouicha,Bilel Zerouali,Muhammed A Hassan,Alban Kuriqi,Basharat Jamil,Ilhami Colak,Adel Khalil,Abdelhameed Ibrahim

Journal

Neural Computing and Applications

Published Date

2024/3/27

Electrical Power Output Prediction of Combined Cycle Power Plants Using a Recurrent Neural Network Optimized by Waterwheel Plant Algorithm

It is difficult to analyze and anticipate the power output of Combined Cycle Power Plants (CCPPs) when considering operational thermal variables such as ambient pressure, vacuum, relative humidity, and temperature. Our data visualization study shows strong non-linearity in the experimental data. We observe that CCPP energy production increases linearly with temperature but not pressure. We offer the Waterwheel Plant Algorithm (WWPA), a unique metaheuristic optimization method, to fine-tune Recurrent Neural Network hyperparameters to improve prediction accuracy. A robust mathematical model for energy production prediction is built and validated using anticipated and experimental data residuals. The residuals’ uniformity above and below the regression line suggests acceptable prediction errors. Our mathematical model has an R-squared value of 0.935 and 0.999 during training and testing, demonstrating its outstanding predictive accuracy. This research provides an accurate way to forecast CCPP energy output, which could improve operational efficiency and resource utilization in these power plants.

Authors

Mohammed Saeed,El-Sayed M El-kenawy,Abdelhameed Ibrahim,Abdelaziz Abdelhamid,Marwa Eid,FATEN KHALID KARIM,Doaa Khafaga,Laith Abualigah

Journal

Frontiers in Energy Research

Published Date

2023/9

Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

The rapid population growth results in a crucial problem in the early detection of diseases in medical research. Among all the cancers unveiled, breast cancer is considered the second most severe cancer. Consequently, an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses. Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective, reliable, and rapid responses, which could help in decreasing the death risk. In this paper, we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers, namely, guided whale and dipper throated optimizers. The proposed algorithm is evaluated using four publicly available breast cancer datasets. The evaluation results show the effectiveness of the proposed approach from the accuracy and speed perspectives. To prove the superiority of the proposed algorithm, a set of competing feature

Authors

Ghada Atteia,El-Sayed M El-kenawy,Nagwan Abdel Samee,Mona M Jamjoom,Abdelhameed Ibrahim,Abdelaziz A Abdelhamid,Ahmad Taher Azar,Nima Khodadadi,Reham A Ghanem,Mahmoud Y Shams

Journal

Computers, Materials and Continua

Published Date

2023

Professor FAQs

What is Abdelhameed Ibrahim's h-index at Mansoura University?

The h-index of Abdelhameed Ibrahim has been 61 since 2020 and 61 in total.

What are Abdelhameed Ibrahim's research interests?

The research interests of Abdelhameed Ibrahim are: Artificial intelligence, Machine Learning, Engineering Optimization, Metaheuristics, Swarm

What is Abdelhameed Ibrahim's total number of citations?

Abdelhameed Ibrahim has 8,195 citations in total.

What are the co-authors of Abdelhameed Ibrahim?

The co-authors of Abdelhameed Ibrahim are Nadhir Al-Ansari, Wei Hong Lim, Tarek Gaber, Nima Khodadadi, Takahiko Horiuchi, Mahmoud Y. Shams.

Co-Authors

H-index: 65
Nadhir Al-Ansari

Nadhir Al-Ansari

Luleå tekniska Universitet

H-index: 32
Wei Hong Lim

Wei Hong Lim

UCSI University

H-index: 30
Tarek Gaber

Tarek Gaber

University of Salford

H-index: 29
Nima Khodadadi

Nima Khodadadi

Iran University of Science and Technology

H-index: 23
Takahiko Horiuchi

Takahiko Horiuchi

Chiba University

H-index: 18
Mahmoud Y. Shams

Mahmoud Y. Shams

Kafrelsheikh University

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