Emad Mabrouk

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Verified

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• Doctor of Philosophy
• Assistant Professor at American University of the Middle East

The emergence of plastic pollution as a global threat to both terrestrial and aquatic organisms has seen efforts geared toward minimizing its production and monitoring widespread distribution within the ecosystem. Though with distinct characteristics and sources, plastic debris and sediments often interact in the natural environment through a compl...

Reservoir sedimentation is mitigated by diverting sediment-laden floodwaters through Sediment Bypass Tunnels (SBTs). However, these tunnels face significant challenges due to hydro-abrasive damage, threatening their long-term sustainability. Predicting this abrasion is challenging due to the complex interactions between flow hydraulics and sediment...

Reservoir sedimentation is a critical issue that impacts dam operations by reducing storage capacity and increasing management costs. This study evaluated the effectiveness of memetic programming (MP) in predicting the suspended sediment concentration (SSC) in the Miwa Reservoir, Japan. The Miwa Dam faces challenges due to its high sediment yield a...

Sediment Bypass Tunnels (SBTs) effectively mitigate reservoir sedimentation by diverting flood-laden flows, but they face significant challenges due to hydroabrasive erosion, which compromises their sustainability. Predicting this abrasion is complex due to the intricate interactions between flow hydraulics and sediment transport, along with limite...

Reservoir sedimentation is a critical issue that impacts dam operations by reducing storage capacity and increasing management costs. This study evaluated the effectiveness of memetic programming (MP) in predicting the suspended sediment concentration (SSC) in the Miwa Reservoir, Japan. The Miwa Dam faces challenges due to its high sediment yield a...

This study aims to examine three machine learning (ML) techniques, namely random forest (RF), LightGBM, and CatBoost for flooding susceptibility maps (FSMs) in the Vietnamese Vu Gia-Thu Bon (VGTB). The results of ML are compared with those of the rainfall-runoff model, and different training dataset sizes are utilized in the performance assessment....

Many renewable energy resources, including wind energy, are uncertain and often unavailable when needed, with high variability and dependency on atmospheric and climatic conditions. Variability and uncertainty of wind energy follow those of wind speed and occurs at multiple timescales, from seconds to minutes to hours, and require movement of other...

The foundation of machine learning is to enable computers to automatically solve certain problems. One of the main tools for achieving this goal is genetic programming (GP), which was developed from the genetic algorithm to expand its scope in machine learning. Although many studies have been conducted on GP, there are many questions about the disr...

This study presents two machine learning models, namely, the light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), for the first time for predicting flash flood susceptibility (FFS) in the Wadi System (Hurghada, Egypt). A flood inventory map with 445 flash flood sites was produced and randomly divided into two groups for t...

The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance’s consistency. An accurate global solar radiation (GSR) predictio...

The minimum dominating set (MDSet) comprises the smallest number of graph nodes, where other graph nodes are connected with at least one MDSet node. The MDSet has been successfully applied to extract proteins that control protein-protein interaction (PPI) networks and to reveal the correlation between structural analysis and biological functions. A...

In network science, controlling the elements of complex networks with a few numbers of nodes has recently become a significant subject of research and a major challenge. Nowadays, the minimum dominating set (MDS) represents an important modern network topic in this context. During the last decade, many methods have been developed to solve the MDS p...

Classification is one of the most popular techniques of data mining. This paper presents an evolutionary approach for designing classifiers for two-class classification problems using an enhanced version of the genetic programming (GP) algorithm, called the Memetic Programming (MP) algorithm. MP can discover relationships between observed data and...

In wireless sensor/ad hoc networks, all wireless nodes frequently flood the network channel by transmitting control messages causing “broadcast storm problem”. Thus, inspired by the physical backbone in wired networks, a Virtual Backbone (VB) in wireless sensor/ad hoc networks can help achieve efficient broadcasting. A well-known and well-researche...

The Minimum Dominating Set (MDSet) problem appears in general network applications and biological networks. MDSet problem aims to find a minimum set of nodes that covers all nodes in a given graph. A graph may contain many MDSets. However, determining the more appropriate MDSet for the given graph is still a challenging problem. During the last dec...

This paper introduces an automatic strategy for the segmentation of medical images from Magnetic Resonance Imaging (MRI) and Computed Topography (CT). A new segmentation technique is proposed to combine a new evolutionary algorithm, called the Immune System Programming (ISP) algorithm, with the Region Growing (RG) technique. The ISP algorithm with...

The main target of the two-class classification problem is to design a classifier that discriminates between two objects from a seen dataset, then use this classifier to predict the object's class for unseen instances. Different methods have been used to solve the two-class classification problem, such as Genetic algorithm (GA) and Genetic Programm...

University courses timetable problem is one of the most important problems facing educational institutions, e.g. University faculty. In this problem, a University faculty plans to allocate their courses to specific time slots, rooms, lecturers etc. Set of hard and soft constraints must be considered while solving this problem, which makes the solut...

This paper presents a new strategy for the segmentation of brain images from the volumetric Magnetic Resonance Imaging (MRI). We propose a new segmentation technique that hybridize an evolutionary algorithm, called the Memetic Programming (MP) algorithm, with the Region Growing (RG) technique. The MP algorithm generates new threshold functions and...

This paper presents a new strategy for the segmentation of brain images from the volumetric Magnetic Resonance Imaging (MRI). We propose a new segmentation technique that hybridizes an evolutionary algorithm, called the Memetic Programming (MP) algorithm, with the Region Growing (RG) technique. The MP algorithm generates new threshold functions and...

For centuries, the study of prime numbers has been regarded as a subject of pure mathematics in number theory. Recently, this vision has changed and the importance of prime numbers has increased rapidly, especially in information technology, e.g., public key cryptography algorithms, hash tables, and pseudo-random number generators. One of the most...

Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been raised about the complexity of the GP algorithm and the dis...

Applications of Artificial Intelligence (AI) are rapidly increasing especially in the infrastructure of every industry, and researchers continually try to develop new efficient AI algorithms or improve the current ones to maximize their benefits. In this paper, we introduce a new hybrid evolutionary algorithm, called the Memetic Programming (MP) al...

Meta-heuristics are general frameworks of heuristics methods for solving combinatorial optimization problems, where exploring the exact solutions for these problems becomes very hard due to some limitations like extremely large running time. In this paper, new local searches over tree space are defined. Using these local searches, various meta-heur...

The modified exponential interval schemes are introduced for the solution of singularly perturbed initial value problems. We give the outline of constructing the schemes of k-th order, then we construct four schemes for k = 1 and k = 2. These schemes are uniformly convergent of second and third order accuracy. Also, we introduce the idea of optimal...

The core of artificial intelligence and machine learning is to get computers to solve problems automatically. One of the great tools that attempt to achieve that goal is Genetic Programming (GP). GP is a generalization procedure of the well-known meta-heuristic of Genetic Algorithms (GAs). Meta-heuristics have shown successful performance in solvin...