Mohammad Shehab- PhD
- Professor (Assistant) at Amman Arab University
Mohammad Shehab
- PhD
- Professor (Assistant) at Amman Arab University
About
74
Publications
105,487
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2,826
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Introduction
Mohammad Shehab received his B.Sc. from Al-Zaytoonah University of Jordan, Software Engineering, Jordan, in 2009. He received the master degree from the Universiti Sains Malaysia, Computer Science/ Artificial Intelligence, in 2012. Also, He received a Ph.D. degree from Universiti Sains Malaysia, Computer Science/ Artificial Intelligence and Software Engineering, in 2018. Dr. Shehab's research focuses on Metaheuristic algorithms, particularly in the areas of population modeling and parameter control.
Current institution
Additional affiliations
September 2019 - August 2020
Publications
Publications (74)
The digital landscape and rapid advancement of Information and Communication Technology have significantly increased social interactions, but it has also led to a rise in harmful behaviours such as offensive language, cyberbullying, and HS. Addressing online harassment is critical due to its severe consequences. This study offers a comprehensive ev...
The Reptile Search Algorithm (RSA) is a powerful modern optimization technique that effectively solves intricate problems across various fields. Despite its notable success, the local search aspect of RSA requires enhancement to overcome issues such as limited solution variety, a pattern of falling into local optimal traps, and the possibility of e...
Due to increased inspection of internet traffic and the pervasive surveillance practices put in place by many organizations, virtual private networks (VPNs) are now frequently used to safeguard online privacy and circumvent censorship. However, existing VPN protocols are susceptible to methods of detection and blockage employed by network administr...
Citation: Alhamad, H.A. ; Shehab, M.; Shambour, M.K.Y.; Abu-Hashem, M.A.; Abuthawabeh, A.; Al-Aqrabi, H.; Daoud, M.S.; Shannaq, F.B. Handwritten Recognition Techniques: A Comprehensive Review. Symmetry 2024, 16, 681. https://doi. Abstract: Given the prevalence of handwritten documents in human interactions, optical character recognition (OCR) for d...
Recognizing handwritten characters remains a critical and formidable challenge within the realm of computer vision. Although considerable strides have been made in enhancing English handwritten character recognition through various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexity arises from the diver...
Artificial Intelligence techniques, such as optimization algorithms, have become essential for success in many fields. Therefore, most researchers, especially in computer and engineering sciences, focused their efforts and abilities on adapting the optimization algorithms for solving various problems. This review introduces one of the recent nature...
The objective function used in global optimization issues as often as possible features a big computing complexity, conditionality, and a nonclear scene. Such jobs are immensely useful, and a variety of methodologies have been proposed as a foundation for solving them. In this study, we will discuss the krill herd (KH), an ecologically inspired app...
Arabic handwritten script recognition presents an energetic area of study. These types of recognitions face several obstacles, such as vast open databases, boundless diversity in individuals' penmanship, and freestyle writing. Thus, Arabic handwriting requires effective techniques to achieve better recognition results. On the other hand, Multilayer...
Recently, the Metaheuristic Algorithms (MAs) field has seen a noteworthy rise in proposed Algorithms. MAs have been picking up ubiquity in a long time due to their capacity to fathom complex optimization issues in different areas, including building, funds, healthcare, and transportation. These Algorithms are based on heuristic methodologies that m...
Chimp Optimization Algorithm (ChOA) is one of the recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also, it...
Detecting a pothole can help prevent damage to your vehicle and potentially prevent an accident. Different techniques, including machine learning, deep learning models, sensor methods, stereo vision, the internet of things (IoT), and black-box cameras, have already been applied to address the problem. However, studies have shown that machine learni...
Chimp Optimization Algorithm (ChOA) is one of the most efficient recent optimization algorithms, which proved its ability to deal with different problems in various do- mains. However, ChOA suffers from the weakness of the local search technique which leads to a loss of diversity, getting stuck in a local minimum, and procuring premature convergenc...
The coronavirus disease (COVID-19) changed the world's lifestyle switching many techno-services to be provided remotely instead of direct usual physical interactions between people. This study focused on university students' perceptions of this virtual technology-engineering change as discrepancies to be analyzed. The research surveyed 777 differen...
Harris Hawks Optimization (HHO) algorithm was proposed recently under the metaheuristic algorithms, which can fix many problems in various domains. However, it needs to improve in local search, which may lead to a loss of diversity, stuck in a local minimum, which procures premature convergence. Two steps have been introduced in this paper to avoid...
This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where d...
The utilization of artificial neural networks (ANNs) has gained widespread popularity in various domains because it is based on the combination of intelligent control and the working principle of the neurons in a brain. Therefore it has achieved encouraging progress in the networks field, medical applications, image processing, and data science. In...
Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive r...
Multi-verse optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step is using opposition-...
![Various shapes of Arabic characters [30]](publication/359577027/figure/fig1/AS:1181185506058251@1658627978676/Various-shapes-of-Arabic-characters-30_Q320.jpg)
The challenge in handwriting recognition, especially in the segmentation process, took the researchers’ attention. These Arabic handwritten text processes are a challenging job because their characters are generally both cursive and unconstrained. In this paper, a new segmentation technique is proposed for solving the problem of Arabic handwritten...
This paper introduces a comprehensive survey of a new swarm intelligence optimization algorithm so-called Harris hawks optimization (HHO) and analyzes its major features. HHO is counted as an example of the most effective Optimization algorithm and utilized in different problems in various domains, successfully. For example, energy and Power Flow,...
Data clustering is one of the most common and challenging problems in the machine learning domain. It requires an efficient method to be addressed. This paper proposed a new version of the Flow Direction Algorithm (FDA) to solve various optimization problems. The proposed method is called FDAOA, which enhanced the performance of the original Flow D...
This paper proposes a hybrid version of the Salp Swarm Algorithm (SSA) and the hill climbing (HC) technique using various selection schemes to solve engineering design problems. The proposed algorithm consists of two stages. In the first stage, the basic SSA is hybridized with HC local search to improve its exploitation capabilities; we refer to th...
Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas, such as medical, financial, environmental, marketing, security, and industrial applications. ML methods are characterized by their ability to examine many data and discover exciting relationships...
This paper provides an in-depth literature review of the Black Hole Algorithm (BHA) which is considered as a recent metaheuristic. BHA has been proven to be very efficient in different applications. There has been several modifications and variants of this algorithm in the literature, so this work reviews various variants of the BHA. The applicatio...
Nonvolatile memory (NVM), such as NAND flash memory, resistive random access memory (ReRAM), ferroelectric RAM (FeRAM), etc., has been used widely in Internet of Thing (IoT) systems as a secondary storage. These types have the benefits of high performance, high scalability, and less area space. However, NVMs still have many challenges such as lifes...
This paper presents two levels of enhancing the basic Moth flame optimization (MFO) algorithm. The first step is hybridizing MFO and the local-based algorithm, hill climbing (HC), called MFOHC. The proposed algorithm takes the advantages of HC to speed up the searching, as well as enhancing the learning technique for finding the generation of candi...
![Fig. 2 Different phases of Harris Hawks optimization (HHO) algorithm [67]](profile/Mohammad-Al-Shinwan/publication/353213771/figure/fig1/AS:1045782148964352@1626345302047/Different-phases-of-Harris-Hawks-optimization-HHO-algorithm-67_Q320.jpg)
![Fig. 3 The Pseudo-code of the Harris Hawks optimization algorithm [67]](profile/Mohammad-Al-Shinwan/publication/353213771/figure/fig2/AS:1045782148939776@1626345302104/The-Pseudo-code-of-the-Harris-Hawks-optimization-algorithm-67_Q320.jpg)
![Fig. 4 The behavior of E through 2 runs and 500 iterations [67]](profile/Mohammad-Al-Shinwan/publication/353213771/figure/fig3/AS:1045782148939777@1626345302140/The-behavior-of-E-through-2-runs-and-500-iterations-67_Q320.jpg)
Harris Hawks optimization (HHO) is a recent population-based optimization algorithm that has been recently proposed to address several different problems. Sometimes, poor exploitation (intensification) ability influences the performance of Harris Hawks optimization. This chapter proposes a new hybridization strategy, namely, hybrid Harris Hawks opt...
The Internet of Things (IoT) is a novel technology that makes smart devices the essential building blocks in the growth of smart pervasive frameworks. Health-related problems are considered as one of the major issues that directly affect the quality of the life. Among other applications enabled by the IoT, healthcare technologies might be the most...
In this paper, a novel feature selection method is introduced to tackle the problem of high-dimensional features in the text clustering application. Text clustering is a prevailing direction in big text mining; in this manner, documents are grouped into cohesive groups by using neatly selected informative features. Swarm-based optimization techniqu...
Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-bas...
This paper thoroughly introduces a comprehensive review of the so-called Dragonfly algorithm (DA) and highlights its main characteristics. DA is considered one of the promising swarm optimization algorithms because it successfully applied in a wide range of optimization problems in several fields, such as engineering design, medical applications, i...
This paper completely introduces an exhaustive and a comprehensive review of the so-called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of the efficient recent meta-heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields, such as machi...
Text clustering is employed in several domains like text mining, data processing, pattern recognition, image clustering. The vector house model may be a mutual arrangement pattern utilized in the text field to simplify a document's parts as an array. This chapter shows the methodology of the projected hybrid feature section technique supported by t...
This paper thoroughly presents a comprehensive review of the so-called moth–flame optimization (MFO) and analyzes its main characteristics. MFO is considered one of the promising metaheuristic algorithms and successfully applied in various optimization problems in a wide range of fields, such as power and energy systems, economic dispatch, engineer...
Artificial Intelligence (AI) and Software Engineering are considered as significant fields to solve various problems. However, there are weaknesses in certain problem-solving in each field. Thus, this paper is a broad-based review of using artificial intelligence (AI) to improve software engineering (SE), and vice versa. As well as it intends to re...
Due to the advantages offered by AI in containment the COVID-19 pandemic, the number of AI techniques has increased greatly. Although these techniques provide an acceptable start to COVID-19 pandemic control, they differ in terms of purpose, AI synthesis methods, datasets, validation approach. This increase and diversity in the numbers of proposed...
This paper introduces a comprehensive overview of the Ant Lion Optimizer (ALO). ALO is a novel metaheuristic swarm-based approach introduced by Mirjalili in 2015 to emulate the hunting behavior of ant lions in nature life. The review is highlighted the applications that are utilized ALO algorithm to solve various optimization problems. In ALO, the...
Organizations seek to employ the power of big data (BD) to improve their decision-making and biasness process. Infrastructure as a service (IaaS) enabling them to take advantage of BD. However, increasing the acceptance of BD is difficult due to common issues such as accountability of cloud providers. Securing BD has its own distinctive challenges...
The genetic algorithm (GA) is a powerful metaheuristic inspired by the genes selection behavior of the natural human. In addition, it is clear, easy to use, soft, and has a special ability to discover the right balance between the exploration and exploitation strategies during the search, which points to positive convergence. Therefore, the genetic...
The most important tasks of the Bioinformatics of the simple of (DNA)and (RNA) and protein and the difficulties of the large size of the search , the Genetic Algorithms has been a ready for optimizing combinatorial problem and of next serious. That homologies exist and can be discovered and recognized is of central importance to comparative and evo...
Digital medical images play a major role in clincal detection of the patient and determine the appropriate treatment for the condition. Because of the exposure of medical digital images to several factors that affect their clarity, accuracy, noise or quality, there was a need for techniques to improve medical images and maintain the information in...
![Figure 1: The main processes of KDD [16].](profile/Laith-Abualigah/publication/338434718/figure/fig1/AS:844759329435651@1578417727539/The-main-processes-of-KDD-16_Q320.jpg)
Feature selection technique is one of the important data pre-processing steps in data mining; it is used to find the important features subset in order to create a new subset of informative features. The model that used the informative subset such that a classification model built only with this subset would get better predictive accuracy than mode...
Feature Selection (FS) method is one of the most important data pre-processing steps in data mining domain, it is used to find the essential features subset in order to make a new subset of informative features. The model that used the informative subset such that a classification model built only with this subset would get better predictive accura...
BCSA has constantly attracted the interest of investigators from diverse disciplines worldwide since its introduction in 2009. This interest has led to various hybridizations for improving the performance of the basic BCSA. These hybridizations can improve MCSA and achieve favorable results.
In this chapter, an initial investigation of adapting the basic cuckoo search algorithm (BCSA) for the orientation distribution function (ODF) is presented. This chapter aims to extract the maxima of the ODF using BCSA, namely, CSA-ODF.
This chapter discusses and summarizes the research methodology that has been provided to achieve the objectives of the present research. This methodology includes three main stages.
The brain is the most complex organ in the human body because it consists of about 100 billion neurons and one million billion (\(10^{15}\)) interconnections (Azevedo et al. 2009). This organ is the control for the sensorimotor such as walking and breathing, cognitive functions such as talking, reasoning, memory and more complex functions such as e...
This chapter provides a summary of the research work and contributions against the research objectives together with the conclusions as to what has been achieved. As well as recommending some future work directions for those interested in the field.
In this chapter, the CSAHC-ODF is employed to extracting the maximum of the ODF from the real human brain data. CSAHC-ODF has been used because it got the best version ((Shehab et al. 2017b, c, 2018a, b)) based on the experiments in Section 7.5.
In this section, the modified CSA (MCSA) is presented for extracting the ODF maxima (MCSA-ODF). CSA was proposed by Yang and Deb in 2009. To date, work on this algorithm has significantly increased, and CSA currently has its rightful place among other optimization methodologies (Shehab et al. 2017). MCSA is based on replacing the random selection p...
Decoding human brain structures and their interconnecting trajectories are very exciting research areas since they have numerous applications in the clinical diagnosis and management of brain disorders. For that, a lot of variety of invasive tools have been introduced to study brain white matter structural connectivity and configuration. Neverthele...
Optimization problem exists in many domains, such as engineering, energy, economics, medical, and computer science. It is mainly concerned with finding the optimal values for several decision variables to form a solution to optimization problem. This solution is considered optimal when the decision maker is satisfied with it. An optimization proble...
Sentiment analysis is one of data mining types that estimates the direction of personality’s sentiment analysis within natural language processing. Analyzing the text computational linguistics are used to deduce and analyze mental knowledge of Web, social media and related references. The examined data quantifies the global society’s attitudes or f...
![Fig. 1 Text summarization techniques [4]](profile/Hamzeh-Alabool/publication/337653638/figure/fig1/AS:831661906358275@1575295058441/Text-summarization-techniques-4_Q320.jpg)
Text Summarization is the process of creating a summary of a certain document that contains the most important information of the original one, the purpose of it is to get a summary of the main points of the document. Abstractive summarization of multi-documents aims to generate a concentrated version of the document while keeping the main informat...
This book focuses on the use of artificial intelligence to address a specific problem in the brain – the orientation distribution function. It discusses three aspects: (i) Preparing, enhancing and evaluating one of the cuckoo search algorithms (CSA); (ii) Describing the problem: Diffusion-weighted magnetic resonance imaging (DW-MRI) is used for non...
Cuckoo search algorithm (CSA) is considered one of the promising metaheuristic algorithms applied to solve numerous problems in different fields. Although it employed the Levy flight to guide the search process. But, it has drawbacks, such as utilization of global search; in certain cases, this technique may surround local optima. Also, the results...
The diffusion-weighted magnetic resonance imaging (DW-MRI) is a promising method for non-invasive investigation of anatomical connectivity in the human brain. Q-ball imaging (QBI) is a diffusion MRI reconstruction technique which has been proven very successful in resolving multiple intravoxel fibre orientations in MRI (i.e., fibre crossing) based...
The diffusion-weighted magnetic resonance imaging (DW-MRI) is a promising method for non-invasive investigation of anatomical connectivity in the human brain. Q-ball imaging (QBI) is a diffusion MRI reconstruction technique which has been proven very successful in resolving multiple intravoxel fibre orientations in MRI (i.e., fibre crossing) based...
Background: Cuckoo Search Algorithm (CSA) was introduced by Yang and Deb in 2009. It considers as one of the most successful in various fields compared with the metaheuristic algorithms. However, random selection is used in the original CSA which means there is no a high chance for the best solution to selected, also, losing the diversity.
Discuss...
Cuckoo search algorithm is considered one of the promising metaheuristic algorithms applied to solve numerous problems in different fields. However, it undergoes the premature convergence problem for high dimensional problems because the algorithm converges rapidly. Therefore, we proposed a robust approach to solve this issue by hybridizing optimiz...
Cuckoo search algorithm is considered one of the promising metaheuristic algorithms applied to solve numerous problems in different fields. However, it undergoes the premature convergence problem for high dimensional problems because the algorithm converges rapidly. Therefore, we proposed a robust approach to solve this issue by hybridizing optimiz...
In this paper, modified cuckoo search algorithm (MCSA) is presented for solving global optimization problems. Cuckoo Search Algorithm (CSA) was proposed by Yang and Deb in 2009. To date, work on this algorithm has significantly increased, and the CSA has succeeded in having its rightful place among other optimization methodologies. The modified ver...
Regarding the increasing volume of document information (text) on Internet network pages, recent applications, and so on, the dealing with this knowledge has become incredibly complex because of the size. The text clustering is a proper technique used to arrange a tremendous amount of text information by classifying into a subset of clusters. In th...
This paper introduces a comprehensive and exhaustive overview of the cuckoo search algorithm (CSA). CSA is a metaheuristic swarm-based approach established by Yang and Deb in 2009 to emulate the cuckoo breeding behavior. Owing to the successful application of CSA for a wide variety of optimization problems, since then, researchers have developed se...
In Evolutionary Algorithms (EA), the selection scheme is a pivotal component, where it relies on the fitness value of individuals to apply the Darwinian principle of survival of the fittest. In Particle Swarm Optimization (PSO) there is only one place employed the idea of selection scheme in global best operator in which the components of best solu...
In Evolutionary Algorithms (EA), the selection scheme is a pivotal component, where it relies on the fitness value of individuals to apply the Darwinian principle of survival of the fittest. In Particle Swarm Optimization (PSO) there is only one place employed the idea of selection scheme in global best operator in which the components of best solu...
Questions
Questions (19)
Data Science (DS): An area that manages, manipulates, extracts, and interprets knowledge from tremendous amount of data.
Artificial Intelligence (AI): in the present, are complex and effective but nowhere near human intelligence. Humans use the data present around them and the data accumulated in the past to figure out anything and everything.
I look for an example (free code) for the integration of AI and DS.
When I plan to write a comprehensive review paper about a particular topic (X). My personal opinion, I should collect all articles about X, without care about the quality of these articles. After I summarize all of them, then I can create a section called "Evaluate X", in this section, I should put the high-quality articles. i.e., I should give a chance for all articles which published about X.
Is this right?
Hi,
As known, both of FSL and MRIcron are used to analyze the medical images. I want to convert the DICOM to NIfTI and continue in the other processing. I wanna to know which software is better to use and what's the main steps of the analysis.
