Laith Abualigah

Al al-Bayt University · Department of Computer Science

The global health crisis of COVID-19 has ushered in an era of unprecedented data generation, encompassing the virus’s transmission patterns, societal consequences, and governmental responses. Data mining has emerged as a pivotal tool for extracting invaluable insights from this voluminous dataset, offering critical support for informed decision-mak...

Diabetic macular edema (DME) poses a significant threat to vision. It is characterized by the enlargement of the macula due to the accumulation of plasma in the extracellular space of the retina. Detection of DME, crucial for timely intervention, traditionally relies on manual inspection of images, which is time-consuming and prone to human error....

Alzheimer’s disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolution neural n...

The ability to interpret and create code is known as cryptography and has been used to exchange information between peer parties securely. An encryption algorithm is a type of network security model that consists of designing and putting into practice cryptographic algorithms and the supporting framework to help secure data. This cryptosystem is in...

This paper is a thorough examination of the modeling of sleep disorders based on machine learning that is applied to the sleep-health-and-lifestyle data. The use of the Dipper Throated Optimization Algorithm for feature selection and Logistic Regression for classification is the basis of the study that explores the effectiveness of predictive model...

In this paper, a gradient-based optimizer (GBO) algorithm is presented to optimize the parameters of a proportional integral derivative (PID) controller in DC motor control. The GBO algorithm which mathematically models and mimics is inspired by the gradient-based Newton method. It was developed to address various optimization issues. To determine...

This paper introduces a novel metaheuristic technique, the Greater Cane Rat Algorithm (GCRA), for solving optimization problems. GCRA's optimization process is inspired by the intelligent foraging behaviours of greater cane rats during and outside the mating season. These nocturnal animals leave trails as they forage through reeds and grass, which...

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong...

An innovative approach to controlling aircraft pitch is shown in this research. This approach is accomplished by adopting a proportional-integral-derivative with filter (PID-F) mechanism. A novel metaheuristic approach that we propose is called the sinh cosh optimizer (SCHO), and it is intended to further optimize the settings of the PID-F controll...

The study presents a segmented dataset comprising dental periapical X-ray images from both healthy and diseased patients. The ability to differentiate between normal and abnormal dental periapical X-rays is pivotal for accurate diagnosis of dental pathology. These X-rays contain crucial information, offering in- sights into the physiological and pa...

Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the opera...

Environmental disasters such as earthquakes, tsunamis, floods, and landslides disrupt natural resources, human casualties, and so on. Early warning and alert of such events will help for preparedness to avoid human and economic causalities. Managing a disaster or emergency scenario is a difficult endeavor. These episodes of mass devastation, whethe...

The Marine Predators Algorithm (MPA) is among the recently proposed metaheuristic algorithms (MAs), and it got its inspiration from the ocean predators’ foraging behaviour based on Brownian and Levy motions. Good exploration, convergence accuracy, ease of implementation, easy parameter settings, fewer parameters, etc., are some of its strengths. Ne...

Distributed data mining (DDM) has emerged as a useful method for analyzing data that is spread across multiple sources. Nevertheless, DDM has other challenges that restrict its effectiveness, such as autonomy, privacy, efficiency, and implementation. DDM's rigidity and lack of adaptability may render it unsuitable for numerous applications due to i...

Decalcification is crucial in histological processing, particularly for studying mineralized tissues like bone. The choice of decalcification method can significantly impact the quality of histological sections and the preservation of tissue morphology. This study aims to establish a standardized protocol for decalcifying rat calvarial bone using a...

In the field of multi-objective optimization algorithms (MOAs), a primary challenge is identifying an optimal solution that effectively converges to the true Pareto Front while maintaining a high level of diversity, especially in the initial phases of evolution. To tackle this challenge, this study introduces a novel algorithm named Many-Objective...

Crayfish Optimization Algorithm (COA) is innovative and easy to implement, but the crayfish search efficiency decreases in the later stage of the algorithm, and the algorithm is easy to fall into local optimum. To solve these problems, this paper proposes an modified crayfish optimization algorithm (MCOA). Based on the survival habits of crayfish,...

Efficient production is paramount for all types of institutions, hinging upon the attainment of predefined employee targets and their subsequent outcomes. In contemporary times, a pervasive issue plaguing institutions is declining production, primarily stemming from employee absenteeism due to various reasons, ultimately eroding profitability. In o...

Driver drowsiness detection is a critical field of research within automotive safety, aimed at identifying signs of fatigue in drivers to prevent accidents. Drowsiness impairs a driver’s reaction time, decision-making ability, and overall alertness, significantly increasing the risk of collisions. Nowadays, the challenge is to detect drowsiness usi...

This study addresses the challenges in accurately estimating photovoltaic (PV) parameters for solar energy applications by enhancing parameter extraction processes to improve the efficiency of PV models. An information gap in PV solar cell and module parameters provided by vendors obstructs accurate simulation. Traditional numerical techniques face...

Wheat (Triticum aestivum) yield predictions can be improved by using multispectral remote sensing to identify different genotypes and crop growth stages. We propose an innovative machine learning technique aimed at classifying diverse wheat crop genotypes and providing accurate estimations of plant age. Multispectral reflectance data was obtained f...

In recent times, the landscape of power systems has undergone significant evolution, particularly with the integration of diverse renewable energy sources (RESs). This advancement presents an invaluable opportunity to enhance energy efficiency in the modern power grid, primarily by bolstering the role of stochastic RESs. The challenge lies in the o...

In the rapidly evolving realm of remote sensing technology, the classification of Hyperspectral Images (HSIs) is a pivotal yet formidable task. Hindered by inherent limitations in hyperspectral imaging, enhancing the accuracy and efficiency of HSI classification remains a critical and much-debated issue. This review study focuses on a key applicati...

Privacy and security present significant challenges in wireless sensor networks (WSNs). In order to enhance security, the sensor network is equipped with high throughput. While the importance of both source node (SN) and base station (BS) location privacy and security is acknowledged, recent research has predominantly focused on location privacy. A...

Agriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer’s life. Recently, adoption of modern technologies, such as the Internet...

Cross-Site Scripting (XSS) attacks continue to pose a significant threat to web applications, compromising the security and integrity of user data. XSS is a web application vulnerability where malicious scripts are injected into websites, allowing attackers to execute arbitrary code in the victim’s browser. The consequences of XSS attacks can be se...

This research introduces a novel multi-objective adaptation of the Geometric Mean Optimizer (GMO), termed the Multi-Objective Geometric Mean Optimizer (MOGMO). MOGMO melds the traditional GMO with an elite non-dominated sorting approach, allowing it to pinpoint Pareto optimal solutions through offspring creation and selection. A Crowding Distance (...

The use of mesenchymal stem cells (MSCs) in cartilage regeneration has gained significant attention in regenerative medicine. This paper reviews the molecular mechanisms underlying MSC-based cartilage regeneration and explores various therapeutic strategies to enhance the efficacy of MSCs in this context. MSCs exhibit multipotent capabilities and c...

This article presents a modified method of mountain gazelle optimizer (MMGO) as a direct current (DC) motor control. Mountain gazelle optimizer (MGO) is an algorithm inspired by the life of the mountain gazelle animal in nature. This animal concept has five essential steps that are duplicated in mathematical modeling. This article uses two tests to...

Identifying counterfeit banknotes is crucial in financial transactions, as the process of identification cannot be handled by ATMs or vending machines. The recent developments in technology, particularly the smart systems that are integrated with cameras and artificial intelligence (AI) tools, allow for the distinction of currency and the detection...

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to...

The efficacy of automatic voltage regulator (AVR) systems is contingent on crucial parameters like voltage regulation, response time, stability, and efficiency. Integration of controllers with AVR systems facilitates centralized monitoring and regulation, enhancing voltage output efficiency. This study employs a modified sinh cosh optimizer (m-SCHO...

The precise control of aircraft pitch angles is critical in aviation for maintaining specific attitudes during flight, including straight and level flight, ascents, and descents. Traditional control strategies face challenges due to the non-linear and uncertain dynamics of flight. To address these issues, this study introduces a novel approach empl...

In an era where Intelligent Decision Support Systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time...

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization A...

Users of computer networks may benefit from cloud computing, which is a fairly new abstraction that offers features like processing as well as the sharing and storing of data. As a result of the services it provides, cloud computing is drawing significant investments from across the world. Despite this, Cloud Computing Security continues to be one...

This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. Th...

This study aimed to synthesize novel compounds as more effective carbonic anhydrase II inhibitors. For this purpose, 2-(3-methoxy-4-(prop-2-yn-1-yloxy)phenyl)-4,5-diphenyl-1H-imidazole (3) was reacted with 3-methoxy-4-(prop-2-yn-1-yloxy)benzaldehyde through the glyoxal reaction to produce a series of imidazole-based 1,2,3-triazoles 5a-f. The synthe...

The optimization of the vaccination campaign and medication distribution in rural regions of Morocco conducted by the Ministry of Health can be significantly improved by employing metaheuristic algorithms in conjunction with a tour planning system. This research proposes the utilization of six metaheuristic algorithms: genetic algorithm, rat swarm...

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...

Computer vision has extensive applications in various sports domains, and cricket, a complex game with different event types, is no exception. Recognizing umpire signals during cricket matches is essential for fair and accurate decision-making in gameplay. This paper presents the Cricket Umpire Action Video dataset (CUAVd), a novel dataset designed...

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm (SSOA). The SSOA combines the principles of swarm intelligence and synergistic cooperation to search for optimal solutions efficiently. A synergistic cooperation mechanism is employed, where particles exchange information and learn from each...

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...

This chapter provides an introduction to the crow search algorithm (CSA) as well as a discussion to keep scholars engaged in swarm intelligence techniques and optimization problem-solving. CSA is a newly created swarm intelligence program that mimics crow behavior in the storage and retrieval of surplus food. There is a solution that can be found b...

To solve new real-world problems, many metaheuristic optimization methods have been invented. One of these methods is called Henry gas solubility optimization (HGSO); it is a physics-based algorithm which simulates the manners managed by Henry’s law to resolve contesting optimization issues. This survey shows the procedure of Henry’s law and the re...

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as...

Internet-of-Things technology is being increasingly important in our daily lives. As IoT technology evolved, IoT devices face a data protection hazard, particularly smart home IoT gateway devices, which became evident. The demand for a low-cost, secure smart home gateway device or router among smart home users. The problem is that as the internet o...

This study aimed to assess and manage bacterial contamination in multiple batches of mesenchymal stem cell (MSC) cultures derived from rabbit bone marrow. Routine visual inspection and microscopic examination were employed for the detection of the contaminated cultures. The contaminated cultures were cultured on Nutrient agar and multiple isolated...

Gait recognition stands as a pivotal biometric technology in individual identification, yet its real-world implementation faces challenges stemming from intra-subject disparities. The task of extracting consistent features to distinguish among various subjects becomes onerous due to factors such as image noise and magnitude divergence, significantl...

Deep Convolutional Neural Networks (DCNNs) have shown remarkable success in image classification tasks, but optimizing their hyperparameters can be challenging due to their complex structure. This paper develops the Adaptive Habitat Biogeography-Based Optimizer (AHBBO) for tuning the hyperparameters of DCNNs in image classification tasks. In compli...

Sonar sound datasets are of significant importance in the domains of underwater surveillance and marine research as they enable experts to discern intricate patterns within the depths of the water. Nevertheless, the task of classifying sonar sound datasets continues to pose significant challenges. In this study, we present a novel approach aimed at...

Deep learning and metaheuristic algorithms have recently increased in various sciences, including financial accounting information systems (FAISs). However, the existence of large datasets has dramatically increased the complexity of these hybrid networks, so to address this shortcoming, this paper aims to develop a quantum‐behaved chimp optimizati...

Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted p...

The increase in road accidents underscores the urgent need for effective methodologies to evaluate and prioritize road safety improvements. Traditional decision-making processes in road safety management often confront challenges due to the lack of a comprehensive approach, particularly in handling multiple evaluation criteria. This study introduce...

Modern power distribution networks are incredibly complex due to the growing incorporation of distributed generators in the past few years. The coordination of Directional Overcurrent Relays (DORs) in interconnected systems with many relays is significantly hindered by this complexity. In a nonlinear and constrained optimization problem, optimal DO...

The traditional threshold methods used for image segmentation are effective for bi-level thresholds. In the case of complex images that contain many objects or color images, the computational complexity is significantly elevated. Multi-level threshold methods for the segmentation of color images can be seen as a complicated optimization problem. In...

This study delves into the exploration of a novel Multi-objective Snow Ablation Optimizer (MOSAO) algorithm, tailored for addressing expansive Optimal Power Flow (OPF) challenges inherent in intricate power systems. These systems are often complemented with the integration of renewable energy modalities and the state-of-the-art Flexible AC Transmis...

Multi-objective truss optimization has garnered relatively less research attention compared to single-objective scenarios. This paper presents a prescriptive and predictive analysis of nine recent multi-objective algorithms based on the Non-dominated Sorting Genetic Algorithm-2 (NSGA-2) framework. The algorithms under consideration are NSGA-2, Dyna...

In the world of technology, the electronic and technical development of the fields of communication and the internet has increased, which has caused a renaissance in the virtual world. This development has greatly impacted virtual communities for the ease and speed of communication and information transfer through social media platforms, making the...

Images from chest X-rays (CXR) are thought to help observe and research various kinds of pulmonary illnesses. Several works were suggested in the literature for recognizing unique lung diseases, and only a few studies were focused on developing a model to identify joint classes of lung diseases. A patient with a negative diagnosis for one condition...

In this paper, the method of flight and movement of Martial Eagle (Polemaetus Bellicosus) for hunting prey is used to design and Meta-Heuristic (MH) algorithm called Martial Eagle Optimizer (MEO). Martial Eagle is a large hunting bird of the falcon category, which lives in southern Africa. Its prey is very diverse and varies according to its habita...

Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability. This study proposes a novel approach for designing a fractional order proportional-integral-derivative (FOPID) controller that utilizes a modified elite opposition-based artificial hummingbird algorithm (m-AHA) for optimal p...

The stability of voltage in a power system is a critical factor that impacts the system’s performance. Automatic voltage regulator system plays a vital role in maintaining stable voltage levels, ensuring efficient and reliable electricity delivery. However, this system may face challenges, such as oscillating transient response, steady-state errors...

In this study, we tackle the challenge of optimizing the design of a Brushless Direct Current (BLDC) motor. Utilizing an established analytical model, we introduced the Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) method, a biomimetic approach based on Pareto optimality, dominance, and external archiving. We initially teste...

This study delves into the application of hybrid extreme machine-based techniques for solar radiation prediction in Adrar, Algeria. The models under evaluation include the Extreme Learning Machine (ELM), Weighted Extreme Learning Machine (WELM), and Self-Adaptive Extreme Learning Machine (SA-ELM), with a comparative analysis based on various perfor...

Sentiment analysis, a branch of natural language processing (NLP), has gained significant attention for its applications in various domains. This study focuses on utilizing machine learning and deep learning algorithms for sentiment analysis in the context of analyzing Monkeypox using Arabic sentiment text. The objective is to develop an accurate a...

This paper introduces a module which is used to transport goods or people from one place to another without any driver assistant. It is mainly used in big industries to save the time and energy. This module is built around an RFID sensor. RFID technology uses fields of electromagnetic waves to track and monitor tags attached to objects. When trigge...

Big data is a massive amount of information, measurements, and observations, where it has the power to provide a solution to the impossibilities. Recently, it has become the most trending topic in the field of data analysis because of its amazing potentials in extracting the hidden facts. Which attracted various sectors all over the world to collec...

Deaf-and-dumb humans make up about 5% of the world's population, and they need special care by providing them alternative methods that help them to communicate with the outside world, whereas the sense of hearing is the main element of human communications, which is indispensable. From the standpoint of introducing helpful applications that help de...

The exponential distribution optimizer (EDO) represents a heuristic approach, capitalizing on exponential distribution theory to identify global solutions for complex optimization challenges. This study extends the EDO's applicability by introducing its multi-objective version, the multi-objective EDO (MOEDO), enhanced with elite non-dominated sort...

Optimization techniques, particularly meta-heuristic algorithms, are highly effective in optimizing and enhancing efficiency across diverse models and systems, renowned for their ability to attain optimal or near-optimal solutions within a reasonable timeframe. In this work, the Puma Optimizer (PO) is proposed as a new optimization algorithm inspir...

Healthcare and technology have a long history of interaction but eHealth adoption has been delayed due to a lack of infrastructure, capacity, and political will. It is called a health information technology system (HITS) or smart health system (SHS) when healthcare adopts health technology. Customers should expect improved service in terms of effic...

Nowadays, the speed of solving optimization problems by increasing various issues and the number of variables is critical. The Harris Hawk optimization method is a brand-new, intelligent system that resolves optimization issues by mathematically simulating the natural behavior of hawks. In this study, the Harris Hawks optimization method and the La...