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Now showing 1 - 14 of 14

A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48
(2023)
Al-Manaseer, Hitham
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Abualigah, Laith
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Alsoud, Anas Ratib
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Abu Zitar, Raed
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Ezugwu, Absalom E.
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Jia, Heming
In this study, the possibility of using and applying the capabilities of artificial intelligence (AI) and machine learning (ML) to increase the effectiveness of Internet of Things (IoT) and big data in developing a system that supports decision makers in the medical fields was studied. This was done by studying the performance of three well-known classification algorithms Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree-J48 (J48), to predict the probability of heart attack. The performance of the algorithms for accuracy was evaluated using the Healthcare (heart attack possibility) dataset, freely available on kagle. The data was divided into three categories consisting of (303, 909, 1808) instances which were analyzed on the WEKA platform. The results showed that the RFC was the best performer.
53Scopus© Citations 9
Animal migration optimization algorithm: novel optimizer, analysis, and applications
(2024)
Abualigah, Laith
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Ahmad, Esraa Nasser
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Ikotun, Abiodun M.
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Abu Zitar, Raed
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Alsoud, Anas Ratib
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Khodadadi, Nima
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Ezugwu, Absalom E.
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Jia, Heming
A new heuristic optimization algorithm was proposed in 2013 called the animal migration optimization (AMO) algorithm. It is based on the animal actions and habits in migration seasons. Optimization algorithms are applied to find the optimal solutions in many domains and fields such as image processing, machine learning, and others. In this paper, we present an overview of the AMO algorithm, describe the algorithm procedure, summarize some of the recent works and applications that use AMO algorithms, classify these works in many application domains, and display the robustness points, weak points, and limitations for AMO. This paper can help and direct the researchers in future works in using AMO in their studies.
Scopus© Citations 1 16
Aquila optimizer: review, results and applications
(2024)
Abualigah, Laith
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Sbenaty, Batool
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Ikotun, Abiodun M.
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Abu Zitar, Raed
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Alsoud, Anas Ratib
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Khodadadi, Nima
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Ezugwu, Absalom E.
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Hanandeh, Essam Said
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Jia, Heming
This survey presents a new optimization method named aquila optimizer (AO), motivated by the hunting behavior of aquila. This optimizer is divided into four steps: choosing the search area from high rise and vertical downhill; reconnoitering from a close search area by outlining aviation with low slip pounce; reconnoitering from a close search area by short aviation with slow going down pounce; attack by walk and seize victim. In this survey, we take some research papers and analyze how they are utilized to solve various optimization problems.
9
Arabic Text Classification Using Modified Artificial Bee Colony Algorithm for Sentiment Analysis: The Case of Jordanian Dialect
(2023)
Habeeb, Abdallah
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Otair, Mohammed A
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Abualigah, Laith
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Alsoud, Anas Ratib
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Elminaam, Diaa Salama Abd
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Abu Zitar, Raed
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Ezugwu, Absalom E
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Jia, Heming
Arab customers give their comments and opinions daily, and it increases dramatically through online reviews of products or services from companies, in both Arabic, and its dialects. This text describes the user’s condition or needs for satisfaction or dissatisfaction, and this evaluation is either negative or positive polarity. Based on the need to work on Arabic text sentiment analysis problem, the case of the Jordanian dialect. The main purpose of this paper is to classify text into two classes: negative or positive which may help the business to maintain a report about service or product. The first phase has tools used in natural language processing; the stemming, stop word removal, and tokenization to filtering the text. The second phase, modified the Artificial Bee Colony (ABC) Algorithm, with Upper Confidence Bound (UCB) Algorithm, to promote the exploitation ability for the minimum dimension, to get the minimum number of the optimal feature, then using forward feature selection strategy by four classifiers of machine learning algorithms: (K-Nearest Neighbors (KNN), Support vector machines (SVM), Naïve-Bayes (NB), and Polynomial Neural Networks (PNN). This proposed model has been applied to the Jordanian dialect database, which contains comments from Jordanian telecom company’s customers. Based on the results of sentiment analysis few suggestions can be provided to the products or services to discontinue or drop, or upgrades it. Moreover, the proposed model is applied to the database of the Algerian dialect, which contains long Arabic texts, in order to see the efficiency of the proposed model for short and long texts. Four performance evaluation criteria were used: precision, recall, f1-score, and accuracy. For a future step, in order to build on or use for the classification of Arabic dialects, the experimental results show that the proposed model gives height accuracy up to 99% by applying to the Jordanian dialect, and a 82% by applying to the Algerian dialect.
28Scopus© Citations 3
Arithmetic optimization algorithm: a review and analysis
(2024)
Abualigah, Laith
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Abusaleem, Aya
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Ikotun, Abiodun M.
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Abu Zitar, Raed
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Alsoud, Anas Ratib
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Khodadadi, Nima
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Ezugwu, Absalom E.
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Hanandeh, Essam Said
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Jia, Heming
A metaheuristic algorithm called arithmetic optimization algorithm (AOA) is developed to solve various optimization problems. It is inspired by various math operators, such as subtraction, division, addition, and multiplication. As a result, it can perform diverse optimization tasks in different search spaces. This article reviews the behaviors of mathematics operations that inspire the main features of AOA, which is a metaheuristic algorithm. This paper reviews the original version of the algorithm and discusses the various variants of the algorithm. It also explores the applications of the algorithm in different fields, which will be of help for future researchers.
21
Comparative Study on Arabic Text Classification: Challenges and Opportunities
(2023)
Abualigah, Laith
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Oliva, Diego
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Abu Zitar, Raed
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Hussien, Abdelazim G.
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Melhem, Mohammed K. Bani
There have been great improvements in web technology over the past years which heavily loaded the Internet with various digital contents of different fields. This made finding certain text classification algorithms that fit a specific language or a set of languages a difficult task for researchers. Text Classification or categorization is the practice of allocating a given text document to one or more predefined labels or categories, it aims to obtain valuable information from unstructured text documents. This paper presents a comparative study based on a list of chosen published papers that focus on improving Arabic text classifications, to highlight the given models and the used classifiers besides discussing the faced challenges in these types of researches, then this paper proposes the expected research opportunities in the field of text classification research. Based on the reviewed researches, SVM and Naive Bayes were the most widely used classifiers for Arabic text classification, while more effort is needed to develop and to implement flexible Arabic text classification methods and classifiers.
32Scopus© Citations 4
Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning
(2023)
Abdo, Ahmed
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Hong, Chin Jun
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Kuan, Lee Meng
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Pauzi, Maisarah Mohamed
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Sumari, Putra
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Abu Zitar, Raed
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Abualigah, Laith
Fruit recognition becomes more and more important in the agricultural industry. Traditionally, we need to manually identify and label all the fruits in the production line, which is labor intensive, error-prone, and ineffective. Therefore, a lot of fruit recognition systems are created to automate the process, but fruit recognition system for Malaysia local fruit is limited. Thus, this project will focus on classifying one of the Malaysia local fruits which is markisa/passion fruit. We proposed two CNN models for markisa classification. The performances of the proposed models are evaluated on our own dataset collection and produces an accuracy of 97% and 65% respectively. The results indicated that the architecture of CNN model is very important because different architecture can produce different results. Therefore, first CNN model is selected because it can classify 4 types of markisa with a higher accuracy. In the proposed work, we also inspected two transfer learning methods in the classification of markisa which are VGG-16 and InceptionV3. The results showed that the performance of the first proposed CNN model outperforms VGG-16 (95% accuracy) and InceptionV3 (65% accuracy).
Scopus© Citations 3 49
Particle swarm optimization algorithm: review and applications
(2024)
Abualigah, Laith
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Sheikhan, Ahlam
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M. Ikotun, Abiodun
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Abu Zitar, Raed
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Alsoud, Anas Ratib
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Al-Shourbaji, Ibrahim
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Hussien, Abdelazim G.
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Jia, Heming
Particle swarm optimization (PSO) is a heuristic global optimization technique and an optimization algorithm that is swarm intelligence-based. It is based on studies into the movement of bird flocks. Individual birds share information about their position, speed, and fitness while searching the food source, and the flock's behavior is affected to enhance the likelihood of migration to high-fitness areas. This paper surveys the published papers in PSO algorithms. Twenty research papers are analyzed and classified according to the implementation area used by the PSO algorithm (neural networks, feature selection, and data clustering). The main procedure of the PSO algorithm is presented. Future researchers can use the collected data in this survey as baseline information on the PSO and PSO's applications.
Scopus© Citations 4 12
Salak Image Classification Method Based Deep Learning Technique Using Two Transfer Learning Models
(2023)
Theng, Lau Wei
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San, Moo Mei
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Cheng, Ong Zhi
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Shen, Wong Wei
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Sumari, Putra
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Abualigah, Laith
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Abu Zitar, Raed
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Izci, Davut
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Jamei, Mehdi
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Al-Zu’bi, Shadi
Salak is one of the fruits plants in Southeast Asia; there are at least 30 cultivars of salak. The size, shape, skin color, sweetness or even flesh color will be different depending on the cultivar. Thus, classification of salak based on their cultivar become a daily job for the fruit farmers. There are many techniques that can be used for fruit classification using computer vision technology. Deep learning is the most promising algorithm compared to another Machine Learning (ML) algorithm. This paper presents an image classification method on 4 types of salak (salak pondoh, salak gading, salak sideempuan and salak affinis) using a Convolutional Neural Network (CNN), VGG16 and ResNet50. The dataset consists of 1000 images which having 250 of images for each type of salak. Pre-processing on the dataset is required to standardize the dataset by resizing the image into 224 * 224 pixels, convert into jpg format and augmentation. Based on the accuracy result from the model, the best model for the salak classification is ResNet50 which gave an accuracy of 84% followed by VGG16 that gave an accuracy of 77% and CNN which gave 31%.
132Scopus© Citations 5
Salp swarm algorithm: survey, analysis, and new applications
(2024)
Abualigah, Laith
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Hawamdeh, Worod
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Abu Zitar, Raed
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AlZu’bi, Shadi
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Mughaid, Ala
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Hanandeh, Essam Said
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Alsoud, Anas Ratib
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El-kenawy, El-Sayed M.
This chapter offers the sea salmon-associated polyp (SALP) swarm algorithm (SSA) and multipurpose SSA (MSSA) as new optimization algorithms for solving optimization problems with single and multiple objectives. The behavior of the species when traveling and foraging in the waters is the main source of SSA and MSSA. These two algorithms are put to test on a variety of mathematical optimization functions to see how they behave when it comes to finding the best solutions to optimization problems. The results of the mathematical functions reveal that the SSA technique may improve the initial random solutions more effectively and efficiently. The findings of the MSSA method show that it can approach optimal Pareto solutions with strong convergence and coverage. The research also explains how to use SSA and MSSA to solve a number of computationally challenging and expensive engineering design issues (e.g., airfoil design and marine propeller design). The benefits of the proposed algorithms in addressing real-world issues with challenging and unknown search areas are demonstrated by the outcomes of real-world case studies. In this paper, the most important literature and previous studies related to the subject of the study were presented, where nearly 30 researches were referred to develop a theoretical framework related to SSA and other improved algorithms and to compare SSA with other systems. The MSSA approach has been linked to a large number of previously published algorithms. Many standard criteria that require individual and multiple objectives are included, and the most important findings of this study and the most important conclusions related to the subject of the study are included.
Scopus© Citations 1 15
Social spider optimization algorithm: survey and new applications
(2024)
Abualigah, Laith
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Al Turk, Ahmad A.
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Ikotun, Abiodun M.
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Abu Zitar, Raed
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Alsoud, Anas Ratib
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Khodadadi, Nima
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Hussien, Abdelazim G.
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Jia, Heming
The behavior of insects and animals has inspired particle swarm optimization (PSO). An insect’s settlement acts as an integrated part that works as a speeded unit, also doing construction for huge projects. Besides the connections between insect societies, they are communicated internally between their members. Each spider has a weight based on the value of fitness. This algorithm consists of two search spiders called agents: males and females. This algorithm has been developed over time, resulting in many versions besides theories and findings. One of the PSO algorithms or versions is the social spider optimization (SSO) algorithm, a simulation of the interaction between spider groups, males and females. Based on gender, evolutionary factors simulate different behaviors usually found at their settlement based on the biological aspect. This survey studied the SSO and compared it with other PSO algorithms to find the best-performing algorithm based on a benchmark. This survey also studied the main applications of this algorithm in different fields, including medical, mathematical, artificial intelligence, engineering, and data engineering, and how this algorithm affected, impacted, and supported the different fields. Finally, this chapter provides an expectation of the fields that need to work with this algorithm to improve problem-solving and the fields that have a growing number studies that use this algorithm.
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Spider monkey optimizations: application review and results
(2024)
Abualigah, Laith
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Alshatti, Sahar M.
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Ikotun, Abiodun M.
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Abu Zitar, Raed
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Alsoud, Anas Ratib
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Khodadadi, Nima
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Ezugwu, Absalom E.
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Hanandeh, Essam Said
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Jia, Heming
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Zare, Mohsen
Optimization algorithms are applied to find efficient solutions in different problems in several fields such as the routing in wireless networks, cloud computing, big data, image processing and scheduling, and so forth. In this paper, we survey one of the metaheuristic algorithms: the spider monkey optimization (SMO). We present the algorithm procedure, discuss some of the recent related works and applications, and then highlight the strong and weak points of the SMO algorithm. This paper can assist in potential future research that involve use of the SMO.
Scopus© Citations 1 17
Teaching–learning-based optimization algorithm: analysis study and its application
(2024)
Abualigah, Laith
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Abu-Dalhoum, Eman
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Ikotun, Abiodun M.
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Abu Zitar, Raed
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Alsoud, Anas Ratib
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Khodadadi, Nima
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Ezugwu, Absalom E.
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Hanandeh, Essam Said
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Jia, Heming
The teaching–learning-based optimization (TLBO) algorithm is a novel nature-based optimization approach that has attracted a lot of interest from researchers because of its great capacity to handle optimization problems. This method is built on a teaching and learning philosophy that is used to efficiently tackle multidimensional, linear, and nonlinear problems. The fundamentals of the TLBO algorithm have recently been updated by many researchers to improve its exploration and exploitation capabilities as well as performance. Therefore, the effective TLBO algorithm investigations over the last 5 years are reviewed in this publication. The structure of TLBO algorithms was first explained. Then, the implementation and the enhanced TLBO were examined and discussed in various areas such as engineering, electrical engineering, technology and science, mechanical design, artificial intelligence, classification problem, healthcare, and economics. Finally, we discussed our perspectives on TLBO's open difficulties and challenges, as well as future areas of research.
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Whale optimization algorithm: analysis and full survey
(2024)
Abualigah, Laith
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Abualigah, Roa’a
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Ikotun, Abiodun M.
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Abu Zitar, Raed
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Alsoud, Anas Ratib
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Khodadadi, Nima
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Ezugwu, Absalom E.
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Hanandeh, Essam Said
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Jia, Heming
The whale optimization algorithm (WOA) is a metaheuristic algorithm inspired by the hunting behavior of humpback whales. This paper presents a comprehensive analysis and survey of the WOA, examining its key components, variations, and applications. The algorithm's encircling prey, bubble-net feeding, and search for prey steps are explained in detail, highlighting their role in balancing exploration and exploitation. Various adaptations and hybridizations of the WOA are reviewed, including adaptive strategies, constrained and multiobjective optimization extensions, and combinations with other algorithms. The survey further discusses the algorithm's performance on a wide range of optimization problems, showcasing its competitiveness and effectiveness. Finally, the paper concludes with insights into the strengths, limitations, and potential future directions of the WOA. This analysis and survey aim to provide researchers and practitioners with a comprehensive understanding of the WOA and its applications, fostering further advancements in the field of nature-inspired optimization algorithms.
Scopus© Citations 2 13

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