Ammar Kamal Abasi

Mohamed bin Zayed University of Artificial Intelligence

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Sharif Naser Makhadmeh

University of Jordan

Mohammed Azmi Al‐Betar

Ajman University

Zaid Abdi Alkareem Alyasseri

University of Kufa

Osama Ahmad Alomari

University of Sharjah

Mohammed A. Awadallah

Al-Aqsa University

Ahamad Tajudin Khader

Universiti Sains Malaysia

Iyad Abu Doush

American University of Kuwait

Moayad Aloqaily

Mohamed bin Zayed University of Artificial Intelligence

Syibrah Naim

University of Strathclyde

Mohsen Guizani

Qatar University

Cooperative Institutions

Universiti Sains Malaysia

Ajman University

Al-Balqa Applied University

University of Kufa

Hospital Universiti Sains Malaysia

National University of Malaysia

Mohamed bin Zayed University of Artificial Intelligence

Al-Ahliyya Amman University

Universiti Sains Islam Malaysia

Malaysia University of Science and Technology

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Bare-Bones Based Salp Swarm Algorithm for Text Document Clustering

IEEE Access (2023)

Mohammed Azmi Al‐Betar Ammar Kamal Abasi Ghazi Al‐Naymat Kamran Arshad Sharif Naser Makhadmeh

Text Document Clustering (TDC) is a challenging optimization problem in unsupervised machine learning and text mining. The Salp Swarm Algorithm (SSA) has been found to be effective in solving complex optimization problems. However, the SSA's exploitation phase requires improvement to solve the TDC problem effectively. In this paper, we propose a new approach, known as the Bare-Bones Salp Swarm Algorithm (BBSSA), which leverages Gaussian search equations, inverse hyperbolic cosine control strategies, and greedy selection techniques to create new individuals and guide the population towards solving the TDC problem. We evaluated the performance of the BBSSA on six benchmark datasets from the text clustering domain and six scientific papers datasets extracted from the top eight UAE universities. The experimental results demonstrate that the BBSSA algorithm outperforms traditional SSA and nine other optimization algorithms. Furthermore, the BBSSA algorithm achieves better results than the five traditional clustering techniques.

Benchmark (surveying)

Swarm intelligence

10.1109/access.2023.3314589

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Citations (4)

CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization

Expert Systems with Applications (2022)

Mohammed A. Awadallah Mohammed Azmi Al‐Betar Iyad Abu Doush Sharif Naser Makhadmeh Zaid Abdi Alkareem Alyasseri

Benchmark (surveying)

10.1016/j.eswa.2021.116431

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Citations (17)

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

Expert Systems (2021)

Zaid Abdi Alkareem Alyasseri Mohammed Azmi Al‐Betar Iyad Abu Doush Mohammed A. Awadallah Ammar Kamal Abasi

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

Pandemic

Coronavirus

10.1111/exsy.12759

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Citations (139)

EEG Feature Fusion for Person Identification Using Efficient Machine Learning Approach

Zaid Abdi Alkareem Alyasseri Mohammed Azmi Al‐Betar Mohammed A. Awadallah Sharif Naser Makhadmeh Osama Ahmad Alomari

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. This paper proposed a new method for EEG feature extraction based on fusing different EEG features. In general, EEG feature extraction can be categorized into three types which are time domain, frequency domain, and time-frequency domain features. This paper also applied several supervised learning approaches to select the efficient classifier for EEG-based person identification. The performance of the proposed method is tested using standard EEG datasets, namely, EEG Motor Movement/Imagery Dataset. The results are evaluated using four common criteria which are: accuracy rate (ACC EEC ), sensitivity (Sen EEC ), specificity (Spe EEC ) and F-score (FS EEC ). The experiment results show that the fusion approach achieves better results compared with a traditional EEG feature extraction approach. The proposed fusion feature method is recommended to apply in more challenging signal problem instances, such as user authentication or early detection of epilepsy based on EEG signals.

Feature (linguistics)

Identification

10.1109/picict53635.2021.00029

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Citations (4)

A hybrid flower pollination withβ-hill climbing algorithm for global optimization

Journal of King Saud University - Computer and Information Sciences (2021)

Zaid Abdi Alkareem Alyasseri Mohammed Azmi Al‐Betar Mohammed A. Awadallah Sharif Naser Makhadmeh Ammar Kamal Abasi

In this paper, the β-hill climbing optimizer is hybridized with the flower pollination algorithm (FPA) as a local refinement operator for global optimization problems. The proposed method is called HyFPAβ-hc. Such hybridization aims to enhance the balance between exploration and exploitation processes during the search, thus improving the quality of the outcomes. β-hill climbing optimizer is a recent trajectory-based algorithm with a powerful digging the niche to search and find the local optimum, while FPA is a recent population-based algorithm with robust mining several niches in the search space without proper concentration. The proposed HyFPAβ-hc is evaluated using 15 unimodal and multimodal test functions established in IEEE-CEC2015. The results show significant improvement in the convergence behaviour of the proposed HyFPAβ-hc over FPA using different dimensions of the test function. The comparative evaluation is also conducted against 26 state-of-the-art methods. The experiments consider three problem sizes (with dimensions 10, 30, and 50) to show the proposed HyFPAβ-hc performance against all comparative methods, where the proposed method outperformed all compared methods in optimizing 8, 7, 4 out of 15 test functions for 10, 30, 50 dimensions, respectively. Accordingly, the achieved results prove the efficiency of the proposed HyFPAβ-hc in optimizing various problem dimensions. In conclusion, the proposed hybrid metaheuristic method can search powerfully in the niches of optimization problems search space and produces very fruitful outcomes.

Hill climbing

10.1016/j.jksuci.2021.06.015

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Citations (10)

Lemurs Optimizer: A New Metaheuristic Algorithm for Global Optimization

Applied Sciences (2022)

Ammar Kamal Abasi Sharif Naser Makhadmeh Mohammed Azmi Al‐Betar Osama Ahmad Alomari Mohammed A. Awadallah

The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm’s primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is first benchmarked on twenty-three standard optimization functions. Additionally, the LO is used to solve three real-world problems to evaluate its performance and effectiveness. In this direction, LO is compared to six well-known algorithms: Salp Swarm Algorithm (SSA), Artificial Bee Colony (ABC), Sine Cosine Algorithm (SCA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and JAYA algorithm. The findings show that the proposed algorithm outperforms these algorithms in fourteen standard optimization functions and proves the LO’s robust performance in managing its exploration and exploitation capabilities, which significantly leads LO towards the global optimum. The real-world experimental findings demonstrate how LO may tackle such challenges competitively.

Optimization algorithm

10.3390/app121910057

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Citations (37)

Hybrid multi-verse optimizer with grey wolf optimizer for power scheduling problem in smart home using IoT

The Journal of Supercomputing (2022)

Sharif Naser Makhadmeh Ammar Kamal Abasi Mohammed Azmi Al‐Betar

Home Automation

10.1007/s11227-022-04325-6

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Citations (16)

AN IMPROVED MULTI-VERSE OPTIMIZER FOR TEXT DOCUMENTS CLUSTERING

Kufa Journal of Engineering (2022)

Ammar Kamal Abasi Ahamad Tajudin Khader Mohammed Azmi Al‐Betar

Text document clustering (TDC) represents a key task in text mining and unsupervised machine learning, which partitions a specific documents' collection into varied K-groups according to certain similarity/dissimilarity criterion. The multi-verse optimizer algorithm (MVO) is a stochastic population-based algorithm, which was recently introduced and successfully utilized to tackle many optimization problems that are complex. The original MVO performance is limited to the utilization of only the best solution in the exploitation phase (local search capability), which makes it suffer from entrapment in local optima and low convergence rate. This paper aims to propose a novel method of modifying the MVO algorithm called link-based multi-verse optimizer algorithm (LBMVO) to enhance the exploitation phase in the original MVO. Generally, LBMVO has outperformed or at least showed that it is profoundly competitive compared with the original MVO algorithm.

Local optimum

Similarity (geometry)

Document Clustering

10.30572/2018/kje/130203

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Citations (4)

A Communication-Efficient Federated Learning Framework for Sustainable Development Using Lemurs Optimizer

Algorithms (2024)

Mohammed Azmi Al‐Betar Ammar Kamal Abasi Zaid Abdi Alkareem Alyasseri F.M.A. Salam Raghad Falih Mohammed

The pressing need for sustainable development solutions necessitates innovative data-driven tools. Machine learning (ML) offers significant potential, but faces challenges in centralized approaches, particularly concerning data privacy and resource constraints in geographically dispersed settings. Federated learning (FL) emerges as a transformative paradigm for sustainable development by decentralizing ML training to edge devices. However, communication bottlenecks hinder its scalability and sustainability. This paper introduces an innovative FL framework that enhances communication efficiency. The proposed framework addresses the communication bottleneck by harnessing the power of the Lemurs optimizer (LO), a nature-inspired metaheuristic algorithm. Inspired by the cooperative foraging behavior of lemurs, the LO strategically selects the most relevant model updates for communication, significantly reducing communication overhead. The framework was rigorously evaluated on CIFAR-10, MNIST, rice leaf disease, and waste recycling plant datasets representing various areas of sustainable development. Experimental results demonstrate that the proposed framework reduces communication overhead by over 15% on average compared to baseline FL approaches, while maintaining high model accuracy. This breakthrough extends the applicability of FL to resource-constrained environments, paving the way for more scalable and sustainable solutions for real-world initiatives.

Transformative Learning

10.3390/a17040160

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Citations (0)

A Hybrid Artificial Intelligence Model for Detecting Keratoconus

Applied Sciences (2022)

Zaid Abdi Alkareem Alyasseri Ali H. Al‐Timemy Ammar Kamal Abasi Alexandru Lavric Husam Jasim Mohammed

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 and 579 KCN4) from Department of Ophthalmology and Visual Sciences, Paulista Medical School, Federal University of São Paulo, São Paulo in Brazil and 1531 eyes (Healthy = 400, KCN1 = 378, KCN2 = 285, KCN3 = 200, KCN4 = 88) from Department of Ophthalmology, Jichi Medical University, Tochigi in Japan and used several accuracy metrics including Precision, Recall, F-Score, and Purity. We compared the proposed method with three other standard unsupervised algorithms including k-means, Kmedoids, and Spectral cluster. Based on two independent datasets, the proposed model outperformed the other algorithms, and thus could provide improved identification of the corneal status of the patients with keratoconus.

10.3390/app122412979

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Citations (2)