Abu Zitar, Raed

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Name
Abu Zitar, Raed
Main Affiliation
Email
raed.zitar@sorbonne.ae
ORCID
0000-0003-2693-2132
Scopus Author ID
8858522100

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  • Publication
    A hybrid Harris Hawks optimizer for economic load dispatch problems
    (2023)
    Al-Betar, Mohammed Azmi
    ;
    Awadallah, Mohammed A.
    ;
    Makhadmeh, Sharif Naser
    ;
    Abu Doush, Iyad
    ;
    Abu Zitar, Raed 
    ;
    Alshathri, Samah
    ;
    Abd Elaziz, Mohamed
    This paper proposes a hybridized version of the Harris Hawks Optimizer (HHO) with adaptive-hill-climbing optimizer to tackle economic load dispatch (ELD) problems. ELD is an important problem in power systems that is tackled by finding the optimal schedule of the generation units that minimize fuel conceptions under a set of constraints. Due to the complexity of ELD search space, as it is rigid and deep, the exploitation of HHO is improved by hybridizing it with a recent local search method called adaptive-hill climbing. The HHO can navigate several potential search space regions, while adaptive-hill climbing is used to deeply search for the local optimal solution in each potential region. To evaluate the proposed approach, six versions of ELD cases with various complexities and constraints have been used which are the 6 generation units with 1263 MW of load demand, 13 generation units with 1800 MW of load demand, 13 generation units with 2520 MW of load demand, 15 generation units with 2630 MW of load demand, 40 generation units with 10500 MW of load demand, and 140 generation units with 49342 MW of load demand. Furthermore, the proposed algorithm is evaluated on two ELD real-world cases which are 6 units-1263 MW and 15units-2630 MW. The results show that the proposed algorithm can achieve a significant performance for the majority of the experimented cases. It can achieve the best-reported solution for the ELD case with 15 generation units when compared to 15 well-established methods. Additionally, it obtains the second-best for the ELD case with 140 generation units when compared to 10 well-established methods. In conclusion, the proposed method can be an alternative to solve ELD problems which is efficient.
      29  1
  • Publication
    A modified coronavirus herd immunity optimizer for capacitated vehicle routing problem
    (2022)
    Abu Zitar, Raed 
    Capacitated Vehicle routing problem is NP-hard scheduling problem in which the main concern is to findthe best routes with minimum cost for a number of vehicles serving a number of scattered customersunder some vehicle capacity constraint. Due to the complex nature of the capacitated vehicle routingproblem, metaheuristic optimization algorithms are widely used for tackling this type of challenge.Coronavirus Herd Immunity Optimizer (CHIO) is a recent metaheuristic population-based algorithm thatmimics the COVID-19 herd immunity treatment strategy. In this paper, CHIO is modified for capacitatedvehicle routing problem. The modifications for CHIO are accomplished by modifying its operators to pre-serve the solution feasibility for this type of vehicle routing problems. To evaluate the modified CHIO, twosets of data sets are used: the first data set has ten Synthetic CVRP models while the second is an ABEFMPdata set which has 27 instances with different models. Moreover, the results achieved by modified CHIOare compared against the results of other 13 well-regarded algorithms. For the first data set, the modifiedCHIO is able to gain the same results as the other comparative methods in two out of ten instances andacceptable results in the rest. For the second and the more complicated data sets, the modified CHIO isable to achieve very competitive results and ranked the first for 8 instances out of 27. In a nutshell,the modified CHIO is able to efficiently solve the capacitated vehicle routing problem and can be utilizedfor other routing problems in the future such as multiple travelling salesman problem
    Scopus© Citations 4  131  54
  • Publication
    A Non-convex Economic Load Dispatch Using Hybrid Salp Swarm Algorithm
    (2021)
    Abu Zitar, Raed 
    In this paper, the economic load dispatch (ELD) problem with valve point effect is tackled using a hybridization between salp swarm algorithm (SSA) as a population-based algorithm and β-hill climbing optimizer as a single point-based algorithm. The proposed hybrid SSA is abbreviated as HSSA. This is to achieve the right balance between the intensification and diversification of the ELD search space. ELD is an important problem in the power systems which is concerned with scheduling the generation units in active generators in optimal way to minimize the fuel cost in accordance with equality and inequality constraints. The proposed HSSA is evaluated using six real-world ELD systems: 3-unit generator, two cases of 13-unit generator, 40-unit generator, 80-unit generator, and 140-unit generator system. These ELD systems are well circulated in the previous literature. The comparative results against 66 well-regarded algorithms are conducted. The results show that the proposed HSSA is able to produce viable and competitive solutions for ELD problems.
    Scopus© Citations 9  93  24
  • Publication
    A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education
    (2022)
    Abu Zitar, Raed 
    ;
    AlZu’bi, Shadi
    ;
    Hawashin, Bilal
    ;
    Abu Shanab, Samia
    ;
    Zraiqat, Amjed
    ;
    Mughaid, Ala
    ;
    Almotairi, Khaled H.
    ;
    Abualigah, Laith
    Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used; the findings suggest that this technique operates well. Based on emotional analysis, several deep learning techniques are applied to train and test the emotion classification process. Transfer learning for a pre-trained deep neural network is used as well to increase the accuracy of the emotion classification stage. The obtained results show that the performance of the proposed method is promising using both techniques, as presented in the Experimental Results Section.
      50  6
  • Publication
    A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence
    (2023)
    Raza, Ali
    ;
    Al Nasar, Mohammad Rustom
    ;
    Hanandeh, Essam Said
    ;
    Abu Zitar, Raed 
    ;
    Nasereddin, Ahmad Yacoub
    ;
    Abualigah, Laith
    Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach.
      2
  • Publication
    An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection
    (2022)
    Zitar, Raed 
    ;
    Awadallah, Mohammed A.
    ;
    Al-Betar, Mohammed Azmi
    ;
    Braik, Malik Shehadeh
    ;
    Hammouri, Abdelaziz I.
    ;
    Abu Doush, Iyad
    In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is still open for further investigations since no FS method gives cutting-edge results for all cases. In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. The best performance is achieved by BERSOC followed by BERSO and then BRSO. These proposed versions are comparatively assessed against 25 well-regarded metaheuristic methods and five filter-based approaches. The obtained results underline their superiority by producing new best results for some datasets.
      82  1
  • Publication
    An intelligent cybersecurity system for detecting fake news in social media websites
    (2022)
    Zitar, Raed 
    ;
    Mughaid, Ala
    ;
    Al-Zu'bi, Shadi
    ;
    Arjan, A
    ;
    Al-Amrat, Rula
    ;
    Alajmi, Rathaa
    ;
    Abualigah, Laith
    ;
    Maalej, Ahmed
    People worldwide suffer from fake news in many life aspects, healthcare, transportation, education, economics, and many others. Therefore, many researchers have considered seeking techniques for automatically detecting fake news in the last decade. The most popular news agencies use e-publishing on their websites; even websites can publish any news they want. However, thus before quotation any news from a website, there should be a close look at news resource ranking by using a trusted websites classifier, such as the website world rank, which reflects the repute of these websites. This paper uses the world rank of news websites as the main factor of news accuracy by using two widespread and trusted websites ranking. Moreover, a secondary factor is proposed to compute the news accuracy similarity by comparing the current news with fakes news and getting the possible news accuracy. Experiments results are conducted on several benchmark datasets. The results showed that the proposed method got promising results compared to other comparative methods in defining the news accuracy.
    Scopus© Citations 3  36  45
  • Publication
    An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications
    (2022)
    Zitar, Raed 
    ;
    Al‑Betar, Mohammed Azmi
    ;
    Awadallah, Mohammed A.
    ;
    Abu Doush, Iyad
    ;
    Assaleh, Khaled
    In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of JAYA algorithm are carefully analyzed. Thereafter, the proposed versions of JAYA algorithm have been surveyed such as modified, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of JAYA algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of JAYA algorithm are identified to provide enrich resources for JAYA research communities. The critical analysis of JAYA algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of JAYA algorithm. The reader of this overview will determine the best domains and applications used by JAYA algorithm and can justify their JAYA-related contributions.
    Scopus© Citations 11  114  32
  • Publication
    Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering
    (2022)
    Zitar, Raed 
    ;
    Abualigah, Laith
    ;
    Abd Elaziz, Mohamed
    ;
    Yousri, Dalia
    ;
    Al-qaness, Mohammed A. A.
    ;
    Ewees, Ahmed A.
    This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. A novel MH optimization algorithm, called arithmetic optimization algorithm (AOA), was proposed to address complex optimization tasks. Math operations inspire the AOA, and it showed significant performance in dealing with different optimization problems. However, the traditional AOA faces some limitations in its search process. Thus, we develop a new variant of the AOA, namely, Augmented AOA (AAOA), integrated with the opposition-based learning (OLB) and Lévy flight (LF) distribution. The main idea of applying OLB and LF is to improve the traditional AOA exploration and exploitation trends in order to find the best clusters. To evaluate the AAOA, we implemented extensive experiments using twenty-three well-known benchmark functions and eight data clustering datasets. We also evaluated the proposed AAOA with extensive comparisons to different optimization algorithms. The outcomes verified the superiority of the AAOA over the traditional AOA and several MH optimization algorithms. Overall, the applications of the LF and OLB have a significant impact on the performance of the conventional AOA.
      28  1
  • Publication
    Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images
    (2023)
    Alaiad, Ahmed
    ;
    Migdady, Aya
    ;
    Al-Khatib, Raed M
    ;
    Alzoubi, Omar
    ;
    Abu Zitar, Raed 
    ;
    Abualigah, Laith
    Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.
      13