Now showing 1 - 10 of 90
  • Publication
    A hybrid Harris Hawks optimizer for economic load dispatch problems
    (2022) ;
    Al-Betar, Mohammed Azmi
    ;
    Awadallah, Mohammed A.
    ;
    Makhadmeh, Sharif Naser
    ;
    Abu Doush, Iyad
    ;
    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.
      17  1
  • Publication
    A Low-Temperature-Resistant Flexible Organic Crystal with Circularly Polarized Luminescence
    (2022) ;
    Pan, Xiuhong
    ;
    Zheng, Anyi
    ;
    Di, Qi
    ;
    Duan, Pengfei
    ;
    Ye, Kaiqi
    ;
    Naumov, Panče
    ;
    Zhang, Hongyu
    ;
    Yu, Xu
    Flexible organic crystals with unique mechanical properties and excellent optical properties are of paramount significance for their wide applications in various research fields such as adaptive optics and soft robotics. However, low-temperature-resistant flexible organic crystal with circularly polarized luminescence (CPL) ability has never been reported. Herein, chiral organic crystals with CPL activity and low-temperature flexibility (77 K) are fabricated by the solvent diffusion method from chiral Schiff bases, S(R)-4- b romo-2-(((1- p henyl e thyl)imino) m ethyl) p henol (S(R)-BPEMP). The corresponding chiroptical properties for the two enantiomeric crystals were thoroughly investigated, including the measurements of circular dichroism (CD) and CPL. To the best of our knowledge, this is the first report on low-molecular-weight flexible organic crystals with CPL activity, and we believe that the results will give a new impetus to the research of organic crystals.
    Scopus© Citations 1  26  13
  • Publication
    A modified coronavirus herd immunity optimizer for capacitated vehicle routing problem
    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  118  47
  • Publication
    A New Method for Generalizing Burr and Related Distributions
    (2022) ;
    Das, Suchismita
    ;
    Chattopadhyay, Swarup
    A new method has been proposed to generalize Burr-XII distribution, also called Burr distribution, by adding an extra parameter to an existing Burr distribution for more flexibility. In this method, the exponent of the Burr distribution is modeled using a nonlinear function of the data and one additional parameter. The models of this newly introduced generalized Burr family can significantly increase the flexibility of the former Burr distribution with respect to the density and hazard rate shapes. Families expanded using the method proposed here is heavy-tailed and belongs to the maximum domain of attractions of the Frechet distribution. The method is further applied to yield three-parameter classical Pareto and generalized exponentiated distributions which shows the broader application of the proposed idea of generalization. A relevant model of the new generalized Burr family has been considered in detail, with particular emphasis on the hazard functions, stochastic orders, estimation procedures, and testing methods are derived. Finally, as empirical evidence, the new distribution is applied to the analysis of large-scale heavy-tailed network data and compared with other commonly used distributions available for fitting degree distributions of networks. Experimental results suggest that the proposed Burr distribution with nonlinear exponent better fits the large-scale heavy-tailed networks better than the popularly used Marhsall-Olkin generalization of Burr and exponentiated Burr distributions.
      31  5
  • Publication
    A Non-convex Economic Load Dispatch Using Hybrid Salp Swarm Algorithm
    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  77  24
  • Publication
    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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    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.
      4
  • Publication
    A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education
    (2022) ;
    AlZu’bi, Shadi
    ;
    Hawashin, Bilal
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    Abu Shanab, Samia
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    Zraiqat, Amjed
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    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.
      43  6
  • Publication
    A Review for the Genetic Algorithm and the Red Deer Algorithm Applications
    The Red Deer algorithm (RD), a contemporary population-based meta heuristic algorithm, applications are thoroughly examined in this paper. The RD algorithm blends evolutionary algorithms' survival of the fittest premise with the productivity and richness of heuristic search approaches. On the other a well-known and relatively older evolutionary based algorithm called the Genetic Algorithm applications are also shown. The contemporary algorithm; the RDA, and the older algorithm; the GA have wide applications in computer science and engineering. This paper sheds the light on all those applications and enable researchers to exploit the possibilities of adapting them in any applications they may have either in engineering, computer science, or business.
      24
  • Publication
    An electro-elastic theory for the mechanically-assisted photo-induced spin transition in core-shell spin-crossover nanoparticles
    (2018) ;
    Boukheddaden, Kamel
    The development of heterostructure materials may lead to new features that cannot be obtained with natural materials. Here we simulate a model structurally hybrid core-shell nanoparticle with different lattice parameters between an electronically inert shell and an active spin crossover core. The nanoparticle consists of a 2D core with 20 × 20 size with square symmetry, surrounded by a shell made of 10 atomic layers. The low temperature photoexcitation of the core shows a significant environment-dependent behavior. In particular, we demonstrate that a shell with a large lattice parameter accelerates the low-spin to high-spin photoexcitation process of the core through the single domain nucleation mechanism while a moderate shell lattice parameter leads to spatially-homogeneous growth of the high-spin fraction. We found that the mechanical retro-action of the shell may cause elastic instability of the core leading to efficient control and manipulation of its photo-conversion.
    Scopus© Citations 9  174  28
  • Publication
    An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection
    (2022) ;
    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.
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