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A catalytic enantioselective stereodivergent aldol reaction
(2023)
Rahman, Md. Ataur
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Cellnik, Torsten
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Ahuja, Brij Bhushan
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Li, Liang
;
Healy, Alan R.
The aldol reaction is among the most powerful and strategically important carbon–carbon bond–forming transformations in organic chemistry. The importance of the aldol reaction in constructing chiral building blocks for complex small-molecule synthesis has spurred continuous efforts toward the development of direct catalytic variants. The realization of a general catalytic aldol reaction with control over both the relative and absolute configurations of the newly formed stereogenic centers has been a longstanding goal in the field. Here, we report a decarboxylative aldol reaction that provides access to all four possible stereoisomers of the aldol product in one step from identical reactants. The mild reaction can be carried out on a large scale in an open flask, and generates CO2 as the only by-product. The method tolerates a broad substrate scope and generates chiral β-hydroxy thioester products with substantial downstream utility.
31 291Scopus© Citations 6
A hybrid Harris Hawks optimizer for economic load dispatch problems
(2023)
Al-Betar, Mohammed Azmi
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Awadallah, Mohammed A.
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Makhadmeh, Sharif Naser
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Abu Doush, Iyad
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Abu Zitar, Raed
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Alshathri, Samah
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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.
Scopus© Citations 33 43 10
A Low-Temperature-Resistant Flexible Organic Crystal with Circularly Polarized Luminescence
(2022)
Li, Liang
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Pan, Xiuhong
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Zheng, Anyi
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Di, Qi
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Duan, Pengfei
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Ye, Kaiqi
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Naumov, Panče
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Zhang, Hongyu
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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 34 43 272
A modified coronavirus herd immunity optimizer for capacitated vehicle routing problem
(2022)
Abu Zitar, Raed
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Mohammad Dalbah, Lamees
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Al-Betar, Mohammed Azmi
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Awadallah, Mohammed A.
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 26 151 107
A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System
(2023)
Ekinci, Serdar
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Izci, Davut
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Abualigah, Laith
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Abu Zitar, Raed
In this work, we propose a real proportional-integral-derivative plus second-order derivative (PIDD2) controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation. In this regard, this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system. We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism. We also propose a simple yet effective objective function to increase the performance of the proposed algorithm (CmOBL-AO) to adjust the real PIDD2 controller's parameters effectively. We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm, gravitational search algorithm, African vultures optimization, and the Aquila Optimizer using well-known unimodal, multimodal benchmark functions. CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm. For the vehicle cruise control system, we confirm the more excellent performance of the proposed method against particle swarm, gray wolf, salp swarm, and original Aquila optimizers using statistical, Wilcoxon signed-rank, time response, robustness, and disturbance rejection analyses. We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective. The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds. Lastly, we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases. We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.
39Scopus© Citations 48
A New Method for Generalizing Burr and Related Distributions
(2022)
Chakraborty, Tanujit
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Das, Suchismita
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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.
Scopus© Citations 2 42 45
A non-convex economic load dispatch problem using chameleon swarm algorithm with roulette wheel and Levy flight methods
(2023)
Abu Zitar, Raed
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Braik, Malik Sh.
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Awadallah, Mohammed A.
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Al-Betar, Mohammed Azmi
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Hammouri, Abdelaziz I.
An Enhanced Chameleon Swarm Algorithm (ECSA) by integrating roulette wheel selection and Lévy flight methods is presented to solve non-convex Economic Load Dispatch (ELD) problems. CSA has diverse strategies to move towards the optimal solution. Even so, this algorithm’s performance faces some hurdles, such as early convergence and slumping into local optimum. In this paper, several enhancements were made to this algorithm. First, it’s position updating process was slightly tweaked and took advantage of the chameleons’ randomization as well as adopting several time-varying functions. Second, the Lévy flight operator is integrated with roulette wheel selection method and both are combined with ECSA to augment the exploration behavior and lessen its bias towards exploitation. Finally, an add-on position updating strategy is proposed to develop a further balance between exploration and exploitation conducts. The optimization performance of ECSA is shown by testing it on five various real ELD cases with a generator having 3, 13, 40, 80 and 140 units, each with different constraints. The results of the ELD systems’ analysis depict that ECSA is better than the parent CSA and other state-of-the art methods. Further, the efficacy of ECSA was experimented on several benchmark test functions, and its performance was compared to other well-known optimization methods. Experimental results show that ECSA surpasses other methods on complex benchmark functions with modest computational burdens. The superiority and practicality of ECSA is demonstrated by getting new best solutions for large-scale ELD cases such as 40-unit and 140-unit test systems.
37Scopus© Citations 14
A Non-convex Economic Load Dispatch Using Hybrid Salp Swarm Algorithm
(2021)
Abu Zitar, Raed
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Alkoffash, Mahmud Salem
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Awadallah, Mohammed A.
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Alweshah, Mohammed
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Assaleh, Khaled
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Al-Betar, Mohammed Azmi
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 31 117 24
A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education
(2022)
Abu Zitar, Raed
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AlZu’bi, Shadi
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Hawashin, Bilal
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Abu Shanab, Samia
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Zraiqat, Amjed
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Mughaid, Ala
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Almotairi, Khaled H.
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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.
Scopus© Citations 22 63 8
A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence
(2023)
Raza, Ali
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Al Nasar, Mohammad Rustom
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Hanandeh, Essam Said
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Abu Zitar, Raed
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Nasereddin, Ahmad Yacoub
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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.
19Scopus© Citations 14
A real-time automatic pothole detection system using convolution neural networks
(2023)
Bharat, Ricardo
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Ikotun, Abiodun M
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Ezugwu, Absalom E.
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Abualigah, Laith
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Shehab, Mohammad
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Abu Zitar, Raed
Detecting a pothole can help prevent damage to your vehicle and potentially prevent an accident. Different techniques, including machine learning, deep learning models, sensor methods, stereo vision, the internet of things (IoT), and black-box cameras, have already been applied to address the problem. However, studies have shown that machine learning and deep learning techniques successfully detect potholes. However, because most of these successful attempts are peculiar to the location of the study, we found no study which has addressed the peculiarity of potholes in South Africa using a tailored-trained deep learning model. In this study, we propose using a convolutional neural network (CNN), a type of deep learning model, to address this growing problem on South African roads. To achieve this, a CNN model was designed from scratch and trained with image samples obtained from the context of the study. The classifier was adapted to distinguish between a binary class which identifies the presence or absence of potholes. Results showed a significant performance enhancement at a classification accuracy of 92.72%. The outcome of this study showed that this machine learning approach holds great potential for addressing the challenge of potholes and road bumps in the region and abroad.
23 1
A Survey of cuckoo search algorithm: optimizer and new applications
(2024)
Abualigah, Laith
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Ababneh, Ashraf
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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
Cuckoo search (CS) is an efficient swarm intelligence-based algorithm that has come a long way since its start in 2009. Due to its simplicity and effectiveness, CS provides several advantages in tackling highly nonlinear optimization problems in engineering work applications. We offer an overview of the latest enhancements to this method in the past 5 years, explore the theoretical underpinning and potential research areas for future advances, and introduce novel metaheuristics to optimize computations based on explicit boosting tasks. Early studies reveal that the algorithm is viable and can exceed existing methods in performance. The CS algorithm is further validated for solving numerous technological optimization challenges. The proposed search technique is compared with the existing standard optimization algorithms. The current CS computations are merged with Lévy flight and it was the first time used to solve nonlinear benchmark challenges.
Scopus© Citations 1 16
A Thermosalient and Mechanically Compliant Organic Crystalline Optical Waveguide Switcher
(2024)
Di, Qi
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Al‐Handawi, Marieh B.
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Li, Liang
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Naumov, Panče
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Zhang, Hongyu
The dense and ordered molecular arrangements endow dynamic molecular crystals with fast response, rapid energy conversion, low energy dissipation, and strong coupling between heat/light and mechanical energy. Most of the known dynamic crystals can only respond to a single stimulus, and materials that can respond to multiple stimuli are rare. Here, we report an organic crystalline material that can be bent plastically and is also thermosalient, as its crystals can move when they undergo a reversible phase transition. The crystals transmit light regardless of their shape or crystalline phase. The combination of light transduction and reversible thermomechanical deformation provides an opportunity to switch the waveguiding capability of the material in a narrow temperature range, which holds a tremendous potential for applications in heat‐averse electronic components, such as central processing units. Unlike existing electronics, the material we report here is completely organic and therefore much lighter, potentially reducing the overall weight of electronic circuits.
11
Adapted arithmetic optimization algorithm for multi-level thresholding image segmentation: a case study of chest x-ray images
(2023)
Abu Zitar, Raed
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Otair, Mohammad
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Abualigah, Laith
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Tawfiq, Saif
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Alshinwan, Mohammad
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Ezugwu, Absalom E.
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Sumari, Putra
Particularly in recent years, there has been increased interest in determining the ideal thresholding for picture segmentation. The best thresholding values are found using various techniques, including Otsu and Kapur-based techniques. These techniques work well for bi-level thresholding, but when used to find the appropriate thresholds for multi-level thresholding, there will be issues with long calculation times, high computational costs, and the need for accuracy improvements. This work investigates the capability of the Arithmetic Optimization Algorithm to discover the best multilayer thresholding for picture segmentation to circumvent this issue. The leading mathematical arithmetic operators' distributional nature is used by the AOA method. The picture histogram was used to construct the candidate solutions in the modified algorithms, which were then updated according to the algorithm's features. The solutions are evaluated using Otsu's fitness function throughout the optimization process. The picture histogram is used to display the algorithm's potential solutions. The proposed approach is tested on five frequent photos from the Berkeley University database. The fitness function, root-mean-squared error, peak signal-to-noise ratio, and other widely used assessment metrics were utilized to assess the performance of the suggested segmentation approach. Many benchmark pictures were employed to verify the suggested technique's effectiveness and evaluate it against other well-known optimization methods described in the literature.
Scopus© Citations 1 23
Algorithms for Pixelwise Shape Deformations Preserving Digital Convexity
(2022)
Tarsissi, Lama
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Kenmochi, Y
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Djerroumi, H
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Coeurjolly, D.
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Romon, P.
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Borel, JP
In this article, we propose algorithms for pixelwise deformations of digital convex sets preserving their convexity using the combinatorics on words to identify digital convex sets via their boundary words, namely Lyndon and Christoffel words. The notion of removable and insertable points are used with a geometric strategy for choosing one of those pixels for each deformation step. The worst-case time complexity of each deflation and inflation step, which is the atomic deformation, is also analysed.
17 150
An efficient artificial intelligence approach for early detection of cross-site scripting attacks
(2024)
Younas, Faizan
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Raza, Ali
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Thalji, Nisrean
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Abualigah, Laith
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Abu Zitar, Raed
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Jia, Heming
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 severe, ranging from financial losses to compromising sensitive user information. XSS attacks enable attackers to deface websites, distribute malware, or launch phishing campaigns, compromising the trust and reputation of affected organizations. This study proposes an efficient artificial intelligence approach for the early detection of XSS attacks, utilizing machine learning and deep learning approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as the Term Frequency-Inverse Document Frequency (TFIDF), are applied and compared to evaluate results. We introduce a novel approach named LSTM-TFIDF (LSTF) for feature extraction, which combines temporal and TFIDF features from the cross-site scripting dataset, resulting in a new feature set. Extensive research experiments demonstrate that the random forest method achieved a high performance of 0.99, outperforming state-of-the-art approaches using the proposed features. A k-fold cross-validation mechanism is utilized to validate the performance of applied methods, and hyperparameter tuning further enhances the performance of XSS attack detection. We have applied Explainable Artificial Intelligence (XAI) to understand the interpretability and transparency of the proposed model in detecting XSS attacks. This study makes a valuable contribution to the growing body of knowledge on XSS attacks and provides an efficient model for developers and security practitioners to enhance the security of web applications.
Scopus© Citations 7 21 3
An electro-elastic theory for the mechanically-assisted photo-induced spin transition in core-shell spin-crossover nanoparticles
(2018)
Slimani, Ahmed
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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 12 188 52
An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection
(2022)
Abu Zitar, Raed
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Awadallah, Mohammed A.
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Al-Betar, Mohammed Azmi
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Braik, Malik Shehadeh
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Hammouri, Abdelaziz I.
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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.
Scopus© Citations 54 88 31
An ensemble neural network approach to forecast Dengue outbreak based on climatic condition
(2023)
Panja, Madhurima
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Chakraborty, Tanujit
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Nadim. Sk Shahid
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Ghosh, Indrajit
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Kumar, Uttam
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Liu, Nan
Dengue fever is a virulent disease spreading over 100 tropical and subtropical countries in Africa, the Americas, and Asia. This arboviral disease affects around 400 million people globally, severely distressing the healthcare systems. The unavailability of a specific drug and ready-to-use vaccine makes the situation worse. Hence, policymakers must rely on early warning systems to control intervention-related decisions. Forecasts routinely provide critical information for dangerous epidemic events. However, the available forecasting models (e.g., weather-driven mechanistic, statistical time series, and machine learning models) lack a clear understanding of different components to improve prediction accuracy and often provide unstable and unreliable forecasts. This study proposes an ensemble wavelet neural network with exogenous factor(s) (XEWNet) model that can produce reliable estimates for dengue outbreak prediction for three geographical regions, namely San Juan, Iquitos, and Ahmedabad. The proposed XEWNet model is flexible and can easily incorporate exogenous climate variable(s) confirmed by statistical causality tests in its scalable framework. The proposed model is an integrated approach that uses wavelet transformation into an ensemble neural network framework that helps in generating more reliable long-term forecasts. The proposed XEWNet allows complex non-linear relationships between the dengue incidence cases and rainfall; however, mathematically interpretable, fast in execution, and easily comprehensible. The proposal's competitiveness is measured using computational experiments based on various statistical metrics and several statistical comparison tests. In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.
Scopus© Citations 12 32
An Improved Genghis Khan Optimizer based on Enhanced Solution Quality Strategy for Global Optimization and Feature Selection Problems
(2024)
Abdel-Salam, Mahmoud
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Alzahrani, Ahmed Ibrahim
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Alblehai, Fahad
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Abu Zitar, Raed
;
Abualigah, Laith
Feature selection (FS) is the activity of defining the most contributing feature subset among all used features to improve the superiority of datasets with a large number of dimensions by selecting significant features and eliminating redundant and irrelevant ones. Therefore, this process can be seen as an optimization process. The primary goals of feature selection are to decrease the number of dimensions and enhance classification accuracy in many domains, such as text classification, large-scale data analysis, and pattern recognition. Several metaheuristics, such as the Genghis Khan Shark Optimizer Algorithm (GKSO), can assist in optimizing the FS issue. However, these methods tend to converge towards local solutions with a low convergence rate. In order to address this issue in GKSO, a more refined version called I-GKSO is implemented. The I-GKSO suggested introducing a new approach to modify solutions that have low fitness values. In addition, it employs the Enhanced Solution Quality (ESQ) strategy to enhance the exploration phase. It utilizes Quasi-opposite-based learning (QOBL) to enhance the best solution obtained and, consequently, the entire population. The algorithm presented aims to solve the FS problem and has been assessed using benchmark optimization problems from the CEC’2017 and CEC’2022. To assess the efficacy of the I-GKSO, it has been subjected to comparisons with multiple different algorithms. The trials conducted using FS datasets yield a quantitative consideration of the I-GKSO's capacity to attain the most optimal subset of features. Furthermore, Wilcoxon and Friedman's non-parametric tests were accomplished to support the performance of the proposed method.
Scopus© Citations 3 19

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