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Urban electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

Computers, materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel Ali Alhussan Doaa Sami Khafaga el-sayed m.el-kenawy marwa m.Eid Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： electric vehicle graylag goose optimization metaheuristics optimization machine learning

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Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

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Computers, materials & Continua 2023年第1期74卷 19-36页

作者： motasem S.Alsawadi el-sayed m.el-kenawy miguel Rio electronic and electrical Engineering Department University College LondonLondonWC1E 7JEEngland Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

The ever-growing available visual data(i.e.,uploaded videos and pictures by internet users)has attracted the research community’s attention in the computer vision ***,finding efficient solutions to extract knowledge from these sources is ***,the BlazePose system has been released for skeleton extraction from images oriented to mobile *** this skeleton graph representation in place,a Spatial-Temporal Graph Convolutional Network can be implemented to predict the *** hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest,it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR ***,in this study,we present the first implementation of the BlazePose skeleton topology upon this architecture for action ***,we propose the Enhanced-BlazePose topology that can achieve better results than its ***,we propose different skeleton detection thresholds that can improve the accuracy performance even *** reached a top-1 accuracy performance of 40.1%on the Kinetics *** the NTU-RGB+D dataset,we achieved 87.59%and 92.1%accuracy for Cross-Subject and Cross-View evaluation criteria,respectively.

关键词： Action recognition BlazePose graph neural network OpenPose skeleton spatial temporal graph convolution network

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Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks

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Computers, materials & Continua 2022年第3期70卷 5091-5106页

作者： Abdullah Ali Salamai Ather Abdulrahman Ageeli el-sayed m.el-kenawy Community college Jazan UniversityJazanKingdom of Saudi Arabia Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt

E-commerce refers to a system that allows individuals to purchase and sell things *** primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a *** will purchase the item online and have it delivered to their home within a few *** goal of this research was to develop machine learning algorithms that might predict e-commerce platformsales.A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search(SFS)based on a Guided Whale Optimization Algorithm(WOA)to optimize the parameter weights of the Bidirectional Recurrent Neural Networks(BRNN).Furthermore,a time series dataset is tested in the experiments of e-commerce demand ***,the results were compared to many versions of the state-of-the-art optimization techniques such as the Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),and Genetic Algorithm(GA).A statistical analysis has proven that the proposed algorithm can work significantly better by statistical analysis test at the P-value less than 0.05 with a one-way analysis of variance(ANOVA)test applied to confirm the performance of the proposed ensemble *** proposed Algorithm achieved a root mean square error of RmSE(0.0000359),mean(0.00003593)and Standard Deviation(0.000002162).

关键词： Neural networks e-commerce forecasting risk management machine learning

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Optimization Ensemble Weights model for Wind Forecasting System

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Computers, materials & Continua 2022年第11期73卷 2619-2635页

作者： Amel Ali Alhussan el-sayed m.el-kenawy Hussah Nasser AlEisa m.el-SAID sayed A.Ward Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt electrical Engineering Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Delta Higher Institute of Engineering and Technology(DHIET) Mansoura35111Egypt Department of electrical Engineering Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Faculty of Engineering Delta University for Science and TechnologyGamasaMansoura11152Egypt

Effective technology for wind direction forecasting can be realized using the recent advances in machine ***,the stability and safety of power systems are expected to be significantly ***,the unstable and unpredictable qualities of the wind predict the wind direction a challenging *** paper proposes a practical forecasting approach based on the weighted ensemble of machine learning *** weighted ensemble is optimized using a whale optimization algorithm guided by particle swarm optimization(PSO-Guided WOA).The proposed optimized weighted ensemble predicts the wind direction given a set of input *** conducted experiments employed the wind power forecasting dataset,freely available on Kaggle and developed to predict the regular power generation at seven wind farms over forty-eight *** recorded results of the conducted experiments emphasize the effectiveness of the proposed ensemble in achieving accurate predictions of the wind *** addition,a comparison is established between the proposed optimized ensemble and other competing optimized ensembles to prove its ***,statistical analysis using one-way analysis of variance(ANOVA)and Wilcoxon’s rank-sum are provided based on the recorded results to confirm the excellent accuracy achieved by the proposed optimized weighted ensemble.

关键词： Guided Whale Optimization Algorithm(Guided WOA) forecasting machine learning weighted ensemble model wind direction

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Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

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Computers, materials & Continua 2022年第7期72卷 1465-1481页

作者： Ali E.Takieldeen el-sayed m.el-kenawy mohammed Hadwan Rokaia m.Zaki Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaizYemen Intelligent Analytics Group(IAG) College of ComputerQassim UniversityBuraydahSaudi Arabia Higher Institute of Engineering and Technology KafrelsheikhEgypt Department of electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated *** has its unique hunting technique by performing rapid bowing *** show the efficiency of the proposed algorithm,DTO is tested and compared to the algorithms of Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),Grey Wolf Optimizer(GWO),and Genetic Algorithm(GA)based on the seven unimodal benchmark ***,ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization ***,to demonstrate the proposed algorithm’s suitability for solving complex realworld issues,DTO is used to solve the feature selection *** strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine(UCI)*** findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues,demonstrating the proposed algorithm’s capabilities to solve complex real-world situations.

关键词： metaheuristic optimization swarmoptimization feature selection function optimization

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An Optimized Ensemble model for Prediction the Bandwidth of metamaterial Antenna

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Computers, materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy mohamed mohamad Fouad el-sayed m.el-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(mL)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the metamaterial *** base models are used namely:multilayer Perceptron(mLP)and Support Vector machines(SVm).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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Detection Collision Flows in SDN Based 5G Using machine Learning Algorithms

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Computers, materials & Continua 2023年第1期74卷 1413-1435页

作者： Aqsa Aqdus Rashid Amin Sadia Ramzan Sultan S.Alshamrani Abdullah Alshehri el-sayed m.el-kenawy Department of Computer Science University of Engineering and Technology Taxila47050Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology Al Baha UniversityAl BahaSaudi Arabia Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt

The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of *** traffic control and data forwarding functions are decoupled in software-defined networking(SDN)and allow the network to be *** switch in SDN keeps track of forwarding information in a flow *** SDN switches must search the flow table for the flow rules that match the packets to handle the incoming *** to the obvious vast quantity of data in data centres,the capacity of the flow table restricts the data plane’s forwarding ***,the SDN must handle traffic from across the whole *** flow table depends on Ternary Content Addressable memorable memory(TCAm)for storing and a quick search of regulations;it is restricted in capacity owing to its elevated cost and energy *** the flow table is abused and overflowing,the usual regulations cannot be executed *** this case,we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don’t fit the flow table at a low *** study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN,using Feed ForwardNeuralNetwork(FFNN),K-means,and Decision Tree(DT).We generate two network topologies,Fat Tree and Simple Tree Topologies,with the mininet simulator and coupled to the OpenDayLight(ODL)*** efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query,propagation delay,overall dropped packets,energy consumption,bandwidth usage,latency rate,and *** findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G *** putting the proposed flow method

关键词： 5G networks software-defined networking(SDN) OpenFlow load balancing machine learning(mL) feed forward neural network(FFNN) k-means and decision tree(DT)

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Novel Optimized Feature Selection Using metaheuristics Applied to Physical Benchmark Datasets

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Computers, materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga el-sayed m.el-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bmGWO),binary multiverse optimization(bmVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CmC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

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Intelligent Automation & Soft Computing 2023年第8期37卷 2117-2132页

作者： Doaa Sami Khafaga el-sayed m.el-kenawy Amel Ali Alhussan marwa m.Eid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansouraEgypt

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and ***,the accurate prediction can be realized using the recent advances in machine learning and predictive *** research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTm)*** proposed optimization algorithm is used to optimize the parameters of the LSTm-based model to boost its forecasting *** optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic *** prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTm-based model,and the results are compared with that of the proposed *** results show that the proposed DDTOSFS+LSTm can accurately forecast the energy consumption with root mean square error RmSE of 0.00013,which is the best among the recorded results of the other *** addition,statistical experiments are conducted to prove the statistical difference of the proposed *** results of these tests confirmed the expected outcomes.

关键词： Stochastic fractal search dipper throated optimization energy consumption long short-term memory prediction models

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machine Learning Techniques for Detecting Phishing URL Attacks

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Computers, materials & Continua 2023年第4期75卷 1271-1290页

作者： Diana T.mosa mahmoud Y.Shams Amr AAbohany el-sayed m.el-kenawy m.Thabet Department of Cyber Security College of Engineering and Information TechnologyBuraydah Private CollegesBuraydah51418Kingdom of Saudi Arabia Faculty of Computers and Information Kafrelsheikh UniversityKafrelsheikh33516Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33516Egypt Delta Higher Institute of Engineering and Technology Mansoura35111Egypt Faculty of Computers and Information Fayoum UniversityFayoumEgypt

Cyber Attacks are critical and destructive to all industry *** affect social engineering by allowing unapproved access to a Personal Computer(PC)that breaks the corrupted system and threatens *** defense of security requires understanding the nature of Cyber Attacks,so prevention becomes easy and accurate by acquiring sufficient knowledge about various features of Cyber ***-Security proposes appropriate actions that can handle and block attacks.A phishing attack is one of the cybercrimes in which users follow a link to illegal websites that will persuade them to divulge their private *** of the online security challenges is the enormous number of daily transactions done via phishing *** Cyber-Security have a priority for all organizations,Cyber-Security risks are considered part of an organization’s risk management *** paper presents a survey of different modern machine-learning approaches that handle phishing problems and detect with high-quality accuracy different phishing attacks.A dataset consisting of more than 11000 websites from the Kaggle dataset was utilized and studying the effect of 30 website features and the resulting class label indicating whether or not it is a phishing website(1 or−1).Furthermore,we determined the confusion matrices of machine Learning models:Neural Networks(NN),Na飗e Bayes,and Adaboost,and the results indicated that the accuracies achieved were 90.23%,92.97%,and 95.43%,respectively.

关键词： Cyber security phishing attack URL phishing online social networks machine learning

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