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Alumina Effect on the Phase Transformation of 3Y-TZP Ceramics

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Journal of materials Science & Technology 2008年第6期24卷 873-877页

作者： Ezzat S.elshazly m.el-sayed ali S.m.el-Hout Department of metallurgy Nuclear Research CenterAtomic Energy AuthorityCairoEgypt

The isothermal tetragonal-to-monoclinic phase transformation of 3 mol fraction Y2O3-ZrO2 ceramics contain- ing different amounts of Al2O3 during ageing in water at 130℃ for periods of time up to 40 h was investigated to explore the effect of Al2O3 addition on this transformation. The propagation of the transformation into the specimen interiors was suppressed by the addition of Al2O3. The transformation kinetics showed a nucleation and growth mechanism on the specimen surface to be dominant in the low temperature ageing in water environment.

关键词： Phase transformation 3Y-TZP Al2O3 Kinetics

A Synergistic multi-Attribute Decision-making method for Educational Institutions Evaluation Using Similarity measures of Possibility Pythagorean Fuzzy Hypersoft Sets

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Computer modeling in Engineering & Sciences 2025年第1期142卷 501-530页

作者： Khuram ali Khan Saba mubeen Ishfaq Atiqe Ur Rahman Salwa el-morsy Department of mathematics University of SargodhaSargodha40100Pakistan Department of mathematics University of Management and TechnologyLahore54000Pakistan Department of mathematics College of ScienceQassim UniversityBuraydah51452Saudi Arabia

Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be *** concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such *** study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and *** results related to these operations are also modified for ***,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and ***,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(mADm)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested *** algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical ***,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.

关键词： Hypersoft set Pythagorean fuzzy hypersoft set computational complexity multi-attribute decision-making optimization similarity measures uncertainty

Dynamic Voting Classifier for Risk Identification in Supply Chain 4.0

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Computers, materials & Continua 2021年第12期69卷 3749-3766页

作者： Abdullah ali Salamai el-sayed m.el-kenawy Ibrahim Abdelhameed Community college Jazan UniversityJazanKingdom of Saudi Arabia Delta Higher Institute of Engineering and Technology MansouraEgypt Faculty of Engineering Mansoura UniversityMansouraEgypt

Supply chain 4.0 refers to the fourth industrial revolution’s supply chain management systems,which integrate the supply chain’s manufacturing operations,information technology,and telecommunication *** supply chain 4.0 aims to improve supply chains’production systems and profitability,it is subject to different operational and disruptive *** risks are a big challenge in the cycle of supply chain 4.0 for controlling the demand and supply operations to produce and deliver products across IT *** paper proposes a voting classifier to identify the operational risks in the supply chain 4.0 based on a Sine Cosine Dynamic Group(SCDG)*** and exploitation mechanisms of the basic Sine Cosine Algorithm(CSA)are adjusted and controlled by two groups of agents that can be changed dynamically during the *** and internal features were collected and analyzed from different data sources of service level agreements and transaction data from various KSA firms to validate the proposed algorithm’s efficiency.A balanced accuracy of 0.989 and a mean Square Error(mSE)of 0.0476 were achieved compared with other optimization-based classifier techniques.A one-way analysis of variance(ANOVA)and Wilcoxon rank-sum tests were performed to show the superiority of the proposed SCDG ***,the experimental results indicate the effectiveness of the proposed SCDG algorithm-based voting classifier.

关键词： machine learning artificial intelligence supply chain 4.0 risk factors risk management

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

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

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

Advance Artificial Intelligence Technique for Designing Double T-Shaped monopole Antenna

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Computers, materials & Continua 2021年第12期69卷 2983-2995页

作者： el-sayed m.el-kenawy Hattan F.Abutarboush ali Wagdy mohamed Abdelhameed Ibrahim Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt electrical Engineering Department College of EngineeringTaibah UniversityMedina 42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza 12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

machine learning(mL)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design *** is a massive area within artificial intelligence(AI)that focuses on obtaining valuable information out of data,explaining why mL has often been related to stats and data *** advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture *** algorithm is designed,depending on the hybrid between the Sine Cosine Algorithm(SCA)and the Grey Wolf Optimizer(GWO),to train neural networkbased multilayer Perceptron(mLP).The proposed optimization algorithm is a practical,versatile,and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole *** proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and *** offers the superiority and validation stability evaluation of the predicted results to verify the procedures’accuracy.

关键词： Antenna optimization machine learning artificial intelligence multilayer perceptron sine cosine algorithm grey wolf optimizer

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

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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

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