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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

Computers, Materials & Continua 2022年第10期73卷 865-881页

作者： doaa Sami Khafaga Amel Ali Alhussan el-Sayed M.el-kenawy Abdelhameed Ibrahim Said H.Abd elkhalik Shady Y.el-Mashad Abdelaziz A.Abdelhamid 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 Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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Computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： doaa Sami Khafaga el-Sayed M.el-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.elsheweikh 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 TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia 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 Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(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 hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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Computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat el-Sayed M.el-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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Computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid el-Sayed M.el-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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The optimum conditions for methanol conversion to dimethyl ether over modified sulfated zirconia catalysts prepared by different methods

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燃料化学学报 2021年第1期49卷 63-71页

作者： doaa S.el-DESOUKI Amina H.IBRAHIM Samira M.ABDelAZIM Noha A.K.ABOUL-GHEIT Dalia R.ABDel-HAFIZAR Processes Development Department EPRICairo 11727Egypt Refining Department EPRICairo 11727Egypt

Sulfated zirconia(SZ)and two promoted 1% Mn/SZ catalysts which have been prepared via sol gel(Mn/SZ-S)and impregnation(Mn/SZ-I)methods were *** morphology of the catalysts was characterized by XRD,BET,NH3-TPD,ICP,SEM and FT-IR *** conversion of methanol to dimethyl ether and hydrocarbons was carried out in the temperature range of 120−300℃.The Mn/SZ-S showed the highest activity due to the high surface area with suitable *** optimum condition of Mn/SZ-S catalyst was investigated at 200℃ and LHSV of 0.02 h^−1 in a time range from 30 to 210 *** was found that the total conversion decreased from 80.18% to 53.26% at 210 *** reusability of this catalyst was studied at the optimum condition up till four cycles for 1 *** characterization of the reused catalyst showed a significant change in the structure and surface acidity due to the blockage of the surface acid sited by carbonaceous materials.

关键词： catalysts dehydration DME sulfate content sulfated zirconia

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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Computer Systems Science & Engineering 2023年第7期46卷 883-896页

作者： el-Sayed M.el-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi doaa Sami Khafaga Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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Nanofibrous ε-polycaprolactone scaffolds containing Ag-doped magnetite nanoparticles: Physicochemical characterization and biological testing for wound dressing applications in vitro and in vivo

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Bioactive Materials 2021年第7期6卷 2070-2088页

作者： M.K.Ahmed M.A.Zayed S.I.el-dek Mayssa Abdel Hady doaa H.el Sherbiny Vuk Uskokovic Faculty of Nanotechnology for Postgraduate studies Cairo UniversityEl-Sheikh Zayed 12588Egypt Department of Physics Faculty of ScienceSuez UniversitySuez 43518Egypt Chemistry Department Faculty of ScienceCairo University12613GizaEgypt Materials Science and Nanotechnology Department Faculty of Postgraduate Studies for Advanced SciencesBeni-Suef UniversityBeni SuefEgypt Pharmaceutical Technology Department National Research CentreDokiiGizaEgypt Advanced Materials and Nanobiotechnology Laboratory TardigradeNano LLCIrvineCA92604USA Department of Biochemistry Faculty of DentistryModern University for Technology and InformationMokattamCairoEgypt

Skin wounds can lead to numerous complications with dangerous health *** this work,magnetite nanoparticles were doped with different concentrations of antimicrobial silver(Ag)ions and incorporated into the electrospun nanofibrous ε-polycaprolactone(PCL)*** and scaffolds with various Ag contents were characterized using a range of physicochemical *** entered magnetite as cations and preferentially positioned at tetrahedral sites,introducing lattice distortions and topographic *** of the structure due to accommodation of Ag expanded the lattice in the bulk and contracted it on the surface,where broadened distribution of Fe-O coordinations was *** spin canting and diminishing the double exchange interaction through altered distribution of ferric and ferrous ions,Ag softened the magnetism of *** making the nanoparticle structure more defective,Ag modified the interface with the polymer and promoted the protrusion of the nanoparticles from the surface of the polymeric nanofibers,thus increasing their roughness and hydrophilicity,with positive repercussions on cell adhesion and *** the viability of human melanocytes and the antibacterial activity against *** and *** increased with the concentration of Ag in the magnetite phase of the *** wound healing rate in rats also increased in direct proportion with the concentration of Ag in the magnetite phase,and no abnormalities in the dermal and epidermal tissues were visible on day 10 in the treatment *** results imply an excellent potential of these composite nanofibrous scaffolds for use as wound dressings and in other reconstructive skin therapies.

关键词： Tissue engineering Wound healing Skin Magnetic nanoparticles Nanofiber

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Diffusion-weighted magnetic resonance imaging and micro-RNA in the diagnosis of hepatic fibrosis in chronic hepatitis C virus

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World Journal of Gastroenterology 2019年第11期25卷 1366-1377页

作者： Tarek Besheer Hatem elalfy Mohamed Abd el-Maksoud Ahmed Abd el-Razek Saher Taman Khaled Zalata Wagdy elkashef Hossam Zaghloul Heba elshahawy doaa Raafat Wafaa elemshaty Eman elsayed Abdel-Hady el-Gilany Mahmoud el-Bendary Department of Tropical Medicine and Hepatology Mansoura Faculty of Medicine-Mansoura University Department of Diagnostic Radiology Mansoura Faculty of Medicine-Mansoura University Department of Pathology Mansoura Faculty of Medicine-Mansoura University Department of Clinical Pathology Mansoura Faculty of Medicine-Mansoura University Department of Public Health and Preventive Medicine Mansoura Faculty of Medicine-Mansoura University

BACKGROUND Diffusion-weighted magnetic resonance imaging has shown promise in the detection and quantification of hepatic fibrosis. In addition, the liver has numerous endogenous micro-RNAs(miRs) that play important roles in the regulation of biological processes such as cell proliferation and hepatic *** To assess diffusion-weighted magnetic resonance imaging and miRs in diagnosing and staging hepatic fibrosis in patients with chronic hepatitis *** This prospective study included 208 patients and 82 age-and sex-matched controls who underwent diffusion-weighted magnetic resonance imaging of the abdomen, miR profiling, and liver biopsy. Pathological scoring was classified according to the METAVIR scoring system. The apparent diffusion coefficient (ADC) and miR were calculated and correlated with pathological *** The ADC value decreased significantly with the progression of fibrosis, from controls(F0) to patients with early fibrosis(F1 and F2) to those with late fibrosis(F3 and F4)(median 1.92, 1.53, and 1.25 × 10^(-3) mm^2/s, respectively)(P = 0.001).The cut-off ADC value used to differentiate patients from controls was 1.83 × 10^(-3) mm^2/s with an area under the curve(AUC) of 0.992. Combining ADC and miR-200 b revealed the highest AUC(0.995) for differentiating patients from controls with an accuracy of 96.9%. The cut-off ADC used to differentiate early fibrosis from late fibrosis was 1.54 × 10^(-3) mm^2/s with an AUC of 0.866. The combination of ADC and miR-200 b revealed the best AUC(0.925) for differentiating early fibrosis from late fibrosis with an accuracy of 80.2%. The ADC correlated with miR-200 b(r =-0.61, P = 0.001), miR-21(r =-0.62, P = 0.001), and miR-29(r = 0.52,P = 0.001).CONCLUSION Combining ADC and miRs offers an alternative surrogate non-invasive diagnostic tool for diagnosing and staging hepatic fibrosis in patients with chronic hepatitis C.

关键词： Diffusion Magnetic resonance imaging Fibrosis Liver Hepatitis C virus Micro-RNA

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Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

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Computer Systems Science & Engineering 2023年第5期45卷 1917-1934页

作者： el-Sayed M.el-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan doaa Sami Khafaga Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： Metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

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Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

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Computers, Materials & Continua 2022年第10期73卷 749-765页

作者： doaa Sami Khafaga Amel Ali Alhussan el-Sayed M.el-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid 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 Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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