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Wind Power Prediction Based on machine Learning and Deep Learning models

Computers, materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek mahmoud Y.Shams Ahmed m.elshewey el-Sayed m.el-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid mohamed A.el-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt 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 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 Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTm),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTm *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(mAE),Nash Sutcliffe Efficiency(NSE),mean square error(mSE),coefficient of determination(R2),root mean squared error(RmSE).The experimental results illustrated that the proposed optimization of LSTm using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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Deterministic and Stochastic Fractional Order model for Lesser Date moth

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Computer Systems Science & Engineering 2022年第2期40卷 749-764页

作者： moustafa el-shahed Asmaa m.Al-Dububan Department of mathematics Unaizah College of Sciences and ArtsQassim UniversityP.O.Box 3771Unaizah51911Saudi Arabia Department of mathematics Buraydah College of Sciences and ArtsQassim UniversityP.O.Box 1162Buraydah51431Saudi Arabia

In this paper,a deterministic and stochastic fractional order model for lesser date moth(LDm)using mating disruption and natural enemies is proposed and *** interaction between LDm larvae,fertilized LDm female,unfertilized LDm female,LDm male and the natural enemy is *** order to clarify the characteristics of the proposed deterministic fractional order model,the analysis of existence,uniqueness,non-negativity and boundedness of the solutions of the proposed fractional-order model are *** addition,some sufficient conditions are obtained to ensure the local and global stability of equilibrium *** occurrence of local bifurcation near the equilibrium points is investigated with the help of Sotomayor’s *** simulations are conducted to illustrate the properties of the proposed fractional order model with respect to the intrinsic growth rate of the LDm larvae,natural enemy’s mortality rate,predation rate,sex pheromone trap parameter,fractional order and environmental *** impact of mating disruption on lesser date moth is ***,a numerical approximation method is developed for the proposed stochastic fractional-order model.

关键词： Lesser date moth stochastic stability natural enemies Sotomayor’s theorem mating disruption

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Effect of single subconjunctival injection of bevacizumab on primary pterygium:clinical,histopathological and immunohistochemical study

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International Journal of Ophthalmology(English edition) 2018年第5期11卷 797-801页

作者： Tarek A mohamed wael Soliman Ahmed m.Fathalla Abeer el Refaie Department of Ophthalmology Assiut University Hospital Department of Pathology Faculty of MedicineAssiut University

AIm：To evaluate the effect（clinically,histopathologically and immunohistochemically） and safety of a single intrapterygium injection of ***：Prospective interventional study comprised 40 eyes of 40 patients with primary fleshy pterygia who attended the Outpatient Clinic of Department of Ophthalmology,Assiut University Hospitals,Egypt from may 2015 to may *** were randomly classified into 2 groups：the first group received a single intralesional injection of bevacizumab（Avastin;Genentech,San Francisco,CA,USA）;the second group comprised patients who did not receive subconjunctival *** of pterygium and conjunctival auto graft was done in both *** excised pterygium tissues were subjected to histopathological and immunohistochemical ***：The study comprised 40 eyes of 40 patients（33 men,7 women） of age range from 31-58 *** study group included 22 *** control group included 18 eyes.A decrease in the vascularity of the pterygium was noted in all injected *** mean vessel count was higher in non-injected pterygia than that in injected pterygia and the difference was statistically significant（P=0.001）.Also,the mean vessel count in both groups was significantly higher than normal conjunctive（P=0.005 and 0.001）.A statistically significant difference in vascular endothelial growth factor（VEGF） expression between injected and non-injected cases was detected in the epithelial,stromal and endothelial cells（P=0.0001,0.016,0.014）.No serious intraoperative complications occurred in both ***：The use of single intra lesional injection of Avastin in pterygium decreased vascularity and decreased VEGF expression in injected pterygium after one *** study proved the effect of single intra lesional injection of Avastin on *** studies may enable limiting the need for surgery and improve quality of life for patients with pterygia.

关键词： pterygiurn vascularity bevacizumab vessel count

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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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Influence of preceding crop and tillage system on forage yield and quality of selected summer grass and legume forage crops under arid conditions

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Journal of Integrative Agriculture 2022年第11期21卷 3329-3344页

作者： Hend H.m.HASSAN el-Sayed E.A.el-SOBKY elsayed mANSOUR Ahmed S.m.el-KHOLY mohamed F.AWAD Hayat ULLAH Avishek DATTA Forage Crops Research Department Field Crops Research InstituteAgricultural Research CenterGiza 12619Egypt Agronomy Department Faculty of AgricultureZagazig UniversityZagazig 44519Egypt Department of Biology College of ScienceTaif UniversityTaif 21944Saudi Arabia Agricultural Systems and Engineering Department of FoodAgriculture and BioresourcesSchool of EnvironmentResources and DevelopmentAsian Institute of TechnologyKlong LuangPathum Thani 12120Thailand

Among the crop production factors,preceding crop and tillage management affect the sustainable use of soil resources and ultimately crop growth and *** study aimed at investigating the impact of preceding winter crops(grass or legume)and different tillage systems on forage yield,quality and nutritive values of three summer grass(Sudan grass,pearl millet and teosinte)and two legume forage crops(cowpea and guar)under arid *** results exhibited that growing forage crops after legumes(as berseem clover)produced the highest fresh and dry forage yields and quality attributes compared with grasses(as wheat)with the exception of crude fiber content,which was ***,tillage practices showed positive impact on forage yields and quality *** maximum forage yields and quality parameters were recorded under conventional tillage(CT)practice compared with reduced tillage(RT)and no-tillage(NT)*** the evaluated crops,the highest yields of fresh forage,dry forage,crude fiber,crude protein and total digestible nutrient were exhibited by grass forage crops(Sudan grass,pearl millet and teosinte),whereas the highest crude protein content and the digestible energy values were produced by legume forage crops(cowpea and guar).The maximum fresh forage,dry forage,crude fiber,crude protein,total digestible nutrient and digestible crude protein yields were produced by pearl millet followed by Sudan grass under CT and RT after berseem *** highest net return was recorded by sowing pearl millet after berseem clover and applying CT followed by RT practices,which could be recommended for the commercial ***,it could be assumed that the combination of growing grass forage crops after legume crops under CT or RT systems could enhance forage crop yield and quality with an improvement in soil properties for sustainable agriculture with low cost and the highest net income.

关键词： soil tillage preceding crop forage quality nutritive values net return

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Optimized Ensemble Algorithm for Predicting metamaterial Antenna Parameters

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Computers, materials & Continua 2022年第6期71卷 4989-5003页

作者： el-Sayed m.el-kenawy Abdelhameed Ibrahim Seyedali mirjalili Yu-Dong Zhang Shaima elnazer Rokaia m.Zaki 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 Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the metamaterial *** Vector machines(SVm),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root mean Squared Error(RmSE)of(0.0102)for bandwidth and RmSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

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Computer Systems Science & Engineering 2022年第10期43卷 87-102页

作者： Safaa el-Gazar Walid el-Shafai Ghada el-Banby Hesham F.A.Hamed Gerges m.Salama mohammed Abd-elnaby Fathi E.Abd el-Samie Department of electrical Engineering Faculty of EngineeringEgyptian-Russian UniversityCairoEgypt Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last *** achieves safe access to systems using biometrics instead of the traditional *** utilization of a biometric in its original format makes it usable only ***,a cancelable biometric template should be used,so that it can be replaced when it is *** biometrics aims to enhance the security and privacy of biometric *** encryption is an efficient technique to be used in order to generate cancelable biometric *** this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification *** considered biometric in this paper is the speech *** speech signal is transformed into its ***,the spectrogram is encrypted using two cascaded optical encryption *** first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption *** OSH encrypted spectrogram is encrypted using Double Random Phase Encoding(DRPE)by implementing two Random Phase masks(RPms).After the two cascaded optical encryption algorithms,the cancelable template is *** verification is implemented through correlation estimation between enrolled and test templates in their encrypted *** the correlation value is larger than a threshold value,the user is *** threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two *** implementation of optical encryption is adopted using its software rather than the optical *** efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation *** can improve the biometric data security without deteriorating the recognition *** results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Charact

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

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Role of the injected water salinity and ion concentrations on the oil recovery in carbonate reservoirs

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Petroleum Research 2022年第3期7卷 394-400页

作者： m.Fouad Snosy mahmoud Abu el ela Ahmed el-Banbi Helmy Sayyouh Petroleum Engineer General Petroleum CompanyEgypt Professor of Petroleum Engineering Cairo UniversityEgypt Professor of Petroleum Engineering The American University in CairoEgypt

Low salinity water flooding(LSWF)was initially considered using water with a low concentration of dissolved salts and was later extended to include modifying the ionic content of injected *** work investigates the effects of changing water salinity and composition along with the concentration of sulfate and iodide ions on oil recovery in carbonate reservoirs during the tertiary recovery *** experimental study was carried out using crude oil of 29°API,8 core samples extracted from the Eocene carbonate reservoir(Egypt),and 6 different water *** results showed additional oil recovery up to 5%of the original oil in place(OOIP)in the tertiary recovery stage with changing water salinity and water *** of high salinity(HS)and low salinity(LS)brines with high sulfate concentrations increased the incremental oil recovery by a value ranging from 1.7 to 3.8%of the *** the contrary,injection of HS and LS brines with low sulfate concentrations showed insignificant incremental oil recovery(less than 1%of the OOIP).Furthermore,injection of water with potassium iodide(KI)after injection of water with high sulfate brines showed additional oil recovery of about 1.7%of the *** the other hand,injection of water with KI after injection of water with low sulfate concentration showed insignificant incremental oil recovery(less than 0.4%of the OOIP).The concentration of sulfate in the injected water appeared to be key parameter to achieve effective waterflooding(WF)projects in carbonate ***,the results revealed that the multi-component ion exchange(mIE)mechanism seems to be the primary recovery mechanism for LSWF in carbonate *** results and conclusions of this study can be used to develop guidelines for designing waterflooding projects in carbonate reservoirs with optimum salinity.

关键词： Low salinity waterflooding Carbonate reservoirs Waterflooding LSWF

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Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network

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Computers, materials & Continua 2022年第10期73卷 1141-1158页

作者： m.m.Lotfy Hazem m.el-Bakry m.m.elgayar Shaker el-Sappagh G.Abdallah m.I A.A.Soliman Kyung Sup Kwak Department of mathematics Faculty of ScienceArish UniversityArish45511Egypt Department of Information Systems Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Centro Singular de Investigación en Tecnoloxías Intelixentes(CiTIUS) Universidade de Santiago de CompostelaSantiago de Compostela15705Spain Information Systems Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13512Egypt Department of Information and Communication Engineering Inha UniversityIncheon22212Korea

Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people,and its high number of deaths also by 7%.For that purpose,a proposed model of several stages was *** first stage is optimizing the images using dynamic adaptive histogram equalization,performing a semantic segmentation using DeepLabv3Plus,then augmenting the data by flipping it horizontally,rotating it,then flipping it *** second stage builds a custom convolutional neural network model using several pre-trained ***,the model compares the pre-trained data to the new output,while repeatedly trimming the best-performing models to reduce complexity and improve memory *** experiments were done using different techniques and ***,the proposed model achieved an average accuracy of 99.6%and an area under the curve of 0.996 in the Covid-19 *** paper will discuss how to train a customized intelligent convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.

关键词： SARS-COV2 COVID-19 pneumonia deep learning network semantic segmentation smart classification

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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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