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Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

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Computer Systems Science & Engineering 2023年第6期45卷 3195-3213页

作者： Zahraa tarek Samaa m.Shohieb Abdelghafar m.elhady el-Sayed m.el-kenawy mahmoud Y.Shams Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Deanship of Scientific Research Umm Al-Qura UniversityMakkah21955KSA Delta Higher Institute of Engineering and Technology Mansoura35111Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The ability to detect and localize the human eye is critical for use in security applications and human identification and verification *** is because eye recognition algorithms have multiple challenges,such as multi-pose variations,ocular parts,and ***,the modern security applica-tions fail to detect facial expressions from eye *** this paper,a Speeded-Up Roust Feature(SURF)Algorithm was utilized to localize the face images of the enrolled *** highlighted on eye and pupil parts to be detected based on SURF,Hough Circle Transform(HCT),and Local Binary Pattern(LBP).Afterward,Deep Belief Neural Networks(DBNN)were used to classify the input features results from the SURF *** further determined the correctly and wrongly classified subjects using a confusion matrix with two class labels to classify people whose eye images are correctly *** apply Stochastic Gradient Descent(SGD)optimizer to address the overfitting problem,and the hyper-parameters arefine-tuned based on the applied *** accuracy of the proposed system is determined based on SURF,LBP,and DBNN classifier achieved 95.54%for the ORL dataset,94.07%for the BioID,and 96.20%for the CASIA-V5 *** proposed approach is more reliable and more advanced when compared with state-of-the-art algorithms.

关键词： Eye localization classification DBNN detection feature extraction LBP feature extraction SURF

Weight Prediction Using the Hybrid Stacked-LSTm Food Selection model

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Computer Systems Science & Engineering 2023年第7期46卷 765-781页

作者： Ahmed m.elshewey mahmoud Y.Shams Zahraa tarek mohamed megahed el-Sayed m.el-kenawy mohamed A.el-dosuky Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentMansoura University35561Egypt Faculty of Physical Education Track and Field Competitions DepartmentArish UniversityEgypt Delta Higher Institute of Engineering and Technology Mansoura35111Egypt Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Food choice motives(i.e.,mood,health,natural content,convenience,sensory appeal,price,familiarities,ethical concerns,and weight control)have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the *** from several domains have presented several models addressing issues influencing food choice over the ***,a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making *** this paper,four Deep Learning(DL)models and one machine Learning(mL)model are utilized to predict the weight in pounds based on food *** Long Short-Term memory(LSTm)model,stacked-LSTm model,Conventional Neural Network(CNN)model,and CNN-LSTm model are the used deep learning *** the applied mL model is the K-Nearest Neighbor(KNN)*** efficiency of the proposed model was determined based on the error rate obtained from the experimental *** findings indicated that mean Absolute Error(mAE)is 0.0087,the mean Square Error(mSE)is 0.00011,the median Absolute Error(medAE)is 0.006,the Root mean Square Error(RmSE)is 0.011,and the mean Absolute Percentage Error(mAPE)is ***,the results demonstrated that the stacked LSTm achieved improved results compared with the LSTm,CNN,CNN-LSTm,and KNN regressor.

关键词： Weight prediction machine learning deep learning LSTm CNN KNN

Wind Power Prediction Based on machine Learning and Deep Learning models

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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

metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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Computers, materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee el-Sayed m.el-Kenawy Ghada Atteia mona m.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.el-Attar tarek Gaber Adam Slowik mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia 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 Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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

Impact Assessment and mitigation Techniques for High Penetration Levels of Renewable Energy Sources in Distribution Networks:Voltage-control Perspective

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Journal of modern Power Systems and Clean Energy 2022年第2期10卷 450-458页

作者： Ahmed S.A.Awad Dave Turcotte tarek H.m.el-Fouly Natural Resources Canada Can-metENERGYVarennesCanadaon leave from the Electrical Power and Machines DepartmentFaculty of EngineeringAin Shams UniversityCairo 11566Egyp Natural Resources Canada CanmetENERGYVarennesCanada Advanced Power and Energy Center Khalifa UniversityAbu DhabiUnited Arab Emirates

The integration of renewable distributed generation(RDG)into distribution networks is promising and increasing ***,high penetration levels of distributed generation(DG)are often limited as they may have an adverse effect on the operation of distribution *** of the operation challenges is the interaction between DG and voltage-control equipment,e.g.,an under-load tap changer(ULTC),which is basically designed to compensate for voltage changes caused by slow load *** integration of variable DGs leads to rapid voltage fluctuations,which can negatively affect the tap operation of *** paper investigates the impact of high penetration levels of RDG on the tap operation of ULTC in distribution networks through *** mitigation techniques that can alleviate this impact are also *** these techniques,constant power-factor mode is regarded as the best trade-off between the simplicity and effectiveness of minimizing the number of tap *** are performed on a Canadian benchmark rural distribution feeder using OpenDSS software.

关键词： Renewable energy distributed generation(DG) voltage regulator load tap changer distribution network.

Smart prediction of liquefaction-induced lateral spreading

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Journal of Rock mechanics and Geotechnical Engineering 2024年第6期16卷 2310-2325页

作者： muhammad Nouman Amjad Raja tarek Abdoun Waleed el-Sekelly New York University(NYU)Abu Dhabi Abu Dhabi129188United Arab Emirates University of management and Technology Lahore54372Pakistan Department of Civil and Environmental Eng. Rensselaer Polytechnic Institute(RPI)1108th StreetJEC 4049TroyNY12180USA Department of Structural Engineering Mansoura UniversityMansoura35516Egypt

The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical *** this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping *** with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,*** process regression(GPR),relevance vector machine(RVm),sequential minimal optimization regression(SmOR),and m5-tree,were developed and compared with the GEP *** results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RmSE)and mean absolute error(mAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,*** overall performance for the free-face topology was ranked as follows:GEP>RVm>m5-tree>GPR>SmOR,with a total score of 40,32,24,15,and 10,*** the gently sloping condition,the performance was ranked as follows:GEP>RVm>GPR>m5-tree>SmOR with a total score of 40,32,21,19,and 8,***,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.

关键词： Lateral spreading Intelligent modeling Gene expression programming(GEP) Closed-form solution Feature importance

Development and characterization of a regional lunar soil simulant for emirates lunar missions

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International Journal of mining Science and Technology 2024年第9期34卷 1305-1315页

作者： Bo Peng Prabu Thannasi Waleed el-Sekelly tarek Abdoun Kemal Celik Division of Engineering New York University Abu DhabiAbu Dhabi PO Box 129188United Arab Emirates Structural Engineering Department Faculty of EngineeringMansoura UniversityMansoura 35516Egypt

Lunar soil simulants(LSS)have been pivotal in the success of past and current lunar *** have enhanced the design of lander and rover wheels through interaction studies on the lunar testbed under simulated environmental *** have also been used to evaluate the geotechnical,geomechanical,and dynamic characteristics of the lunar soil(LS),which is vital for future lunar *** most simulants have focused on either the chemical and mineralogical or geotechnical properties,developing a comprehensive LSS that mimics the lunar soil is of paramount importance for lander and rover soft-landing and future lunar *** article presents the significant development and characterization of the new LSS NYUAD-1,the first regional soil simulant tailored for the planned Emirates lunar *** material and geotechnical properties of NYUAD-1,including chemical composition,mineralogy,particle size,morphology,specific gravity,density,shear strength,and compressibility behaviors,were assessed through laboratory tests conducted per ASTm *** analysis with authentic lunar regolith and various regolith simulants confirms the significance and applicability of NYUAD-1 for lunar-based research,offering a promising step toward future lunar habitation.

关键词： LSS Chemical composition mineralogy Density Shear strength Compressibility

Assessing effects of deficit irrigation techniques on water productivity of tomato for subsurface drip irrigation system

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International Journal of Agricultural and Biological Engineering 2018年第4期11卷 156-167页

作者： mahmoud S Hashem tarek Zin el-Abedin Hussein m Al-Ghobari Agricultural Engineering Department College of Food and Agriculture SciencesKing Saud UniversityRiyadh 11451Saudi Arabia Agricultural Engineering Research Institute(AEnRI) Agricultural Research CenterGizaEgypt Agricultural Engineering Department College of AgricultureAlexandria UniversityAlexandriaEgypt

Water resources are subjected to ever-increasing supply constraints due to extensive agricultural water demand for irrigated ***,water-saving irrigation strategies need to be *** present study was conducted to explore the possibilities of using regulated deficit irrigation(RDI)and partial root zone drying irrigation(PRD)methods as water-saving irrigation techniques for subsurface *** objective of this study are to assess the effects of RDI and PRD irrigation on the water productivity of vegetable crops(tomato)under SSD systems in arid climatic conditions,and to compare the responses of tomato crops to PRD,RDI,and FI under an SSD system in terms of productivity,crop quality,and the amount of water *** field experiment was conducted during the fall 2014-2015 and 2015-2016 seasons in an experimental field located on an educational farm owned by the Faculty of Food and Agriculture Sciences Department,King Saud University,Riyadh,Kingdom of Saudi *** area of 102.7 m^(2)(13 m×7.9 m)was allocated for the experiment to manage three treatments:RDI,PRD,and full irrigation(FI).The RDI and PRD treatments received 70%of the irrigation water volume of *** was replicated three *** most important results indicated that the soil water content(SWC)for the RDI and PRD treatments was lower than that of the FI *** had the highest stomatal conductance values(gs),while PRD had the lowest stomatal conductance *** photosynthetic rate(A_(n))was lower under RDI and PRD compared to ***,there was no significant change in A_(n) between treatments for most readings taken during both time periods,which means that the water saving treatments(PRD and RDI)did not affect the net photosynthesis rate,thereby enhancing irrigation water use efficiency(IWUE)under DI *** water-saving irrigation techniques decreased transpiration rate(T)compared to the FI *** values of the abscisic acid(ABA)contents were higher

关键词： full irrigation(FI) regulated deficit irrigation(RDI) partial root zone drying(PRD) irrigation water use efficiency(IWUE)