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Framework for COVID-19 Segmentation and Classification Based on Deep Learning of Computed Tomography Lung Images

Journal of Electronic Science and Technology 2022年第3期20卷 246-256页

作者： Wessam M.Salama Moustafa H.Aly the Department of Basic Science Faculty of EngineeringPharos UniversityAlexandria12455Egypt the Department of Electronics and Communications Engineering College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

Corona Virus Disease 2019(COVID-19) has affected millions of people worldwide and caused more than6.3 million deaths(World health Organization, June 2022). Increased attempts have been made to develop deep learning methods to diagnose COVID-19 based on computed tomography(CT) lung images. It is a challenge to reproduce and obtain the CT lung data, because it is not publicly available. This paper introduces a new generalized framework to segment and classify CT images and determine whether a patient is tested positive or negative for COVID-19 based on lung CT images. In this work, many different strategies are explored for the classification ***50 and VGG16 models are applied to classify CT lung images into COVID-19 positive or negative. Also,VGG16 and ReNet50 combined with U-Net, which is one of the most used architectures in deep learning for image segmentation, are employed to segment CT lung images before the classifying process to increase system performance. Moreover, the image size dependent normalization technique(ISDNT) and Wiener filter are utilized as the preprocessing techniques to enhance images and noise suppression. Additionally, transfer learning and data augmentation techniques are performed to solve the problem of COVID-19 CT lung images deficiency, therefore the over-fitting of deep models can be avoided. The proposed frameworks, which comprised of end-to-end, VGG16,ResNet50, and U-Net with VGG16 or ResNet50, are applied on the dataset that is sourced from COVID-19 lung CT images in Kaggle. The classification results show that using the preprocessed CT lung images as the input for U-Net hybrid with ResNet50 achieves the best performance. The proposed classification model achieves the 98.98%accuracy(ACC), 98.87% area under the ROC curve(AUC), 98.89% sensitivity(Se), 97.99 % precision(Pr), 97.88%F-score, and 1.8974-seconds computational time.

关键词： Augmentation classification computed tomography(CT) Corona Virus Disease 2019(COVID-19) deep learning ResNet50 segmentation U-Net VGG16

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Secure and Robust Optical Multi-Stage Medical Image Cryptosystem

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Computers, Materials & Continua 2022年第1期70卷 895-913页

作者： Walid El-Shafai Moustafa H.Aly Abeer D.Algarni Fathi E.Abd El-Samie Naglaa F.Soliman Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Communications Engineering College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt

Due to the rapid growth of telemedicine and healthcare services,color medical image security applications have been expanded precipitously.In this paper,an asymmetric PTFrFT(Phase Truncated Fractional Fourier Transform)-based color medical image cryptosystem is suggested.Two different phases in the fractional Fourier and output planes are provided as deciphering keys.Accordingly,the ciphering keys will not be employed for the deciphering procedure.Thus,the introduced PTFrFT algorithm comprises asymmetric ciphering and deciphering processes in contrast to the traditional optical symmetric OSh(Optical Scanning holography)and DRPE(Double Random Phase Encoding)algorithms.One of the principal impacts of the introduced asymmetric cryptosystem is that it eliminates the onedimensionality aspects of the related symmetric cryptosystems due to its remarkable feature of phase nonlinear truncation components.More comparisons on various colormedical images are examined and analyzed to substantiate the cryptosystem efficacy.The achieved experimental outcomes ensure that the introduced cryptosystem is robust and secure.It has terrific cryptography performance compared to conventional cryptography algorithms,even in the presence of noise and severe channel attacks.

关键词： Optical encryption medical image security symmetric and asymmetric encryption OSh DRPE PTFrFT

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Automatic PV Grid Fault Detection System with IoT and LabVIEW as Data Logger

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Computers, Materials & Continua 2021年第11期69卷 1709-1723页

作者： Rohit Samkria Mohammed Abd-Elnaby Rajesh Singh Anita Gehlot Mamoon Rashid Moustafa H.Aly Walid El-Shafai Project Engineer NWTFIndian Institute of TechnologyKanpurIndia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Electronics and Electrical Engineering Lovely Professional UniversityJalandharIndia School of Computer Science and Engineering Lovely Professional UniversityJalandharIndia Department of Electronics and Communications Engineering College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria 1029Egypt Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Fault detection of the photovoltaic(PV)grid is necessary to detect serious output power reduction to avoid PV modules’damage.To identify the fault of the PV arrays,there is a necessity to implement an automatic system.In this IoT and LabVIEW-based automatic fault detection of 3×3 solar array,a PV system is proposed to control and monitor Internet connectivity remotely.hardware component to automatically reconfigure the solar PV array from the series-parallel(SP)to the complete cross-linked array underneath partial shading conditions(PSC)is centered on the Atmega328 system to achieve maximum power.In the LabVIEW environment,an automated monitoring system is developed.The automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem,and provides an indicator of the faulty solar panel over the built-in Interface LabVIEW.The managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV system.Node MCU in the receiver section enables transmission of the fault status of PV arrays via Internet connectivity.The IoT-based Blynk app is employed for visualizing the fault status of the 3×3 PV array.The dashboard of Blynk visualizes every array with the status.

关键词： Blynk app IoT LabVIEW node MCU PV array RF modem WSN

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hybrid Single Image Super-Resolution Algorithm for Medical Images

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Computers, Materials & Continua 2022年第9期72卷 4879-4896页

作者： Walid El-Shafai Ehab Mahmoud Mohamed Medien Zeghid Anas MAli Moustafa H.Aly Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Engineering Department College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia Electrical Engineering Department Aswan UniversityAswan81542Egypt Electronics and Micro-Electronics Laboratory(E.μ.E.L) Faculty of SciencesUniversity of MonastirMonastir5000Tunisia Alexandria higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

high-quality medical microscopic images used for diseases detection are expensive and difficult to store.Therefore,low-resolution images are favorable due to their low storage space and ease of sharing,where the images can be enlarged when needed using Super-Resolution(SR)techniques.however,it is important to maintain the shape and size of the medical images while enlarging them.One of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image resolution.Consequently,this paper suggests a multi-SR and classification framework based on Generative Adversarial Network(GAN)to generate high-resolution images with higher quality and finer details to reduce blurring.The proposed framework comprises five GAN models:Enhanced SR Generative Adversarial Networks(ESRGAN),Enhanced deep SR GAN(EDSRGAN),Sub-Pixel-GAN,SRGAN,and Efficient Wider Activation-B GAN(WDSR-b-GAN).To train the proposed models,we have employed images from the famous Breakhis dataset and enlarged them by 4×and 16×upscale factors with the ground truth of the size of 256×256×3.Moreover,several evaluation metrics like Peak Signal-to-Noise Ratio(PSNR),Mean Squared Error(MSE),Structural Similarity Index(SSIM),Multiscale Structural Similarity Index(MS-SSIM),and histogram are applied to make comprehensive and objective comparisons to determine the best methods in terms of efficiency,training time,and storage space.The obtained results reveal the superiority of the proposed models over traditional and benchmark models in terms of color and texture restoration and detection by achieving an accuracy of 99.7433%.

关键词： GAN medical images SSIM MS-SSIM PSNR SISR

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Real-Time Multi-Class Infection Classification for Respiratory Diseases

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Computers, Materials & Continua 2022年第11期73卷 4157-4177页

作者： Ahmed El.Shafee Walid El-Shafai Abdulaziz Alarifi Mohammed Amoon Aman Singh Moustafa H.Aly Department of Electrical Engineering Faculty of EngineeringAhram Canadian UniversityGizaEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia higher Polytechnic School Universidad Europea del AtlánticoSantander39011Spain Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

Real-time disease prediction has emerged as the main focus of study in the field of computerized medicine.Intelligent disease identification framework can assist medical practitioners in diagnosing disease in a way that is reliable,consistent,and timely,successfully lowering mortality rates,particularly during endemics and pandemics.To prevent this pandemic’s rapid and widespread,it is vital to quickly identify,confine,and treat affected individuals.The need for auxiliary computer-aided diagnostic(CAD)systems has grown.Numerous recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 virus.Utilizing advanced convolutional neural network(CNN)architectures in conjunction with radiological imaging makes it possible to provide rapid,accurate,and extremely useful susceptible classifications.This research work proposes a methodology for real-time detection of COVID-19 infections caused by the Corona Virus.The purpose of this study is to offer a two-way COVID-19(2WCD)diagnosis prediction deep learning system that is built on Transfer Learning Methodologies(TLM)and features customized fine-tuning on top of fully connected layered pre-trained CNN architectures.2WCD has applied modifications to pre-trained models for better performance.It is designed and implemented to improve the generalization ability of the classifier for binary and multi-class models.Along with the ability to differentiate COVID-19 and No-Patient in the binary class model and COVID-19,No-Patient,and Pneumonia in the multi-class model,our framework is augmented with a critical add-on for visually demonstrating infection in any tested radiological image by highlighting the affected region in the patient’s lung in a recognizable color pattern.The proposed system is shown to be extremely robust and reliable for real-time COVID-19 diagnostic prediction.It can also be used to forecast other lung-related disorders.As the system can assist medical practitioners in diagnosing the greatest number of patients

关键词： COVID-19 real-time computerized disease prediction intelligent disease identification framework CAD systems X-rays CT-scans CNN real-time detection of COVID-19 infections

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Statistical Time Series Forecasting Models for Pandemic Prediction

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Computer Systems Science & Engineering 2023年第10期47卷 349-374页

作者： Ahmed ElShafee Walid El-Shafai Abeer D.Algarni Naglaa F.Soliman Moustafa H.Aly Department of Electrical Engineering Faculty of EngineeringAhram Canadian University6th October CityGizaEgypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be projected using different methodologies.Thus,this work aims to gauge the spread of the outbreak severity over time.Furthermore,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus infections.We have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML models.Examples of these models are Logistic Regression,Lasso,Ridge,ElasticNet,huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive Bayes.Furthermore,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance.Then,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.

关键词： Forecasting COVID-19 predictive models medical viruses mathematical model market research diseases

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Diverse features of magnetization curves of uniaxial crystals：A simulation study

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Chinese Physics B 2017年第1期26卷 454-465页

作者： hala A.Sobh Samy h.aly Department of Physics Faculty of Science at Damietta Damietta University P. O. Box 89 New Damietta 34517 Egypt

We present a simulation of the magnetization curves, energy, probability, and torque landscapes of uniaxial systems with up to five anisotropy constants. The total energy used in the simulation is the sum of the anisotropy and Zeeman energies. The exchange interaction is not considered in the present work in which we treat single-domain-particle systems within a classical mechanics-based model. Diverse features of the calculated magnetization curves are highlighted for the studied systems. These diverse features are strongly dependent on the sign and magnitude of the simulation parameters.The model is versatile enough to handle both hypothetical and real material systems, e.g. hoFe（11）Ti and Y2Co（17）.

关键词： magnetization curve saturation moments magnetic anisotropy ferromagnetic metals and alloys

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Analytical Solution for Peristaltic Transport of Viscous Nanofluid in an Asymmetric Channel with Full Slip and Convective Conditions

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Communications in Theoretical Physics 2017年第7期67卷 96-102页

作者： Abdelhalim Ebaid Emad h.aly K.Vajravelu Department of Mathematics Faculty of Science University of Tabuk Department of Mathematics Faculty of Science University of Jeddah Department of Mathematics Faculty of Education Ain Shams University Department of Mathematics Department of Mechanical Materials and Aerospace Engineering University of Central Florida

The peristaltic flow of nanofluids is a relatively new area of research. Scientists are of the opinion that the no-slip conditions at the boundaries are no longer valid and consequently, the first and the second order slip conditions should be addressed. In this paper, the effects of slip conditions and the convective boundary conditions at the boundary walls on the peristaltic flow of a viscous nanofluid are investigated for. Also, the exact analytical solutions are obtained for the model. The obtained results are presented through graphs and discussed. The results reveal that the two slip parameters have strong effects on the temperature and the nanoparticles volume fraction profiles. Moreover, it has been seen that the temperature and nanoparticles volume fraction profiles attain certain values when the first slip condition exceeds a specified value. however, no limit value for the second slip parameter has been detected. Further, the effects of the various emerging parameters on the flow and heat transfer characteristics have been presented.

关键词：对流边界条件精确解析解粘性流体非对称信道滑移蠕动颗粒体积分数运输

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Exact solutions for magnetohydrodynamic nanofluids flow and heat transfer over a permeable axisymmetric radially stretching/shrinking sheet

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Chinese Physics B 2024年第2期33卷 108-114页

作者： U.S.Mahabaleshwar G.P.Vanitha L.M.Pérez Emad h.aly I.Pop Department of Studies in Mathematics Davangere UniversityShivagangothriDavangereIndia Departamento de F´ısica FACIUniversidad de Tarapac´aCasilla 7DAricaChile Department of Mathematics Faculty of EducationAin Shams UniversityRoxyCairoEgypt Department of Mathematics Babes-Bolyai University400084 Cluj-NapocaRomania

We report on the magnetohydrodynamic impact on the axisymmetric flow of Al_(2)O_(3)/Cu nanoparticles suspended in h_(2)O past a stretched/shrinked sheet.With the use of partial differential equations and the corresponding thermophysical characteristics of nanoparticles,the physical flow process is illustrated.The resultant nonlinear system of partial differential equations is converted into a system of ordinary differential equations using the suitable similarity transformations.The transformed differential equations are solved analytically.Impacts of the magnetic parameter,solid volume fraction and stretching/shrinking parameter on momentum and temperature distribution have been analyzed and interpreted graphically.The skin friction and Nusselt number were also evaluated.In addition,existence of dual solution was deduced for the shrinking sheet and unique solution for the stretching one.Further,Al_(2)O_(3)/h_(2)O nanofluid flow has better thermal conductivity on comparing with Cu/h_(2)O nanofluid.Furthermore,it was found that the first solutions of the stream are stable and physically realizable,whereas those of the second ones are unstable.

关键词： magnetohydrodynamic nanofluid stretching/shrinking sheet axisymmetric flow analytical solution suction/injection

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Endophytes and associated marine derived fungi—ecological and chemical perspectives

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Fungal Diversity 2012年第6期57卷 45-83页

作者： Abdessamad Debbab Amal h.aly Peter Proksch heinrich-heine-Universität Düsseldorf Institut für Pharmazeutische Biologie und BiotechnologieUniversitätsstr.1Geb.26.2340225 DüsseldorfGermany

The potential of endophytes and algal or invertebrate associated marine derived fungi as promising sources of structurally unprecedented bioactive natural products is undeniable and continues to attract broad attention.This review highlights new bioactive fungal metabolites reported in 2011 until April 2012,as well as known compounds for which novel biological activities have been disclosed.All compounds are grouped according to their reported biological activities which include cytotoxic,anti-infective,as well as radical scavenging,enzyme inhibition,anti-fouling and anti-parasitic activities.Overall,178 fungal metabolites,including 138 new natural products are presented.Furthermore,new insights into fungal-host interaction,chemical communication,and chemo-ecological roles of fungal metabolites,as well as new strategies for bioprospecting are presented.

关键词： Fungi Endophytes Associated marine fungi Bioactive metabolites Natural products Biosynthetic pathways

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