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An Efficient GCD-Based Cancelable Biometric Algorithm for Single and Multiple Biometrics

Computers, Materials & Continua 2021年第11期69卷 1571-1595页

作者： Naglaa F.Soliman Abeer D.Algarni walid el-Shafai Fathi E.Abd el-Samie Ghada M.el Banby Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Cancelable biometrics are required in most remote access applications that need an authentication stage such as the cloud and Internet of Things(IoT)*** objective of using cancelable biometrics is to save the original ones from hacking attempts.A generalized algorithm to generate cancelable templates that is applicable on both single and multiple biometrics is proposed in this paper to be considered for cloud and IoT *** original biometric is blurred with two co-prime ***,it can be recovered as the Greatest Common Divisor(GCD)between its two blurred *** changes if induced in the biometric image prior to processing with co-prime operators prevents the recovery of the original biometric image through a GCD ***,the ability to change cancelable templates is guaranteed,since the owner of the biometric can pre-determine and manage the minimal change induced in the biometric ***,we test the utility of the proposed algorithm in the single-and multi-biometric *** multi-biometric scenario depends on compressing face,fingerprint,iris,and palm print images,simultaneously,to generate the cancelable *** metrics such as Equal Error Rate(EER)and Area and Receiver Operator Characteristic curve(AROC)are *** results on single-and multi-biometric scenarios show high AROC values up to 99.59%,and low EER values down to 0.04%.

关键词： Cloud IoT cancelable biometrics GCD single-and multi-biometrics security applications

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An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

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Computers, Materials & Continua 2022年第3期70卷 4393-4410页

作者： Abeer D.Algarni walid el-Shafai Ghada M.el Banby Fathi E.Abd el-Samie Naglaa F.Soliman Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 84428Saudi Arabia Department of electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia 44519Egypt

COVID-19 remains to proliferate precipitously in the *** has significantly influenced public health,the world economy,and the persons’***,there is a need to speed up the diagnosis and precautions to deal with COVID-19 *** this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical *** paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)*** proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT *** classification stage is firstly applied to detect and classify the input medical CT ***,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT *** classification stage is implemented based on a simple and efficient CNN deep learning *** model comprises four Rectified Linear Units(RelUs),four batch normalization layers,and four convolutional(Conv)*** layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection *** the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid *** proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,*** summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis *** the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the *** region of interest can be assesses both automatically and through ***

关键词： COVID-19 segmentation classification CNN SPCNN CT images

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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 ***,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)***,it is important to maintain the shape and size of the medical images while enlarging *** of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image ***,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 *** 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×***,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 *** 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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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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Analysis of BrainMRI: AI-Assisted Healthcare Framework for the Smart Cities

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Intelligent Automation & Soft Computing 2023年第2期35卷 1843-1856页

作者： walid el-Shafai Randa Ali Ahmed Sedik Taha el-Sayed Taha Mohammed Abd-elnaby Fathi E.Abd el-Samie Department electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of ROBOTICS and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The use of intelligent machines to work and react like humans is vital in emerging smart ***-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare *** AI(Artiﬁcial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchﬁelds that have become attractive to *** are several modalities of imaging performed on the brain for the pur-pose of tumor *** paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)*** proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal *** dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:*** main contribution of this paper is introdu-cing an optimum deep learning structure of CNN *** simulation results are obtained for 50 epochs in the training *** simulation results reveal that the optimum CNN architecture consists of four layers.

关键词： Healthcare smart cities clinical automation CNN machine learning brain tumor medical diagnosis

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Automated COVID-19 Detection Based on Single-Image Super-Resolution and CNN Models

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Computers, Materials & Continua 2022年第1期70卷 1141-1157页

作者： walid el-Shafai Anas M.Ali el-Sayed M.el-Rabaie Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd el-Samie Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

In developing countries,medical diagnosis is expensive and time ***,automatic diagnosis can be a good cheap *** task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks(CNNs).These tools can be used on medical images to speed up the diagnosis process and save the efforts of *** deep CNNs allow direct learning from the medical ***,the accessibility of classified data is still the largest challenge,particularly in the field of medical *** learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image ***,because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19,transfer learning is not usually a robust ***-Image Super-Resolution(SISR)can facilitate learning to enhance computer vision functions,apart from enhancing perceptual image ***,it helps in showing the main features of *** by the challenging dilemma of Pneumonia and COVID-19 diagnosis,this paper introduces a hybrid CNN model,namely SIGTra,to generate super-resolution versions of X-ray and CT *** depends on aGenerative Adversarial Network(GAN)for the super-resolution reconstruction ***,Transfer learning with CNN(TCNN)is adopted for the classification of *** different categories of chest X-ray and CT images can be classified with the proposed model.A comparison study is presented between the proposed SIGTra model and the other relatedCNNmodels for COVID-19 detection in terms of precision,sensitivity,and accuracy.

关键词： Medical images SIGTra GAN CT and X-ray images SISR TCNN

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Correlation of oxidation reduction potential and total motile sperm count:its utility in the evaluation of male fertility potential

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Asian Journal of Andrology 2020年第3期22卷 317-322页

作者： Ahmad Majzoub Mohamed Arafa walid el Ansari Mohammed Mahdi Ashok Agarwal Sami Al-Said Haitham elbardisi Department of Urology Hamad Medical CorporationDoha 00974Qatar Department of Urology Weill Cornell Medicine-QatarDoha 00974Qatar Department of Andrology Cairo UniversityCairo 12613Egypt Department of Surgery Hamad Medical CorporationDoha 00974Qatar College of Medicine Qatar UniversityDoha 00974Qatar School of Health and Education University of SkovdeSkovde 54128Sweden department of Urology Glickman Urology and Kidney InstituteCleveland Clinic FoundationOH 44195USA.

Oxidative stress(OS)is detri me ntal to sperm fun ctio ns,and the oxidation reductio n pote ntial(ORP)is a good measure of OS as it considers the balance betwee n oxidants and reducta *** motile sperm count(TMSC)is viewed as the single most im porta nt semen analysis parameter that can predict male infertility severity,and its correlation with ORP has never been *** objectives of this study were to assess the correlation between ORP and TMSC,to identify the ORP cutoff value based on the TMSC result,and to compare this cutoff value with previously reported ORP cutoff values in *** thousand one hundred and sixty-eight infertile patients and 100 fertile controls were *** and semen data of the participants were retrieved and *** on's rank-sum test compared variables betwee n infertile men and fertile controls;Spearman's correlation assessed the static ORP(sORP)-TMSC relationship for the whole sample and among each group *** a 20×10^6TMSC threshold,receiver operator characteristic(ROC)analysis determined the sORP cutoff associated with the highest predictive *** was significantly negatively correlated with sORP across all participants(r=0.86,Pelded the highest overall diagnostic accuracy in the evaluation of in fertile men.

关键词： male in fertility oxidati on reduction pote ntial oxidative stress total motile sperm

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Efficient Deep-Learning-Based Autoencoder Denoising Approach for Medical Image Diagnosis

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Computers, Materials & Continua 2022年第3期70卷 6107-6125页

作者： walid el-Shafai Samy Abd el-Nabi el-Sayed Mel-Rabaie Anas M.Ali Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd el-Samie Department of electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Effective medical diagnosis is dramatically expensive,especially in third-world *** of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant ***,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific ***,experimentation and utilization of different models of transfer learning do not achieve satisfactory *** this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related *** model is based on classification,denoising autoencoder,and transfer ***,pre-processing is employed to prepare the medical *** depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis ***,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect *** proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray ***,a comparative study is introduced for the classification performance with and without the denoising *** proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and *** demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN *** achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the exis

关键词： Medical images CADTra AD CT and X-ray images autoencoder

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Tribological and Physicochemical Analysis of Squid Pen High-density Polyethylene Biocomposite for Medical Application

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Journal of Bionic Engineering 2022年第5期19卷 1481-1492页

作者： Besma Sidia walid Bensalah Mechanical Engineering Laboratory LR99ES32National Engineering School of MonastirUniversity of MonastirMonastirTunisia

High-Density Polyethylene(HDPE)wear debris generated in the hip joint prothesis leads to its *** aim of this study was to evaluate the potential of Squid Pen(SP)on the tribological and physicochemical properties of HDPE *** filled with 0,5,10,15 and 20 wt.%SP were elaborated by hot compression *** tests were carried out using a reciprocating pin-on-disc *** hardness,Fourier-Transform infra-red(FTIR)analysis,Scanning electron Microscopy(SEM)of the biocomposite were *** analysis results of the biocomposites showed that an increase in the crystallinity rate was obtained with the addition of SP *** 10 wt.%of SP has a significant effect on the hardness of the *** correlation between the friction coefficient and the wear resistance of the composite was *** 5 wt.%SP-HDPE biocomposite has the lowest friction coefficient value with a decrease in the specific wear rate,compared to the unfilled *** SEM results showed that SP wear debris played an important role as a third roller body at the interface reducing the friction coefficient of the *** was concluded that the HDPE biocomposite could be successfully reinforced with 5 wt.%of SP.

关键词： Squid pen biomaterial Tribological behaviours FTIR analysis Wear morphology Bionic

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Proposed Biometric Security System Based on Deep Learning and Chaos Algorithms

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Computers, Materials & Continua 2023年第2期74卷 3515-3537页

作者： Iman Almomani walid el-Shafai Aala AlKhayer Albandari Alsumayt Sumayh S.Aljameel Khalid Alissa Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Computer Science Department King Abdullah II School of Information TechnologyThe University of Jordan11942Jordan Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Applied CollegeImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Science Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Nowadays,there is tremendous growth in biometric authentication and cybersecurity ***,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private ***,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be *** work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and *** proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their *** employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric *** obtained features are utilized to generate two random chaos *** first random chaos matrix is used to permute the pixels of biometric *** contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)*** assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were *** assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing *** example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.

关键词： Biometric security deep learning AE network 2D CLM cybersecurity and authentication applications feature extraction unsupervised learning

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