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International Journal of Sediment Research 2016年第2期31卷 164-172页

作者： Ahmed el Nemr ghada f.el-said Azza Khaled Safaa Ragab Environmental Division National Institute of Oceanography and Fisheries

The ecological risk assessment for Al,Zn,Cu,Ni,V,Pb,Cd,and Hg in surface sediment collected from the Egyptian Red Sea coast was evaluated using the Geo-accumulation Index(Igeo).Sediment Enrichment factor（SEf） and Potential Ecological Risk Index（PERI） methods.The predominant heavy metal,aluminum,showed high concentrations along both of Aqaba Gulf（4378.8 ± 2554.1 μg/g） and southern part of the Red Sea（2972.8 + 1527.5 μg/g）.while it recorded the lowest concentration in Suez Gulf（829.7 ± 398.2 μg/g）.The determined heavy metal concentrations had the order of Al > Zn >-Ni > V > Pb > Cu > Cd > Hg.The statistical analyses showed some correlations among the heavy metals contents.Several international sediment quality guidelines were used to estimate the quality of the collected sediments.Interestingly,the recorded average heavy metals concentrations were lower than those of the permissible contents for sediment quality guidelines.The Geo-accumulation index calculations(Igeo) proved that the investigated region could be classified as an unpolluted area.Sediment Enrichment factor(KSEf) study showed high values in Suez Gulf region.The single pollution index analysis of heavy metals in the sediments（Ci_f） indicated that Al,Zn,V,and Pb were of natural origin,while Ni,Cd and Hg were seriously affected by human activities.Interestingly,amongst,all the determined heavy metals,Cd and Hg gave moderate ecological risk indicators.

关键词： Heavy metals Coastal surface sediments Ecological risk assessment Suez and Aqaba Gulfs Red Sea

Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

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Computers, Materials & Continua 2024年第5期79卷 3193-3209页

作者： Israa Ismail ghada eltaweel Mohamed Meselhy eltoukhy Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.Super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

关键词： Super-resolution shearlet transform shearlet coefficients enhanced deep super-resolution network

Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

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Journal of Computer and Communications 2024年第3期12卷 160-183页

作者： Hend S. Salem Mohamed A. Mead ghada S. el-Taweel Department of Computer Sciences Suez Canal University Ismailia Egypt

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

关键词： Big COVID-19 Data Machine Learning Hyperparameter Optimization Particle Swarm Optimization Computational Intelligence

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 world.It has significantly influenced public health,the world economy,and the persons’lives.Hence,there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients.With this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical images.This paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)images.The proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT images.The classification stage is firstly applied to detect and classify the input medical CT images.Then,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT images.The classification stage is implemented based on a simple and efficient CNN deep learning model.This model comprises four Rectified Linear Units(RelUs),four batch normalization layers,and four convolutional(Conv)layers.TheConv 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 process.for the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid approach.The proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,accurately.To 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 system.Once 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 images.The region of interest can be assesses both automatically and through experts.This strategy helps somuch in saving the time and ef

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

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 decades.It achieves safe access to systems using biometrics instead of the traditional passwords.The utilization of a biometric in its original format makes it usable only once.Therefore,a cancelable biometric template should be used,so that it can be replaced when it is attacked.Cancelable biometrics aims to enhance the security and privacy of biometric authentication.Digital encryption is an efficient technique to be used in order to generate cancelable biometric templates.In this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification systems.The considered biometric in this paper is the speech signal.The speech signal is transformed into its spectrogram.Then,the spectrogram is encrypted using two cascaded optical encryption algorithms.The first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption tool.The 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 obtained.The verification is implemented through correlation estimation between enrolled and test templates in their encrypted format.If the correlation value is larger than a threshold value,the user is authorized.The threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two curves.The implementation of optical encryption is adopted using its software rather than the optical setup.The efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation results.It can improve the biometric data security without deteriorating the recognition accuracy.Simulation results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Characteristic(AROC)curve.

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

Efficient Deep Learning Modalities for Object Detection from Infrared Images

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Computers, Materials & Continua 2022年第8期72卷 2545-2563页

作者： Naglaa f.Soliman E.A.Alabdulkreem Abeer D.Algarni ghada M.el Banby fathi E.Abd el-Samie Ahmed Sedik 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 Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of.electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafr el-sheikhEgypt

for military warfare purposes,it is necessary to identify the type of a certain weapon through video stream tracking based on infrared(IR)video frames.Computer vision is a visual search trend that is used to identify objects in images or video frames.for military applications,drones take a main role in surveillance tasks,but they cannot be confident for longtime missions.So,there is a need for such a system,which provides a continuous surveillance task to support the drone mission.Such a system can be called a Hybrid Surveillance System(HSS).This system is based on a distributed network of wireless sensors for continuous surveillance.In addition,it includes one or more drones to make short-time missions,if the sensors detect a suspicious event.This paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks(CNNs)and Convolutional Long Short-Term Memory(ConvLSTM).Based on initial results,the importance of video frame enhancement is obvious to improve the visibility of objects in video streams.The accuracy of the proposed methods reach 99%,which reflects the effectiveness of the presented solution.In addition,the experimental results prove that the proposed methods provide superior performance compared to traditional ones.

关键词： Deep learning object detection military applications OfDM SPIHT IoT

An Efficient Medical Image Deep fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid el-Shafai Noha A.el-Hag Ahmed Sedik ghada elbanby fathi E.Abd el-Samie Naglaa f.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of.electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy.Deep learning provides a high performance for several medical image analysis applications.This paper proposes a deep learning model for the medical image fusion process.This model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR images.Then,an additional process is executed on the extracted features.After that,the fused feature map is reconstructed to obtain the resulting fused image.finally,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality.Different realistic datasets of different modalities and diseases are tested and implemented.Also,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

Enhanced Robotic Vision System Based on Deep Learning and Image fusion

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Computers, Materials & Continua 2022年第10期73卷 1845-1861页

作者： E.A.Alabdulkreem Ahmed Sedik Abeer D.Algarni ghada M.el Banby fathi E.Abd el-Samie Naglaa f.Soliman Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafrelsheikh33511Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of.electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt

Image fusion has become one of the interesting f.elds that attract researchers to integrate information from different image sources.It is involved in several applications.One of the recent applications is the robotic vision.This application necessitates image enhancement of both infrared(IR)and visible images.This paper presents a Robot Human Interaction System(RHIS)based on image fusion and deep learning.The basic objective of this system is to fuse visual and IR images for efficient feature extraction from the captured images.Then,an enhancement model is carried out on the fused image to increase its quality.Several image enhancement models such as fuzzy logic,Convolutional Neural Network(CNN)and residual network(ResNet)pre-trained model are utilized on the fusion results and they are compared with each other and with the state-of-the-art works.Simulation results prove that the fuzzy logic enhancement gives the best results from the image quality perspective.Hence,the proposed system can be considered as an efficient solution for the robotic vision problem with multi-modality images.

关键词： Deep learning fuzzy logic image fusion IR images

Latent Space Representational Learning of Deep features for Acute Lymphoblastic Leukemia Diagnosis

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Computer Systems Science & Engineering 2023年第4期45卷 361-376页

作者： ghada Emam Atteia Information Technology Department College of Computer&Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11461Saudi Arabia

Acute Lymphoblastic Leukemia(ALL)is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow.Early prognosis of ALL is indispensable for the effectual remediation of this disease.Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images,a process which is time-consuming and prone to errors.Therefore,many deep learning-based computer-aided diagnosis(CAD)systems have been established to automatically diagnose ALL.This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images.The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks(CNNs)to identify the existence of ALL in blood smears.An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images.A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set.The latent features are used to perform image classification using Support Vector Machine(SVM)classifier.The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features.Moreover,the classification performance of the system with various sizes of the latent feature set is evaluated.The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.

关键词： Autoencoder deep learning CNN leukemia diagnosis computeraided diagnosis

Gauss Gradient and SURf features for Landmine Detection from GPR Images

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Computers, Materials & Continua 2022年第6期71卷 4457-4486页

作者： fatma M.el-Ghamry Walid el-Shafai Mahmouad I.Abdalla ghada M.el-Banby Abeer D.Algarni Moawad I.Dessouky Adel S.elfishawy fathi E.Abd el-Samie Naglaa f.Soliman Department of.electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia44519Egypt Department of.electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Recently,ground-penetrating radar(GPR)has been extended as a well-known area to investigate the subsurface objects.However,its output has a low resolution,and it needs more processing for more interpretation.This paper presents two algorithms for landmine detection from GPR images.The first algorithm depends on a multi-scale technique.A Gaussian kernel with a particular scale is convolved with the image,and after that,two gradients are estimated;horizontal and vertical gradients.Then,histogram and cumulative histogram are estimated for the overall gradient image.The bin values on the cumulative histogram are used for discrimination between images with and without landmines.Moreover,a neural classifier is used to classify images with cumulative histograms as feature vectors.The second algorithm is based on scale-space analysis with the number of speeded-up robust feature(SURf)points as the key parameter for classification.In addition,this paper presents a framework for size reduction of GPR images based on decimation for efficient storage.The further classification steps can be performed on images after interpolation.The sensitivity of classification accuracy to the interpolation process is studied in detail.

关键词： GPR images cumulative histogram gradient image neural classifier SURf