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Gauss Gradient and SURF Features for Landmine Detection from GPR Images

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 ***,its output has a low resolution,and it needs more processing for more *** paper presents two algorithms for landmine detection from GPR *** 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 ***,histogram and cumulative histogram are estimated for the overall gradient *** bin values on the cumulative histogram are used for discrimination between images with and without ***,a neural classifier is used to classify images with cumulative histograms as feature *** second algorithm is based on scale-space analysis with the number of speeded-up robust feature(SURF)points as the key parameter for *** addition,this paper presents a framework for size reduction of GPR images based on decimation for efficient *** further classification steps can be performed on images after *** sensitivity of classification accuracy to the interpolation process is studied in detail.

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

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The germline genetics of mild-to-moderate penetrance:An intriguing role of PRAmE in multiple carcinogenesis

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Genes & Diseases 2024年第3期11卷 7-10页

作者： Diaddin Hamdan Van Tai Nguyen Justine Paris Christophe Leboeuf morad el Bouchtaoui marc Espie Anne Janin Geraldine Falgarone melanie Di Benedetto Guithem Bousquet Universite de Paris Cite INSERMUMR_S942MASCOTParis F-75006France Hopital La Porte Verte Versailles F-78004France National Cancer Hospital Cancer Research and Clinical Trials CenterHaNoi100000VietNam AP-HP-Hopital Saint Louis Centre de Maladies du SeinParisF-75010France Universite Sorbonne Paris Nord Bobigny F-93000France AP-HP-Hopital Avicenne Unite de Medecine AmbulatoireBobigny F-93000France

Germline genetics of high penetrance such as BRCA genes,mutations might be sufficient for carcinogenesis,but this only explains fewer than 10%of *** assume that most cancers are the result of germline mutations of low to moderate penetrance,combined with acquired mutations due to external carcinogenic *** their accumulation over time,aging is associated with an increased risk of multiple cancer development in a given *** decades,germline genetics have been based on familial linkage studies enabling most high-penetrance genes to be *** recently,population-wide studies have led to the identification of considerable numbers of polymorphisms associated with cancer ***,most of them are of unknown functional value,thus limiting their translational application.

关键词： carcinogenesis BRCA linkage

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Double Deep Q-Network method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

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Computer Systems Science & Engineering 2023年第7期46卷 73-92页

作者： mohamed Amine Ouamri Reem Alkanhel Daljeet Singh el-sayed m.el-kenaway Sherif S.m.Ghoneim University Grenoble Alpes CNRSGrenoble INPLIGDRAKKAR Teams38000GrenobleFrance Laboratoire d’informatique médical Universitéde BejaiaTarga OuzemourQ22R+475Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Research and Development Centre for Space ResearchSchool of Electronics and Electrical EngineeringLovely Professional UniversityPhagwara144411India Department of Communication and electronics Delta Higher Institute of Engineering and TechnologyMansouraEgypt electrical Engineering Department College of EngineeringTaif UniversityP.O.BOX 11099Taif21944Saudi Arabia

Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation(5G)***,it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access *** meet these access conditions and improve Quality of Service,resource allocation(RA)should be carefully ***,RA problems are nonconvex optimizations,which are performed using heuristic methods,such as genetic algorithm,particle swarm optimization,and simulated ***,the application of these approaches remains computationally expensive and unattractive for dense cellular ***,artificial intelligence algorithms are used to improve traditional RA *** learning is a promising tool for addressing resource management problems in wireless *** this study,we investigate a double deep Q-network-based RA framework that maximizes energy efficiency(EE)and total network throughput in unmanned aerial vehicle(UAV)-assisted terrestrial ***,the system is studied under the constraints of ***,the optimization problem is formulated as a mixed integer nonlinear *** this framework,we evaluated the effect of height and the number of UAVs on EE and ***,in accordance with the experimental results,we compare the proposed algorithm with several artificial intelligence *** results indicate that the proposed approach can increase EE with a considerable throughput.

关键词： UAV terrestrial network reinforcement learning mmWave resource allocation

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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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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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HDLIDP: A Hybrid Deep Learning Intrusion Detection and Prevention Framework

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Computers, materials & Continua 2022年第11期73卷 2293-2312页

作者： magdy m.Fadel Sally m.el-Ghamrawy Amr m.T.Ali-eldin mohammed K.Hassan Ali I.el-Desoky Computer Engineering and Systems Department Faculty of EngineeringMansoura UniversityMansoura35516DKEgypt Head of Communications and Computer Engineering Department MISR Higher Institute for Engineering and TechnologyMansoura35111DKEgypt mechatronics Department Faculty of EngineeringHours University in Egypt(HUE)New Damietta34517DTEgypt

Distributed denial-of-service(DDoS)attacks are designed to interrupt network services such as email servers and webpages in traditional computer ***,the enormous number of connected devices makes it difficult to operate such a network *** defined networks(SDN)are networks that are managed through a centralized control system,according to *** controller is the brain of any SDN,composing the forwarding table of all data plane network *** the advantages of SDN controllers,DDoS attacks are easier to perpetrate than on traditional *** the controller is a single point of failure,if it fails,the entire network will *** paper offers a Hybrid Deep Learning Intrusion Detection and Prevention(HDLIDP)framework,which blends signature-based and deep learning neural networks to detect and prevent *** framework improves detection accuracy while addressing all of the aforementioned *** validate the framework,experiments are done on both traditional and SDN datasets;the findings demonstrate a significant improvement in classification accuracy.

关键词： Software defined networks(SDN) distributed denial of service attack(DDoS) signature-based detection whale optimization algorism(WOA) deep learning neural network classifier

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Combining ability of Egyptian cotton(Gossypium barbadense L.)reveals genetic potential for improved yield and fiber quality

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Journal of Cotton Research 2024年第2期7卷 109-122页

作者： Abdelghany Ahmed m. el-Banna Aly A.A. Lamlom Sobhi F el-Sorady Gawhara A. Salama Ehab A.A. Ren Honglei Shaibu Abdulwahab S. Yehia Waleed m.B. Crop Science Department Faculty of AgricultureDamanhour UniversityDamanhour 22516Egypt Plant Production Department Faculty of Agriculture Saba BashaAlexandria UniversityAlexandria 21531Egypt Agricultural Botany Department Faculty of Agriculture Saba BashaAlexandria UniversityAlexandria 21531Egypt Heilongjiang Academy of Agricultural Sciences Soybean Research InstituteHarbin 150086China Department of Agronomy Bayero University KanoKano 700001Nigeria Cotton Breeding Department Cotton Research InstituteAgriculture Research CenterGizaEgypt

Background As the most widely cultivated fiber crop,cotton production depends on hybridization to unlock the yield potential of current varieties.A deep understanding of genetic dissection is crucial for the cultivation of enhanced hybrid plants with desired traits,such as high yield and fine fiber *** this study,the general combining ability(GCA)and specific combining ability(SCA)of yield and fiber quality of nine cotton parents(six lines and three testers)and eighteen F1 crosses produced using a line×tester mating design were *** The results revealed significant effects of genotypes,parents,crosses,and interactions between parents and crosses for most of the studied ***,the effects of both additive and non-additive gene actions played a notably significant role in the inheritance of most of the yield and fiber quality *** F1 hybrids of(Giza 90×Aust)×Giza 86,Uzbekistan 1×Giza 97,and Giza 96×Giza 97 demonstrated superior performance due to their favorable integration of high yield attributes and premium fiber quality *** analysis revealed that lint yield has the highest positive direct effect on seed cotton yield,while lint percentage showed the highest negative direct effect on seed cotton *** component analysis identified specific parents and hybrids associated with higher cotton yield,fiber quality,and other agronomic *** This study provides insights into identifying potential single-and three-way cross hybrids with superior cotton yield and fiber quality characteristics,laying a foundation for future research on improving fiber quality in cotton.

关键词： Gossypium barbadense L. Combining ability Seed cotton yield Fiber quality Cluster analysis Path analysis

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metaheuristic Optimization of Time Series models for Predicting Networks Traffic

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Computers, materials & Continua 2023年第4期75卷 427-442页

作者： Reem Alkanhel el-Sayed m.el-kenawy D.L.elsheweikh Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Department of Information Technology 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 TechnologyMansoura35712Egypt Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTm) network as an efficient timeseries modeling approach which is widely used in sequence prediction *** prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTm, and the results were *** evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.

关键词： Network traffic soft computing LSTm metaheuristic optimization

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Advanced Guided Whale Optimization Algorithm for Feature Selection in BlazePose Action Recognition

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Intelligent Automation & Soft Computing 2023年第9期37卷 2767-2782页

作者： motasem S.Alsawadi el-Sayed m.el-kenawy miguel Rio electronic and electrical Engineering Department University College LondonLondonWC1E7JEEngland Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt

The BlazePose,which models human body skeletons as spatiotem-poral graphs,has achieved fantastic performance in skeleton-based action *** extraction from photos for mobile devices has been made possible by the BlazePose system.A Spatial-Temporal Graph Con-volutional Network(STGCN)can then forecast the *** Spatial-Temporal Graph Convolutional Network(STGCN)can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of *** the other hand,existing approaches require the user to manually set the graph’s topology and then fix it across all input layers and *** research shows how to use the Statistical Fractal Search(SFS)-Guided whale optimization algorithm(GWOA).To get the best solution for the GWOA,we adopt the SFS diffusion algorithm,which uses the random walk with a Gaussian distribution method common to growing *** values are transformed into binary to apply to the feature-selection problem in conjunction with the BlazePose skeletal topology and stochastic fractal search to construct a novel implementation of the BlazePose topology for action *** our experiments,we employed the Kinetics and the NTU-RGB+D *** achieved actiona accuracy in the X-View is 93.14%and in the X-Sub is 96.74%.In addition,the proposed model performs better in numerous statistical tests such as the Analysis of Variance(ANOVA),Wilcoxon signed-rank test,histogram,and times analysis.

关键词： BlazePose metaheuristics convolutional networks feature selection action recognition

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Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

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Computers, materials & Continua 2023年第11期77卷 1855-1872页

作者： Heba m.el-Hoseny Heba F.elsepae Wael A.mohamed Ayman S.Selmy Department of Computer Science the Higher Future Institute for Specialized Technological StudiesObour11828Egypt Department of electrical Engineering Benha Faculty of EngineeringBenha UniversityBenha13511Egypt

Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision *** methods of diagnosing and treating the disease are time-consuming and ***,machine learning and deep transfer learning(DTL)techniques have shown promise in medical applications,including detecting,classifying,and segmenting diabetic *** advanced techniques offer higher accuracy and *** Diagnosis(CAD)is crucial in speeding up classification and providing accurate disease ***,these technological advancements hold great potential for improving the management of diabetic *** study’s objective was to differentiate between different classes of diabetes and verify the model’s capability to distinguish between these *** robustness of the model was evaluated using other metrics such as accuracy(ACC),precision(PRE),recall(REC),and area under the curve(AUC).In this particular study,the researchers utilized data cleansing techniques,transfer learning(TL),and convolutional neural network(CNN)methods to effectively identify and categorize the various diseases associated with diabetic retinopathy(DR).They employed the VGG-16CNN model,incorporating intelligent parameters that enhanced its *** outcomes surpassed the results obtained by the auto enhancement(AE)filter,which had an ACC of over 98%.The manuscript provides visual aids such as graphs,tables,and techniques and frameworks to enhance *** study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of *** manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.

关键词： No diabetic retinopathy(NDR) convolution layers(CNV layers) transfer learning data cleansing convolutional neural networks a visual geometry group(VGG16)

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