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Artificial Intelligence Techniques Based Learner Authentication in Cybersecurity Higher Education Institutions

Computers, Materials & Continua 2022年第8期72卷 3131-3144页

作者： Abdullah Saad AL-Malaise AL-Ghamdi mahmoud Ragab Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt

Education 4.0 is being authorized more and more by the design of artificial intelligence(AI)*** education institutions(HEI)have started to utilize Internet technologies to improve the quality of the service and boost *** to the unavailability of information technology(IT)infrastructures,HEI is vulnerable to *** authentication can be used to authenticate a person based on biological features such as face,fingerprint,iris,and so *** study designs a novel search and rescue optimization with deep learning based learning authentication technique for cybersecurity in higher education institutions,named SRODLLAC *** proposed SRODL-LAC technique aims to authenticate the learner/student in HEI using fingerprint ***,the SRODLLACtechnique designs a median filtering(MF)based preprocessing approach to improving the quality of the *** addition,the Densely Connected Networks(DenseNet-77)model is applied for the extraction of ***,search and rescue optimization(SRO)algorithm with deep neural network(DNN)model is utilized for the classification ***,template matching process is done for fingerprint identification.A wide range of simulation analyses is carried out and the results are inspected under several *** experimental results reported the effective performance of the SRODL-LAC technique over the other methodologies.

关键词： Cybersecurity authentication biometrics security higher education institution deep learning

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Energy-Efficient UAVs Coverage Path Planning Approach

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 3239-3263页

作者： Gamil Ahmed Tarek Sheltami Ashraf mahmoud Ansar Yasar Computer Engineering Department Interdisciplinary Research Center of Smart Mobility and LogisticsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Transportation Research Institute(IMOB) Hasselt UniversityHasselt3500Belgium

Unmanned aerial vehicles(UAVs),commonly known as drones,have drawn significant consideration thanks to their agility,mobility,and flexibility *** play a crucial role in modern reconnaissance,inspection,intelligence,and surveillance *** path planning(CPP)which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest(ROI).However,the flight time of the UAV is limited due to a battery limitation and may not cover the whole region,especially in large ***,energy consumption is one of the most challenging issues that need to be *** this paper,we propose an energy-efficient coverage path planning algorithm to solve the CPP *** objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole *** do so,the flight path is optimized and the number of turns is reduced to minimize the energy *** proposed approach first decomposes the ROI into a set of cells depending on a UAV camera ***,the coverage path planning problem is formulated,where the exact solution is determined using the CPLEX *** small-scale problems,the CPLEX shows a better solution in a reasonable ***,the CPLEX solver fails to generate the solution within a reasonable time for large-scale ***,to solve the model for large-scale problems,simulated annealing forCPP is *** results show that heuristic approaches yield a better solution for large-scale problems within amuch shorter execution time than the CPLEX ***,we compare the simulated annealing against the greedy *** results show that simulated annealing outperforms the greedy algorithm in generating better solution quality.

关键词： Coverage path planning MILP CPLEX solver energy model optimization region of interest area of interest

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Deep Learning Prediction Model for NiCrAlY Diffusion Barrier Thickness for Tungsten Wires

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Computers, Materials & Continua 2022年第12期73卷 5755-5769页

作者： Amal H.Alharbi Hanan A.Hosni mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In the last decades,technology has used Copper for IC interconnect and it has been the best material used in the wire ***,Copper is now showing inefficiency as downscaling is getting *** research starts to show Tungsten(W)as a possible replacement,for its better downsizing *** scaling-down of interconnects dimension has to be augmented with thin diffusion *** is crucial to subdue tungsten diffusion in the nickel-based thermal spray Flexicord(NiCrAlY)coating ***,diffusion barriers with thicknesses less than 4.3 nm do not to execute *** the introduction of two dimensional layers,hexagonal boron has been recommended as a substitute for Tungsten diffusion barrier layers with thicknesses less than 1.5 Nano meters(nm).Nevertheless,vacancies flaws may develop into a Tungsten dissemination path,which is a problematic issue in the manufacturing of diffusion *** energy layer density,of Tungsten atom diffusion via a di-vacancy in NiCrAlY,is computed by density functions *** has complex energy barrier which is thicker than other materials such as *** is due to the sturdier contact and charge variance of NI and Cr in ***,we utilize the energy barriers of several vacancy constructions and produce a dataset to be employed in the proposed 3-imensional deep learning model(3D-DNN).Our trained deep learning neural model can predict the energy barrier of Tungsten diffusion through arbitrarily configured NiCrAlY with accuracy greater than 98.4%in 5×5 *** results generate directors on selecting barriers through energy computation.

关键词： Deep learning tungsten NiCrAlY thickness barrier

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Proposed Framework for Detection of Breast Tumors

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Computers, Materials & Continua 2023年第2期74卷 2927-2944页

作者： Mostafa Elbaz Haitham Elwahsh Ibrahim mahmoud El-Henawy Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt

Computer vision is one of the significant trends in computer *** plays as a vital role in many applications,especially in the medical *** detection and segmentation of different tumors is a big challenge in the medical *** proposed framework uses ultrasound images from Kaggle,applying five diverse models to denoise the images,using the best possible noise-free image as input to the U-Net model for segmentation of the tumor,and then using the Convolution Neural Network(CNN)model to classify whether the tumor is benign,malignant,or *** main challenge faced by the framework in the segmentation is the speckle ***’s is a multiplicative and negative issue in breast ultrasound imaging,because of this noise,the image resolution and contrast become reduced,which affects the diagnostic value of this imaging *** result,speckle noise reduction is very vital for the segmentation *** framework uses five models such as Generative Adversarial Denoising Network(DGAN-Net),Denoising U-Shaped Net(D-U-NET),Batch Renormalization U-Net(Br-UNET),Generative Adversarial Network(GAN),and Nonlocal Neutrosophic ofWiener Filtering(NLNWF)for reducing the speckle noise from the breast ultrasound images then choose the best image according to peak signal to noise ratio(PSNR)for each level of *** five used methods have been compared with classical filters such as Bilateral,Frost,Kuan,and Lee and they proved their efficiency according to PSNR in different levels of *** five diverse models are achieved PSNR results for speckle noise at level(0.1,0.25,0.5,0.75),(33.354,29.415,27.218,24.115),(31.424,28.353,27.246,24.244),(32.243,28.42,27.744,24.893),(31.234,28.212,26.983,23.234)and(33.013,29.491,28.556,25.011)forDGAN,Br-U-NET,D-U-NET,GANand NLNWF *** to the value of PSNR and level of speckle noise,the best image passed for segmentation using U-Net and classification usingCNNto detect tumor *** experiments proved

关键词： Breast tumor speckle noise GAN model U-Net model neutrosophic

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A Deep Real-Time Fire Prediction Parallel D-CNN Model on UDOO BOLT V8

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Computers, Materials & Continua 2022年第12期73卷 6237-6252页

作者： Amal H.Alharbi Hanan A.Hosni mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Hazardous incidences have significant influences on human life,and fire is one of the foremost causes of such hazard in most *** prediction and classification model from a set of fire images can decrease the risk of losing human lives and *** promotion of fire emergency can be of great ***,construction of these prediction models is relevant and *** article proposes an operative fire prediction model that depends on a prediction unit embedded in the processor UDOO BOLT V8 hardware to predict fires in real time.A fire image database is improved to enhance the images quality prior to classify them as either fire or *** proposed deep learning-based Very Deep Convolutional Networks Visual Geometry Group(VGG-16)model(Parallel VGG-16)is an enhanced version of the VGG-16 model,by incorporating parallel convolution layers and a fusion module for better *** experimental results validate the performance of the Parallel VGG-16 which achieves an accuracy of 97%,compared to the compared state-of-the-art ***,we integrate the prediction module into a UDOO BOLT V8 computer,which precisely controlled the fire alarm so that it can cautious people from fire in real *** this paper we propose a complete fire prediction model using a camera and the UDOO BOLT V8 embedded *** experiments validate the effectiveness and applicability of the proposed fire model.

关键词： Fire prediction UDOO BOLT V8 deep learning

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Surgical outcomes of complicated cataract with pediatric trematode granulomatous uveitis

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International Journal of Ophthalmology(English edition) 2023年第3期16卷 354-360页

作者： mahmoud M.Farouk Amr Mounir Mortada Ahmed Abozaid Engy Mohamed Mostafa Marwa mahmoud Abdellah Department of Ophthalmology Sohag Faculty of MedicineSohag UniversitySohag 82524Egypt

AIM:To describe the clinical characteristics and surgical outcomes of complicated cataract with pediatric trematodal granulomatous uveitis(TGU).METHODS:Patients of cataract with TGU in the membranous(inactive) stage underwent cataract surgery with intraocular lens(IOL) *** history and ophthalmic examination were conducted for all cases,whereas Schimphlug imaging and corneal topography were done for some *** follow up was done on the 1st,2nd,and 5th postoperative days after ***,it was done at least at one,three,and six months *** and postoperative complications and the methods of their management were ***:Twelve eyes of 12 male children were included in this study ranging from 8 to *** mean best corrected Snellen visual acuity(BCVA) was significantly improved from 0.09±0.06 preoperatively to 0.37±0.11 at the final visit 6mo postoperatively(P<0.001).Schimphlug imaging and corneal topography showed flattening of the anterior surface of the inferior *** difficulties and complications included the poor dilatability of the drown down pupil,strong posterior synechia between the anterior lens capsule and the iris at the site of the inferior retrocorneal vascularized membrane and *** cases underwent primary hydrophobic IOL ***:Surgery for this type of cataract is relatively safe and *** is associated with some specific difficulties and complications that should be considered during surgery and follow up.

关键词： complicated cataract trematode granulomatous uveitis surgical outcomes

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ExtSpecR:An R Package and Tool for Extracting Tree Spectra from UAV-Based Remote Sensing

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Plant Phenomics 2023年第4期5卷 893-904页

作者： Zhuo Liu mahmoud AI-Sarayreh Cong Xu Federico Tomasetto Yanjie Li State Key Laboratory of Tree Genetics and Breeding Research Institute of Subtropical ForestryChinese Academy of ForestryHangzhouZhejiang 311400China Key Laboratory of Tree Breeding of Zhejiang Province Research Institute of Subtropical ForestryChinese Academy of ForestryHangzhouZhejiang 311400China Department of Computer Engineering German Jordanian UniversityAmman 11180Jordan School of Forestry University of CanterburyPrivate Bag 4800Christchurch 8140New Zealand AgResearch Ltd. Christchurch 8140New Zealand

The development of unmanned aerial vehicle(UAV)remote sensing has been increasingly applied in forestry for high-throughput and rapid acquisition of tree phenomics traits for various research ***,the detection of individual trees and the extraction of their spectral data remain a challenge,often requiring manual *** several software-based solutions have been developed,they are far from being widely *** paper presents ExtSpecR,an open-source tool for spectral extraction of a single tree in forestry with an easy-to-use interactive web *** reduces the time required for single tree detection and annotation and simplifies the entire process of spectral and spatial feature extraction from UAV-based *** addition,ExtSpecR provides several functionalities with interactive dashboards that allow users to maximize the quality of information extracted from UAV *** can promote the practical use of UAV remote sensing data among forest ecology and tree breeding researchers and help them to further understand the relationships between tree growth and its physiological traits.

关键词： interactive Remote Tool

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Deep Learning Prediction Model for Heart Disease for Elderly Patients

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Intelligent Automation & Soft Computing 2023年第2期35卷 2527-2540页

作者： Abeer Abdulaziz AlArfaj Hanan Ahmed Hosni mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The detection of heart disease is a problematic task in medical *** diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical *** massive advances in deep learning models pursue the devel-opment of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things ***,in this paper,we propose a deep learning model for elderly patients to aid and enhance the diag-nosis of heart *** proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning *** mod-el performance is veriﬁed with a full and minimum set of *** features enhance the processing time of the classiﬁcation process while the accuracy is *** performance of classiﬁers with less features has been analyzed with experimental *** proposed system is built on the Internet of Things Platform for medical data for the classiﬁcation process which aids medical profes-sionals to detect heart diseases through cloud *** results accuracy is matched to classical learning models such as Convolutional Neural Network(CNN),Deep CNN,and neural ensemble *** analysis of the proposed diagnostic system can determine the heart disease risks efﬁ*** results demonstrate thatﬂexible modeling and tuning of the hyperparameters can attain an accuracy of up to 97.11%.

关键词： Heart disease internet of things deep learning

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Investigating the Effects of Eichhornia Crassipes Biodiesel and Liquefied Petroleum Gas on the Performance and Emissions of a Dual-Fuel Engine

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Fluid Dynamics & Materials Processing 2023年第9期19卷 2329-2343页

作者： Hawraa S.Mohammed mahmoud A.Mashkour Mechanical Engineering Department University of Technology-IraqBagdadIraq

This study considers the effect of Eichhornia Crassipes Biodiesel(ECB)blends on the performances,combustion,and emission characteristics of a direct injection compression ignition engine operated in a dual-fuel mode(DFM)and equipped with an Exhaust gas recirculation technique(EGR).In particular,a single-cylinder,four-stroke,water-cooled diesel engine was utilized and four modes of fuel operation were considered:mode I,the engine operated with an ordinary diesel fuel;mode II,the engine operated with the addition of 2.4 L/min of lique-ﬁed petroleum gas(LPG)and 20%EGR;mode III,20%ECB with 2.4 L/min LPG and 20%EGR;mode IV,40%ECB with 2.4 L/min LPG and 20%*** operation conditions were constant engine speed(1500 rpm),var-iation of load(25%,50%,75%,and 100%),full load,with a compression ratio of 18,and a time injection of 23°BTDC(Before top died center).With regard to engine emissions,carbon dioxide(CO_(2)),carbon monoxide(CO),hydrocarbons(UHC),and nitrogen oxide(NOX)were measured using a gas *** smoke opacity was measured using an OPABOX smoke *** comparing the results related to the different modes with mode I at full load,the BTE(Brake thermal efﬁciency)increased by 20.17%,11.45%,and 12.66%with modes II,III,and IV,*** comparison to the results for mode II,the BTE decreased due to the combustion of ECB blends by 7.26%and 6.24%for mode III and mode IV,respectively,at full *** comparison to mode II,the Brake speciﬁc energy consumption(BSEC)increased with the ECB *** ECB blends,there is a noticeable decrease in the CO,CO_(2),and UHC emissions at a partial ***,the 20%ECB has no effect on CO emissions at full *** modes II and IV,the CO_(2)increased by 33.33%and 19%,respectively,while the UHC emissions were reduced by 14.49%for mode III and 26.08%for mode *** smoke of mode III was lower by 7.21%,but for mode IV,it was higher by 12.37%.In addition,with mode III and mode IV,the NOx emissions increased by 30.50%

关键词： Eichhornia Crassipes Biodiesel dual-fuel engine liqueﬁed petroleum gas combustion diesel engine brake thermal efﬁciency

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Geochemical characteristics of natural gases and source rocks in Obayied sub-basin,north Western Desert,Egypt:implications for gas-source correlation

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Acta Geochimica 2023年第2期42卷 241-255页

作者： Mostafa Hassan mahmoud Leila Mohammed Ahmed Ghalib Issa Omar Hegab Geology Department Faculty of ScienceMansoura UniversityMansoura 35516Egypt Egyptian General Petroleum Corporation CairoEgypt

Western Desert represents a major oil and gas province in Egypt producing more than 50%of the country’s oil *** and gas blend occurs in most producing fields,however,the genetic link between gas and liquid hydrocarbon phases are not *** sub-basin in the Western Desert where oil and gas phases coexist in the Middle Jurassic sandstones of the Khatatba Formation provides an ideal place to investigate the link between oil and gas *** analyses on rock samples(Rock–Eval pyrolysis,vitrinite reflectance,R_(o))and gases(molecular and isotopic composition)were conducted in order to identify the genetic characteristics of the hydrocarbon phases ***-relevant parameters(Rock–Eval T_(max),vitrinite reflectance R_(o))elucidate that only Middle Jurassic Khatatba organofacies capable of generating wet and dry hydrocarbon ***,the enrichment of C_(7)normal alkanes,mono-branched alkanes relative to polybranched components in the Obayied gases reflect their generation via cracking of *** modelling results confirm gas generation through both primary and secondary ***,secondary cracking of liquid hydrocarbon phases is volumetrically more *** cracking of the Khatatba organofacies likely predate and catalyze the secondary cracking of the liquid phases and therefore the volume of generated gas increases incrementally eastward where both processes *** present study highlights the significant role of secondary cracking in the generation and accumulation of huge gas accumulations in the basins containing oil-prone source intervals.

关键词： Primary and secondary cracking Gas geochemistry Jurassic Khatatba Obayied Western Desert

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