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Computationally efficient MUSIC based DOA estimation algorithm for FMCW radar

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Journal of Electronic Science and Technology 2023年第1期21卷 46-64页

作者： Bakhtiar Ali Karim haitham Kareem Ali Department of Communication Engineering Sulaimani Polytechnic UniversitySulaymaniyah70-236Iraq

This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)*** existing subspace-based DOA estimation algorithms suffer fromeither high computational costs or low *** aim to solve such contradictory relation between complexity and accuracy by using randomizedmatrix ***,we apply an easily-interpretablerandomized low-rank approximation to the covariance matrix(CM)and R∈C^(M×M)throughthresketch maties in the fom of R≈OBQ^(H).Here the approximately compute its *** is,we first approximate matrix Q∈C^(M×z)contains the orthonormal basis for the range of the sketchmatrik C∈C^(M×z)cwe whichis etrated fom R using randomized unifom counsampling and B∈C^(z×z)is a weight-matrix reducing the approximation *** on such approximation,we are able to accelerate the subspacecomputation by the orders of the magnitude without compromising estimation ***,we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the *** validated by the simulation results,the DOA estimation accuracy of the proposed algorithm,eficient multiple signal classification(E-MUSIC)s high,closely tracks standardMUSIC,and outperforms the well-known algorithms with tremendouslyreduced time ***,the devised method can realize high-resolutionreal-time target detection in the emerging multiple input and multiple output(MIMO)automotive radar systems.

关键词： Computational complexity Direction of arrival(DOA)estimation Frequency-modulated continuous-wave(FMCW)radar Subspace algorithms

Using Capsule Networks for Android Malware Detection Through Orientation-Based Features

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Computers, Materials & Continua 2022年第3期70卷 5345-5362页

作者： Sohail Khan Mohammad Nauman Suleiman Ali alsaif Toqeer Ali Syed Hassan Ahmad Eleraky Deanship of Preparatory Year and Supporting Studies Computer Science DepartmentImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia National University of Computer and Emerging Sciences Pakistan Department of Computer Science Islamic University of MedinaMedinaSaudi Arabia

Mobile phones are an essential part of modern *** two popular mobile phone platforms,Android and iPhone Operating System(iOS),have an immense impact on the lives of millions of *** these two,Android currently boasts more than 84%market ***,any personal data put on it are at great risk if not properly *** the other hand,more than a million pieces of malware have been reported on Android in just 2021 till *** and mitigating all this malware is extremely difficult for any set of human *** to this reason,machine learning-and specifically deep learning-has been utilized in the recent past to resolve this ***,deep learning models have primarily been designed for image *** this line of research has shown promising results,it has been difficult to really understand what the features extracted by deep learning models are in the domain of ***,due to the translation invariance property of popular models based on ConvolutionalNeural Network(CNN),the true potential of deep learning for malware analysis is yet to be *** resolve this issue,we envision the use of Capsule Networks(CapsNets),a state-of-the-art model in deep *** argue that since CapsNets are orientation-based in terms of images,they can potentially be used to capture spatial relationships between different features at different locations within a sequence of *** design a deep learning-based architecture that efficiently and effectively handles very large scale malware datasets to detect Androidmalware without resorting to very deep *** leads tomuch faster detection as well as increased *** achieve state-of-the-art F1 score of 0.987 with an FPR of just 0.002 for three very large,real-world malware *** code is made available as open source and can be used to further enhance our work with minimal effort.

关键词： Malware security Android deep learning capsule networks

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

The Odd Log-Logistic Weibull-G Family of Distributions with Regression and Financial Risk Models

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Journal of the Operations Research Society of China 2022年第1期10卷 133-158页

作者： Mahdi Rasekhi Emrah Altun Morad Alizadeh haitham M.Yousof Department of Statistics Malayer UniversityMalayerIran Department of Mathematics Bartin UniversityBartinTurkey Department of Statistics Persian Gulf UniversityBushehrIran Department of Statistics Mathematics and InsuranceBenha UniversityBenhaEgypt

A new generalization of the Weibull-G family is proposed with two extra shape param*** mathematical properties are derived in great *** the Weibull and normal distributions as baseline distributions,two models are *** first model is a location-scale regression model based on a new extension of the Weibull *** second model is a new two-step financial risk model to forecast the daily value at *** flexibility and applicability of the proposed models are investigated by means of five real data sets on the lifetime and financial *** findings of the study show that proposed models work well and produce better results than other well-known models for financial risk modeling and censored lifetime data analysis.

关键词： Odd log-logistic-G family Weibull-G family Regression model Value at risk Simulation Maximum likelihood Financial risk modeling

Aspartate transferase-to-platelet ratio index-plus: A new simplified model for predicting the risk of mortality among patients with COVID-19

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World Journal of Gastroenterology 2022年第16期28卷 1671-1680页

作者： Ali Madian Ahmed Eliwa Hytham Abdalla haitham A Azeem Aly Department of Internal Medicine Faculty of MedicineAl-Azhar University-AssiutAssiut 71524Egypt Department of Internal Medicine Faculty of MedicineAl-Azhar University-CairoCairo 11754Egypt Department of Chest Diseases Faculty of MedicineAl-Azhar University-AssiutAssiut 71524Egypt

BACKGROUND Coronavirus disease 2019(COVID-19)has a spectrum of clinical syndromes with serious involvement of the lung and frequent effection of the liver and hemostatic *** biomarkers are affordable,rapid,objective,and useful in the evaluation and prognostication of COVID-19 *** To investigate the association between aspartate transferase-to-platelet ratio index(APRI)and in-hospital mortality to develop a COVID-19 mortality prediction *** A multicenter cohort study with a retrospective design was *** records of all consecutive adult patients admitted to Al-Azhar University Hospital(Assiut,Egypt)and Chest Hospital(Assiut,Egypt)with confirmed COVID-19 from July 1,2020 to October 1,2020,were retrieved and *** patient cohort was classified into the following two categories based on the APRI:(1)COVID-19 presenting with APRI≤0.5;and(2)COVID-19 presenting with APRI(>0.5 and≤1.5).The association between APRI and all-cause in-hospital mortality was analyzed,and the new model was developed through logistic regression *** Of the 353 patients who satisfied the inclusion criteria,10%were admitted to the intensive care unit(n=36)and 7%died during the hospital stay(n=25).The median age was 40 years and 50.7%were *** admission,49%had aspartate transferase-dominant liver *** admission,APRI(>0.5 and≤1.5)was independently associated with all-cause in-hospital mortality in unadjusted regression analysis and after adjustment for age and sex;after stepwise adjustment for several clinically relevant confounders,APRI was still significantly associated with all-cause inhospital *** admission,APRI(>0.5 and≤1.5)increased the odds of mortality by fivetimes(P<0.006).From these results,we developed a new predictive model,the APRI-plus,which includes the four predictors of age,aspartate transferase,platelets,and serum *** for mortality was very good,with an area under the receiver operating curve

关键词： COVID-19 Aspartate transferase-to-platelet ratio index Aspartate transferase All-cause inhospital mortality Serum ferritin SARS-CoV-2

Personality Assessment Based on Natural Stream of Thoughts Empowered with Machine Learning

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Computers, Materials & Continua 2023年第7期76卷 1-17页

作者： Mohammed Salahat Liaqat Ali Taher M.Ghazal haitham M.Alzoubi College of Engineering and Technology University of Science and Technology of FujairahFujairahUAE School of Information Technology Skyline University CollegeUniversity City Sharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)43600Malaysia School of Business Skyline University CollegeUniversity City Sharjah1797SharjahUAE

Knowing each other is obligatory in a multi-agent collaborative *** may develop the desired know-how of each other in various aspects such as habits,job roles,status,and *** different distinguishing characteristics related to a person,personality traits are an effective predictive tool for an individual’s behavioral *** has been observed that when people are asked to share their details through questionnaires,they intentionally or unintentionally become *** knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about *** writings can effectively assess an individual’s personality traits that may yield enormous possibilities for applications such as forensic departments,job interviews,mental health diagnoses,*** of consciousness,collected by James Pennbaker and Laura King,is one such way of writing,referring to a narrative technique where the emotions and thoughts of the writer are presented in a way that brings the reader to the fluid through the mental states of the ***-over,computationally,various attempts have been made in an individual’s personality traits assessment through deep learning algorithms;however,the effectiveness and reliability of results vary with varying word embedding *** article proposes an empirical approach to assessing personality by applying convolutional networks to text *** Encoder Representations from Transformers(BERT)word embedding technique is used for word vector generation to enhance the contextual meanings.

关键词： Personality traits convolutional neural network deep learning word embedding

An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

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Computers, Materials & Continua 2022年第2期70卷 3311-3319页

作者： Khattab Alheeti Abdulkareem Alaloosy haitham Khalaf Abdulkareem Alzahrani Duaa Al_Dosary Department of Computer Networking System College of Computer Sciences and Information TechnologyUniversity of AnbarRamadiIraq College of Medicine University of AnbarRamadiIraq Department of Computer Science Faculty of Computer Science and Information TechnologyAlbaha UniversityAlbahaSaudi Arabia College of Computer Sciences and Information Technology University of AnbarRamadiIraq

Self-driving and semi-self-driving cars play an important role in our daily *** effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital ***,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution *** this case,roadside units are considered one of themost important communication *** distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage *** this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these ***,this approach is based primarily on k-means to find the optimal location of each roadside *** addition,this approach supports dynamicmobility with a long period of connectivity for each ***,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and ***,the system can achieve a high connectivity rate with a low error rate while reducing costs.

关键词： Self-driving cars roadside unit autonomous vehicles distributed systems connectivity

Characterization of Unsaturated Polyester Filled with Waste Coconut Shells, Walnut Shells, and Carbon Fibers

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Fluid Dynamics & Materials Processing 2023年第10期19卷 2449-2469页

作者： Marwah Subhi Attallah Reem Alaa Mohammed Ruaa haitham Abdel-Rahim Department of Materials Engineering University of Technology-IraqBaghdadIraq

This study aims to evaluate the erosion behavior and the hardness of hybrid composites made of varying amounts of coconut shells,walnut shells,and carbonﬁbers dispersed in a polyester ***(L16)Taguchi experiments were used to determine the optimal combination of *** particular,an erosion device con-sisting of a motor with a constantﬂow rate of 45 L/min,a pump with a diameter of 40 mm,a nozzle with a dia-meter of 5 mm,and a tank made of“perspex glass”55 cm long,30 cm tall,and 25 cm wide was *** tests were conducted by varying the sample-to-nozzle distance,the pattern angle,and the sand particle *** results have revealed that the presence of 7.5%by weight of waste coconut shell,for conditions corresponding to 90°angle,sand size 425μm,stand distance 30 cm,gives the best wear resistance(3.04×10^(-5) g/g).Theﬁller content and sand particle size affect the erosive rate,with the angle playing a secondary *** distance between the sample and the nozzle has a weaker effect on erosive *** hardness results show that the models(UP-5%carbonﬁber-2.5-3.5-4.5-5.5-6.5-7.5 wt.%waste coconut shell)give the best values for prayer compared to the samples(UP-5 wt.%carbonﬁber-2.5-3.5-4.5-5.5-6.5-7.5 wt.%waste walnut shell).

关键词： Unsaturated polyester resin carbonﬁber waste coconut shell walnut shell erosive behavior Taguchi’s method L16

Optimal Deep Transfer Learning Based Colorectal Cancer Detection and Classification Model

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Computers, Materials & Continua 2023年第2期74卷 3279-3295页

作者： Mahmoud Ragab Maged Mostafa Mahmoud Amer H.Asseri Hani Choudhry haitham A.Yacoub Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Cancer Biology Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Sciences Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah22252Saudi Arabia Department of Molecular Genetics and Enzymology Human Genetics and Genome Research InstituteNational Research CentreCairo12622Egypt Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Cell Biology Department Biotechnology Research InstituteNational Research CentreGiza12622Egypt

Colorectal carcinoma(CRC)is one such dispersed cancer globally and also prominent one in causing cancer-based ***,pathologists execute CRC diagnosis through visible scrutinizing under the microscope the resected tissue samples,stained and fixed through Haematoxylin and Eosin(H&E).The advancement of graphical processing systems has resulted in high potentiality for deep learning(DL)techniques in interpretating visual anatomy from high resolution medical *** study develops a slime mould algorithm with deep transfer learning enabled colorectal cancer detection and classification(SMADTL-CCDC)*** presented SMADTL-CCDC technique intends to appropriately recognize the occurrence of colorectal *** accomplish this,the SMADTLCCDC model initially undergoes pre-processing to improve the input image *** addition,a dense-EfficientNet technique was employed to extract feature vectors from the pre-processed ***,SMA with Discrete Hopfield neural network(DHNN)method was applied for the recognition and classification of colorectal *** utilization of SMA assists in appropriately selecting the parameters involved in the DHNN approach.A wide range of experiments was implemented on benchmark datasets to assess the classification performance.A comprehensive comparative study highlighted the better performance of the SMADTL-CDC model over the recent approaches.

关键词： Colorectal cancer deep transfer learning slime mould algorithm hyperparameter optimization biomedical imaging