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Predictors of renal angioembolization outcome:A retrospective analysis with 148 patients at a tertiary urology institute

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Asian Journal of Urology 2022年第2期9卷 103-108页

作者： hashim mohamed farg mohamed mohamed Elawdy Karim Ali Soliman mohamed Ali Badawy Ali Elsorougy Abdalla Abdelhamid Tarek Mohsen Tarek El-Diasty Department of Radiology Urology and Nephrology CentreMansoura UniversityMansouraEgypt Department of Urology Urology and Nephrology CentreMansoura UniversityMansouraEgypt

Objective:The aim of this study was to evaluate the predictor of unsuccessful outcome of renal angioembolization(RAE).Knowing those predictors may help in avoiding unnecessary RAE procedures and their associated side effects,while helping to prepare for an alternate procedure and improving patient's overall ***:A retrospective analysis between January 2006 and December 2018 was performed,and the indications for RAE were classified into post-traumatic,iatrogenic,renal tumors,and *** who underwent RAE prior to nephrectomy were *** tomography angiography was performed in patients with normal renal function and those who had no contrast allergy,otherwise magnetic resonance angiography was *** the purpose of statistical analysis,we stratified patients into two main categories based on the final outcome—successful or ***:Of 180 patients,32 with negative angiography were eliminated,leaving 148 patients;136(91%)had successful outcomes after one or more trials and 12 had unsuccessful *** mean age was 45±15 years,and 105(71%)were *** gender,side of the lesion,presence of hematuria,indication for RAE,nor the type of lesion affected the *** the other hand,renal anatomy with presence of accessory artery was the only predictor to failed RAE(p=0.001).Failed RAE trial was a predictor for nephrectomy as a secondary procedure(p=0.03).Conclusion:No pre-procedural predictors could anticipate the RAE outcome,and different indications can be scheduled to RAE,which is equally *** presence of accessory renal artery on diagnostic angiography is the only factor that may predict the failure of the procedure.

关键词： Angioembolization Renal angioembolization Embolization Renal trauma Outcome

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The Impact of Urban and Rural Education Gap on Student Achievement Differences

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Journal of Contemporary Educational Research 2024年第9期8卷 145-150页

作者： Weiping Fu Abdul Talib bin mohamed hashim Faculty of Human Development Universiti Pendidikan Sultan Idris35900 Tanjong MalimPerakMalaysia

With our country’s social development,the gap between urban and rural education has become increasingly obvious,especially the difference in student *** study aims to explore the impact of the urban-rural education gap on student *** study adopts a quantitative analysis method to collect and analyze the academic performance,learning resources,and social resources of urban and rural *** results show that the unbalanced distribution of educational resources in urban and rural areas is the key factor leading to the gap in students’*** example,urban education resources are abundant while rural education resources are relatively *** the same time,the difference in social environment and family economic status also have a significant impact on student *** differences between urban and rural areas,particularly in terms of family economic conditions and their ability to invest in education,further widen the gap between urban and rural *** results of this study can provide a reference for reducing the gap between urban and rural education and improving the academic performance of rural students.A fair education policy is essential to narrow the gap between urban and rural education resources distribution,so as to improve the equality of education in society.

关键词： Urban and rural education gap Student achievement differences Allocation of educational resources Social and environmental differences Family economic situation

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Sensor Faults Observer Design with H_∞ Performance for Non-linear T-S systems

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International Journal of Automation and computing 2013年第6期10卷 563-570页

作者： Imen Haj Brahim Maha Bouattour Driss Mehdi mohamed Chaabane Ghani hashim Laboratory of Sciences and Techniques of Automatic Control&Computer Engineering(Lab-STA) National School of Engineering of SfaxUniversity of Sfax LIAS-ENSIP University of PoitiersLIAS-ENSIP2 rue Pierre Brousse Computer Science Department Cihan University

This paper deals with the problem of the state estimation and the sensor faults detection for nonlinear perturbed systems described by Takagi-Sugeno （T-S） fuzzy models with unmeasurable premise variables. Indeed, a T-S observer is synthesized, in descriptor form, to estimate both the system states and the sensor faults simultaneously. The idea of the proposed approach is to introduce the sensor fault as an auxiliary variable in the state vector. Besides, the T-S model with unmeasurable premise variables is reduced to a perturbed model with measurable variables. Convergence conditions are established with Lyapunov theory and the H∞ performance in order to guarantee the best robustness to disturbances. These conditions are expressed in terms of linear matrix inequalities （LMIs）. The parameters of the observer are computed using the solution of the LMI conditions. Finally, a numerical example is given to illustrate the design procedures. Simulation results show the satisfactory performances.

关键词： Takagi-Sugeno (T-S) fuzzy system sensor faults H∞ performance descriptor approach observer linear matrix inequality(LMI).

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Synthesis of ZrC Nanoparticles in the ZrO_2–Mg–C–Fe System Through Mechanically Activated Self-Propagating High-Temperature Synthesis

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Acta Metallurgica Sinica(English Letters) 2014年第6期27卷 1144-1151页

作者： Abdollah Hajalilou Mansor hashim Halimah mohamed kamari Kazem Javadi Samikannu Kanagesan Mohammad Parastegari Materials Synthesis and Characterization Laboratory Institute of Advanced Technology (ITMA) University Putra Malaysia Physics Department University Putra Malaysia Irankhodrow Industry Mechanical Testing Laboratory Materials Synthesis and Characterization Laboratory Isfahan University of Technology

ZrC nanoparticles in the matrix of Fe were produced by the mechanically activated self-propagating hightemperature method using ZrO2/C/Mg/Fe powder mixtures. The effects of milling time, Fe content, and combustion temperature as well as the formation route for synthesizing ZrC powder particles were studied. The samples were characterized by XRD, SEM, TEM, and DTA. The XRD results revealed that, after 18 h of mechanical activation, ZrO2/ZC/Mg/Fe reacted with the self-propagating combustion（SHS） mode at 870 °C producing the ZrC–Fe nanocomposite. It was also found that both mechanical activation and Fe content played key roles in the ZrC synthesis temperature. With a Fe content of（5–40） wt%, the SHS reaction proceeded favorably and both the ZrC formation temperature and the adiabatic temperature（Tad） decreased. The Mg O content was removed from the final products using a leaching test process by dissolving in hydrochloric and acetic acids.

关键词： Mechanical activation(MA) Combustion synthesis Fe–ZrC composite Leaching process

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Identification of potential soil water retention using hydric numerical model at arid regions by land-use changes

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International Soil and Water Conservation Research 2015年第4期3卷 305-315页

作者： mohamed Abu-hashim Elsayed mohamed Abd-ElAziz Belal Soil Science Department Zagazig UniversityZagazig44511Egypt National Authority for Remote Sensing and Space Sciences(NARSS) Cairo19765Egypt

Assessment of soil water retention in arid region is an input required parameter in precision water management at large *** were carried out in Tanta catchment in the middle Nile Delta,Egypt(30145 N,30155 E),where collecting soil samples covered different hydrological soil groups and *** on the natural resource conservation service curve number model(NRCS-CN),CN approach was used to investigate the effect of spatio-temporal variations of different land-uses on soil water *** soil water retention from 1990 to 2015 was reduced by 118.1 m^(3) per hectare with decreasing cropland *** encroachment from 1990 to 2015 was increased by 2.13%by decreasing cropland with 15.3%(5300 ha in 2015).This resulted in losing the potential soil water retention by 625,968.42 m^(3) water for the whole catchment *** of land degradation was pronounced,where 2.65%,29.35%,and 1.11%of the initial crop land-use in 1990 were converted to bare soil,fallow,and urban area,respectively in *** of(S)value of the NRCS-CN model with GIS technique provides useful measure to identify land-use changes of potential water storage capacity at catchment scale.

关键词： Curve number model Land-use Potential water retention Spatio-temporal variations

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Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

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Computers, Materials & Continua 2022年第12期73卷 6019-6035页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla hashim Heba G.mohamed Mohammad Alamgeer mohamed K.Nour Anas Abdelrahman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariJhnujhunuRajasthanIndia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Mechanical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication *** SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity *** the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s *** advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in *** this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)*** aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient *** attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform ***,WWO algorithm is applied to choose an optimal subset of features from the pre-processed ***,Deep Belief Network(DBN)model is followed to predict the stability level of ***,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN *** order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was *** simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.

关键词： Smart grid stability prediction deep learning energy systems machine learning metaheursitics

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Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

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Computer Systems Science & Engineering 2023年第5期45卷 2033-2047页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla hashim Heba G.mohamed Lubna A.Alharbi mohamed K.Nour Abdullah mohamed Ahmed S.Almasoud Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk47512Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

Recent developments on Internet and social networking have led to the growth of aggressive language and hate *** provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize *** advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social *** this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for *** presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the *** achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature *** addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of ***,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier *** experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.

关键词： Cybersecurity cyberbullying online social network deep learning spotted hyena optimizer

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Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

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Computers, Materials & Continua 2023年第1期74卷 607-621页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla hashim Heba G.mohamed mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia 53100 Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital *** Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their *** may result in compromised security of the systems,scams,and other such *** attacks hijack huge quantities of the available data,incurring heavy financial *** the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify *** this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)*** presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine *** attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding *** addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious ***,AFSA is applied to the proposed model to enhance the efficiency of GRU *** proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle *** simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

关键词： Malicious URL cybersecurity deep learning machine learning metaheuristics gated recurrent unit

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Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

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Computers, Materials & Continua 2023年第3期74卷 6195-6212页

作者： T.Gopalakrishnan mohamed Yacin Sikkandar Raed Abdullah Alharbi P.Selvaraj Zahraa H.Kareem Ahmed Alkhayyat Ali hashim Abbas Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipalIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Public Health Department College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computing Technologies College of Engineering and TechnologyFaculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University CollegeBabylonIraq College of Technical Engineering The Islamic UniversityNajafIraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq

The Coronavirus Disease(COVID-19)pandemic has exposed the vulnerabilities of medical services across the globe,especially in underdeveloped *** the aftermath of the COVID-19 outbreak,a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional *** imaging has become a crucial component in the disease diagnosis process,whereas X-rays and Computed Tomography(CT)scan imaging are employed in a deep network to diagnose the *** general,four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks,such as network training,feature extraction,model performance testing and optimal feature *** current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion(CFPADLDF)approach for detecting and classifying *** presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical ***,the proposed CFPA-DLDF technique employs the Gabor Filtering(GF)approach to pre-process the input *** addition,a weighted voting-based ensemble model is employed for feature extraction,in which both VGG-19 and the MixNet models are ***,the CFPA with Recurrent Neural Network(RNN)model is utilized for classification,showing the work’s novelty.A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model,and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches.

关键词： Deep learning medical imaging fusion model chaotic models ensemble model COVID-19 detection

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Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

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Computers, Materials & Continua 2022年第12期73卷 6579-6594页

作者： Manar Ahmed Hamza Aisha Hassan Abdalla hashim Heba G.mohamed Saud S.Alotaibi Hany Mahgoub Amal S.Mehanna Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt

Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and *** recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected *** the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful *** and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT *** presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT *** proposed IMVO-DLIDS model follows a three-stage *** first,data pre-processing is performed to convert the actual data into useful *** addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature ***,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for ***,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU *** experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct *** extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

关键词： Internet of everything deep learning feature selection classification intrusion detection cybersecurity

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