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Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

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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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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Chaotic Pigeon Inspired Optimization Technique for Clustered Wireless Sensor Networks

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Computers, Materials & Continua 2022年第12期73卷 6547-6561页

作者： Anwer Mustafa Hilal aisha hassan abdalla hashim Sami Dhahbi Dalia H.Elkamchouchi Jaber S.Alzahrani Mrim M.Alnfiai Amira Sayed A.Aziz Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia53100Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifP.O.Box 11099Taif21944Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Wireless Sensor Networks(WSN)interlink numerous Sensor Nodes(SN)to support Internet of Things(loT)*** the data gathered from SNs can be divulged,tempered,and *** WSN data processes manage the data in a centralized format at terminal *** devices are prone to attacks and the security of systems can get *** is a distributed and decentralized technique that has the ability to handle security issues in *** security issues include transactions that may be copied and spread across numerous nodes in a peer-peer network *** breaches the mutual trust and allows data immutability which in turn permits the network to go *** some instances,few nodes die or get compromised due to heavy power *** current article develops an Energy Aware Chaotic Pigeon Inspired Optimization based Clustering scheme for Blockchain assisted WSN technique abbreviated as EACPIO-CB *** primary objective of the proposed EACPIO-CB model is to proficiently group the sensor nodes into clusters and exploit Blockchain(BC)for inter-cluster communication in the *** select ClusterHeads(CHs)and organize the clusters,the presented EACPIO-CB model designs a fitness function that involves distinct input ***,BC technology enables the communication between one CH and the other and with the Base Station(BS)in the *** authors conducted comprehensive set of simulations and the outcomes were investigated under different *** simulation results confirmed the better performance of EACPIO-CB method over recent methodologies.

关键词： Blockchain wireless sensor networks clustering energy efficiency security data transmission

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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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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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Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

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Computer Systems Science & Engineering 2023年第5期45卷 1711-1726页

作者： Manar Ahmed Hamza aisha hassan abdalla hashim Dalia H.Elkamchouchi Nadhem Nemri Jaber S.Alzahrani Amira Sayed A.Aziz Mnahel Ahmed Ibrahim 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 Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt College of Science and Humanities Studies in Alquwayiyah Shaqra UniversitySaudi Arabia

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the *** the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the *** node clustering is a popular approach for ***,the sensor nodes are grouped to form clusters in a cluster-based WSN *** battery performance of the sensor nodes is likewise *** a result,the energy efficiency of WSNs is *** specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in *** the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was *** selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed *** provides the best routing path and increases the lifetime and energy efficiency of the ***-to-end delay and packet loss rate have also been *** proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor *** to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

关键词： Wireless sensor networks energy-efficient load balancing energy consumption network’s lifetime cluster heads grey wolf optimization tabu search particle swarm optimization

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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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Renal arterial embolization:Indications,angiographic findings,and outcomes in a series of 170 patients

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Current Urology 2023年第3期17卷 213-218页

作者： hashim Mohamed Farg Mohamed Elawdy Karim Ali Soliman Mohamed Ali Badawy Ali Elsorougy Tarek Mohsen Tarek El-Diasty abdalla Abdelhamid Urology Department Urology and Nephrology CenterMansoura UniversityMansoura35516Egypt

Background:The lack of overall experience and reporting on angiographic findings in previously published studies of renal arterial embolization(RAE)compelled us to report our overall experience on a series of *** and methods:A retrospective study was performed analyzing data of patients enrolled for RAE between 2010 and ***,physical examination,and laboratory data were reviewed for all *** ultrasound was the initial imaging study,and all patients underwent subsequent computed tomography or magnetic resonance *** outcome of RAE was determined based on radiographic and clinical ***:Data from 202 patients were analyzed,with a mean age of 45±15 years,and 71.3%of patients were *** injury was the most common indication for RAE(54%),followed by renal tumors,trauma,and spontaneous,in 27.7%,10.4%,and 8.4%of patients,*** angiography revealing pseudoaneurysm alone or with other pathology in the lower pole of the kidney was the most common finding(40.6%),whereas no lesions were identified on angiography in 32 patients(15.8%),after which RAE was subsequently *** arterial embolization was successful in 158 of 170 patients(92.9%)after 1 or more trials(maximum of 4).Microcoil alone or with other embolic materials was the most commonly used material for embolization(85%).Conclusions:latrogenic injury was the most common indication for *** alone or with other lesions was the most common lesion on renal angiography;however,angiography showed a negative result in 16%of patients,even those with *** lesions are present on angiography,the overall success of repeated trials of RAE reached 92.9%.

关键词： Angiographic findings Embolization Renal angioembolization Renal arterial embolization

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A rare condition: Larsen Syndrome

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TMR Integrative Nursing 2020年第6期4卷 215-218页

作者： aisha Alshdefat Abdu Mueed Bidchol Harshita Prabhakaran abdalla Alshdaifat Department of Maternal and Child Health College of NursingSultan Qaboos UniversitySultanate of Oman Genetic Reporting Manager Al Ward Medical ServicesSultanate of Oman Lecturer Department of Maternal and Child HealthCollege of NursingSultan Qaboos UniversitySultanate of Oman Medical Student University of LodzRepublic of Poland

Larsen Syndrome is a rare inherited severe disease that described for the first time in the medical field as a distinct disease by *** Larsen in *** is characterized by congenital dislocation of multiple joints along with other anomalies,including heart,face,hands,and *** present a two years five months male who presented with facial dysmorphic features like low set of ears,depressed nasal bridge,micro-retrognathia,and broad forehead.

关键词： Larsen Syndrome inherited newborn distinct disease

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Marfan Syndrome with Systemic Lupus erythematosus

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TMR Integrative Nursing 2020年第5期4卷 172-175页

作者： aisha Alshdefat Abdu Mueed Bidchol abdalla Alshdaifat Rasha Abu Baker Department of Maternal and Child Health College of NursingSultan Qaboos UniversitySultanate of Oman Genetic Reporting Manager Al Ward Medical ServicesSultanate of Oman Medical Student University of LodzRepublic of Poland 不详

One of the autosomal dominant tissue disorders is Marfan syndrome that affects different organ ***,Marfan syndrome causing abnormalities in the heart,blood vessels,eyes,bones,and *** Often,features of Marfan syndrome are vision problems,defects in the large blood vessel-like aorta,tall and very thin,have long fingers and toes(arachnodactyly),and have an arm span exceeding the height of their ***,Other common features include a long and narrow face,crowded teeth,and scoliosis,or *** presented a thirty-three years old female known case of Systemic Lupus erythematosus(SLE)and had Marfan syndrome,presented to the emergency department with complaints of headache and fever for two *** patient denied any complaining of vomiting,blurred vision,dizziness.

关键词： Marfan Syndrome autosomal subdural subacute on chronic hemorrhage Systemic Lupus erythematosus

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