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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 abd.lla 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 abd.lla 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 abd.lla 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 abd.lla 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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Treating amyotrophic lateral sclerosis with allogeneic Schwann cell-derived exosomal vesicles: a case report

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Neural Regeneration Research 2025年第4期20卷 1207-1216页

作者： Pascal J.Goldschmidt-Clermont aisha Khan George Jimsheleishvili Patricia Graham Adriana Brooks Risset Silvera Alexander J.P.Goldschmidt Damien D.Pearse W.Dalton Dietrich Allan D.Levi James D.Guest Miller School of Medicine University of MiamiMiamiFLUSA Stem Cell Institute Miller School of MedicineUniversity of MiamiMiamiFLUSA The Miami Project to Cure Paralysis Miller School of MedicineUniversity of MiamiMiamiFLUSA Alzady International LLC MiamiFLUSA Neurological Surgery Miller School of MedicineUniversity of MiamiMiamiFLUSA

Schwann cells are essential for the maintenance and function of motor neurons,axonal networks,and the neuromuscular *** amyotrophic lateral sclerosis,where motor neuron function is progressively lost,Schwann cell function may also be ***,important signaling and potential trophic activities of Schwann cell-derived exosomal vesicles have been *** case report describes the treatment of a patient with advanced amyotrophic lateral sclerosis using serial intravenous infusions of allogeneic Schwann cell-derived exosomal vesicles,marking,to our knowledge,the first instance of such *** 81-year-old male patient presented with a 1.5-year history of rapidly progressive amyotrophic lateral *** initial diagnosis,the patient underwent a combination of generic riluzole,sodium phenylbutyrate for the treatment of amyotrophic lateral sclerosis,and *** patient volunteered to participate in an FDA-approved single-patient expanded access treatment and received weekly intravenous infusions of allogeneic Schwann cell-derived exosomal vesicles to potentially restore impaired Schwann cell and motor neuron *** confirmed that cultured Schwann cells obtained from the amyotrophic lateral sclerosis patient via sural nerve biopsy appeared impaired(senescent)and that exposure of the patient’s Schwann cells to allogeneic Schwann cell-derived exosomal vesicles,cultured expanded from a cadaver donor improved their growth capacity in *** a period of observation lasting 10 weeks,during which amyotrophic lateral sclerosis Functional Rating Scale-Revised and pulmonary function were regularly monitored,the patient received weekly consecutive infusions of 1.54×1012(×2),and then consecutive infusions of 7.5×1012(×6)allogeneic Schwann cell-derived exosomal vesicles diluted in 40 mL of Dulbecco’s phosphate-buffered *** of the infusions were associated with adverse events such as infusion reactions(allergic or otherwise)or

关键词： allogeneic amyotrophic lateral sclerosis exosomes infusion neuromuscular junction Schwann cell

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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 abd.lla hashim Heba G.Mohamed Lubna A.Alharbi Mohamed K.Nour abd.llah 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 abd.lla 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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Entropies of the Y-Junction Type Nanostructures

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Computer Modeling in Engineering & Sciences 2023年第6期135卷 2665-2679页

作者： Ricai Luo aisha Javed Muhammad Azeem Muhammad Kamran Jamil hassan Raza Muhammad Yasir Ilyas School of Mathematics and Physics Hechi UniversityYizhou456300China abdus Salam School of Mathematical Sciences Government College UniversityLahore54000Pakistan Department of Mathematics Riphah Institute of Computing and Applied SciencesRiphah International UniversityLahore54000Pakistan School of Mathematical Sciences College of Science and TechnologyWenzhou-Kean UniversityWenzhou325060China Department of Physics COMSATS University IslamabadLahore54000Pakistan

Recent research on nanostructures has demonstrated their importance and application in a variety of *** are used directly or indirectly in drug delivery systems,medicine and pharmaceuticals,biological sensors,photodetectors,transistors,optical and electronic devices,and so *** discovery of carbon nanotubes with Y-shaped junctions is motivated by the development of future advanced electronic *** of their interactionwithY-junctions,electronic switches,amplifiers,and three-terminal transistors are of particular *** is a concept that determines the uncertainty of a system or *** concepts are also used in biology,chemistry,and applied *** on the requirements,entropy in the form of a graph can be classified into several *** 1955,graph-based entropy was *** of the types of entropy is edgeweighted *** examined the abstract form of Y-shaped junctions in this *** edge-weight-based entropy formulas for the generic view of Y-shaped junctions were created,and some edge-weighted and topological index-based concepts for Y-shaped junctions were discussed in the present paper.

关键词： Y-shaped junctions three-terminal junctions nanostuctures entropy carbon nanotube

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A view of compact configurations from the R+γe^xT scenario

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Communications in Theoretical Physics 2020年第3期72卷 38-45页

作者： aisha Siddiqa Department of Mathematics Virtual University of Pakistan54-Lawrence RoadLahorePakistan

This paper presents a picture of compact structures for the f(R,T)model of the form R+γe^χT,withγandχbeing *** poly tropic equation of state and the MIT bag model reflect the pressure density relation for these compact *** this purpose,a system of two differential equations involving pressure as well as the mass of the stellar structure is obtained from field equations and the Tolman-Oppenheimer-Volkoff(TOV)*** numerical solution of this system gives a graphical description of various characteristics of these compact *** examine the viability and stability of assumed configurations,the energy conditions,causality relation and adiabatic index are discussed for the presumed scenario.

关键词： f(R,T)gravity neutron and quark stars equation of state

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Viscosity Effects on Anisotropic Universe in Curvature-Matter Coupling Gravity

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Communications in Theoretical Physics 2018年第5期69卷 537-543页

作者： M.Sharif aisha Siddiqa Department of Mathematics University of the PunjabQuaid-e-Azam Campus

In this paper, we study evolution of the universe in the background of f(R, T) gravity using LRS Bianchi type-Ⅰ model. We discuss scale factors as well as deceleration parameter in dark energy dominated era for different bulk viscosity models. The occurrence of big-rip singularity is also examined. It is concluded that expansion is faster when bulk viscosity is proportional to Hubble parameter as compared to other models.

关键词： f(R T) gravity anisotropic universe bulk viscosity

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