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Science China Mathematics 2008年第10期51卷 1775-1786页

作者： Thabet Abdeljawad MARAABA fahd JARAD Dumitru BalEANU Department of Mathematics and Computer Sciences ankaya University06530 Ankara Turkey Department of Mathematics and Computer Sciences ankaya University06530 Ankara Turkey On leave of absence from Institute of Space Sciences P.O.BOX MG-23 R 76900 Magurele Romania

Local and global existence and uniqueness theorems for a functional delay fractional differential equation with bounded delay are investigated. The continuity with respect to the initial function is proved under Lipsc... 详细信息

关键词： fractional Caputo derivative C r -condition delay Lipschitz 26A33

Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks

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Computers, Materials & Continua 2022年第7期72卷 1195-1212页

作者： Ibrahim Abunadi Maha M.althobaiti fahd N.al-Wesabi Anwer Mustafa Hilal Mohammad Medani Manar Ahmed Hamza Mohammed Rizwanullah Abu Serwar Zamani Department of Information Systems Prince Sultan UniversityRafha StreetRiyadh11586Saudi Arabia Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer and Self Development Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia

The evolving“Industry 4.0”domain encompasses a collection of future industrial developments with cyber-physical systems(CPS),Internet of things(IoT),big data,cloud computing,***,the industrial Internet of things(IIoT)directs data from systems for monitoring and controlling the physical world to the data processing system.A major novelty of the IIoT is the unmanned aerial vehicles(UAVs),which are treated as an efficient remote sensing technique to gather data from large *** are commonly employed in the industrial sector to solve several issues and help decision *** the strict regulations leading to data privacy possibly hinder data sharing across autonomous *** learning(FL)becomes a recent advancement of machine learning(ML)which aims to protect user *** this aspect,this study designs federated learning with blockchain assisted image classification model for clustered UAV networks(FLBIC-CUAV)on IIoT *** proposed FLBIC-CUAV technique involves three major processes namely clustering,blockchain enabled secure communication and FL based image *** UAV cluster construction process,beetle swarm optimization(BSO)algorithm with three input parameters is designed to cluster the UAVs for effective *** addition,blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud ***,the cloud server uses an FL with Residual Network model to carry out the image classification process.A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV *** experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.

关键词： UAV networks clustering secure communication blockchain federated learning image classification

Intelligent Deep Convolutional Neural Network Based Object DetectionModel for Visually Challenged People

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Computer Systems Science & Engineering 2023年第9期46卷 3191-3207页

作者： S.Kiruthika Devi Amani Abdulrahman albraikan fahd N.al-Wesabi Mohamed K.Nour Ahmed Ashour Anwer Mustafa Hilal Department of Computing Technology SRM Institute of Science and TechnologyIndia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Engineering Mathematics and Physics Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)and Computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged *** detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so *** test images include all these variants as *** detection models alert them about their surroundings when they want to walk *** study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),Inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition *** on the analysis performed on these test images,the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and *** further improve the performance of the Inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the *** accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization ***,Inception V3 can handle images of different *** makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.

关键词： Pre-trained models object detection visually challenged people deep learning Inception V3 DenseNet-121

Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

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Computers, Materials & Continua 2022年第7期72卷 907-923页

作者： Amani Abdulrahman albraikan Siwar Ben Haj Hassine Suliman Mohamed Fati fahd Nal-Wesabi Anwer Mustafa Hilal Abdelwahed Motwakel Manar Ahmed Hamza Mesfer al Duhayyim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self-Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Cyberbullying(CB)is a distressing online behavior that disturbs mental health *** studies have employed statistical and Machine Learning(ML)techniques for CB *** this motivation,the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification(ODL-CDC)technique for CB detection in social *** proposed ODL-CDC technique involves different processes such as pre-processing,prediction,and hyperparameter *** addition,GloVe approach is employed in the generation of word ***,the pre-processed data is fed into BidirectionalGated Recurrent Neural Network(BiGRNN)model for ***,hyperparameter tuning of BiGRNN model is carried out with the help of Search and Rescue Optimization(SRO)*** order to validate the improved classification performance of ODL-CDC technique,a comprehensive experimental analysis was carried out upon benchmark dataset and the results were inspected under varying aspects.A detailed comparative study portrayed the superiority of the proposed ODL-CDC technique over recent techniques,in terms of performance,with the maximum accuracy of 92.45%.

关键词： Cybersecurity cyberbullying social networks parameter tuning deep learning metaheuristics

Increasing Dye-Sensitized Solar Cell Efficiency by ZnO Spin-Coating of the TiO<sub>2</sub>Electrode: Effect of ZnO Amount

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Energy and Power Engineering 2013年第10期5卷 591-595页

作者： fahd al-Juaid Amar Merazga Physics Department Faculty of Science Taif University Taif Saudi Arabia

This paper is concerned with the improvement of dye-sensitized solar cell (DSSC) efficiency upon ZnO-coating of the TiO2 electrode. Sol-gel ZnO of controlled amount by varying the number of sol drops during spin-coating is shown to increase the DSSC efficiency. The highest efficiency is obtained at a single sol drop with enhancement of 40%, while beyond this amount the efficiency falls down sharply to zero. Based on measured optical absorption spectra of the different dye-loaded electrodes, it is concluded that this amount of ZnO sol corresponds to the thinnest layer that can create the energy barrier to minimize the electron recombination rate without seriously affecting the dye adsorption efficiency of the TiO2 film.

关键词： ZnO Spin-Coating Dye-Sensitized Solar Cell Efficiency

Indoor Electromagnetic Radiation Intensity Relationship to Total Energy of Household Appliances

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Computers, Materials & Continua 2022年第3期70卷 5421-5435页

作者： Murad A.A.almekhlafi Lamia Osman Widaa fahd N.al-Wesabi Mohammad alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The rapid technological developments in the modern era have led to increased electrical equipment in our daily lives,work,and *** this standpoint,the main objective of this study is to evaluate the potential relationship between the intensity of electromagnetic radiation and the total energy of household appliances in the living environment within the building by measuring and analyzing the strength of the electric field and the entire electromagnetic radiation flux density of electrical devices operating at frequencies(5 Hz to 1 kHz).The living room was chosen as a center for measurement at 15 homes in three different environmental regions(urban,suburbs,and open areas).The three measurement methods are(Mode 1:people in a sitting position with electrical appliances *** 2:People in a standing position with electrical appliances *** 3:People are in the upright positionwhile turning off the electrical devices)in the living *** measurement methods and their results reinforce the importance of this *** results showed that the average electric field strengthmeasured inMode 2 ismuch greater than the two methods,and we also found less electromagnetic radiation in Mode 3 than in the two *** results remain within the recommended overall exposure developed by the International Committee for the Prevention of Non-Ionizing Radiation and the International Electrotechnical Commission.

关键词： Exposure assessment electromagnetic field household appliances indoor various environments epidemiology

Thrust characteristics of nano-carbon/al/oxygenated salt nanothermites for micro-energetic applications

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Defence Technology（防务技术） 2023年第12期30卷 55-69页

作者： Ahmed fahd alex Baranovsky Charles Dubois Jamal Chaouki Sherif Elbasuney Shady Shokry Chemical Engineering Department Ecole Polytechnique de MontrealMontrealH3C 3A7Canada Energetic Materials and Nanotechnology Research Center Technical Research CenterCairo11765Egypt Mechanical and Mechatronics Engineering Department University of WaterlooOntarioN2L 3G1Canada Head of Nanotechnology Research Center Military Technical CollegeCairo11767Egypt Laser and Optoelectronics Research Center Technical Research CenterCairo11765Egypt

Combustion within small motors is key in the application-specific development of nanothermite-based micro-energetic systems. This study evaluates the performance of nanothermite mixtures in a converging-diverging nozzle and an open tube. Mixtures were prepared using nano-aluminum(n-al),potassium perchlorate(KClO_(4)), and different carbon nanomaterials(CNMs) including graphene-oxide(GO), reduced GO, carbon nanotubes(CNTs) and nanofibers(CNFs). The mixtures were packed at different densities and ignited by laser beam. Performance was measured using thrust measurement,high-speed imaging, and computational fluid dynamics modeling, respectively. Thrust, specific impulse(ISP), volumetric impulse(ISV), as well as normalized energy were found to increase notably with CNM content. Two distinctive reaction regimes(fast and slow) were observed in combustion of low and high packing densities(20% and 55%TMD), respectively. Total impulse(IFT) and ISPwere maximized in the 5%GO/al/KClO_4 mixture, producing 7.95 m N·s and 135.20 s respectively at 20%TMD, an improvement of 57%compared to a GO-free sample(5.05 m N·s and 85.88 s). CFD analysis of the motors over predicts the thrust generated but trends in nozzle layout and packing density agree with those observed experimentally;peak force was maximized by reducing packing density and using an open tube. The numerical force profiles fit better for the nozzle cases than the open tube scenarios due to the rapid nature of combustion. This study reveals the potential of GO in improving oxygenated salt-based nanothermites,and further demonstrates their applicability for micro-propulsion and micro-energetic applications.

关键词： Nanothermites Graphene oxide Reduced graphene oxide Carbon nano material Oxygenated salts Laser ignition Computational fluid dynamics

A Joint Resource allocation algorithm for D2D Communication

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Computers, Materials & Continua 2022年第2期70卷 3751-3762页

作者： Abdul Kadir Hamid Lamia Osman Widaa fahd N.al-Wesabi Imran Khan Anwer Mustafa Hilal Manar Ahmed Hamza Abu Sarwar Zaman Mohammed Rizwanullah Department of Electrical&Computer Engineering University of SharjahSharjahUnited Arab Emirates Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKSA&Faculty of Computer and ITSana’a UniversityYemen Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The emergence of multimedia services has meant a substantial increase in the number of devices in mobile networks and driving the demand for higher data transmission *** result is that,cellular networks must technically evolve to support such higher rates,to be equipped with greater capacity,and to increase the spectral and energy *** with 4G technology,the 5G networks are being designed to transmit up to 100 times more data volume with devices whose battery life is 10 times ***,this new generation of networks has adopted a heterogeneous and ultra-dense architecture,where different technological advances are combined such as device-to-device(D2D)communication,which is one of the key elements of 5G *** has immediate applications such as the distribution of traffic load(data offloading),communications for emergency services,and the extension of cellular coverage,*** this communication model,two devices can communicate directly if they are close to each other without using a base station or a remote access ***,eliminating the interference between theD2Dand cellular communication in the *** interference management has become a hot issue in current *** order to address this problem,this paper proposes a joint resource allocation algorithm based on the idea of mode selection and resource *** results showthat the proposed algorithm effectively improves the systemperformance and reduces the interference as compared with existing algorithms.

关键词： D2D communication resource allocation wireless networks mobile communication

CryptoNight Mining algorithm with YAC Consensus for Social Media Marketing Using Blockchain

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Computers, Materials & Continua 2022年第5期71卷 3921-3936页

作者： Anwer Mustafa Hil fahd N.al-Wesabi Hadeel alsolai Ola Abdelgney Omer ali Nadhem Nemri Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah 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&Faculty of Computer and ITSana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia

Social media is a platform in which user can create,share and exchange the knowledge/*** media marketing is to identify the different consumer’s demands and engages them to create marketing *** popular social media platforms are Microsoft,Snapchat,Amazon,Flipkart,Google,eBay,Instagram,Facebook,Pin interest,and *** main aim of social media marketing deals with various business partners and build good relationship with millions of customers by satisfying their *** technology is replacing old approaches in the social media marketing to new technology-based ***,this disruptive technology creates some issues like fake news,insecure,inconsistency,inaccuracy and so *** issues contribute economic instability in the society,diminishing the level of *** overcome these issues,this paper we present blockchain as disruptive technology for social media *** plays a vital role on social media marketing by providing secure to the company page in the *** properties of disruptive potential of blockchain on social media marketing is transparency,security,reliability and *** paper presents a new framework for disruptive technology in blockchain social media marketing using fusion of CryptoNight mining algorithm with YAC consensus algorithm[BCDSMM-CNYAC].This mining algorithm provides high CPU efficiency,high dimensionality of secure and detecting falsifying data attack in the social media *** the data analysis we proposed ANOVA analysis method regarding to the factors of age,time,frequency visiting times of social media *** reliability analysis of data Cronbach’s alpha tests are implemented.

关键词： Cryptonight disruptive technology smart contracts social media security YAC

Design of Intelligent alzheimer Disease Diagnosis Model on CIoT Environment

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Computers, Materials & Continua 2022年第6期71卷 5979-5994页

作者： Anwer Mustafa Hilal fahd Nal-Wesabi Mohamed Tahar Ben Othman Khaled Mohamad almustafa Nadhem Nemri Mesfer al Duhayyim Manar Ahmed Hamza Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer Science College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Information Systems College of Computer and Information SystemsPrince Sultan UniversitySaudi Arabia Department of Information Systems King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the healthcare sector to avail effective decision *** the other hand,alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare *** this aspect,this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features(BRIEF)Detector(ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare *** initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest(RoI)detection processes are carried *** addition,the ORBOANN model includes ORB based feature extractor and principal component analysis(PCA)based feature ***,artificial neural network(ANN)model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.

关键词： Cognitive internet of things machine learning parameter tuning alzheimer’s disease healthcare decision making