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第43届中国控制会议

作者： Lai Peng He Lu Dezheng Hua Qiyu Wang Yurui Shen Xinhua Liu School of Mechatronic Engineering China University of Mining and Technology

In order to describe the nonlinear characteristics under the influence of hysteresis on magnetorheological brake(MRB), a hysteresis model based on the Jiles-Atherton model is proposed. Firstly, the structure model of MRB is presented and the hysteresis characteristics of MRB are analyzed. Secondly, the hysteresis model based on the Jiles-Atherton Model is constructed for MRB. Secondly, the methodology of parameter identification using the white shark optimizer is proposed and its framework is shown. Finally, two experiments for parameter identification are carried out to demonstrate the feasibility and efficiency of this approach. The results of the comparison indicate that the proposed approach outperforms other algorithms.

关键词： Magnetorheological brake Hysteresis characteristics Jiles-Atherton model Parameter identification white shark optimizer

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Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

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Computer Systems Science & Engineering 2023年第9期46卷 2947-2961页

作者： IbrahimM.Alwayle Badriyya B.Al-onazi Mohamed K.Nour Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Amal S.Mehanna Abdelwahed Motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Online reviews regarding purchasing services or products offered are the main source of users’*** gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or *** practice is called review *** the last few years,various techniques have been recommended to solve the problem of spam *** spam detection study focuses on English reviews,with a lesser interest in other *** review detection in Arabic online sources is an innovative topic despite the vast amount of data ***,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion *** presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and ***,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible ***,unigram and bigram feature extractors are *** SGRU model is employed in this study to identify and classify Arabic spam *** the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU *** experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC *** extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

关键词： Arabic text spam reviews machine learning deep learning white shark optimizer

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Securing Cloud Computing from Flash Crowd Attack Using Ensemble Intrusion Detection System

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Computer Systems Science & Engineering 2023年第10期47卷 453-469页

作者： Turke Althobaiti Yousef Sanjalawe Naeem Ramzan Department of Computer Science Faculty of ScienceNorthern Border University(NBU)Arar73222Saudi Arabia Remote Sensing Unit Northern Border University(NBU)Arar73222Saudi Arabia Deparment of Cybersecurity American University of Madaba(AUM)Amman11821Jordan School of Engineering and Computing University of West of ScotlandPaisleyPA12BEUK

Flash Crowd attacks are a form of Distributed Denial of Service(DDoS)attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing(CC).Botnets are often used by attackers to perform a wide range of DDoS *** advancements in technology,bots are now able to simulate DDoS attacks as flash crowd events,making them difficult to *** it comes to application layer DDoS attacks,the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate *** is mainly because it can imitate typical user behavior,leading to a substantial influx of requests that can overwhelm the server by consuming either its network bandwidth or ***,identifying these types of attacks on web servers has become crucial,particularly in the *** this article,an efficient intrusion detection method is proposed based on white shark optimizer and ensemble classifier(Convolutional Neural Network(CNN)and LighGBM).Experiments were conducted using a CICIDS 2017 dataset to evaluate the performance of the proposed method in real-life *** proposed IDS achieved superior results,with 95.84%accuracy,96.15%precision,95.54%recall,and 95.84%F1 *** crowd attacks are challenging to detect,but the proposed IDS has proven its effectiveness in identifying such attacks in CC and holds potential for future improvement.

关键词： Cloud computing CNN flash crowd attack intrusion detection system LightGBM white shark optimizer

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