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Indentation Creep Behavior and Microstructure of Cu-Ge Ferrites

Materials Sciences and Applications 2011年第8期2卷 1076-1082页

作者： Hesham Mohaned Zaki ali Mohamed Abdel-Daiem Yahia Ibrahim Swilem Farid El-Tantawy Fahad Masoud al-Marzouki Ahmed Abdallah al-Ghamdi Saleh al-Heniti Farag Said al-Hazmi talal sadaka al-harbi 不详

Cu-Ge ferrite was prepared using the standard ceramic method. The creep rate of polycrystalline Cu1+xGexFe2-2xO4 ferrite has been measured as a function of time at room temperature. It is found that the indentation length increases with the increase of both time and applied load. A regime of individual creep curves is observed for the first and second stages. It is not possible to record the third stage of the curve as usually happened in an ordinary creep test, because fracture of the samples does not occur. The slope is found to increase with increasing germanium and copper content in the steady state region. The high value of n (stress exponent factor) indicates that the dislocation creep is the dominate mechanism. The porosity arrangements developed within the specimens were examined using optical microscope. The results are discussed with regard to models describing the role of the steady state creep rate of metals. The morphology of the samples shows that the porosity is increased by increasing both copper and germanium ions.

关键词： Ferrites Indentation Creep Microstructure

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Development of PCCNN-Based Network Intrusion Detection System for EDGE Computing

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Computers, Materials & Continua 2022年第4期71卷 1769-1788页

作者： Mohd Anul Haq Mohd Abdul Rahim Khan talal al-harbi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah 11952Saudi Arabia

Intrusion Detection System(IDS)plays a crucial role in detecting and identifying the DoS and DDoS type of attacks on IoT ***,anomaly-based techniques do not provide acceptable accuracy for efficacious intrusion ***,we found many difficulty levels when applying IDS to IoT devices for identifying attempted *** this background,we designed a solution to detect intrusions using the Convolutional Neural Network(CNN)for Enhanced Data rates for GSM Evolution(EDGE)*** created two separate categories to handle the attack and non-attack events in the *** findings of this study indicate that this approach was significantly *** attempted both multiclass and binary *** the case of binary,we clustered all malicious traffic data in a single ***,we developed 13 layers of Sequential 1-D CNN for IDS detection and assessed them on the public dataset *** Component Analysis(PCA)was implemented to decrease the size of the feature vector based on feature extraction and *** approach proposed in the current investigation obtained accuracies of 99.34%and 99.13%for binary and multiclass classification,respectively,for the NSL-KDD *** experimental outcomes showed that the proposed Principal Component-based Convolution Neural Network(PCCNN)approach achieved greater precision based on deep learning and has potential use in modern intrusion detection for IoT systems.

关键词： IDS edge computing machine learning NSL-KDD IoT

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Indentation Creep Behavior and Microstructure of Cu-Ge Ferrites

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Materials Sciences and Applications（材料科学与应用期刊） 2011年第8期

作者： Hesham Mohaned Zaki ali Mohamed Abdel-Daiem Yahia Ibrahim Swilem Farid El-Tantawy Fahad Masoud al-Marzouki Ahmed Abdallah al-Ghamdi Saleh al-Heniti Farag Said al-Hazmi talal sadaka al-harbi

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Chaotic Krill Herd with Deep Transfer Learning-Based Biometric Iris Recognition System

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Computers, Materials & Continua 2022年第12期73卷 5703-5715页

作者： harbi al-Mahafzah Tamer AbuKhalil Bassam A.Y.alqaralleh Department of Computer Science Faculty of Information TechnologyAl-Hussein Bin Talal University Ma’an71111Jordan MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia

Biometric verification has become essential to authenticate the individuals in public and private *** several biometrics,iris has peculiar features and its working mechanism is complex in *** recent developments in Machine Learning and Deep Learning approaches enable the development of effective iris recognition *** this motivation,the current study introduces a novel Chaotic Krill Herd with Deep Transfer Learning Based Biometric Iris Recognition System(CKHDTL-BIRS).The presented CKHDTL-BIRS model intends to recognize and classify iris images as a part of biometric *** achieve this,CKHDTL-BIRS model initially performs Median Filtering(MF)-based preprocessing and segmentation for iris *** addition,MobileNetmodel is also utilized to generate a set of useful feature ***,Stacked Sparse Autoencoder(SSAE)approach is applied for *** last,CKH algorithm is exploited for optimization of the parameters involved in SSAE *** proposed CKHDTL-BIRS model was experimentally validated using benchmark dataset and the outcomes were examined under several *** comparison study results established the enhanced performance of CKHDTL-BIRS technique over recent approaches.

关键词： Biometric verification iris recognition deep learning parameter tuning machine learning

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